id	sid	tid	token	lemma	pos
cana-1008	1	1	communications	communication	NOUN
cana-1008	1	2	on	on	ADP
cana-1008	1	3	applied	apply	VERB
cana-1008	1	4	nonlinear	nonlinear	ADJ
cana-1008	1	5	analysis	analysis	NOUN
cana-1008	1	6	issn	issn	NOUN
cana-1008	1	7	:	:	PUNCT
cana-1008	1	8	1074	1074	NUM
cana-1008	1	9	-	-	PUNCT
cana-1008	1	10	133x	133x	NUM
cana-1008	1	11	vol	vol	NOUN
cana-1008	1	12	31	31	NUM
cana-1008	1	13	no	no	NOUN
cana-1008	1	14	.	.	PUNCT
cana-1008	2	1	5s	5s	NUM
cana-1008	2	2	(	(	PUNCT
cana-1008	2	3	2024	2024	NUM
cana-1008	2	4	)	)	PUNCT
cana-1008	2	5	138	138	NUM
cana-1008	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1008	2	7	diabetic	diabetic	ADJ
cana-1008	2	8	prediction	prediction	NOUN
cana-1008	2	9	based	base	VERB
cana-1008	2	10	on	on	ADP
cana-1008	2	11	machine	machine	NOUN
cana-1008	2	12	learning	learning	NOUN
cana-1008	2	13	using	use	VERB
cana-1008	2	14	pima	pima	PROPN
cana-1008	2	15	indian	indian	PROPN
cana-1008	2	16	dataset	dataset	NOUN
cana-1008	2	17	merdin	merdin	PROPN
cana-1008	2	18	shamal	shamal	PROPN
cana-1008	2	19	salih1	salih1	PROPN
cana-1008	2	20	,	,	PUNCT
cana-1008	2	21	rowaida	rowaida	PROPN
cana-1008	2	22	khalil	khalil	PROPN
cana-1008	3	1	ibrahim2	ibrahim2	PROPN
cana-1008	4	1	*	*	PROPN
cana-1008	4	2	,	,	PUNCT
cana-1008	4	3	subhi	subhi	PROPN
cana-1008	4	4	r.	r.	PROPN
cana-1008	4	5	m.	m.	PROPN
cana-1008	4	6	zeebaree3	zeebaree3	PROPN
cana-1008	4	7	,	,	PUNCT
cana-1008	4	8	dilovan	dilovan	NOUN
cana-1008	4	9	asaad	asaad	NOUN
cana-1008	4	10	zebari4	zebari4	PROPN
cana-1008	4	11	,	,	PUNCT
cana-1008	4	12	lozan	lozan	NOUN
cana-1008	4	13	m.	m.	NOUN
cana-1008	4	14	abdulrahman5	abdulrahman5	PROPN
cana-1008	4	15	,	,	PUNCT
cana-1008	4	16	nasiba	nasiba	PROPN
cana-1008	4	17	mahdi	mahdi	PROPN
cana-1008	4	18	abdulkareem6	abdulkareem6	PROPN
cana-1008	5	1	1information	1information	NUM
cana-1008	5	2	technology	technology	NOUN
cana-1008	5	3	dept	dept	NOUN
cana-1008	5	4	.	.	PROPN
cana-1008	5	5	,	,	PUNCT
cana-1008	5	6	technical	technical	PROPN
cana-1008	5	7	college	college	PROPN
cana-1008	5	8	of	of	ADP
cana-1008	5	9	informatics	informatics	PROPN
cana-1008	5	10	-	-	PUNCT
cana-1008	5	11	akre	akre	ADJ
cana-1008	5	12	,	,	PUNCT
cana-1008	5	13	akre	akre	ADJ
cana-1008	5	14	university	university	PROPN
cana-1008	5	15	for	for	ADP
cana-1008	5	16	applied	apply	VERB
cana-1008	5	17	science	science	NOUN
cana-1008	5	18	,	,	PUNCT
cana-1008	5	19	akre	akre	ADJ
cana-1008	5	20	,	,	PUNCT
cana-1008	5	21	42004	42004	NUM
cana-1008	5	22	,	,	PUNCT
cana-1008	5	23	iraq	iraq	PROPN
cana-1008	5	24	,	,	PUNCT
cana-1008	5	25	merdin.shamal@auas.edue.krd	merdin.shamal@auas.edue.krd	VERB
cana-1008	5	26	2computer	2computer	NUM
cana-1008	5	27	science	science	NOUN
cana-1008	5	28	dept	dept	NOUN
cana-1008	5	29	.	.	PROPN
cana-1008	5	30	,	,	PUNCT
cana-1008	5	31	faculty	faculty	NOUN
cana-1008	5	32	of	of	ADP
cana-1008	5	33	science	science	NOUN
cana-1008	5	34	,	,	PUNCT
cana-1008	5	35	university	university	NOUN
cana-1008	5	36	of	of	ADP
cana-1008	5	37	zakho	zakho	PROPN
cana-1008	5	38	,	,	PUNCT
cana-1008	5	39	zakho	zakho	PROPN
cana-1008	5	40	,	,	PUNCT
cana-1008	5	41	42002	42002	NUM
cana-1008	5	42	,	,	PUNCT
cana-1008	5	43	iraq	iraq	PROPN
cana-1008	5	44	,	,	PUNCT
cana-1008	6	1	rowaida.ibrahim@uoz.edu.krd	rowaida.ibrahim@uoz.edu.krd	PROPN
cana-1008	6	2	3energy	3energy	NUM
cana-1008	6	3	eng	eng	PROPN
cana-1008	6	4	.	.	PROPN
cana-1008	6	5	dept	dept	PROPN
cana-1008	6	6	.	.	PROPN
cana-1008	6	7	,	,	PUNCT
cana-1008	6	8	technical	technical	PROPN
cana-1008	6	9	college	college	NOUN
cana-1008	6	10	of	of	ADP
cana-1008	6	11	engineering	engineering	NOUN
cana-1008	6	12	,	,	PUNCT
cana-1008	6	13	duhok	duhok	NOUN
cana-1008	6	14	polytechnic	polytechnic	ADJ
cana-1008	6	15	university	university	NOUN
cana-1008	6	16	,	,	PUNCT
cana-1008	6	17	duhok	duhok	NOUN
cana-1008	6	18	,	,	PUNCT
cana-1008	6	19	42001	42001	NUM
cana-1008	6	20	,	,	PUNCT
cana-1008	6	21	iraq	iraq	PROPN
cana-1008	6	22	,	,	PUNCT
cana-1008	6	23	subhi.rafeeq@dpu.edu.krd	subhi.rafeeq@dpu.edu.krd	PROPN
cana-1008	6	24	4computer	4computer	NUM
cana-1008	6	25	science	science	NOUN
cana-1008	6	26	dept	dept	NOUN
cana-1008	6	27	.	.	PROPN
cana-1008	6	28	,	,	PUNCT
cana-1008	6	29	college	college	NOUN
cana-1008	6	30	of	of	ADP
cana-1008	6	31	science	science	NOUN
cana-1008	6	32	,	,	PUNCT
cana-1008	6	33	nawroz	nawroz	ADJ
cana-1008	6	34	university	university	NOUN
cana-1008	6	35	,	,	PUNCT
cana-1008	6	36	duhok	duhok	NOUN
cana-1008	6	37	,	,	PUNCT
cana-1008	6	38	42001	42001	NUM
cana-1008	6	39	,	,	PUNCT
cana-1008	6	40	iraq	iraq	PROPN
cana-1008	6	41	,	,	PUNCT
cana-1008	6	42	dilovan.majeed@nawroz.edu.krd	dilovan.majeed@nawroz.edu.krd	PROPN
cana-1008	6	43	5information	5information	NUM
cana-1008	6	44	technology	technology	NOUN
cana-1008	6	45	dept	dept	NOUN
cana-1008	6	46	.	.	PROPN
cana-1008	6	47	,	,	PUNCT
cana-1008	6	48	duhok	duhok	PROPN
cana-1008	6	49	technical	technical	PROPN
cana-1008	6	50	college	college	PROPN
cana-1008	6	51	,	,	PUNCT
cana-1008	6	52	duhok	duhok	NOUN
cana-1008	6	53	polytechnic	polytechnic	ADJ
cana-1008	6	54	university	university	NOUN
cana-1008	6	55	,	,	PUNCT
cana-1008	6	56	duhok	duhok	NOUN
cana-1008	6	57	,	,	PUNCT
cana-1008	6	58	42001	42001	NUM
cana-1008	6	59	,	,	PUNCT
cana-1008	6	60	iraq	iraq	PROPN
cana-1008	6	61	,	,	PUNCT
cana-1008	6	62	lozan.abdulrahman@dpu.edu.krd	lozan.abdulrahman@dpu.edu.krd	PROPN
cana-1008	6	63	6information	6information	NUM
cana-1008	6	64	technology	technology	NOUN
cana-1008	6	65	dept	dept	NOUN
cana-1008	6	66	.	.	PROPN
cana-1008	6	67	,	,	PUNCT
cana-1008	6	68	duhok	duhok	PROPN
cana-1008	6	69	technical	technical	PROPN
cana-1008	6	70	college	college	PROPN
cana-1008	6	71	,	,	PUNCT
cana-1008	6	72	duhok	duhok	NOUN
cana-1008	6	73	polytechnic	polytechnic	ADJ
cana-1008	6	74	university	university	NOUN
cana-1008	6	75	,	,	PUNCT
cana-1008	6	76	duhok	duhok	NOUN
cana-1008	6	77	,	,	PUNCT
cana-1008	6	78	42001	42001	NUM
cana-1008	6	79	,	,	PUNCT
cana-1008	6	80	iraq	iraq	PROPN
cana-1008	6	81	,	,	PUNCT
cana-1008	6	82	nasiba.mahdi@dpu.edu.krd	nasiba.mahdi@dpu.edu.krd	NOUN
cana-1008	6	83	corresponding	correspond	VERB
cana-1008	6	84	author	author	NOUN
cana-1008	6	85	:	:	PUNCT
cana-1008	6	86	rowaida	rowaida	PROPN
cana-1008	6	87	khalil	khalil	PROPN
cana-1008	6	88	ibrahim	ibrahim	PROPN
cana-1008	6	89	,	,	PUNCT
cana-1008	6	90	rowaida.ibrahim@uoz.edu.krd	rowaida.ibrahim@uoz.edu.krd	PROPN
cana-1008	6	91	article	article	NOUN
cana-1008	6	92	history	history	NOUN
cana-1008	6	93	:	:	PUNCT
cana-1008	6	94	received	receive	VERB
cana-1008	6	95	:	:	PUNCT
cana-1008	6	96	18	18	NUM
cana-1008	6	97	-	-	SYM
cana-1008	6	98	05	05	NUM
cana-1008	6	99	-	-	PUNCT
cana-1008	6	100	2024	2024	NUM
cana-1008	6	101	revised	revise	VERB
cana-1008	6	102	:	:	PUNCT
cana-1008	6	103	16	16	NUM
cana-1008	6	104	-	-	SYM
cana-1008	6	105	06	06	NUM
cana-1008	6	106	-	-	PUNCT
cana-1008	6	107	2024	2024	NUM
cana-1008	6	108	accepted	accept	VERB
cana-1008	6	109	:	:	PUNCT
cana-1008	6	110	02	02	NUM
cana-1008	6	111	-	-	PUNCT
cana-1008	6	112	07	07	NUM
cana-1008	6	113	-	-	PUNCT
cana-1008	6	114	2024	2024	NUM
cana-1008	6	115	abstract	abstract	ADJ
cana-1008	6	116	diabetes	diabetes	NOUN
cana-1008	6	117	mellitus	mellitus	NOUN
cana-1008	6	118	,	,	PUNCT
cana-1008	6	119	a	a	DET
cana-1008	6	120	chronic	chronic	ADJ
cana-1008	6	121	condition	condition	NOUN
cana-1008	6	122	,	,	PUNCT
cana-1008	6	123	causes	cause	VERB
cana-1008	6	124	disruptions	disruption	NOUN
cana-1008	6	125	in	in	ADP
cana-1008	6	126	the	the	DET
cana-1008	6	127	metabolic	metabolic	NOUN
cana-1008	6	128	processes	process	NOUN
cana-1008	6	129	of	of	ADP
cana-1008	6	130	carbohydrates	carbohydrate	NOUN
cana-1008	6	131	,	,	PUNCT
cana-1008	6	132	lipids	lipid	NOUN
cana-1008	6	133	,	,	PUNCT
cana-1008	6	134	and	and	CCONJ
cana-1008	6	135	proteins	protein	NOUN
cana-1008	6	136	.	.	PUNCT
cana-1008	7	1	hyperglycemia	hyperglycemia	NOUN
cana-1008	7	2	,	,	PUNCT
cana-1008	7	3	characterised	characterise	VERB
cana-1008	7	4	by	by	ADP
cana-1008	7	5	elevated	elevated	ADJ
cana-1008	7	6	blood	blood	NOUN
cana-1008	7	7	sugar	sugar	NOUN
cana-1008	7	8	levels	level	NOUN
cana-1008	7	9	,	,	PUNCT
cana-1008	7	10	is	be	AUX
cana-1008	7	11	the	the	DET
cana-1008	7	12	primary	primary	ADJ
cana-1008	7	13	distinguishing	distinguish	VERB
cana-1008	7	14	characteristic	characteristic	NOUN
cana-1008	7	15	of	of	ADP
cana-1008	7	16	all	all	DET
cana-1008	7	17	forms	form	NOUN
cana-1008	7	18	of	of	ADP
cana-1008	7	19	diabetes	diabetes	NOUN
cana-1008	7	20	.	.	PUNCT
cana-1008	8	1	diabetes	diabetes	NOUN
cana-1008	8	2	is	be	AUX
cana-1008	8	3	a	a	DET
cana-1008	8	4	disease	disease	NOUN
cana-1008	8	5	that	that	PRON
cana-1008	8	6	has	have	AUX
cana-1008	8	7	significantly	significantly	ADV
cana-1008	8	8	increased	increase	VERB
cana-1008	8	9	in	in	ADP
cana-1008	8	10	prevalence	prevalence	NOUN
cana-1008	8	11	due	due	ADP
cana-1008	8	12	to	to	ADP
cana-1008	8	13	the	the	DET
cana-1008	8	14	contemporary	contemporary	ADJ
cana-1008	8	15	lifestyle	lifestyle	NOUN
cana-1008	8	16	.	.	PUNCT
cana-1008	9	1	consequently	consequently	ADV
cana-1008	9	2	,	,	PUNCT
cana-1008	9	3	it	it	PRON
cana-1008	9	4	is	be	AUX
cana-1008	9	5	essential	essential	ADJ
cana-1008	9	6	to	to	PART
cana-1008	9	7	get	get	VERB
cana-1008	9	8	an	an	DET
cana-1008	9	9	early	early	ADJ
cana-1008	9	10	-	-	PUNCT
cana-1008	9	11	stage	stage	NOUN
cana-1008	9	12	diagnosis	diagnosis	NOUN
cana-1008	9	13	of	of	ADP
cana-1008	9	14	the	the	DET
cana-1008	9	15	illness	illness	NOUN
cana-1008	9	16	.	.	PUNCT
cana-1008	10	1	when	when	SCONJ
cana-1008	10	2	constructing	construct	VERB
cana-1008	10	3	classification	classification	NOUN
cana-1008	10	4	models	model	NOUN
cana-1008	10	5	,	,	PUNCT
cana-1008	10	6	data	datum	NOUN
cana-1008	10	7	pre	pre	ADJ
cana-1008	10	8	-	-	ADJ
cana-1008	10	9	processing	processing	NOUN
cana-1008	10	10	is	be	AUX
cana-1008	10	11	a	a	DET
cana-1008	10	12	crucial	crucial	ADJ
cana-1008	10	13	step	step	NOUN
cana-1008	10	14	.	.	PUNCT
cana-1008	11	1	the	the	DET
cana-1008	11	2	pima	pima	PROPN
cana-1008	11	3	indian	indian	PROPN
cana-1008	11	4	diabetes	diabetes	NOUN
cana-1008	11	5	dataset	dataset	VERB
cana-1008	11	6	,	,	PUNCT
cana-1008	11	7	available	available	ADJ
cana-1008	11	8	in	in	ADP
cana-1008	11	9	the	the	DET
cana-1008	11	10	university	university	PROPN
cana-1008	11	11	of	of	ADP
cana-1008	11	12	california	california	PROPN
cana-1008	11	13	irvine	irvine	PROPN
cana-1008	11	14	(	(	PUNCT
cana-1008	11	15	uci	uci	PROPN
cana-1008	11	16	)	)	PUNCT
cana-1008	11	17	repository	repository	NOUN
cana-1008	11	18	,	,	PUNCT
cana-1008	11	19	is	be	AUX
cana-1008	11	20	a	a	DET
cana-1008	11	21	challenging	challenging	ADJ
cana-1008	11	22	dataset	dataset	NOUN
cana-1008	11	23	with	with	ADP
cana-1008	11	24	a	a	DET
cana-1008	11	25	higher	high	ADJ
cana-1008	11	26	proportion	proportion	NOUN
cana-1008	11	27	of	of	ADP
cana-1008	11	28	missing	miss	VERB
cana-1008	11	29	values	value	NOUN
cana-1008	11	30	(	(	PUNCT
cana-1008	11	31	48	48	NUM
cana-1008	11	32	%	%	NOUN
cana-1008	11	33	)	)	PUNCT
cana-1008	11	34	compared	compare	VERB
cana-1008	11	35	to	to	ADP
cana-1008	11	36	comparable	comparable	ADJ
cana-1008	11	37	datasets	dataset	NOUN
cana-1008	11	38	.	.	PUNCT
cana-1008	12	1	to	to	PART
cana-1008	12	2	improve	improve	VERB
cana-1008	12	3	the	the	DET
cana-1008	12	4	accuracy	accuracy	NOUN
cana-1008	12	5	of	of	ADP
cana-1008	12	6	the	the	DET
cana-1008	12	7	classification	classification	NOUN
cana-1008	12	8	model	model	NOUN
cana-1008	12	9	,	,	PUNCT
cana-1008	12	10	many	many	ADJ
cana-1008	12	11	rounds	round	NOUN
cana-1008	12	12	of	of	ADP
cana-1008	12	13	data	datum	NOUN
cana-1008	12	14	pre	pre	ADJ
cana-1008	12	15	-	-	ADJ
cana-1008	12	16	processing	processing	NOUN
cana-1008	12	17	are	be	AUX
cana-1008	12	18	conducted	conduct	VERB
cana-1008	12	19	on	on	ADP
cana-1008	12	20	the	the	DET
cana-1008	12	21	pima	pima	PROPN
cana-1008	12	22	diabetes	diabetes	NOUN
cana-1008	12	23	dataset	dataset	VERB
cana-1008	12	24	.	.	PUNCT
cana-1008	13	1	the	the	DET
cana-1008	13	2	proposed	propose	VERB
cana-1008	13	3	approach	approach	NOUN
cana-1008	13	4	consists	consist	VERB
cana-1008	13	5	of	of	ADP
cana-1008	13	6	two	two	NUM
cana-1008	13	7	stages	stage	NOUN
cana-1008	13	8	:	:	PUNCT
cana-1008	13	9	outlier	outlier	NOUN
cana-1008	13	10	removal	removal	NOUN
cana-1008	13	11	and	and	CCONJ
cana-1008	13	12	imputation	imputation	NOUN
cana-1008	13	13	in	in	ADP
cana-1008	13	14	the	the	DET
cana-1008	13	15	first	first	ADJ
cana-1008	13	16	stage	stage	NOUN
cana-1008	13	17	,	,	PUNCT
cana-1008	13	18	and	and	CCONJ
cana-1008	13	19	normalisation	normalisation	NOUN
cana-1008	13	20	in	in	ADP
cana-1008	13	21	the	the	DET
cana-1008	13	22	second	second	ADJ
cana-1008	13	23	stage	stage	NOUN
cana-1008	13	24	.	.	PUNCT
cana-1008	14	1	regarding	regard	VERB
cana-1008	14	2	the	the	DET
cana-1008	14	3	feature	feature	NOUN
cana-1008	14	4	aspect	aspect	NOUN
cana-1008	14	5	,	,	PUNCT
cana-1008	14	6	we	we	PRON
cana-1008	14	7	used	use	VERB
cana-1008	14	8	a	a	DET
cana-1008	14	9	method	method	NOUN
cana-1008	14	10	called	call	VERB
cana-1008	14	11	principal	principal	ADJ
cana-1008	14	12	component	component	NOUN
cana-1008	14	13	analysis	analysis	NOUN
cana-1008	14	14	(	(	PUNCT
cana-1008	14	15	pca	pca	NOUN
cana-1008	14	16	)	)	PUNCT
cana-1008	14	17	.	.	PUNCT
cana-1008	15	1	ultimately	ultimately	ADV
cana-1008	15	2	,	,	PUNCT
cana-1008	15	3	to	to	PART
cana-1008	15	4	classify	classify	VERB
cana-1008	15	5	the	the	DET
cana-1008	15	6	pima	pima	PROPN
cana-1008	15	7	dataset	dataset	PROPN
cana-1008	15	8	,	,	PUNCT
cana-1008	15	9	we	we	PRON
cana-1008	15	10	used	use	VERB
cana-1008	15	11	many	many	ADJ
cana-1008	15	12	classifiers	classifier	NOUN
cana-1008	15	13	such	such	ADJ
cana-1008	15	14	as	as	ADP
cana-1008	15	15	support	support	NOUN
cana-1008	15	16	vector	vector	NOUN
cana-1008	15	17	machine	machine	NOUN
cana-1008	15	18	(	(	PUNCT
cana-1008	15	19	svm	svm	PROPN
cana-1008	15	20	)	)	PUNCT
cana-1008	15	21	,	,	PUNCT
cana-1008	15	22	random	random	ADJ
cana-1008	15	23	forest	forest	NOUN
cana-1008	15	24	(	(	PUNCT
cana-1008	15	25	rf	rf	NOUN
cana-1008	15	26	)	)	PUNCT
cana-1008	15	27	,	,	PUNCT
cana-1008	15	28	naïve	naïve	ADJ
cana-1008	15	29	bayes	bayes	PROPN
cana-1008	15	30	(	(	PUNCT
cana-1008	15	31	nb	nb	NOUN
cana-1008	15	32	)	)	PUNCT
cana-1008	15	33	,	,	PUNCT
cana-1008	15	34	and	and	CCONJ
cana-1008	15	35	decision	decision	NOUN
cana-1008	15	36	tree	tree	NOUN
cana-1008	15	37	(	(	PUNCT
cana-1008	15	38	dt	dt	NOUN
cana-1008	15	39	)	)	PUNCT
cana-1008	15	40	.	.	PUNCT
cana-1008	16	1	the	the	DET
cana-1008	16	2	testing	testing	NOUN
cana-1008	16	3	revealed	reveal	VERB
cana-1008	16	4	that	that	SCONJ
cana-1008	16	5	the	the	DET
cana-1008	16	6	maximum	maximum	ADJ
cana-1008	16	7	achievable	achievable	ADJ
cana-1008	16	8	accuracy	accuracy	NOUN
cana-1008	16	9	was	be	AUX
cana-1008	16	10	89.86	89.86	NUM
cana-1008	16	11	%	%	NOUN
cana-1008	16	12	when	when	SCONJ
cana-1008	16	13	80	80	NUM
cana-1008	16	14	%	%	NOUN
cana-1008	16	15	of	of	ADP
cana-1008	16	16	the	the	DET
cana-1008	16	17	data	datum	NOUN
cana-1008	16	18	was	be	AUX
cana-1008	16	19	used	use	VERB
cana-1008	16	20	for	for	ADP
cana-1008	16	21	training	training	NOUN
cana-1008	16	22	.	.	PUNCT
cana-1008	17	1	this	this	PRON
cana-1008	17	2	was	be	AUX
cana-1008	17	3	accomplished	accomplish	VERB
cana-1008	17	4	by	by	ADP
cana-1008	17	5	integrating	integrate	VERB
cana-1008	17	6	the	the	DET
cana-1008	17	7	feature	feature	NOUN
cana-1008	17	8	selection	selection	NOUN
cana-1008	17	9	technique	technique	NOUN
cana-1008	17	10	with	with	ADP
cana-1008	17	11	the	the	DET
cana-1008	17	12	classifier	classifier	NOUN
cana-1008	17	13	.	.	PUNCT
cana-1008	18	1	keywords	keyword	NOUN
cana-1008	18	2	:	:	PUNCT
cana-1008	18	3	diabetic	diabetic	ADJ
cana-1008	18	4	,	,	PUNCT
cana-1008	18	5	pima	pima	PROPN
cana-1008	18	6	dataset	dataset	PROPN
cana-1008	18	7	,	,	PUNCT
cana-1008	18	8	data	datum	NOUN
cana-1008	18	9	pre	pre	ADJ
cana-1008	18	10	-	-	ADJ
cana-1008	18	11	processing	processing	ADJ
cana-1008	18	12	,	,	PUNCT
cana-1008	18	13	pca	pca	PROPN
cana-1008	18	14	,	,	PUNCT
cana-1008	18	15	pima	pima	PROPN
cana-1008	18	16	dataset	dataset	PROPN
cana-1008	18	17	,	,	PUNCT
cana-1008	18	18	classification	classification	NOUN
cana-1008	18	19	.	.	PUNCT
cana-1008	19	1	1	1	X
cana-1008	19	2	.	.	X
cana-1008	19	3	introduction	introduction	NOUN
cana-1008	19	4	1.1	1.1	NUM
cana-1008	19	5	.	.	PUNCT
cana-1008	20	1	diseases	disease	NOUN
cana-1008	20	2	prediction	prediction	NOUN
cana-1008	20	3	based	base	VERB
cana-1008	20	4	on	on	ADP
cana-1008	20	5	machine	machine	NOUN
cana-1008	20	6	learning	learn	VERB
cana-1008	20	7	the	the	DET
cana-1008	20	8	use	use	NOUN
cana-1008	20	9	of	of	ADP
cana-1008	20	10	machine	machine	NOUN
cana-1008	20	11	learning	learning	NOUN
cana-1008	20	12	(	(	PUNCT
cana-1008	20	13	ml	ml	NOUN
cana-1008	20	14	)	)	PUNCT
cana-1008	20	15	in	in	ADP
cana-1008	20	16	illness	illness	NOUN
cana-1008	20	17	prediction	prediction	NOUN
cana-1008	20	18	has	have	AUX
cana-1008	20	19	fundamentally	fundamentally	ADV
cana-1008	20	20	transformed	transform	VERB
cana-1008	20	21	the	the	DET
cana-1008	20	22	healthcare	healthcare	NOUN
cana-1008	20	23	business	business	NOUN
cana-1008	20	24	.	.	PUNCT
cana-1008	21	1	machine	machine	NOUN
cana-1008	21	2	learning	learn	VERB
cana-1008	21	3	algorithms	algorithm	NOUN
cana-1008	21	4	can	can	AUX
cana-1008	21	5	use	use	VERB
cana-1008	21	6	extensive	extensive	ADJ
cana-1008	21	7	medical	medical	ADJ
cana-1008	21	8	data	datum	NOUN
cana-1008	21	9	to	to	PART
cana-1008	21	10	detect	detect	VERB
cana-1008	21	11	patterns	pattern	NOUN
cana-1008	21	12	and	and	CCONJ
cana-1008	21	13	produce	produce	VERB
cana-1008	21	14	communications	communication	NOUN
cana-1008	21	15	on	on	ADP
cana-1008	21	16	applied	apply	VERB
cana-1008	21	17	nonlinear	nonlinear	ADJ
cana-1008	21	18	analysis	analysis	NOUN
cana-1008	21	19	issn	issn	NOUN
cana-1008	21	20	:	:	PUNCT
cana-1008	21	21	1074	1074	NUM
cana-1008	21	22	-	-	PUNCT
cana-1008	21	23	133x	133x	NUM
cana-1008	21	24	vol	vol	NOUN
cana-1008	21	25	31	31	NUM
cana-1008	21	26	no	no	NOUN
cana-1008	21	27	.	.	PUNCT
cana-1008	22	1	5s	5s	NUM
cana-1008	22	2	(	(	PUNCT
cana-1008	22	3	2024	2024	NUM
cana-1008	22	4	)	)	PUNCT
cana-1008	22	5	139	139	NUM
cana-1008	22	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1008	22	7	accurate	accurate	ADJ
cana-1008	22	8	predictions	prediction	NOUN
cana-1008	22	9	on	on	ADP
cana-1008	22	10	the	the	DET
cana-1008	22	11	beginning	beginning	NOUN
cana-1008	22	12	,	,	PUNCT
cana-1008	22	13	course	course	NOUN
cana-1008	22	14	,	,	PUNCT
cana-1008	22	15	and	and	CCONJ
cana-1008	22	16	outcomes	outcome	NOUN
cana-1008	22	17	of	of	ADP
cana-1008	22	18	diseases	disease	NOUN
cana-1008	22	19	.	.	PUNCT
cana-1008	23	1	this	this	DET
cana-1008	23	2	theoretical	theoretical	ADJ
cana-1008	23	3	review	review	NOUN
cana-1008	23	4	explores	explore	VERB
cana-1008	23	5	the	the	DET
cana-1008	23	6	core	core	NOUN
cana-1008	23	7	principles	principle	NOUN
cana-1008	23	8	,	,	PUNCT
cana-1008	23	9	approaches	approach	NOUN
cana-1008	23	10	,	,	PUNCT
cana-1008	23	11	and	and	CCONJ
cana-1008	23	12	difficulties	difficulty	NOUN
cana-1008	23	13	associated	associate	VERB
cana-1008	23	14	with	with	ADP
cana-1008	23	15	using	use	VERB
cana-1008	23	16	machine	machine	NOUN
cana-1008	23	17	learning	learning	NOUN
cana-1008	23	18	for	for	ADP
cana-1008	23	19	illness	illness	NOUN
cana-1008	23	20	prediction	prediction	NOUN
cana-1008	23	21	.	.	PUNCT
cana-1008	24	1	machine	machine	NOUN
cana-1008	24	2	learning	learning	NOUN
cana-1008	24	3	is	be	AUX
cana-1008	24	4	a	a	DET
cana-1008	24	5	subdivision	subdivision	NOUN
cana-1008	24	6	of	of	ADP
cana-1008	24	7	artificial	artificial	ADJ
cana-1008	24	8	intelligence	intelligence	NOUN
cana-1008	24	9	that	that	PRON
cana-1008	24	10	focuses	focus	VERB
cana-1008	24	11	on	on	ADP
cana-1008	24	12	constructing	construct	VERB
cana-1008	24	13	systems	system	NOUN
cana-1008	24	14	that	that	PRON
cana-1008	24	15	can	can	AUX
cana-1008	24	16	acquire	acquire	VERB
cana-1008	24	17	knowledge	knowledge	NOUN
cana-1008	24	18	from	from	ADP
cana-1008	24	19	data	datum	NOUN
cana-1008	24	20	and	and	CCONJ
cana-1008	24	21	make	make	VERB
cana-1008	24	22	forecasts	forecast	NOUN
cana-1008	24	23	or	or	CCONJ
cana-1008	24	24	choices	choice	NOUN
cana-1008	24	25	without	without	ADP
cana-1008	24	26	requiring	require	VERB
cana-1008	24	27	explicit	explicit	ADJ
cana-1008	24	28	programming	programming	NOUN
cana-1008	24	29	.	.	PUNCT
cana-1008	25	1	important	important	ADJ
cana-1008	25	2	principles	principle	NOUN
cana-1008	25	3	in	in	ADP
cana-1008	25	4	machine	machine	NOUN
cana-1008	25	5	learning	learning	NOUN
cana-1008	25	6	encompass	encompass	NOUN
cana-1008	25	7	:	:	PUNCT
cana-1008	25	8	the	the	DET
cana-1008	25	9	mathematical	mathematical	ADJ
cana-1008	25	10	methodologies	methodology	NOUN
cana-1008	25	11	used	use	VERB
cana-1008	25	12	to	to	PART
cana-1008	25	13	represent	represent	VERB
cana-1008	25	14	the	the	DET
cana-1008	25	15	data	datum	NOUN
cana-1008	25	16	.	.	PUNCT
cana-1008	26	1	popular	popular	ADJ
cana-1008	26	2	algorithms	algorithm	NOUN
cana-1008	26	3	used	use	VERB
cana-1008	26	4	in	in	ADP
cana-1008	26	5	illness	illness	NOUN
cana-1008	26	6	prediction	prediction	NOUN
cana-1008	26	7	encompass	encompass	NOUN
cana-1008	26	8	logistic	logistic	ADJ
cana-1008	26	9	regression	regression	NOUN
cana-1008	26	10	,	,	PUNCT
cana-1008	26	11	decision	decision	NOUN
cana-1008	26	12	trees	tree	NOUN
cana-1008	26	13	,	,	PUNCT
cana-1008	26	14	random	random	ADJ
cana-1008	26	15	forests	forest	NOUN
cana-1008	26	16	,	,	PUNCT
cana-1008	26	17	support	support	NOUN
cana-1008	26	18	vector	vector	NOUN
cana-1008	26	19	machines	machine	NOUN
cana-1008	26	20	(	(	PUNCT
cana-1008	26	21	svm	svm	PROPN
cana-1008	26	22	)	)	PUNCT
cana-1008	26	23	,	,	PUNCT
cana-1008	26	24	and	and	CCONJ
cana-1008	26	25	neural	neural	ADJ
cana-1008	26	26	networks	network	NOUN
cana-1008	26	27	.	.	PUNCT
cana-1008	27	1	the	the	DET
cana-1008	27	2	outputs	output	NOUN
cana-1008	27	3	of	of	ADP
cana-1008	27	4	the	the	DET
cana-1008	27	5	learning	learning	NOUN
cana-1008	27	6	process	process	NOUN
cana-1008	27	7	are	be	AUX
cana-1008	27	8	the	the	DET
cana-1008	27	9	patterns	pattern	NOUN
cana-1008	27	10	identified	identify	VERB
cana-1008	27	11	in	in	ADP
cana-1008	27	12	the	the	DET
cana-1008	27	13	training	training	NOUN
cana-1008	27	14	data	datum	NOUN
cana-1008	27	15	.	.	PUNCT
cana-1008	28	1	training	training	NOUN
cana-1008	28	2	entails	entail	VERB
cana-1008	28	3	acquiring	acquire	VERB
cana-1008	28	4	knowledge	knowledge	NOUN
cana-1008	28	5	from	from	ADP
cana-1008	28	6	a	a	DET
cana-1008	28	7	dataset	dataset	NOUN
cana-1008	28	8	,	,	PUNCT
cana-1008	28	9	while	while	SCONJ
cana-1008	28	10	testing	testing	NOUN
cana-1008	28	11	assesses	assess	VERB
cana-1008	28	12	the	the	DET
cana-1008	28	13	model	model	NOUN
cana-1008	28	14	's	's	PART
cana-1008	28	15	effectiveness	effectiveness	NOUN
cana-1008	28	16	on	on	ADP
cana-1008	28	17	unfamiliar	unfamiliar	ADJ
cana-1008	28	18	data	datum	NOUN
cana-1008	28	19	.	.	PUNCT
cana-1008	29	1	the	the	DET
cana-1008	29	2	quantifiable	quantifiable	ADJ
cana-1008	29	3	attributes	attribute	NOUN
cana-1008	29	4	or	or	CCONJ
cana-1008	29	5	traits	trait	NOUN
cana-1008	29	6	of	of	ADP
cana-1008	29	7	the	the	DET
cana-1008	29	8	observed	observed	ADJ
cana-1008	29	9	phenomenon	phenomenon	NOUN
cana-1008	29	10	.	.	PUNCT
cana-1008	30	1	when	when	SCONJ
cana-1008	30	2	predicting	predict	VERB
cana-1008	30	3	diseases	disease	NOUN
cana-1008	30	4	,	,	PUNCT
cana-1008	30	5	the	the	DET
cana-1008	30	6	elements	element	NOUN
cana-1008	30	7	that	that	PRON
cana-1008	30	8	are	be	AUX
cana-1008	30	9	taken	take	VERB
cana-1008	30	10	into	into	ADP
cana-1008	30	11	consideration	consideration	NOUN
cana-1008	30	12	may	may	AUX
cana-1008	30	13	consist	consist	VERB
cana-1008	30	14	of	of	ADP
cana-1008	30	15	patient	patient	ADJ
cana-1008	30	16	demographics	demographic	NOUN
cana-1008	30	17	,	,	PUNCT
cana-1008	30	18	medical	medical	ADJ
cana-1008	30	19	history	history	NOUN
cana-1008	30	20	,	,	PUNCT
cana-1008	30	21	genetic	genetic	ADJ
cana-1008	30	22	information	information	NOUN
cana-1008	30	23	,	,	PUNCT
cana-1008	30	24	and	and	CCONJ
cana-1008	30	25	lifestyle	lifestyle	NOUN
cana-1008	30	26	variables	variable	NOUN
cana-1008	30	27	.	.	PUNCT
cana-1008	31	1	the	the	DET
cana-1008	31	2	model	model	NOUN
cana-1008	31	3	's	's	PART
cana-1008	31	4	objective	objective	NOUN
cana-1008	31	5	is	be	AUX
cana-1008	31	6	to	to	PART
cana-1008	31	7	forecast	forecast	VERB
cana-1008	31	8	the	the	DET
cana-1008	31	9	outcomes	outcome	NOUN
cana-1008	31	10	or	or	CCONJ
cana-1008	31	11	categories	category	NOUN
cana-1008	31	12	.	.	PUNCT
cana-1008	32	1	in	in	ADP
cana-1008	32	2	the	the	DET
cana-1008	32	3	context	context	NOUN
cana-1008	32	4	of	of	ADP
cana-1008	32	5	illness	illness	NOUN
cana-1008	32	6	prediction	prediction	NOUN
cana-1008	32	7	,	,	PUNCT
cana-1008	32	8	labels	label	NOUN
cana-1008	32	9	often	often	ADV
cana-1008	32	10	indicate	indicate	VERB
cana-1008	32	11	whether	whether	SCONJ
cana-1008	32	12	a	a	DET
cana-1008	32	13	certain	certain	ADJ
cana-1008	32	14	disease	disease	NOUN
cana-1008	32	15	is	be	AUX
cana-1008	32	16	present	present	ADJ
cana-1008	32	17	or	or	CCONJ
cana-1008	32	18	not[1	not[1	NUM
cana-1008	32	19	]	]	PUNCT
cana-1008	32	20	.	.	PUNCT
cana-1008	33	1	machine	machine	NOUN
cana-1008	33	2	learning	learn	VERB
cana-1008	33	3	approaches	approach	NOUN
cana-1008	33	4	used	use	VERB
cana-1008	33	5	in	in	ADP
cana-1008	33	6	illness	illness	NOUN
cana-1008	33	7	prediction	prediction	NOUN
cana-1008	33	8	may	may	AUX
cana-1008	33	9	be	be	AUX
cana-1008	33	10	roughly	roughly	ADV
cana-1008	33	11	classified	classify	VERB
cana-1008	33	12	into	into	ADP
cana-1008	33	13	three	three	NUM
cana-1008	33	14	categories	category	NOUN
cana-1008	33	15	:	:	PUNCT
cana-1008	33	16	models	model	NOUN
cana-1008	33	17	are	be	AUX
cana-1008	33	18	trained	train	VERB
cana-1008	33	19	using	use	VERB
cana-1008	33	20	labelled	label	VERB
cana-1008	33	21	data	datum	NOUN
cana-1008	33	22	,	,	PUNCT
cana-1008	33	23	which	which	PRON
cana-1008	33	24	consists	consist	VERB
cana-1008	33	25	of	of	ADP
cana-1008	33	26	known	know	VERB
cana-1008	33	27	input	input	NOUN
cana-1008	33	28	characteristics	characteristic	NOUN
cana-1008	33	29	and	and	CCONJ
cana-1008	33	30	their	their	PRON
cana-1008	33	31	matching	match	VERB
cana-1008	33	32	output	output	NOUN
cana-1008	33	33	labels	label	NOUN
cana-1008	33	34	.	.	PUNCT
cana-1008	34	1	the	the	DET
cana-1008	34	2	most	most	ADV
cana-1008	34	3	prevalent	prevalent	ADJ
cana-1008	34	4	method	method	NOUN
cana-1008	34	5	in	in	ADP
cana-1008	34	6	illness	illness	NOUN
cana-1008	34	7	prediction	prediction	NOUN
cana-1008	34	8	involves	involve	VERB
cana-1008	34	9	activities	activity	NOUN
cana-1008	34	10	such	such	ADJ
cana-1008	34	11	as	as	ADP
cana-1008	34	12	categorising	categorise	VERB
cana-1008	34	13	people	people	NOUN
cana-1008	34	14	as	as	ADP
cana-1008	34	15	either	either	CCONJ
cana-1008	34	16	sick	sick	ADJ
cana-1008	34	17	or	or	CCONJ
cana-1008	34	18	healthy	healthy	ADJ
cana-1008	34	19	.	.	PUNCT
cana-1008	35	1	models	model	NOUN
cana-1008	35	2	use	use	VERB
cana-1008	35	3	unsupervised	unsupervised	ADJ
cana-1008	35	4	learning	learning	NOUN
cana-1008	35	5	to	to	PART
cana-1008	35	6	detect	detect	VERB
cana-1008	35	7	patterns	pattern	NOUN
cana-1008	35	8	in	in	ADP
cana-1008	35	9	data	datum	NOUN
cana-1008	35	10	without	without	ADP
cana-1008	35	11	labelled	label	VERB
cana-1008	35	12	outputs	output	NOUN
cana-1008	35	13	.	.	PUNCT
cana-1008	36	1	this	this	DET
cana-1008	36	2	methodology	methodology	NOUN
cana-1008	36	3	is	be	AUX
cana-1008	36	4	valuable	valuable	ADJ
cana-1008	36	5	for	for	ADP
cana-1008	36	6	uncovering	uncover	VERB
cana-1008	36	7	hidden	hidden	ADJ
cana-1008	36	8	patterns	pattern	NOUN
cana-1008	36	9	in	in	ADP
cana-1008	36	10	data	datum	NOUN
cana-1008	36	11	,	,	PUNCT
cana-1008	36	12	such	such	ADJ
cana-1008	36	13	as	as	ADP
cana-1008	36	14	grouping	group	VERB
cana-1008	36	15	patients	patient	NOUN
cana-1008	36	16	with	with	ADP
cana-1008	36	17	comparable	comparable	ADJ
cana-1008	36	18	symptoms	symptom	NOUN
cana-1008	36	19	.	.	PUNCT
cana-1008	37	1	models	model	NOUN
cana-1008	37	2	acquire	acquire	VERB
cana-1008	37	3	decision	decision	NOUN
cana-1008	37	4	-	-	PUNCT
cana-1008	37	5	making	make	VERB
cana-1008	37	6	abilities	ability	NOUN
cana-1008	37	7	via	via	ADP
cana-1008	37	8	their	their	PRON
cana-1008	37	9	interaction	interaction	NOUN
cana-1008	37	10	with	with	ADP
cana-1008	37	11	an	an	DET
cana-1008	37	12	environment	environment	NOUN
cana-1008	37	13	and	and	CCONJ
cana-1008	37	14	the	the	DET
cana-1008	37	15	subsequent	subsequent	ADJ
cana-1008	37	16	input	input	NOUN
cana-1008	37	17	they	they	PRON
cana-1008	37	18	get	get	VERB
cana-1008	37	19	.	.	PUNCT
cana-1008	38	1	although	although	SCONJ
cana-1008	38	2	less	less	ADV
cana-1008	38	3	prevalent	prevalent	ADJ
cana-1008	38	4	in	in	ADP
cana-1008	38	5	illness	illness	NOUN
cana-1008	38	6	prediction	prediction	NOUN
cana-1008	38	7	,	,	PUNCT
cana-1008	38	8	it	it	PRON
cana-1008	38	9	may	may	AUX
cana-1008	38	10	be	be	AUX
cana-1008	38	11	advantageous	advantageous	ADJ
cana-1008	38	12	for	for	ADP
cana-1008	38	13	tailoring	tailor	VERB
cana-1008	38	14	treatment	treatment	NOUN
cana-1008	38	15	regimens	regimen	NOUN
cana-1008	38	16	to	to	ADP
cana-1008	38	17	individual	individual	ADJ
cana-1008	38	18	needs[2	needs[2	PROPN
cana-1008	38	19	]	]	PUNCT
cana-1008	38	20	.	.	PUNCT
cana-1008	39	1	various	various	ADJ
cana-1008	39	2	strategies	strategy	NOUN
cana-1008	39	3	and	and	CCONJ
cana-1008	39	4	techniques	technique	NOUN
cana-1008	39	5	are	be	AUX
cana-1008	39	6	used	use	VERB
cana-1008	39	7	in	in	ADP
cana-1008	39	8	illness	illness	NOUN
cana-1008	39	9	prediction	prediction	NOUN
cana-1008	39	10	via	via	ADP
cana-1008	39	11	the	the	DET
cana-1008	39	12	application	application	NOUN
cana-1008	39	13	of	of	ADP
cana-1008	39	14	machine	machine	NOUN
cana-1008	39	15	learning	learning	NOUN
cana-1008	39	16	:	:	PUNCT
cana-1008	39	17	the	the	DET
cana-1008	39	18	first	first	ADJ
cana-1008	39	19	phase	phase	NOUN
cana-1008	39	20	is	be	AUX
cana-1008	39	21	cleansing	cleanse	VERB
cana-1008	39	22	and	and	CCONJ
cana-1008	39	23	preparing	prepare	VERB
cana-1008	39	24	the	the	DET
cana-1008	39	25	data	datum	NOUN
cana-1008	39	26	for	for	ADP
cana-1008	39	27	analysis	analysis	NOUN
cana-1008	39	28	.	.	PUNCT
cana-1008	40	1	this	this	PRON
cana-1008	40	2	encompasses	encompass	VERB
cana-1008	40	3	the	the	DET
cana-1008	40	4	tasks	task	NOUN
cana-1008	40	5	of	of	ADP
cana-1008	40	6	managing	manage	VERB
cana-1008	40	7	null	null	ADJ
cana-1008	40	8	values	value	NOUN
cana-1008	40	9	,	,	PUNCT
cana-1008	40	10	standardising	standardise	VERB
cana-1008	40	11	data	datum	NOUN
cana-1008	40	12	,	,	PUNCT
cana-1008	40	13	and	and	CCONJ
cana-1008	40	14	converting	convert	VERB
cana-1008	40	15	categorical	categorical	ADJ
cana-1008	40	16	variables	variable	NOUN
cana-1008	40	17	into	into	ADP
cana-1008	40	18	numerical	numerical	ADJ
cana-1008	40	19	representations	representation	NOUN
cana-1008	40	20	.	.	PUNCT
cana-1008	41	1	the	the	DET
cana-1008	41	2	identification	identification	NOUN
cana-1008	41	3	and	and	CCONJ
cana-1008	41	4	creation	creation	NOUN
cana-1008	41	5	of	of	ADP
cana-1008	41	6	relevant	relevant	ADJ
cana-1008	41	7	characteristics	characteristic	NOUN
cana-1008	41	8	are	be	AUX
cana-1008	41	9	essential	essential	ADJ
cana-1008	41	10	for	for	ADP
cana-1008	41	11	optimising	optimise	VERB
cana-1008	41	12	model	model	NOUN
cana-1008	41	13	performance	performance	NOUN
cana-1008	41	14	.	.	PUNCT
cana-1008	42	1	methods	method	NOUN
cana-1008	42	2	such	such	ADJ
cana-1008	42	3	as	as	ADP
cana-1008	42	4	principal	principal	ADJ
cana-1008	42	5	component	component	NOUN
cana-1008	42	6	analysis	analysis	NOUN
cana-1008	42	7	(	(	PUNCT
cana-1008	42	8	pca	pca	NOUN
cana-1008	42	9	)	)	PUNCT
cana-1008	42	10	and	and	CCONJ
cana-1008	42	11	recursive	recursive	ADJ
cana-1008	42	12	feature	feature	NOUN
cana-1008	42	13	elimination	elimination	NOUN
cana-1008	42	14	(	(	PUNCT
cana-1008	42	15	rfe	rfe	NOUN
cana-1008	42	16	)	)	PUNCT
cana-1008	42	17	are	be	AUX
cana-1008	42	18	used	use	VERB
cana-1008	42	19	to	to	PART
cana-1008	42	20	identify	identify	VERB
cana-1008	42	21	significant	significant	ADJ
cana-1008	42	22	features	feature	NOUN
cana-1008	42	23	.	.	PUNCT
cana-1008	43	1	the	the	DET
cana-1008	43	2	selection	selection	NOUN
cana-1008	43	3	of	of	ADP
cana-1008	43	4	the	the	DET
cana-1008	43	5	most	most	ADV
cana-1008	43	6	suitable	suitable	ADJ
cana-1008	43	7	machine	machine	NOUN
cana-1008	43	8	learning	learning	NOUN
cana-1008	43	9	method	method	NOUN
cana-1008	43	10	is	be	AUX
cana-1008	43	11	contingent	contingent	ADJ
cana-1008	43	12	upon	upon	SCONJ
cana-1008	43	13	the	the	DET
cana-1008	43	14	characteristics	characteristic	NOUN
cana-1008	43	15	of	of	ADP
cana-1008	43	16	the	the	DET
cana-1008	43	17	task	task	NOUN
cana-1008	43	18	at	at	ADP
cana-1008	43	19	hand	hand	NOUN
cana-1008	43	20	and	and	CCONJ
cana-1008	43	21	the	the	DET
cana-1008	43	22	available	available	ADJ
cana-1008	43	23	data	datum	NOUN
cana-1008	43	24	.	.	PUNCT
cana-1008	44	1	logistic	logistic	ADJ
cana-1008	44	2	regression	regression	NOUN
cana-1008	44	3	is	be	AUX
cana-1008	44	4	appropriate	appropriate	ADJ
cana-1008	44	5	for	for	ADP
cana-1008	44	6	binary	binary	ADJ
cana-1008	44	7	classification	classification	NOUN
cana-1008	44	8	,	,	PUNCT
cana-1008	44	9	but	but	CCONJ
cana-1008	44	10	neural	neural	ADJ
cana-1008	44	11	networks	network	NOUN
cana-1008	44	12	are	be	AUX
cana-1008	44	13	more	more	ADV
cana-1008	44	14	effective	effective	ADJ
cana-1008	44	15	for	for	ADP
cana-1008	44	16	intricate	intricate	ADJ
cana-1008	44	17	patterns	pattern	NOUN
cana-1008	44	18	.	.	PUNCT
cana-1008	45	1	models	model	NOUN
cana-1008	45	2	undergo	undergo	VERB
cana-1008	45	3	training	training	NOUN
cana-1008	45	4	using	use	VERB
cana-1008	45	5	a	a	DET
cana-1008	45	6	portion	portion	NOUN
cana-1008	45	7	of	of	ADP
cana-1008	45	8	the	the	DET
cana-1008	45	9	data	datum	NOUN
cana-1008	45	10	and	and	CCONJ
cana-1008	45	11	are	be	AUX
cana-1008	45	12	then	then	ADV
cana-1008	45	13	assessed	assess	VERB
cana-1008	45	14	for	for	ADP
cana-1008	45	15	performance	performance	NOUN
cana-1008	45	16	using	use	VERB
cana-1008	45	17	a	a	DET
cana-1008	45	18	separate	separate	ADJ
cana-1008	45	19	portion	portion	NOUN
cana-1008	45	20	of	of	ADP
cana-1008	45	21	the	the	DET
cana-1008	45	22	data	datum	NOUN
cana-1008	45	23	.	.	PUNCT
cana-1008	46	1	methods	method	NOUN
cana-1008	46	2	such	such	ADJ
cana-1008	46	3	as	as	ADP
cana-1008	46	4	cross	cross	ADJ
cana-1008	46	5	-	-	ADJ
cana-1008	46	6	validation	validation	ADJ
cana-1008	46	7	aid	aid	NOUN
cana-1008	46	8	in	in	ADP
cana-1008	46	9	ensuring	ensure	VERB
cana-1008	46	10	that	that	SCONJ
cana-1008	46	11	the	the	DET
cana-1008	46	12	model	model	NOUN
cana-1008	46	13	exhibits	exhibit	VERB
cana-1008	46	14	good	good	ADJ
cana-1008	46	15	generalisation	generalisation	NOUN
cana-1008	46	16	to	to	ADP
cana-1008	46	17	unfamiliar	unfamiliar	ADJ
cana-1008	46	18	data	datum	NOUN
cana-1008	46	19	.	.	PUNCT
cana-1008	47	1	standard	standard	ADJ
cana-1008	47	2	measures	measure	NOUN
cana-1008	47	3	used	use	VERB
cana-1008	47	4	to	to	PART
cana-1008	47	5	evaluate	evaluate	VERB
cana-1008	47	6	model	model	NOUN
cana-1008	47	7	performance	performance	NOUN
cana-1008	47	8	include	include	VERB
cana-1008	47	9	accuracy	accuracy	NOUN
cana-1008	47	10	,	,	PUNCT
cana-1008	47	11	precision	precision	NOUN
cana-1008	47	12	,	,	PUNCT
cana-1008	47	13	recall	recall	NOUN
cana-1008	47	14	,	,	PUNCT
cana-1008	47	15	f1	f1	NOUN
cana-1008	47	16	score	score	NOUN
cana-1008	47	17	,	,	PUNCT
cana-1008	47	18	and	and	CCONJ
cana-1008	47	19	the	the	DET
cana-1008	47	20	area	area	NOUN
cana-1008	47	21	under	under	ADP
cana-1008	47	22	the	the	DET
cana-1008	47	23	receiver	receiver	NOUN
cana-1008	47	24	operating	operate	VERB
cana-1008	47	25	characteristic	characteristic	ADJ
cana-1008	47	26	curve	curve	NOUN
cana-1008	47	27	(	(	PUNCT
cana-1008	47	28	auc	auc	NOUN
cana-1008	47	29	-	-	PUNCT
cana-1008	47	30	roc)[3	roc)[3	NOUN
cana-1008	47	31	]	]	PUNCT
cana-1008	47	32	.	.	PUNCT
cana-1008	48	1	various	various	ADJ
cana-1008	48	2	machine	machine	NOUN
cana-1008	48	3	learning	learn	VERB
cana-1008	48	4	techniques	technique	NOUN
cana-1008	48	5	are	be	AUX
cana-1008	48	6	often	often	ADV
cana-1008	48	7	used	use	VERB
cana-1008	48	8	for	for	ADP
cana-1008	48	9	illness	illness	NOUN
cana-1008	48	10	prediction	prediction	NOUN
cana-1008	48	11	:	:	PUNCT
cana-1008	48	12	a	a	DET
cana-1008	48	13	logistic	logistic	ADJ
cana-1008	48	14	regression	regression	NOUN
cana-1008	48	15	model	model	NOUN
cana-1008	48	16	is	be	AUX
cana-1008	48	17	a	a	DET
cana-1008	48	18	statistical	statistical	ADJ
cana-1008	48	19	model	model	NOUN
cana-1008	48	20	that	that	PRON
cana-1008	48	21	estimates	estimate	VERB
cana-1008	48	22	the	the	DET
cana-1008	48	23	likelihood	likelihood	NOUN
cana-1008	48	24	of	of	ADP
cana-1008	48	25	a	a	DET
cana-1008	48	26	binary	binary	ADJ
cana-1008	48	27	outcome	outcome	NOUN
cana-1008	48	28	by	by	ADP
cana-1008	48	29	considering	consider	VERB
cana-1008	48	30	one	one	NUM
cana-1008	48	31	or	or	CCONJ
cana-1008	48	32	more	more	ADJ
cana-1008	48	33	predictor	predictor	NOUN
cana-1008	48	34	variables	variable	NOUN
cana-1008	48	35	.	.	PUNCT
cana-1008	49	1	it	it	PRON
cana-1008	49	2	is	be	AUX
cana-1008	49	3	straightforward	straightforward	ADJ
cana-1008	49	4	and	and	CCONJ
cana-1008	49	5	easily	easily	ADV
cana-1008	49	6	understood	understand	VERB
cana-1008	49	7	,	,	PUNCT
cana-1008	49	8	which	which	PRON
cana-1008	49	9	makes	make	VERB
cana-1008	49	10	it	it	PRON
cana-1008	49	11	valuable	valuable	ADJ
cana-1008	49	12	for	for	ADP
cana-1008	49	13	developing	develop	VERB
cana-1008	49	14	early	early	ADJ
cana-1008	49	15	illness	illness	NOUN
cana-1008	49	16	prediction	prediction	NOUN
cana-1008	49	17	models	model	NOUN
cana-1008	49	18	.	.	PUNCT
cana-1008	50	1	decision	decision	NOUN
cana-1008	50	2	tree	tree	NOUN
cana-1008	50	3	models	model	NOUN
cana-1008	50	4	partition	partition	VERB
cana-1008	50	5	the	the	DET
cana-1008	50	6	data	datum	NOUN
cana-1008	50	7	into	into	ADP
cana-1008	50	8	branches	branch	NOUN
cana-1008	50	9	according	accord	VERB
cana-1008	50	10	to	to	ADP
cana-1008	50	11	feature	feature	NOUN
cana-1008	50	12	values	value	NOUN
cana-1008	50	13	in	in	ADP
cana-1008	50	14	order	order	NOUN
cana-1008	50	15	to	to	PART
cana-1008	50	16	generate	generate	VERB
cana-1008	50	17	predictions	prediction	NOUN
cana-1008	50	18	.	.	PUNCT
cana-1008	51	1	they	they	PRON
cana-1008	51	2	are	be	AUX
cana-1008	51	3	easily	easily	ADV
cana-1008	51	4	understandable	understandable	ADJ
cana-1008	51	5	yet	yet	ADV
cana-1008	51	6	susceptible	susceptible	ADJ
cana-1008	51	7	to	to	ADP
cana-1008	51	8	overfitting	overfitte	VERB
cana-1008	51	9	.	.	PUNCT
cana-1008	52	1	a	a	DET
cana-1008	52	2	technique	technique	NOUN
cana-1008	52	3	called	call	VERB
cana-1008	52	4	ensemble	ensemble	ADJ
cana-1008	52	5	learning	learning	NOUN
cana-1008	52	6	that	that	PRON
cana-1008	52	7	enhances	enhance	VERB
cana-1008	52	8	accuracy	accuracy	NOUN
cana-1008	52	9	and	and	CCONJ
cana-1008	52	10	resilience	resilience	NOUN
cana-1008	52	11	by	by	ADP
cana-1008	52	12	combining	combine	VERB
cana-1008	52	13	communications	communication	NOUN
cana-1008	52	14	on	on	ADP
cana-1008	52	15	applied	apply	VERB
cana-1008	52	16	nonlinear	nonlinear	ADJ
cana-1008	52	17	analysis	analysis	NOUN
cana-1008	52	18	issn	issn	NOUN
cana-1008	52	19	:	:	PUNCT
cana-1008	52	20	1074	1074	NUM
cana-1008	52	21	-	-	PUNCT
cana-1008	52	22	133x	133x	NUM
cana-1008	52	23	vol	vol	NOUN
cana-1008	52	24	31	31	NUM
cana-1008	52	25	no	no	NOUN
cana-1008	52	26	.	.	PUNCT
cana-1008	53	1	5s	5s	NUM
cana-1008	53	2	(	(	PUNCT
cana-1008	53	3	2024	2024	NUM
cana-1008	53	4	)	)	PUNCT
cana-1008	53	5	140	140	NUM
cana-1008	53	6	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-1008	53	7	numerous	numerous	ADJ
cana-1008	53	8	decision	decision	NOUN
cana-1008	53	9	trees	tree	NOUN
cana-1008	53	10	[	[	X
cana-1008	53	11	4	4	NUM
cana-1008	53	12	]	]	PUNCT
cana-1008	53	13	.	.	PUNCT
cana-1008	54	1	it	it	PRON
cana-1008	54	2	mitigates	mitigate	VERB
cana-1008	54	3	overfitting	overfitte	VERB
cana-1008	54	4	in	in	ADP
cana-1008	54	5	comparison	comparison	NOUN
cana-1008	54	6	to	to	ADP
cana-1008	54	7	individual	individual	ADJ
cana-1008	54	8	decision	decision	NOUN
cana-1008	54	9	trees	tree	NOUN
cana-1008	54	10	.	.	PUNCT
cana-1008	55	1	an	an	DET
cana-1008	55	2	effective	effective	ADJ
cana-1008	55	3	classification	classification	NOUN
cana-1008	55	4	technique	technique	NOUN
cana-1008	55	5	that	that	PRON
cana-1008	55	6	identifies	identify	VERB
cana-1008	55	7	the	the	DET
cana-1008	55	8	most	most	ADV
cana-1008	55	9	suitable	suitable	ADJ
cana-1008	55	10	hyperplane	hyperplane	NOUN
cana-1008	55	11	for	for	ADP
cana-1008	55	12	separating	separate	VERB
cana-1008	55	13	distinct	distinct	ADJ
cana-1008	55	14	classes	class	NOUN
cana-1008	55	15	inside	inside	ADP
cana-1008	55	16	the	the	DET
cana-1008	55	17	feature	feature	NOUN
cana-1008	55	18	space	space	NOUN
cana-1008	55	19	.	.	PUNCT
cana-1008	56	1	neural	neural	ADJ
cana-1008	56	2	models	model	NOUN
cana-1008	56	3	that	that	PRON
cana-1008	56	4	draw	draw	VERB
cana-1008	56	5	inspiration	inspiration	NOUN
cana-1008	56	6	from	from	ADP
cana-1008	56	7	the	the	DET
cana-1008	56	8	human	human	ADJ
cana-1008	56	9	brain	brain	NOUN
cana-1008	56	10	and	and	CCONJ
cana-1008	56	11	possess	possess	VERB
cana-1008	56	12	the	the	DET
cana-1008	56	13	ability	ability	NOUN
cana-1008	56	14	to	to	PART
cana-1008	56	15	acquire	acquire	VERB
cana-1008	56	16	intricate	intricate	ADJ
cana-1008	56	17	patterns	pattern	NOUN
cana-1008	56	18	.	.	PUNCT
cana-1008	57	1	deep	deep	ADJ
cana-1008	57	2	learning	learning	NOUN
cana-1008	57	3	,	,	PUNCT
cana-1008	57	4	a	a	DET
cana-1008	57	5	specific	specific	ADJ
cana-1008	57	6	branch	branch	NOUN
cana-1008	57	7	of	of	ADP
cana-1008	57	8	neural	neural	ADJ
cana-1008	57	9	networks	network	NOUN
cana-1008	57	10	that	that	PRON
cana-1008	57	11	consists	consist	VERB
cana-1008	57	12	of	of	ADP
cana-1008	57	13	numerous	numerous	ADJ
cana-1008	57	14	layers	layer	NOUN
cana-1008	57	15	,	,	PUNCT
cana-1008	57	16	has	have	AUX
cana-1008	57	17	shown	show	VERB
cana-1008	57	18	significant	significant	ADJ
cana-1008	57	19	potential	potential	NOUN
cana-1008	57	20	in	in	ADP
cana-1008	57	21	the	the	DET
cana-1008	57	22	field	field	NOUN
cana-1008	57	23	of	of	ADP
cana-1008	57	24	illness	illness	NOUN
cana-1008	57	25	prediction	prediction	NOUN
cana-1008	57	26	,	,	PUNCT
cana-1008	57	27	particularly	particularly	ADV
cana-1008	57	28	when	when	SCONJ
cana-1008	57	29	applied	apply	VERB
cana-1008	57	30	to	to	ADP
cana-1008	57	31	picture	picture	NOUN
cana-1008	57	32	and	and	CCONJ
cana-1008	57	33	genomic	genomic	ADJ
cana-1008	57	34	data	datum	NOUN
cana-1008	57	35	[	[	X
cana-1008	57	36	5	5	NUM
cana-1008	57	37	]	]	PUNCT
cana-1008	57	38	.	.	PUNCT
cana-1008	58	1	machine	machine	NOUN
cana-1008	58	2	learning	learn	VERB
cana-1008	58	3	algorithms	algorithm	NOUN
cana-1008	58	4	have	have	AUX
cana-1008	58	5	been	be	AUX
cana-1008	58	6	used	use	VERB
cana-1008	58	7	to	to	PART
cana-1008	58	8	forecast	forecast	VERB
cana-1008	58	9	a	a	DET
cana-1008	58	10	diverse	diverse	ADJ
cana-1008	58	11	array	array	NOUN
cana-1008	58	12	of	of	ADP
cana-1008	58	13	illnesses	illness	NOUN
cana-1008	58	14	,	,	PUNCT
cana-1008	58	15	encompassing	encompass	VERB
cana-1008	58	16	:	:	PUNCT
cana-1008	58	17	utilising	utilise	VERB
cana-1008	58	18	patient	patient	ADJ
cana-1008	58	19	data	datum	NOUN
cana-1008	58	20	such	such	ADJ
cana-1008	58	21	as	as	ADP
cana-1008	58	22	age	age	NOUN
cana-1008	58	23	,	,	PUNCT
cana-1008	58	24	blood	blood	NOUN
cana-1008	58	25	pressure	pressure	NOUN
cana-1008	58	26	,	,	PUNCT
cana-1008	58	27	cholesterol	cholesterol	NOUN
cana-1008	58	28	levels	level	NOUN
cana-1008	58	29	,	,	PUNCT
cana-1008	58	30	and	and	CCONJ
cana-1008	58	31	lifestyle	lifestyle	NOUN
cana-1008	58	32	variables	variable	NOUN
cana-1008	58	33	to	to	PART
cana-1008	58	34	forecast	forecast	VERB
cana-1008	58	35	occurrences	occurrence	NOUN
cana-1008	58	36	of	of	ADP
cana-1008	58	37	heart	heart	NOUN
cana-1008	58	38	attacks	attack	NOUN
cana-1008	58	39	and	and	CCONJ
cana-1008	58	40	strokes	stroke	NOUN
cana-1008	58	41	.	.	PUNCT
cana-1008	59	1	utilising	utilise	VERB
cana-1008	59	2	medical	medical	ADJ
cana-1008	59	3	imaging	imaging	NOUN
cana-1008	59	4	and	and	CCONJ
cana-1008	59	5	genomic	genomic	ADJ
cana-1008	59	6	data	datum	NOUN
cana-1008	59	7	for	for	ADP
cana-1008	59	8	the	the	DET
cana-1008	59	9	prompt	prompt	ADJ
cana-1008	59	10	identification	identification	NOUN
cana-1008	59	11	and	and	CCONJ
cana-1008	59	12	categorization	categorization	NOUN
cana-1008	59	13	of	of	ADP
cana-1008	59	14	tumours	tumour	NOUN
cana-1008	59	15	.	.	PUNCT
cana-1008	60	1	machine	machine	NOUN
cana-1008	60	2	learning	learning	NOUN
cana-1008	60	3	may	may	AUX
cana-1008	60	4	be	be	AUX
cana-1008	60	5	used	use	VERB
cana-1008	60	6	to	to	PART
cana-1008	60	7	detect	detect	VERB
cana-1008	60	8	tumours	tumour	NOUN
cana-1008	60	9	in	in	ADP
cana-1008	60	10	medical	medical	ADJ
cana-1008	60	11	imaging	imaging	NOUN
cana-1008	60	12	and	and	CCONJ
cana-1008	60	13	accurately	accurately	ADV
cana-1008	60	14	forecast	forecast	VERB
cana-1008	60	15	their	their	PRON
cana-1008	60	16	aggressiveness	aggressiveness	NOUN
cana-1008	60	17	.	.	PUNCT
cana-1008	61	1	anticipating	anticipate	VERB
cana-1008	61	2	the	the	DET
cana-1008	61	3	initiation	initiation	NOUN
cana-1008	61	4	of	of	ADP
cana-1008	61	5	diabetes	diabetes	NOUN
cana-1008	61	6	by	by	ADP
cana-1008	61	7	the	the	DET
cana-1008	61	8	examination	examination	NOUN
cana-1008	61	9	of	of	ADP
cana-1008	61	10	patient	patient	ADJ
cana-1008	61	11	data	datum	NOUN
cana-1008	61	12	and	and	CCONJ
cana-1008	61	13	lifestyle	lifestyle	NOUN
cana-1008	61	14	variables	variable	NOUN
cana-1008	61	15	.	.	PUNCT
cana-1008	62	1	models	model	NOUN
cana-1008	62	2	have	have	VERB
cana-1008	62	3	the	the	DET
cana-1008	62	4	ability	ability	NOUN
cana-1008	62	5	to	to	PART
cana-1008	62	6	identify	identify	VERB
cana-1008	62	7	persons	person	NOUN
cana-1008	62	8	who	who	PRON
cana-1008	62	9	are	be	AUX
cana-1008	62	10	at	at	ADP
cana-1008	62	11	a	a	DET
cana-1008	62	12	high	high	ADJ
cana-1008	62	13	risk	risk	NOUN
cana-1008	62	14	and	and	CCONJ
cana-1008	62	15	propose	propose	VERB
cana-1008	62	16	actions	action	NOUN
cana-1008	62	17	to	to	PART
cana-1008	62	18	avert	avert	VERB
cana-1008	62	19	negative	negative	ADJ
cana-1008	62	20	outcomes	outcome	NOUN
cana-1008	62	21	.	.	PUNCT
cana-1008	63	1	identifying	identify	VERB
cana-1008	63	2	disorders	disorder	NOUN
cana-1008	63	3	such	such	ADJ
cana-1008	63	4	as	as	ADP
cana-1008	63	5	alzheimer	alzheimer	PROPN
cana-1008	63	6	's	's	PART
cana-1008	63	7	and	and	CCONJ
cana-1008	63	8	parkinson	parkinson	NOUN
cana-1008	63	9	's	's	PART
cana-1008	63	10	by	by	ADP
cana-1008	63	11	the	the	DET
cana-1008	63	12	examination	examination	NOUN
cana-1008	63	13	of	of	ADP
cana-1008	63	14	brain	brain	NOUN
cana-1008	63	15	scans	scan	NOUN
cana-1008	63	16	,	,	PUNCT
cana-1008	63	17	genetic	genetic	ADJ
cana-1008	63	18	data	datum	NOUN
cana-1008	63	19	,	,	PUNCT
cana-1008	63	20	and	and	CCONJ
cana-1008	63	21	cognitive	cognitive	ADJ
cana-1008	63	22	test	test	NOUN
cana-1008	63	23	outcomes	outcome	NOUN
cana-1008	63	24	.	.	PUNCT
cana-1008	64	1	forecasting	forecast	VERB
cana-1008	64	2	the	the	DET
cana-1008	64	3	dissemination	dissemination	NOUN
cana-1008	64	4	of	of	ADP
cana-1008	64	5	illnesses	illness	NOUN
cana-1008	64	6	such	such	ADJ
cana-1008	64	7	as	as	ADP
cana-1008	64	8	influenza	influenza	NOUN
cana-1008	64	9	and	and	CCONJ
cana-1008	64	10	covid-19	covid-19	PROPN
cana-1008	64	11	by	by	ADP
cana-1008	64	12	the	the	DET
cana-1008	64	13	use	use	NOUN
cana-1008	64	14	of	of	ADP
cana-1008	64	15	transmission	transmission	NOUN
cana-1008	64	16	patterns	pattern	NOUN
cana-1008	64	17	modelling	model	VERB
cana-1008	64	18	and	and	CCONJ
cana-1008	64	19	the	the	DET
cana-1008	64	20	examination	examination	NOUN
cana-1008	64	21	of	of	ADP
cana-1008	64	22	epidemiological	epidemiological	ADJ
cana-1008	64	23	data[6	data[6	NOUN
cana-1008	64	24	]	]	PUNCT
cana-1008	64	25	.	.	PUNCT
cana-1008	65	1	in	in	ADP
cana-1008	65	2	order	order	NOUN
cana-1008	65	3	to	to	PART
cana-1008	65	4	properly	properly	ADV
cana-1008	65	5	use	use	VERB
cana-1008	65	6	machine	machine	NOUN
cana-1008	65	7	learning	learning	NOUN
cana-1008	65	8	for	for	ADP
cana-1008	65	9	illness	illness	NOUN
cana-1008	65	10	prediction	prediction	NOUN
cana-1008	65	11	,	,	PUNCT
cana-1008	65	12	it	it	PRON
cana-1008	65	13	is	be	AUX
cana-1008	65	14	necessary	necessary	ADJ
cana-1008	65	15	to	to	PART
cana-1008	65	16	solve	solve	VERB
cana-1008	65	17	many	many	ADJ
cana-1008	65	18	problems	problem	NOUN
cana-1008	65	19	despite	despite	SCONJ
cana-1008	65	20	the	the	DET
cana-1008	65	21	promise	promise	NOUN
cana-1008	65	22	it	it	PRON
cana-1008	65	23	holds	hold	VERB
cana-1008	65	24	.	.	PUNCT
cana-1008	66	1	training	train	VERB
cana-1008	66	2	robust	robust	ADJ
cana-1008	66	3	models	model	NOUN
cana-1008	66	4	requires	require	VERB
cana-1008	66	5	the	the	DET
cana-1008	66	6	use	use	NOUN
cana-1008	66	7	of	of	ADP
cana-1008	66	8	high	high	ADJ
cana-1008	66	9	-	-	PUNCT
cana-1008	66	10	quality	quality	NOUN
cana-1008	66	11	,	,	PUNCT
cana-1008	66	12	representative	representative	NOUN
cana-1008	66	13	,	,	PUNCT
cana-1008	66	14	and	and	CCONJ
cana-1008	66	15	large	large	ADJ
cana-1008	66	16	-	-	PUNCT
cana-1008	66	17	scale	scale	NOUN
cana-1008	66	18	datasets	dataset	NOUN
cana-1008	66	19	.	.	PUNCT
cana-1008	67	1	nevertheless	nevertheless	ADV
cana-1008	67	2	,	,	PUNCT
cana-1008	67	3	medical	medical	ADJ
cana-1008	67	4	data	datum	NOUN
cana-1008	67	5	often	often	ADV
cana-1008	67	6	lacks	lack	VERB
cana-1008	67	7	information	information	NOUN
cana-1008	67	8	,	,	PUNCT
cana-1008	67	9	contains	contain	VERB
cana-1008	67	10	errors	error	NOUN
cana-1008	67	11	,	,	PUNCT
cana-1008	67	12	or	or	CCONJ
cana-1008	67	13	exhibits	exhibit	VERB
cana-1008	67	14	prejudice	prejudice	NOUN
cana-1008	67	15	.	.	PUNCT
cana-1008	68	1	a	a	DET
cana-1008	68	2	significant	significant	ADJ
cana-1008	68	3	number	number	NOUN
cana-1008	68	4	of	of	ADP
cana-1008	68	5	machine	machine	NOUN
cana-1008	68	6	learning	learning	NOUN
cana-1008	68	7	models	model	NOUN
cana-1008	68	8	,	,	PUNCT
cana-1008	68	9	particularly	particularly	ADV
cana-1008	68	10	those	those	PRON
cana-1008	68	11	based	base	VERB
cana-1008	68	12	on	on	ADP
cana-1008	68	13	deep	deep	ADJ
cana-1008	68	14	learning	learning	NOUN
cana-1008	68	15	,	,	PUNCT
cana-1008	68	16	function	function	VERB
cana-1008	68	17	as	as	ADP
cana-1008	68	18	opaque	opaque	ADJ
cana-1008	68	19	entities	entity	NOUN
cana-1008	68	20	,	,	PUNCT
cana-1008	68	21	hence	hence	ADV
cana-1008	68	22	posing	pose	VERB
cana-1008	68	23	challenges	challenge	NOUN
cana-1008	68	24	in	in	ADP
cana-1008	68	25	comprehending	comprehend	VERB
cana-1008	68	26	the	the	DET
cana-1008	68	27	underlying	underlie	VERB
cana-1008	68	28	mechanisms	mechanism	NOUN
cana-1008	68	29	behind	behind	ADP
cana-1008	68	30	their	their	PRON
cana-1008	68	31	predictions	prediction	NOUN
cana-1008	68	32	.	.	PUNCT
cana-1008	69	1	interpretability	interpretability	NOUN
cana-1008	69	2	is	be	AUX
cana-1008	69	3	essential	essential	ADJ
cana-1008	69	4	for	for	ADP
cana-1008	69	5	the	the	DET
cana-1008	69	6	acceptability	acceptability	NOUN
cana-1008	69	7	of	of	ADP
cana-1008	69	8	clinical	clinical	ADJ
cana-1008	69	9	applications	application	NOUN
cana-1008	69	10	[	[	X
cana-1008	69	11	7	7	NUM
cana-1008	69	12	]	]	PUNCT
cana-1008	69	13	.	.	PUNCT
cana-1008	70	1	models	model	NOUN
cana-1008	70	2	must	must	AUX
cana-1008	70	3	have	have	VERB
cana-1008	70	4	strong	strong	ADJ
cana-1008	70	5	generalisation	generalisation	NOUN
cana-1008	70	6	capabilities	capability	NOUN
cana-1008	70	7	across	across	ADP
cana-1008	70	8	diverse	diverse	ADJ
cana-1008	70	9	populations	population	NOUN
cana-1008	70	10	and	and	CCONJ
cana-1008	70	11	environments	environment	NOUN
cana-1008	70	12	.	.	PUNCT
cana-1008	71	1	overfitting	overfitte	VERB
cana-1008	71	2	to	to	ADP
cana-1008	71	3	individual	individual	ADJ
cana-1008	71	4	datasets	dataset	NOUN
cana-1008	71	5	might	might	AUX
cana-1008	71	6	restrict	restrict	VERB
cana-1008	71	7	the	the	DET
cana-1008	71	8	generalizability	generalizability	NOUN
cana-1008	71	9	of	of	ADP
cana-1008	71	10	the	the	DET
cana-1008	71	11	models	model	NOUN
cana-1008	71	12	.	.	PUNCT
cana-1008	72	1	managing	manage	VERB
cana-1008	72	2	sensitive	sensitive	ADJ
cana-1008	72	3	medical	medical	ADJ
cana-1008	72	4	information	information	NOUN
cana-1008	72	5	requires	require	VERB
cana-1008	72	6	rigorous	rigorous	ADJ
cana-1008	72	7	privacy	privacy	NOUN
cana-1008	72	8	protocols	protocol	NOUN
cana-1008	72	9	and	and	CCONJ
cana-1008	72	10	ethical	ethical	ADJ
cana-1008	72	11	deliberations	deliberation	NOUN
cana-1008	72	12	to	to	PART
cana-1008	72	13	safeguard	safeguard	VERB
cana-1008	72	14	patient	patient	ADJ
cana-1008	72	15	confidentiality	confidentiality	NOUN
cana-1008	72	16	and	and	CCONJ
cana-1008	72	17	get	get	VERB
cana-1008	72	18	permission	permission	NOUN
cana-1008	72	19	.	.	PUNCT
cana-1008	73	1	integrating	integrate	VERB
cana-1008	73	2	machine	machine	NOUN
cana-1008	73	3	learning	learning	NOUN
cana-1008	73	4	models	model	NOUN
cana-1008	73	5	into	into	ADP
cana-1008	73	6	healthcare	healthcare	NOUN
cana-1008	73	7	settings	setting	NOUN
cana-1008	73	8	necessitates	necessitate	VERB
cana-1008	73	9	smooth	smooth	ADJ
cana-1008	73	10	incorporation	incorporation	NOUN
cana-1008	73	11	into	into	ADP
cana-1008	73	12	current	current	ADJ
cana-1008	73	13	systems	system	NOUN
cana-1008	73	14	and	and	CCONJ
cana-1008	73	15	processes	process	NOUN
cana-1008	73	16	,	,	PUNCT
cana-1008	73	17	as	as	ADV
cana-1008	73	18	well	well	ADV
cana-1008	73	19	as	as	ADP
cana-1008	73	20	providing	provide	VERB
cana-1008	73	21	training	training	NOUN
cana-1008	73	22	for	for	ADP
cana-1008	73	23	healthcare	healthcare	NOUN
cana-1008	73	24	personnel	personnel	NOUN
cana-1008	74	1	[	[	X
cana-1008	74	2	8	8	NUM
cana-1008	74	3	]	]	PUNCT
cana-1008	74	4	.	.	PUNCT
cana-1008	75	1	machine	machine	NOUN
cana-1008	75	2	learning	learning	NOUN
cana-1008	75	3	has	have	VERB
cana-1008	75	4	great	great	ADJ
cana-1008	75	5	promise	promise	NOUN
cana-1008	75	6	for	for	ADP
cana-1008	75	7	illness	illness	NOUN
cana-1008	75	8	prediction	prediction	NOUN
cana-1008	75	9	,	,	PUNCT
cana-1008	75	10	since	since	SCONJ
cana-1008	75	11	it	it	PRON
cana-1008	75	12	can	can	AUX
cana-1008	75	13	analyse	analyse	VERB
cana-1008	75	14	intricate	intricate	ADJ
cana-1008	75	15	medical	medical	ADJ
cana-1008	75	16	data	datum	NOUN
cana-1008	75	17	and	and	CCONJ
cana-1008	75	18	make	make	VERB
cana-1008	75	19	early	early	ADJ
cana-1008	75	20	and	and	CCONJ
cana-1008	75	21	precise	precise	ADJ
cana-1008	75	22	predictions	prediction	NOUN
cana-1008	75	23	.	.	PUNCT
cana-1008	76	1	machine	machine	NOUN
cana-1008	76	2	learning	learning	NOUN
cana-1008	76	3	has	have	VERB
cana-1008	76	4	the	the	DET
cana-1008	76	5	potential	potential	NOUN
cana-1008	76	6	to	to	PART
cana-1008	76	7	greatly	greatly	ADV
cana-1008	76	8	improve	improve	VERB
cana-1008	76	9	healthcare	healthcare	NOUN
cana-1008	76	10	results	result	NOUN
cana-1008	76	11	and	and	CCONJ
cana-1008	76	12	provide	provide	VERB
cana-1008	76	13	more	more	ADV
cana-1008	76	14	individualised	individualised	ADJ
cana-1008	76	15	and	and	CCONJ
cana-1008	76	16	efficient	efficient	ADJ
cana-1008	76	17	treatments	treatment	NOUN
cana-1008	76	18	by	by	ADP
cana-1008	76	19	tackling	tackle	VERB
cana-1008	76	20	present	present	ADJ
cana-1008	76	21	difficulties	difficulty	NOUN
cana-1008	76	22	and	and	CCONJ
cana-1008	76	23	using	use	VERB
cana-1008	76	24	forthcoming	forthcoming	ADJ
cana-1008	76	25	developments	development	NOUN
cana-1008	76	26	.	.	PUNCT
cana-1008	77	1	1.2	1.2	NUM
cana-1008	77	2	.	.	PUNCT
cana-1008	77	3	diabetic	diabetic	ADJ
cana-1008	77	4	prediction	prediction	NOUN
cana-1008	77	5	based	base	VERB
cana-1008	77	6	on	on	ADP
cana-1008	77	7	machine	machine	NOUN
cana-1008	77	8	learning	learning	NOUN
cana-1008	77	9	since	since	SCONJ
cana-1008	77	10	the	the	DET
cana-1008	77	11	beginning	beginning	NOUN
cana-1008	77	12	of	of	ADP
cana-1008	77	13	time	time	NOUN
cana-1008	77	14	,	,	PUNCT
cana-1008	77	15	health	health	NOUN
cana-1008	77	16	has	have	AUX
cana-1008	77	17	been	be	AUX
cana-1008	77	18	and	and	CCONJ
cana-1008	77	19	will	will	AUX
cana-1008	77	20	continue	continue	VERB
cana-1008	77	21	to	to	PART
cana-1008	77	22	be	be	AUX
cana-1008	77	23	a	a	DET
cana-1008	77	24	top	top	ADJ
cana-1008	77	25	priority	priority	NOUN
cana-1008	77	26	.	.	PUNCT
cana-1008	78	1	given	give	VERB
cana-1008	78	2	the	the	DET
cana-1008	78	3	significant	significant	ADJ
cana-1008	78	4	amount	amount	NOUN
cana-1008	78	5	of	of	ADP
cana-1008	78	6	progress	progress	NOUN
cana-1008	78	7	that	that	PRON
cana-1008	78	8	has	have	AUX
cana-1008	78	9	been	be	AUX
cana-1008	78	10	made	make	VERB
cana-1008	78	11	in	in	ADP
cana-1008	78	12	the	the	DET
cana-1008	78	13	healthcare	healthcare	NOUN
cana-1008	78	14	industry	industry	NOUN
cana-1008	78	15	,	,	PUNCT
cana-1008	78	16	there	there	PRON
cana-1008	78	17	is	be	VERB
cana-1008	78	18	a	a	DET
cana-1008	78	19	great	great	ADJ
cana-1008	78	20	deal	deal	NOUN
cana-1008	78	21	of	of	ADP
cana-1008	78	22	room	room	NOUN
cana-1008	78	23	for	for	ADP
cana-1008	78	24	research	research	NOUN
cana-1008	78	25	.	.	PUNCT
cana-1008	79	1	it	it	PRON
cana-1008	79	2	is	be	AUX
cana-1008	79	3	imperative	imperative	ADJ
cana-1008	79	4	that	that	SCONJ
cana-1008	79	5	the	the	DET
cana-1008	79	6	current	current	ADJ
cana-1008	79	7	healthcare	healthcare	NOUN
cana-1008	79	8	technology	technology	NOUN
cana-1008	79	9	be	be	AUX
cana-1008	79	10	upgraded	upgrade	VERB
cana-1008	79	11	by	by	ADP
cana-1008	79	12	embracing	embrace	VERB
cana-1008	79	13	the	the	DET
cana-1008	79	14	digitalization	digitalization	NOUN
cana-1008	79	15	of	of	ADP
cana-1008	79	16	medical	medical	ADJ
cana-1008	79	17	information	information	NOUN
cana-1008	79	18	.	.	PUNCT
cana-1008	80	1	this	this	PRON
cana-1008	80	2	applies	apply	VERB
cana-1008	80	3	to	to	ADP
cana-1008	80	4	the	the	DET
cana-1008	80	5	data	datum	NOUN
cana-1008	80	6	that	that	PRON
cana-1008	80	7	is	be	AUX
cana-1008	80	8	provided	provide	VERB
cana-1008	80	9	by	by	ADP
cana-1008	80	10	patients	patient	NOUN
cana-1008	80	11	as	as	ADV
cana-1008	80	12	well	well	ADV
cana-1008	80	13	as	as	ADP
cana-1008	80	14	the	the	DET
cana-1008	80	15	medical	medical	ADJ
cana-1008	80	16	outcomes	outcome	NOUN
cana-1008	80	17	that	that	PRON
cana-1008	80	18	are	be	AUX
cana-1008	80	19	generated	generate	VERB
cana-1008	80	20	by	by	ADP
cana-1008	80	21	modern	modern	ADJ
cana-1008	80	22	equipment	equipment	NOUN
cana-1008	80	23	.	.	PUNCT
cana-1008	81	1	the	the	DET
cana-1008	81	2	fact	fact	NOUN
cana-1008	81	3	that	that	SCONJ
cana-1008	81	4	we	we	PRON
cana-1008	81	5	are	be	AUX
cana-1008	81	6	confronted	confront	VERB
cana-1008	81	7	with	with	ADP
cana-1008	81	8	the	the	DET
cana-1008	81	9	onerous	onerous	ADJ
cana-1008	81	10	task	task	NOUN
cana-1008	81	11	of	of	ADP
cana-1008	81	12	evaluating	evaluate	VERB
cana-1008	81	13	and	and	CCONJ
cana-1008	81	14	comprehending	comprehend	VERB
cana-1008	81	15	the	the	DET
cana-1008	81	16	massive	massive	ADJ
cana-1008	81	17	amounts	amount	NOUN
cana-1008	81	18	of	of	ADP
cana-1008	81	19	data	datum	NOUN
cana-1008	81	20	that	that	PRON
cana-1008	81	21	have	have	AUX
cana-1008	81	22	been	be	AUX
cana-1008	81	23	obtained	obtain	VERB
cana-1008	81	24	communications	communication	NOUN
cana-1008	81	25	on	on	ADP
cana-1008	81	26	applied	apply	VERB
cana-1008	81	27	nonlinear	nonlinear	ADJ
cana-1008	81	28	analysis	analysis	NOUN
cana-1008	81	29	issn	issn	NOUN
cana-1008	81	30	:	:	PUNCT
cana-1008	81	31	1074	1074	NUM
cana-1008	81	32	-	-	PUNCT
cana-1008	81	33	133x	133x	NUM
cana-1008	81	34	vol	vol	NOUN
cana-1008	81	35	31	31	NUM
cana-1008	81	36	no	no	NOUN
cana-1008	81	37	.	.	PUNCT
cana-1008	82	1	5s	5s	NUM
cana-1008	82	2	(	(	PUNCT
cana-1008	82	3	2024	2024	NUM
cana-1008	82	4	)	)	PUNCT
cana-1008	82	5	141	141	NUM
cana-1008	83	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-1008	83	2	is	be	AUX
cana-1008	83	3	a	a	DET
cana-1008	83	4	common	common	ADJ
cana-1008	83	5	consequence	consequence	NOUN
cana-1008	83	6	of	of	ADP
cana-1008	83	7	this	this	DET
cana-1008	83	8	digital	digital	ADJ
cana-1008	83	9	revolution	revolution	NOUN
cana-1008	83	10	.	.	PUNCT
cana-1008	84	1	in	in	ADP
cana-1008	84	2	light	light	NOUN
cana-1008	84	3	of	of	ADP
cana-1008	84	4	the	the	DET
cana-1008	84	5	fact	fact	NOUN
cana-1008	84	6	that	that	SCONJ
cana-1008	84	7	there	there	PRON
cana-1008	84	8	is	be	VERB
cana-1008	84	9	an	an	DET
cana-1008	84	10	enormous	enormous	ADJ
cana-1008	84	11	quantity	quantity	NOUN
cana-1008	84	12	of	of	ADP
cana-1008	84	13	data	datum	NOUN
cana-1008	84	14	,	,	PUNCT
cana-1008	84	15	big	big	ADJ
cana-1008	84	16	data	datum	NOUN
cana-1008	84	17	analytics	analytic	NOUN
cana-1008	84	18	appears	appear	VERB
cana-1008	84	19	to	to	PART
cana-1008	84	20	be	be	AUX
cana-1008	84	21	of	of	ADP
cana-1008	84	22	great	great	ADJ
cana-1008	84	23	assistance	assistance	NOUN
cana-1008	84	24	[	[	X
cana-1008	84	25	9	9	NUM
cana-1008	84	26	]	]	PUNCT
cana-1008	84	27	.	.	PUNCT
cana-1008	85	1	diabetes	diabetes	NOUN
cana-1008	85	2	mellitus	mellitus	NOUN
cana-1008	85	3	(	(	PUNCT
cana-1008	85	4	db	db	PROPN
cana-1008	85	5	)	)	PUNCT
cana-1008	85	6	is	be	AUX
cana-1008	85	7	a	a	DET
cana-1008	85	8	debilitating	debilitate	VERB
cana-1008	85	9	health	health	NOUN
cana-1008	85	10	condition	condition	NOUN
cana-1008	85	11	that	that	PRON
cana-1008	85	12	places	place	VERB
cana-1008	85	13	a	a	DET
cana-1008	85	14	large	large	ADJ
cana-1008	85	15	additional	additional	ADJ
cana-1008	85	16	financial	financial	ADJ
cana-1008	85	17	burden	burden	NOUN
cana-1008	85	18	on	on	ADP
cana-1008	85	19	healthcare	healthcare	NOUN
cana-1008	85	20	providers	provider	NOUN
cana-1008	85	21	all	all	ADV
cana-1008	85	22	over	over	ADP
cana-1008	85	23	the	the	DET
cana-1008	85	24	world	world	NOUN
cana-1008	85	25	in	in	ADP
cana-1008	85	26	terms	term	NOUN
cana-1008	85	27	of	of	ADP
cana-1008	85	28	the	the	DET
cana-1008	85	29	expense	expense	NOUN
cana-1008	85	30	of	of	ADP
cana-1008	85	31	treatment	treatment	NOUN
cana-1008	85	32	.	.	PUNCT
cana-1008	86	1	diabetes	diabetes	NOUN
cana-1008	86	2	type	type	NOUN
cana-1008	86	3	1	1	NUM
cana-1008	86	4	,	,	PUNCT
cana-1008	86	5	sometimes	sometimes	ADV
cana-1008	86	6	referred	refer	VERB
cana-1008	86	7	to	to	ADP
cana-1008	86	8	as	as	ADP
cana-1008	86	9	hyperglycemia	hyperglycemia	NOUN
cana-1008	86	10	,	,	PUNCT
cana-1008	86	11	is	be	AUX
cana-1008	86	12	a	a	DET
cana-1008	86	13	condition	condition	NOUN
cana-1008	86	14	that	that	PRON
cana-1008	86	15	manifests	manifest	VERB
cana-1008	86	16	itself	itself	PRON
cana-1008	86	17	when	when	SCONJ
cana-1008	86	18	the	the	DET
cana-1008	86	19	beta	beta	NOUN
cana-1008	86	20	cells	cell	NOUN
cana-1008	86	21	of	of	ADP
cana-1008	86	22	the	the	DET
cana-1008	86	23	pancreas	pancrea	NOUN
cana-1008	86	24	fail	fail	VERB
cana-1008	86	25	to	to	PART
cana-1008	86	26	secrete	secrete	VERB
cana-1008	86	27	an	an	DET
cana-1008	86	28	adequate	adequate	ADJ
cana-1008	86	29	amount	amount	NOUN
cana-1008	86	30	of	of	ADP
cana-1008	86	31	insulin	insulin	NOUN
cana-1008	86	32	,	,	PUNCT
cana-1008	86	33	resulting	result	VERB
cana-1008	86	34	in	in	ADP
cana-1008	86	35	elevated	elevated	ADJ
cana-1008	86	36	levels	level	NOUN
cana-1008	86	37	of	of	ADP
cana-1008	86	38	glucose	glucose	NOUN
cana-1008	86	39	in	in	ADP
cana-1008	86	40	the	the	DET
cana-1008	86	41	blood	blood	NOUN
cana-1008	86	42	[	[	X
cana-1008	86	43	10	10	NUM
cana-1008	86	44	]	]	PUNCT
cana-1008	86	45	.	.	PUNCT
cana-1008	87	1	people	people	NOUN
cana-1008	87	2	who	who	PRON
cana-1008	87	3	have	have	VERB
cana-1008	87	4	diabetes	diabetes	NOUN
cana-1008	87	5	type	type	NOUN
cana-1008	87	6	2	2	NUM
cana-1008	87	7	have	have	VERB
cana-1008	87	8	a	a	DET
cana-1008	87	9	body	body	NOUN
cana-1008	87	10	that	that	PRON
cana-1008	87	11	is	be	AUX
cana-1008	87	12	unable	unable	ADJ
cana-1008	87	13	to	to	PART
cana-1008	87	14	make	make	VERB
cana-1008	87	15	efficient	efficient	ADJ
cana-1008	87	16	use	use	NOUN
cana-1008	87	17	of	of	ADP
cana-1008	87	18	the	the	DET
cana-1008	87	19	insulin	insulin	NOUN
cana-1008	87	20	that	that	PRON
cana-1008	87	21	is	be	AUX
cana-1008	87	22	available	available	ADJ
cana-1008	87	23	in	in	ADP
cana-1008	87	24	their	their	PRON
cana-1008	87	25	bodies	body	NOUN
cana-1008	87	26	.	.	PUNCT
cana-1008	88	1	furthermore	furthermore	ADV
cana-1008	88	2	,	,	PUNCT
cana-1008	88	3	diabetic	diabetic	ADJ
cana-1008	88	4	retinopathy	retinopathy	NOUN
cana-1008	88	5	may	may	AUX
cana-1008	88	6	result	result	VERB
cana-1008	88	7	in	in	ADP
cana-1008	88	8	numerous	numerous	ADJ
cana-1008	88	9	clinical	clinical	ADJ
cana-1008	88	10	ramifications	ramification	NOUN
cana-1008	88	11	,	,	PUNCT
cana-1008	88	12	such	such	ADJ
cana-1008	88	13	as	as	ADP
cana-1008	88	14	harm	harm	NOUN
cana-1008	88	15	to	to	ADP
cana-1008	88	16	the	the	DET
cana-1008	88	17	neurological	neurological	ADJ
cana-1008	88	18	system	system	NOUN
cana-1008	88	19	,	,	PUNCT
cana-1008	88	20	degeneration	degeneration	NOUN
cana-1008	88	21	of	of	ADP
cana-1008	88	22	the	the	DET
cana-1008	88	23	retina	retina	NOUN
cana-1008	88	24	,	,	PUNCT
cana-1008	88	25	renal	renal	ADJ
cana-1008	88	26	sickness	sickness	NOUN
cana-1008	88	27	,	,	PUNCT
cana-1008	88	28	and	and	CCONJ
cana-1008	88	29	cardiovascular	cardiovascular	ADJ
cana-1008	88	30	disease	disease	NOUN
cana-1008	88	31	[	[	X
cana-1008	88	32	11	11	NUM
cana-1008	88	33	]	]	PUNCT
cana-1008	88	34	.	.	PUNCT
cana-1008	89	1	in	in	ADP
cana-1008	89	2	1980	1980	NUM
cana-1008	89	3	,	,	PUNCT
cana-1008	89	4	there	there	PRON
cana-1008	89	5	were	be	VERB
cana-1008	89	6	108	108	NUM
cana-1008	89	7	million	million	NUM
cana-1008	89	8	people	people	NOUN
cana-1008	89	9	who	who	PRON
cana-1008	89	10	were	be	AUX
cana-1008	89	11	diagnosed	diagnose	VERB
cana-1008	89	12	with	with	ADP
cana-1008	89	13	diabetes	diabetes	NOUN
cana-1008	89	14	.	.	PUNCT
cana-1008	90	1	in	in	ADP
cana-1008	90	2	2014	2014	NUM
cana-1008	90	3	,	,	PUNCT
cana-1008	90	4	the	the	DET
cana-1008	90	5	number	number	NOUN
cana-1008	90	6	of	of	ADP
cana-1008	90	7	persons	person	NOUN
cana-1008	90	8	afflicted	afflict	VERB
cana-1008	90	9	by	by	ADP
cana-1008	90	10	diabetes	diabetes	NOUN
cana-1008	90	11	is	be	AUX
cana-1008	90	12	estimated	estimate	VERB
cana-1008	90	13	to	to	PART
cana-1008	90	14	have	have	AUX
cana-1008	90	15	surpassed	surpass	VERB
cana-1008	90	16	422	422	NUM
cana-1008	90	17	million	million	NUM
cana-1008	90	18	,	,	PUNCT
cana-1008	90	19	which	which	PRON
cana-1008	90	20	is	be	AUX
cana-1008	90	21	a	a	DET
cana-1008	90	22	significant	significant	ADJ
cana-1008	90	23	increase	increase	NOUN
cana-1008	90	24	from	from	ADP
cana-1008	90	25	that	that	DET
cana-1008	90	26	amount	amount	NOUN
cana-1008	90	27	.	.	PUNCT
cana-1008	91	1	in	in	ADP
cana-1008	91	2	addition	addition	NOUN
cana-1008	91	3	,	,	PUNCT
cana-1008	91	4	over	over	ADP
cana-1008	91	5	the	the	DET
cana-1008	91	6	same	same	ADJ
cana-1008	91	7	time	time	NOUN
cana-1008	91	8	period	period	NOUN
cana-1008	91	9	,	,	PUNCT
cana-1008	91	10	the	the	DET
cana-1008	91	11	percentage	percentage	NOUN
cana-1008	91	12	of	of	ADP
cana-1008	91	13	people	people	NOUN
cana-1008	91	14	who	who	PRON
cana-1008	91	15	were	be	AUX
cana-1008	91	16	diagnosed	diagnose	VERB
cana-1008	91	17	with	with	ADP
cana-1008	91	18	diabetes	diabetes	NOUN
cana-1008	91	19	(	(	PUNCT
cana-1008	91	20	also	also	ADV
cana-1008	91	21	known	know	VERB
cana-1008	91	22	as	as	ADP
cana-1008	91	23	people	people	NOUN
cana-1008	91	24	with	with	ADP
cana-1008	91	25	diabetes	diabetes	NOUN
cana-1008	91	26	)	)	PUNCT
cana-1008	91	27	increased	increase	VERB
cana-1008	91	28	from	from	ADP
cana-1008	91	29	4.7	4.7	NUM
cana-1008	91	30	%	%	NOUN
cana-1008	91	31	to	to	ADP
cana-1008	91	32	8.5	8.5	NUM
cana-1008	91	33	%	%	NOUN
cana-1008	91	34	of	of	ADP
cana-1008	91	35	the	the	DET
cana-1008	91	36	total	total	ADJ
cana-1008	91	37	adult	adult	NOUN
cana-1008	91	38	population	population	NOUN
cana-1008	91	39	.	.	PUNCT
cana-1008	92	1	this	this	PRON
cana-1008	92	2	is	be	AUX
cana-1008	92	3	a	a	DET
cana-1008	92	4	significant	significant	ADJ
cana-1008	92	5	challenge	challenge	NOUN
cana-1008	92	6	for	for	ADP
cana-1008	92	7	those	those	PRON
cana-1008	92	8	who	who	PRON
cana-1008	92	9	are	be	AUX
cana-1008	92	10	attempting	attempt	VERB
cana-1008	92	11	to	to	PART
cana-1008	92	12	control	control	VERB
cana-1008	92	13	diabetes	diabetes	NOUN
cana-1008	92	14	.	.	PUNCT
cana-1008	93	1	in	in	ADP
cana-1008	93	2	the	the	DET
cana-1008	93	3	year	year	NOUN
cana-1008	93	4	2012	2012	NUM
cana-1008	93	5	,	,	PUNCT
cana-1008	93	6	high	high	ADJ
cana-1008	93	7	blood	blood	NOUN
cana-1008	93	8	glucose	glucose	NOUN
cana-1008	93	9	levels	level	NOUN
cana-1008	93	10	were	be	AUX
cana-1008	93	11	the	the	DET
cana-1008	93	12	cause	cause	NOUN
cana-1008	93	13	of	of	ADP
cana-1008	93	14	death	death	NOUN
cana-1008	93	15	for	for	ADP
cana-1008	93	16	2.2	2.2	NUM
cana-1008	93	17	million	million	NUM
cana-1008	93	18	persons	person	NOUN
cana-1008	93	19	who	who	PRON
cana-1008	93	20	had	have	VERB
cana-1008	93	21	diabetes	diabetes	NOUN
cana-1008	93	22	[	[	X
cana-1008	93	23	12	12	NUM
cana-1008	93	24	]	]	PUNCT
cana-1008	93	25	.	.	PUNCT
cana-1008	94	1	one	one	NUM
cana-1008	94	2	million	million	NUM
cana-1008	94	3	and	and	CCONJ
cana-1008	94	4	six	six	NUM
cana-1008	94	5	hundred	hundred	NUM
cana-1008	94	6	thousand	thousand	NUM
cana-1008	94	7	people	people	NOUN
cana-1008	94	8	died	die	VERB
cana-1008	94	9	as	as	ADP
cana-1008	94	10	a	a	DET
cana-1008	94	11	result	result	NOUN
cana-1008	94	12	of	of	ADP
cana-1008	94	13	diabetes	diabetes	NOUN
cana-1008	94	14	in	in	ADP
cana-1008	94	15	the	the	DET
cana-1008	94	16	year	year	NOUN
cana-1008	94	17	2015	2015	NUM
cana-1008	94	18	.	.	PUNCT
cana-1008	95	1	when	when	SCONJ
cana-1008	95	2	it	it	PRON
cana-1008	95	3	comes	come	VERB
cana-1008	95	4	to	to	ADP
cana-1008	95	5	achieving	achieve	VERB
cana-1008	95	6	the	the	DET
cana-1008	95	7	objectives	objective	NOUN
cana-1008	95	8	of	of	ADP
cana-1008	95	9	maximizing	maximize	VERB
cana-1008	95	10	treatment	treatment	NOUN
cana-1008	95	11	choices	choice	NOUN
cana-1008	95	12	,	,	PUNCT
cana-1008	95	13	increasing	increase	VERB
cana-1008	95	14	the	the	DET
cana-1008	95	15	quality	quality	NOUN
cana-1008	95	16	of	of	ADP
cana-1008	95	17	life	life	NOUN
cana-1008	95	18	for	for	ADP
cana-1008	95	19	individuals	individual	NOUN
cana-1008	95	20	who	who	PRON
cana-1008	95	21	have	have	VERB
cana-1008	95	22	diabetes	diabetes	NOUN
cana-1008	95	23	,	,	PUNCT
cana-1008	95	24	and	and	CCONJ
cana-1008	95	25	decreasing	decrease	VERB
cana-1008	95	26	death	death	NOUN
cana-1008	95	27	rates	rate	NOUN
cana-1008	95	28	connected	connect	VERB
cana-1008	95	29	with	with	ADP
cana-1008	95	30	the	the	DET
cana-1008	95	31	condition	condition	NOUN
cana-1008	95	32	,	,	PUNCT
cana-1008	95	33	timely	timely	ADJ
cana-1008	95	34	diagnosis	diagnosis	NOUN
cana-1008	95	35	and	and	CCONJ
cana-1008	95	36	early	early	ADJ
cana-1008	95	37	identification	identification	NOUN
cana-1008	95	38	of	of	ADP
cana-1008	95	39	diabetes	diabetes	NOUN
cana-1008	95	40	are	be	AUX
cana-1008	95	41	very	very	ADV
cana-1008	95	42	essential	essential	ADJ
cana-1008	95	43	.	.	PUNCT
cana-1008	96	1	diabetes	diabetes	NOUN
cana-1008	96	2	is	be	AUX
cana-1008	96	3	expected	expect	VERB
cana-1008	96	4	to	to	PART
cana-1008	96	5	become	become	VERB
cana-1008	96	6	the	the	DET
cana-1008	96	7	sixth	sixth	ADV
cana-1008	96	8	largest	large	ADJ
cana-1008	96	9	cause	cause	NOUN
cana-1008	96	10	of	of	ADP
cana-1008	96	11	death	death	NOUN
cana-1008	96	12	throughout	throughout	ADP
cana-1008	96	13	the	the	DET
cana-1008	96	14	globe	globe	NOUN
cana-1008	96	15	by	by	ADP
cana-1008	96	16	the	the	DET
cana-1008	96	17	year	year	NOUN
cana-1008	96	18	2030	2030	NUM
cana-1008	96	19	,	,	PUNCT
cana-1008	96	20	according	accord	VERB
cana-1008	96	21	to	to	ADP
cana-1008	96	22	projections	projection	NOUN
cana-1008	96	23	.	.	PUNCT
cana-1008	97	1	furthermore	furthermore	ADV
cana-1008	97	2	,	,	PUNCT
cana-1008	97	3	a	a	DET
cana-1008	97	4	sizeable	sizeable	ADJ
cana-1008	97	5	proportion	proportion	NOUN
cana-1008	97	6	of	of	ADP
cana-1008	97	7	people	people	NOUN
cana-1008	97	8	who	who	PRON
cana-1008	97	9	have	have	VERB
cana-1008	97	10	impairments	impairment	NOUN
cana-1008	97	11	do	do	AUX
cana-1008	97	12	not	not	PART
cana-1008	97	13	become	become	VERB
cana-1008	97	14	aware	aware	ADJ
cana-1008	97	15	of	of	ADP
cana-1008	97	16	their	their	PRON
cana-1008	97	17	disease	disease	NOUN
cana-1008	97	18	until	until	SCONJ
cana-1008	97	19	a	a	DET
cana-1008	97	20	critical	critical	ADJ
cana-1008	97	21	complication	complication	NOUN
cana-1008	97	22	manifests	manifest	VERB
cana-1008	97	23	itself	itself	PRON
cana-1008	97	24	;	;	PUNCT
cana-1008	97	25	there	there	PRON
cana-1008	97	26	is	be	VERB
cana-1008	97	27	a	a	DET
cana-1008	97	28	correlation	correlation	NOUN
cana-1008	97	29	between	between	ADP
cana-1008	97	30	the	the	DET
cana-1008	97	31	delay	delay	NOUN
cana-1008	97	32	in	in	ADP
cana-1008	97	33	identifying	identify	VERB
cana-1008	97	34	the	the	DET
cana-1008	97	35	start	start	NOUN
cana-1008	97	36	of	of	ADP
cana-1008	97	37	type	type	NOUN
cana-1008	97	38	2	2	NUM
cana-1008	97	39	diabetes	diabetes	NOUN
cana-1008	97	40	in	in	ADP
cana-1008	97	41	its	its	PRON
cana-1008	97	42	early	early	ADJ
cana-1008	97	43	stages	stage	NOUN
cana-1008	97	44	and	and	CCONJ
cana-1008	97	45	an	an	DET
cana-1008	97	46	increased	increase	VERB
cana-1008	97	47	chance	chance	NOUN
cana-1008	97	48	of	of	ADP
cana-1008	97	49	serious	serious	ADJ
cana-1008	97	50	outcomes	outcome	NOUN
cana-1008	97	51	[	[	X
cana-1008	97	52	13	13	NUM
cana-1008	97	53	]	]	PUNCT
cana-1008	97	54	.	.	PUNCT
cana-1008	98	1	a	a	DET
cana-1008	98	2	reliable	reliable	ADJ
cana-1008	98	3	model	model	NOUN
cana-1008	98	4	that	that	PRON
cana-1008	98	5	is	be	AUX
cana-1008	98	6	able	able	ADJ
cana-1008	98	7	to	to	PART
cana-1008	98	8	effectively	effectively	ADV
cana-1008	98	9	depict	depict	VERB
cana-1008	98	10	the	the	DET
cana-1008	98	11	presence	presence	NOUN
cana-1008	98	12	of	of	ADP
cana-1008	98	13	diabetes	diabetes	NOUN
cana-1008	98	14	through	through	ADP
cana-1008	98	15	the	the	DET
cana-1008	98	16	features	feature	NOUN
cana-1008	98	17	that	that	PRON
cana-1008	98	18	are	be	AUX
cana-1008	98	19	input	input	NOUN
cana-1008	98	20	is	be	AUX
cana-1008	98	21	required	require	VERB
cana-1008	98	22	in	in	ADP
cana-1008	98	23	order	order	NOUN
cana-1008	98	24	to	to	PART
cana-1008	98	25	make	make	VERB
cana-1008	98	26	an	an	DET
cana-1008	98	27	accurate	accurate	ADJ
cana-1008	98	28	prediction	prediction	NOUN
cana-1008	98	29	of	of	ADP
cana-1008	98	30	the	the	DET
cana-1008	98	31	illness	illness	NOUN
cana-1008	98	32	.	.	PUNCT
cana-1008	99	1	it	it	PRON
cana-1008	99	2	is	be	AUX
cana-1008	99	3	possible	possible	ADJ
cana-1008	99	4	to	to	PART
cana-1008	99	5	improve	improve	VERB
cana-1008	99	6	the	the	DET
cana-1008	99	7	efficiency	efficiency	NOUN
cana-1008	99	8	of	of	ADP
cana-1008	99	9	diagnosis	diagnosis	NOUN
cana-1008	99	10	by	by	ADP
cana-1008	99	11	the	the	DET
cana-1008	99	12	utilization	utilization	NOUN
cana-1008	99	13	of	of	ADP
cana-1008	99	14	a	a	DET
cana-1008	99	15	reliable	reliable	ADJ
cana-1008	99	16	model	model	NOUN
cana-1008	99	17	and	and	CCONJ
cana-1008	99	18	a	a	DET
cana-1008	99	19	detection	detection	NOUN
cana-1008	99	20	method	method	NOUN
cana-1008	99	21	that	that	PRON
cana-1008	99	22	is	be	AUX
cana-1008	99	23	precise	precise	ADJ
cana-1008	99	24	.	.	PUNCT
cana-1008	100	1	using	use	VERB
cana-1008	100	2	the	the	DET
cana-1008	100	3	forecast	forecast	NOUN
cana-1008	100	4	,	,	PUNCT
cana-1008	100	5	medical	medical	ADJ
cana-1008	100	6	professionals	professional	NOUN
cana-1008	100	7	are	be	AUX
cana-1008	100	8	able	able	ADJ
cana-1008	100	9	to	to	PART
cana-1008	100	10	foresee	foresee	VERB
cana-1008	100	11	the	the	DET
cana-1008	100	12	possibility	possibility	NOUN
cana-1008	100	13	of	of	ADP
cana-1008	100	14	doing	do	VERB
cana-1008	100	15	biomedical	biomedical	ADJ
cana-1008	100	16	diagnosis	diagnosis	NOUN
cana-1008	100	17	with	with	ADP
cana-1008	100	18	the	the	DET
cana-1008	100	19	assistance	assistance	NOUN
cana-1008	100	20	of	of	ADP
cana-1008	100	21	engineering	engineering	NOUN
cana-1008	100	22	tools	tool	NOUN
cana-1008	100	23	that	that	PRON
cana-1008	100	24	are	be	AUX
cana-1008	100	25	able	able	ADJ
cana-1008	100	26	to	to	PART
cana-1008	100	27	automatically	automatically	ADV
cana-1008	100	28	adjust	adjust	VERB
cana-1008	100	29	to	to	ADP
cana-1008	100	30	any	any	DET
cana-1008	100	31	unanticipated	unanticipated	ADJ
cana-1008	100	32	future	future	ADJ
cana-1008	100	33	situations	situation	NOUN
cana-1008	100	34	.	.	PUNCT
cana-1008	101	1	with	with	ADP
cana-1008	101	2	regard	regard	NOUN
cana-1008	101	3	to	to	ADP
cana-1008	101	4	planning	planning	NOUN
cana-1008	101	5	and	and	CCONJ
cana-1008	101	6	provisioning	provisioning	NOUN
cana-1008	101	7	,	,	PUNCT
cana-1008	101	8	a	a	DET
cana-1008	101	9	long	long	ADJ
cana-1008	101	10	-	-	PUNCT
cana-1008	101	11	term	term	NOUN
cana-1008	101	12	prediction	prediction	NOUN
cana-1008	101	13	algorithm	algorithm	NOUN
cana-1008	101	14	has	have	VERB
cana-1008	101	15	the	the	DET
cana-1008	101	16	potential	potential	NOUN
cana-1008	101	17	to	to	PART
cana-1008	101	18	be	be	AUX
cana-1008	101	19	of	of	ADP
cana-1008	101	20	great	great	ADJ
cana-1008	101	21	assistance	assistance	NOUN
cana-1008	101	22	.	.	PUNCT
cana-1008	102	1	in	in	ADP
cana-1008	102	2	reaction	reaction	NOUN
cana-1008	102	3	to	to	ADP
cana-1008	102	4	new	new	ADJ
cana-1008	102	5	experiences	experience	NOUN
cana-1008	102	6	or	or	CCONJ
cana-1008	102	7	shifts	shift	NOUN
cana-1008	102	8	in	in	ADP
cana-1008	102	9	the	the	DET
cana-1008	102	10	functional	functional	ADJ
cana-1008	102	11	relationships	relationship	NOUN
cana-1008	102	12	between	between	ADP
cana-1008	102	13	components	component	NOUN
cana-1008	102	14	,	,	PUNCT
cana-1008	102	15	intelligence	intelligence	NOUN
cana-1008	102	16	systems	system	NOUN
cana-1008	102	17	have	have	VERB
cana-1008	102	18	the	the	DET
cana-1008	102	19	ability	ability	NOUN
cana-1008	102	20	to	to	PART
cana-1008	102	21	learn	learn	VERB
cana-1008	102	22	,	,	PUNCT
cana-1008	102	23	adapt	adapt	VERB
cana-1008	102	24	,	,	PUNCT
cana-1008	102	25	and	and	CCONJ
cana-1008	102	26	adjust	adjust	VERB
cana-1008	102	27	the	the	DET
cana-1008	102	28	functional	functional	ADJ
cana-1008	102	29	dependencies	dependency	NOUN
cana-1008	102	30	,	,	PUNCT
cana-1008	102	31	respectively	respectively	ADV
cana-1008	102	32	[	[	X
cana-1008	102	33	10	10	NUM
cana-1008	102	34	,	,	PUNCT
cana-1008	102	35	13	13	NUM
cana-1008	102	36	]	]	PUNCT
cana-1008	102	37	.	.	PUNCT
cana-1008	103	1	when	when	SCONJ
cana-1008	103	2	it	it	PRON
cana-1008	103	3	comes	come	VERB
cana-1008	103	4	to	to	ADP
cana-1008	103	5	early	early	ADJ
cana-1008	103	6	diagnosis	diagnosis	NOUN
cana-1008	103	7	,	,	PUNCT
cana-1008	103	8	the	the	DET
cana-1008	103	9	forecasting	forecasting	NOUN
cana-1008	103	10	and	and	CCONJ
cana-1008	103	11	identification	identification	NOUN
cana-1008	103	12	of	of	ADP
cana-1008	103	13	the	the	DET
cana-1008	103	14	disease	disease	NOUN
cana-1008	103	15	are	be	AUX
cana-1008	103	16	assessed	assess	VERB
cana-1008	103	17	through	through	ADP
cana-1008	103	18	the	the	DET
cana-1008	103	19	knowledge	knowledge	NOUN
cana-1008	103	20	and	and	CCONJ
cana-1008	103	21	expertise	expertise	NOUN
cana-1008	103	22	of	of	ADP
cana-1008	103	23	a	a	DET
cana-1008	103	24	physician	physician	NOUN
cana-1008	103	25	;	;	PUNCT
cana-1008	103	26	however	however	ADV
cana-1008	103	27	,	,	PUNCT
cana-1008	103	28	this	this	DET
cana-1008	103	29	method	method	NOUN
cana-1008	103	30	is	be	AUX
cana-1008	103	31	not	not	PART
cana-1008	103	32	without	without	ADP
cana-1008	103	33	its	its	PRON
cana-1008	103	34	limitations	limitation	NOUN
cana-1008	103	35	and	and	CCONJ
cana-1008	103	36	can	can	AUX
cana-1008	103	37	be	be	AUX
cana-1008	103	38	subject	subject	ADJ
cana-1008	103	39	to	to	ADP
cana-1008	103	40	error	error	NOUN
cana-1008	103	41	.	.	PUNCT
cana-1008	104	1	the	the	DET
cana-1008	104	2	healthcare	healthcare	NOUN
cana-1008	104	3	industry	industry	NOUN
cana-1008	104	4	gathers	gather	VERB
cana-1008	104	5	a	a	DET
cana-1008	104	6	massive	massive	ADJ
cana-1008	104	7	amount	amount	NOUN
cana-1008	104	8	of	of	ADP
cana-1008	104	9	data	datum	NOUN
cana-1008	104	10	pertaining	pertain	VERB
cana-1008	104	11	to	to	ADP
cana-1008	104	12	healthcare	healthcare	PROPN
cana-1008	104	13	;	;	PUNCT
cana-1008	104	14	however	however	ADV
cana-1008	104	15	,	,	PUNCT
cana-1008	104	16	this	this	DET
cana-1008	104	17	data	data	NOUN
cana-1008	104	18	is	be	AUX
cana-1008	104	19	unable	unable	ADJ
cana-1008	104	20	to	to	PART
cana-1008	104	21	recognize	recognize	VERB
cana-1008	104	22	patterns	pattern	NOUN
cana-1008	104	23	that	that	PRON
cana-1008	104	24	have	have	AUX
cana-1008	104	25	not	not	PART
cana-1008	104	26	yet	yet	ADV
cana-1008	104	27	been	be	AUX
cana-1008	104	28	discovered	discover	VERB
cana-1008	104	29	,	,	PUNCT
cana-1008	104	30	which	which	PRON
cana-1008	104	31	prevents	prevent	VERB
cana-1008	104	32	it	it	PRON
cana-1008	104	33	from	from	ADP
cana-1008	104	34	making	make	VERB
cana-1008	104	35	successful	successful	ADJ
cana-1008	104	36	judgments	judgment	NOUN
cana-1008	104	37	[	[	X
cana-1008	104	38	14	14	NUM
cana-1008	104	39	]	]	PUNCT
cana-1008	104	40	.	.	PUNCT
cana-1008	105	1	because	because	SCONJ
cana-1008	105	2	decisions	decision	NOUN
cana-1008	105	3	made	make	VERB
cana-1008	105	4	manually	manually	ADV
cana-1008	105	5	are	be	AUX
cana-1008	105	6	based	base	VERB
cana-1008	105	7	on	on	ADP
cana-1008	105	8	the	the	DET
cana-1008	105	9	observations	observation	NOUN
cana-1008	105	10	and	and	CCONJ
cana-1008	105	11	judgment	judgment	NOUN
cana-1008	105	12	of	of	ADP
cana-1008	105	13	the	the	DET
cana-1008	105	14	healthcare	healthcare	NOUN
cana-1008	105	15	official	official	NOUN
cana-1008	105	16	,	,	PUNCT
cana-1008	105	17	which	which	PRON
cana-1008	105	18	is	be	AUX
cana-1008	105	19	not	not	PART
cana-1008	105	20	always	always	ADV
cana-1008	105	21	accurate	accurate	ADJ
cana-1008	105	22	,	,	PUNCT
cana-1008	105	23	manual	manual	ADJ
cana-1008	105	24	decisions	decision	NOUN
cana-1008	105	25	can	can	AUX
cana-1008	105	26	be	be	AUX
cana-1008	105	27	extremely	extremely	ADV
cana-1008	105	28	risky	risky	ADJ
cana-1008	105	29	when	when	SCONJ
cana-1008	105	30	it	it	PRON
cana-1008	105	31	comes	come	VERB
cana-1008	105	32	to	to	ADP
cana-1008	105	33	the	the	DET
cana-1008	105	34	early	early	ADJ
cana-1008	105	35	diagnosis	diagnosis	NOUN
cana-1008	105	36	of	of	ADP
cana-1008	105	37	health	health	NOUN
cana-1008	105	38	conditions	condition	NOUN
cana-1008	105	39	.	.	PUNCT
cana-1008	106	1	there	there	PRON
cana-1008	106	2	may	may	AUX
cana-1008	106	3	be	be	AUX
cana-1008	106	4	some	some	DET
cana-1008	106	5	patterns	pattern	NOUN
cana-1008	106	6	that	that	PRON
cana-1008	106	7	are	be	AUX
cana-1008	106	8	not	not	PART
cana-1008	106	9	readily	readily	ADV
cana-1008	106	10	apparent	apparent	ADJ
cana-1008	106	11	,	,	PUNCT
cana-1008	106	12	which	which	PRON
cana-1008	106	13	may	may	AUX
cana-1008	106	14	have	have	VERB
cana-1008	106	15	an	an	DET
cana-1008	106	16	effect	effect	NOUN
cana-1008	106	17	on	on	ADP
cana-1008	106	18	the	the	DET
cana-1008	106	19	observations	observation	NOUN
cana-1008	106	20	and	and	CCONJ
cana-1008	106	21	the	the	DET
cana-1008	106	22	communications	communication	NOUN
cana-1008	106	23	on	on	ADP
cana-1008	106	24	applied	apply	VERB
cana-1008	106	25	nonlinear	nonlinear	ADJ
cana-1008	106	26	analysis	analysis	NOUN
cana-1008	106	27	issn	issn	NOUN
cana-1008	106	28	:	:	PUNCT
cana-1008	106	29	1074	1074	NUM
cana-1008	106	30	-	-	PUNCT
cana-1008	106	31	133x	133x	NUM
cana-1008	106	32	vol	vol	NOUN
cana-1008	106	33	31	31	NUM
cana-1008	106	34	no	no	NOUN
cana-1008	106	35	.	.	PUNCT
cana-1008	107	1	5s	5s	NUM
cana-1008	107	2	(	(	PUNCT
cana-1008	107	3	2024	2024	NUM
cana-1008	107	4	)	)	PUNCT
cana-1008	107	5	142	142	NUM
cana-1008	107	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1008	107	7	results	result	NOUN
cana-1008	107	8	.	.	PUNCT
cana-1008	108	1	patients	patient	NOUN
cana-1008	108	2	are	be	AUX
cana-1008	108	3	receiving	receive	VERB
cana-1008	108	4	a	a	DET
cana-1008	108	5	low	low	ADJ
cana-1008	108	6	quality	quality	NOUN
cana-1008	108	7	of	of	ADP
cana-1008	108	8	service	service	NOUN
cana-1008	108	9	as	as	ADP
cana-1008	108	10	a	a	DET
cana-1008	108	11	consequence	consequence	NOUN
cana-1008	108	12	of	of	ADP
cana-1008	108	13	this	this	PRON
cana-1008	108	14	;	;	PUNCT
cana-1008	108	15	hence	hence	ADV
cana-1008	108	16	,	,	PUNCT
cana-1008	108	17	an	an	DET
cana-1008	108	18	advanced	advanced	ADJ
cana-1008	108	19	mechanism	mechanism	NOUN
cana-1008	108	20	is	be	AUX
cana-1008	108	21	necessary	necessary	ADJ
cana-1008	108	22	for	for	ADP
cana-1008	108	23	early	early	ADJ
cana-1008	108	24	identification	identification	NOUN
cana-1008	108	25	of	of	ADP
cana-1008	108	26	illness	illness	NOUN
cana-1008	108	27	with	with	ADP
cana-1008	108	28	an	an	DET
cana-1008	108	29	automatic	automatic	ADJ
cana-1008	108	30	diagnosis	diagnosis	NOUN
cana-1008	108	31	and	and	CCONJ
cana-1008	108	32	improved	improved	ADJ
cana-1008	108	33	accuracy	accuracy	NOUN
cana-1008	108	34	.	.	PUNCT
cana-1008	109	1	in	in	ADP
cana-1008	109	2	the	the	DET
cana-1008	109	3	process	process	NOUN
cana-1008	109	4	of	of	ADP
cana-1008	109	5	data	datum	NOUN
cana-1008	109	6	mining	mining	NOUN
cana-1008	109	7	and	and	CCONJ
cana-1008	109	8	machine	machine	NOUN
cana-1008	109	9	learning	learning	NOUN
cana-1008	109	10	,	,	PUNCT
cana-1008	109	11	many	many	ADJ
cana-1008	109	12	flaws	flaw	NOUN
cana-1008	109	13	and	and	CCONJ
cana-1008	109	14	hidden	hidden	ADJ
cana-1008	109	15	patterns	pattern	NOUN
cana-1008	109	16	that	that	PRON
cana-1008	109	17	have	have	AUX
cana-1008	109	18	not	not	PART
cana-1008	109	19	been	be	AUX
cana-1008	109	20	discovered	discover	VERB
cana-1008	109	21	give	give	VERB
cana-1008	109	22	rise	rise	NOUN
cana-1008	109	23	to	to	ADP
cana-1008	109	24	a	a	DET
cana-1008	109	25	wide	wide	ADJ
cana-1008	109	26	variety	variety	NOUN
cana-1008	109	27	of	of	ADP
cana-1008	109	28	algorithms	algorithm	NOUN
cana-1008	109	29	that	that	PRON
cana-1008	109	30	are	be	AUX
cana-1008	109	31	capable	capable	ADJ
cana-1008	109	32	of	of	ADP
cana-1008	109	33	producing	produce	VERB
cana-1008	109	34	efficient	efficient	ADJ
cana-1008	109	35	results	result	NOUN
cana-1008	109	36	with	with	ADP
cana-1008	109	37	trustworthy	trustworthy	ADJ
cana-1008	109	38	accuracy	accuracy	NOUN
cana-1008	110	1	[	[	X
cana-1008	110	2	15	15	NUM
cana-1008	110	3	]	]	PUNCT
cana-1008	110	4	.	.	PUNCT
cana-1008	111	1	a	a	DET
cana-1008	111	2	wide	wide	ADJ
cana-1008	111	3	range	range	NOUN
cana-1008	111	4	of	of	ADP
cana-1008	111	5	data	datum	NOUN
cana-1008	111	6	mining	mining	NOUN
cana-1008	111	7	techniques	technique	NOUN
cana-1008	111	8	have	have	AUX
cana-1008	111	9	been	be	AUX
cana-1008	111	10	developed	develop	VERB
cana-1008	111	11	in	in	ADP
cana-1008	111	12	response	response	NOUN
cana-1008	111	13	to	to	ADP
cana-1008	111	14	the	the	DET
cana-1008	111	15	ever	ever	ADV
cana-1008	111	16	-	-	PUNCT
cana-1008	111	17	increasing	increase	VERB
cana-1008	111	18	impact	impact	NOUN
cana-1008	111	19	of	of	ADP
cana-1008	111	20	diabetes	diabetes	NOUN
cana-1008	111	21	on	on	ADP
cana-1008	111	22	a	a	DET
cana-1008	111	23	daily	daily	ADJ
cana-1008	111	24	basis	basis	NOUN
cana-1008	111	25	.	.	PUNCT
cana-1008	112	1	these	these	DET
cana-1008	112	2	algorithms	algorithm	NOUN
cana-1008	112	3	are	be	AUX
cana-1008	112	4	designed	design	VERB
cana-1008	112	5	to	to	PART
cana-1008	112	6	uncover	uncover	VERB
cana-1008	112	7	hidden	hidden	ADJ
cana-1008	112	8	patterns	pattern	NOUN
cana-1008	112	9	within	within	ADP
cana-1008	112	10	enormous	enormous	ADJ
cana-1008	112	11	amounts	amount	NOUN
cana-1008	112	12	of	of	ADP
cana-1008	112	13	healthcare	healthcare	PROPN
cana-1008	112	14	data	datum	NOUN
cana-1008	112	15	.	.	PUNCT
cana-1008	113	1	in	in	ADP
cana-1008	113	2	addition	addition	NOUN
cana-1008	113	3	,	,	PUNCT
cana-1008	113	4	the	the	DET
cana-1008	113	5	data	datum	NOUN
cana-1008	113	6	can	can	AUX
cana-1008	113	7	be	be	AUX
cana-1008	113	8	utilized	utilize	VERB
cana-1008	113	9	for	for	ADP
cana-1008	113	10	the	the	DET
cana-1008	113	11	purpose	purpose	NOUN
cana-1008	113	12	of	of	ADP
cana-1008	113	13	selecting	select	VERB
cana-1008	113	14	features	feature	NOUN
cana-1008	113	15	and	and	CCONJ
cana-1008	113	16	making	make	VERB
cana-1008	113	17	automated	automate	VERB
cana-1008	113	18	predictions	prediction	NOUN
cana-1008	113	19	regarding	regard	VERB
cana-1008	113	20	diabetes	diabetes	NOUN
cana-1008	113	21	.	.	PUNCT
cana-1008	114	1	this	this	DET
cana-1008	114	2	research	research	NOUN
cana-1008	114	3	work	work	NOUN
cana-1008	114	4	's	's	PART
cana-1008	114	5	primary	primary	ADJ
cana-1008	114	6	objective	objective	NOUN
cana-1008	114	7	is	be	AUX
cana-1008	114	8	to	to	PART
cana-1008	114	9	suggest	suggest	VERB
cana-1008	114	10	an	an	DET
cana-1008	114	11	invention	invention	NOUN
cana-1008	114	12	of	of	ADP
cana-1008	114	13	a	a	DET
cana-1008	114	14	prognostic	prognostic	ADJ
cana-1008	114	15	tool	tool	NOUN
cana-1008	114	16	for	for	ADP
cana-1008	114	17	early	early	ADJ
cana-1008	114	18	diabetes	diabetes	NOUN
cana-1008	114	19	forecasting	forecasting	NOUN
cana-1008	114	20	and	and	CCONJ
cana-1008	114	21	diagnosis	diagnosis	NOUN
cana-1008	114	22	that	that	PRON
cana-1008	114	23	is	be	AUX
cana-1008	114	24	more	more	ADV
cana-1008	114	25	accurate	accurate	ADJ
cana-1008	114	26	than	than	ADP
cana-1008	114	27	previous	previous	ADJ
cana-1008	114	28	methods	method	NOUN
cana-1008	114	29	.	.	PUNCT
cana-1008	115	1	the	the	DET
cana-1008	115	2	pima	pima	PROPN
cana-1008	115	3	dataset	dataset	PROPN
cana-1008	115	4	,	,	PUNCT
cana-1008	115	5	which	which	PRON
cana-1008	115	6	has	have	AUX
cana-1008	115	7	been	be	AUX
cana-1008	115	8	utilized	utilize	VERB
cana-1008	115	9	in	in	ADP
cana-1008	115	10	this	this	DET
cana-1008	115	11	work	work	NOUN
cana-1008	115	12	,	,	PUNCT
cana-1008	115	13	is	be	AUX
cana-1008	115	14	one	one	NUM
cana-1008	115	15	of	of	ADP
cana-1008	115	16	the	the	DET
cana-1008	115	17	most	most	ADV
cana-1008	115	18	extensively	extensively	ADV
cana-1008	115	19	used	use	VERB
cana-1008	115	20	datasets	dataset	NOUN
cana-1008	115	21	.	.	PUNCT
cana-1008	116	1	there	there	PRON
cana-1008	116	2	has	have	AUX
cana-1008	116	3	been	be	AUX
cana-1008	116	4	a	a	DET
cana-1008	116	5	substantial	substantial	ADJ
cana-1008	116	6	amount	amount	NOUN
cana-1008	116	7	of	of	ADP
cana-1008	116	8	data	datum	NOUN
cana-1008	116	9	and	and	CCONJ
cana-1008	116	10	datasets	dataset	NOUN
cana-1008	116	11	that	that	PRON
cana-1008	116	12	have	have	AUX
cana-1008	116	13	been	be	AUX
cana-1008	116	14	made	make	VERB
cana-1008	116	15	available	available	ADJ
cana-1008	116	16	on	on	ADP
cana-1008	116	17	the	the	DET
cana-1008	116	18	internet	internet	NOUN
cana-1008	116	19	or	or	CCONJ
cana-1008	116	20	from	from	ADP
cana-1008	116	21	other	other	ADJ
cana-1008	116	22	sources	source	NOUN
cana-1008	116	23	.	.	PUNCT
cana-1008	117	1	svm	svm	PROPN
cana-1008	117	2	,	,	PUNCT
cana-1008	117	3	nb	nb	PROPN
cana-1008	117	4	,	,	PUNCT
cana-1008	117	5	dt	dt	X
cana-1008	117	6	,	,	PUNCT
cana-1008	117	7	and	and	CCONJ
cana-1008	117	8	rf	rf	PRON
cana-1008	117	9	are	be	AUX
cana-1008	117	10	some	some	PRON
cana-1008	117	11	of	of	ADP
cana-1008	117	12	the	the	DET
cana-1008	117	13	data	data	NOUN
cana-1008	117	14	mining	mining	NOUN
cana-1008	117	15	techniques	technique	NOUN
cana-1008	117	16	that	that	PRON
cana-1008	117	17	were	be	AUX
cana-1008	117	18	utilized	utilize	VERB
cana-1008	117	19	in	in	ADP
cana-1008	117	20	this	this	DET
cana-1008	117	21	research	research	NOUN
cana-1008	117	22	effort	effort	NOUN
cana-1008	117	23	,	,	PUNCT
cana-1008	117	24	which	which	PRON
cana-1008	117	25	represents	represent	VERB
cana-1008	117	26	thorough	thorough	ADJ
cana-1008	117	27	studies	study	NOUN
cana-1008	117	28	that	that	PRON
cana-1008	117	29	were	be	AUX
cana-1008	117	30	conducted	conduct	VERB
cana-1008	117	31	on	on	ADP
cana-1008	117	32	the	the	DET
cana-1008	117	33	pima	pima	PROPN
cana-1008	117	34	datasets	dataset	NOUN
cana-1008	117	35	.	.	PUNCT
cana-1008	118	1	2	2	X
cana-1008	118	2	.	.	X
cana-1008	118	3	literature	literature	PROPN
cana-1008	118	4	review	review	PROPN
cana-1008	118	5	data	datum	NOUN
cana-1008	118	6	mining	mining	NOUN
cana-1008	118	7	can	can	AUX
cana-1008	118	8	be	be	AUX
cana-1008	118	9	applied	apply	VERB
cana-1008	118	10	across	across	ADP
cana-1008	118	11	various	various	ADJ
cana-1008	118	12	areas	area	NOUN
cana-1008	118	13	like	like	ADP
cana-1008	118	14	health	health	NOUN
cana-1008	118	15	care	care	NOUN
cana-1008	118	16	,	,	PUNCT
cana-1008	118	17	education	education	NOUN
cana-1008	118	18	,	,	PUNCT
cana-1008	118	19	business	business	NOUN
cana-1008	118	20	,	,	PUNCT
cana-1008	118	21	and	and	CCONJ
cana-1008	118	22	numerous	numerous	ADJ
cana-1008	118	23	other	other	ADJ
cana-1008	118	24	domains	domain	NOUN
cana-1008	118	25	.	.	PUNCT
cana-1008	119	1	data	datum	NOUN
cana-1008	119	2	mining	mining	NOUN
cana-1008	119	3	in	in	ADP
cana-1008	119	4	healthcare	healthcare	PROPN
cana-1008	119	5	facilitates	facilitate	VERB
cana-1008	119	6	the	the	DET
cana-1008	119	7	identification	identification	NOUN
cana-1008	119	8	of	of	ADP
cana-1008	119	9	illnesses	illness	NOUN
cana-1008	119	10	,	,	PUNCT
cana-1008	119	11	prognosis	prognosis	NOUN
cana-1008	119	12	,	,	PUNCT
cana-1008	119	13	and	and	CCONJ
cana-1008	119	14	comprehensive	comprehensive	ADJ
cana-1008	119	15	analysis	analysis	NOUN
cana-1008	119	16	of	of	ADP
cana-1008	119	17	medical	medical	ADJ
cana-1008	119	18	data	datum	NOUN
cana-1008	119	19	.	.	PUNCT
cana-1008	120	1	for	for	ADP
cana-1008	120	2	example	example	NOUN
cana-1008	120	3	,	,	PUNCT
cana-1008	120	4	it	it	PRON
cana-1008	120	5	can	can	AUX
cana-1008	120	6	enhance	enhance	VERB
cana-1008	120	7	comprehension	comprehension	NOUN
cana-1008	120	8	of	of	ADP
cana-1008	120	9	the	the	DET
cana-1008	120	10	relationship	relationship	NOUN
cana-1008	120	11	between	between	ADP
cana-1008	120	12	several	several	ADJ
cana-1008	120	13	chronic	chronic	ADJ
cana-1008	120	14	illnesses	illness	NOUN
cana-1008	120	15	,	,	PUNCT
cana-1008	120	16	including	include	VERB
cana-1008	120	17	type	type	NOUN
cana-1008	120	18	2	2	NUM
cana-1008	120	19	diabetes	diabetes	NOUN
cana-1008	120	20	(	(	PUNCT
cana-1008	120	21	dm	dm	NOUN
cana-1008	120	22	)	)	PUNCT
cana-1008	120	23	,	,	PUNCT
cana-1008	120	24	which	which	PRON
cana-1008	120	25	is	be	AUX
cana-1008	120	26	a	a	DET
cana-1008	120	27	significant	significant	ADJ
cana-1008	120	28	health	health	NOUN
cana-1008	120	29	issue	issue	NOUN
cana-1008	120	30	and	and	CCONJ
cana-1008	120	31	a	a	DET
cana-1008	120	32	leading	lead	VERB
cana-1008	120	33	cause	cause	NOUN
cana-1008	120	34	of	of	ADP
cana-1008	120	35	mortality	mortality	NOUN
cana-1008	120	36	.	.	PUNCT
cana-1008	121	1	the	the	DET
cana-1008	121	2	study	study	NOUN
cana-1008	121	3	[	[	X
cana-1008	121	4	16	16	NUM
cana-1008	121	5	]	]	PUNCT
cana-1008	121	6	included	include	VERB
cana-1008	121	7	a	a	DET
cana-1008	121	8	complete	complete	ADJ
cana-1008	121	9	analysis	analysis	NOUN
cana-1008	121	10	of	of	ADP
cana-1008	121	11	three	three	NUM
cana-1008	121	12	different	different	ADJ
cana-1008	121	13	data	datum	NOUN
cana-1008	121	14	mining	mining	NOUN
cana-1008	121	15	algorithms	algorithm	NOUN
cana-1008	121	16	that	that	PRON
cana-1008	121	17	were	be	AUX
cana-1008	121	18	used	use	VERB
cana-1008	121	19	for	for	ADP
cana-1008	121	20	the	the	DET
cana-1008	121	21	purpose	purpose	NOUN
cana-1008	121	22	of	of	ADP
cana-1008	121	23	predicting	predict	VERB
cana-1008	121	24	diabetes	diabetes	NOUN
cana-1008	121	25	or	or	CCONJ
cana-1008	121	26	prediabetes	prediabete	NOUN
cana-1008	121	27	.	.	PUNCT
cana-1008	122	1	there	there	PRON
cana-1008	122	2	were	be	VERB
cana-1008	122	3	three	three	NUM
cana-1008	122	4	types	type	NOUN
cana-1008	122	5	of	of	ADP
cana-1008	122	6	models	model	NOUN
cana-1008	122	7	that	that	PRON
cana-1008	122	8	were	be	AUX
cana-1008	122	9	used	use	VERB
cana-1008	122	10	in	in	ADP
cana-1008	122	11	the	the	DET
cana-1008	122	12	data	data	NOUN
cana-1008	122	13	mining	mining	NOUN
cana-1008	122	14	process	process	NOUN
cana-1008	122	15	:	:	PUNCT
cana-1008	122	16	decision	decision	NOUN
cana-1008	122	17	trees	tree	NOUN
cana-1008	122	18	(	(	PUNCT
cana-1008	122	19	dt	dt	NOUN
cana-1008	122	20	)	)	PUNCT
cana-1008	122	21	,	,	PUNCT
cana-1008	122	22	artificial	artificial	ADJ
cana-1008	122	23	neural	neural	ADJ
cana-1008	122	24	networks	network	NOUN
cana-1008	122	25	(	(	PUNCT
cana-1008	122	26	anns	anns	NOUN
cana-1008	122	27	)	)	PUNCT
cana-1008	122	28	,	,	PUNCT
cana-1008	122	29	and	and	CCONJ
cana-1008	122	30	logistic	logistic	ADJ
cana-1008	122	31	regression	regression	NOUN
cana-1008	122	32	(	(	PUNCT
cana-1008	122	33	lr	lr	NOUN
cana-1008	122	34	)	)	PUNCT
cana-1008	122	35	.	.	PUNCT
cana-1008	123	1	there	there	PRON
cana-1008	123	2	are	be	VERB
cana-1008	123	3	735	735	NUM
cana-1008	123	4	patients	patient	NOUN
cana-1008	123	5	and	and	CCONJ
cana-1008	123	6	752	752	NUM
cana-1008	123	7	normal	normal	ADJ
cana-1008	123	8	controls	control	NOUN
cana-1008	123	9	included	include	VERB
cana-1008	123	10	in	in	ADP
cana-1008	123	11	the	the	DET
cana-1008	123	12	dataset	dataset	NOUN
cana-1008	123	13	that	that	PRON
cana-1008	123	14	was	be	AUX
cana-1008	123	15	utilized	utilize	VERB
cana-1008	123	16	.	.	PUNCT
cana-1008	124	1	the	the	DET
cana-1008	124	2	distribution	distribution	NOUN
cana-1008	124	3	of	of	ADP
cana-1008	124	4	the	the	DET
cana-1008	124	5	patients	patient	NOUN
cana-1008	124	6	is	be	AUX
cana-1008	124	7	even	even	ADV
cana-1008	124	8	.	.	PUNCT
cana-1008	125	1	twelve	twelve	NUM
cana-1008	125	2	parameters	parameter	NOUN
cana-1008	125	3	were	be	AUX
cana-1008	125	4	used	use	VERB
cana-1008	125	5	in	in	ADP
cana-1008	125	6	the	the	DET
cana-1008	125	7	construction	construction	NOUN
cana-1008	125	8	of	of	ADP
cana-1008	125	9	the	the	DET
cana-1008	125	10	models	model	NOUN
cana-1008	125	11	.	.	PUNCT
cana-1008	126	1	these	these	DET
cana-1008	126	2	parameters	parameter	NOUN
cana-1008	126	3	included	include	VERB
cana-1008	126	4	gender	gender	NOUN
cana-1008	126	5	,	,	PUNCT
cana-1008	126	6	age	age	NOUN
cana-1008	126	7	,	,	PUNCT
cana-1008	126	8	marital	marital	ADJ
cana-1008	126	9	status	status	NOUN
cana-1008	126	10	,	,	PUNCT
cana-1008	126	11	educational	educational	ADJ
cana-1008	126	12	attainment	attainment	NOUN
cana-1008	126	13	,	,	PUNCT
cana-1008	126	14	familial	familial	ADJ
cana-1008	126	15	predisposition	predisposition	NOUN
cana-1008	126	16	to	to	ADP
cana-1008	126	17	diabetes	diabetes	NOUN
cana-1008	126	18	,	,	PUNCT
cana-1008	126	19	body	body	NOUN
cana-1008	126	20	mass	mass	NOUN
cana-1008	126	21	index	index	NOUN
cana-1008	126	22	(	(	PUNCT
cana-1008	126	23	bmi	bmi	PROPN
cana-1008	126	24	)	)	PUNCT
cana-1008	126	25	,	,	PUNCT
cana-1008	126	26	coffee	coffee	NOUN
cana-1008	126	27	consumption	consumption	NOUN
cana-1008	126	28	,	,	PUNCT
cana-1008	126	29	amount	amount	NOUN
cana-1008	126	30	of	of	ADP
cana-1008	126	31	physical	physical	ADJ
cana-1008	126	32	activity	activity	NOUN
cana-1008	126	33	,	,	PUNCT
cana-1008	126	34	duration	duration	NOUN
cana-1008	126	35	of	of	ADP
cana-1008	126	36	sleep	sleep	NOUN
cana-1008	126	37	,	,	PUNCT
cana-1008	126	38	stress	stress	NOUN
cana-1008	126	39	related	relate	VERB
cana-1008	126	40	to	to	ADP
cana-1008	126	41	work	work	NOUN
cana-1008	126	42	,	,	PUNCT
cana-1008	126	43	fish	fish	NOUN
cana-1008	126	44	consumption	consumption	NOUN
cana-1008	126	45	,	,	PUNCT
cana-1008	126	46	and	and	CCONJ
cana-1008	126	47	preference	preference	NOUN
cana-1008	126	48	for	for	ADP
cana-1008	126	49	salty	salty	ADJ
cana-1008	126	50	foods	food	NOUN
cana-1008	126	51	.	.	PUNCT
cana-1008	127	1	in	in	ADP
cana-1008	127	2	order	order	NOUN
cana-1008	127	3	to	to	PART
cana-1008	127	4	collect	collect	VERB
cana-1008	127	5	the	the	DET
cana-1008	127	6	aforementioned	aforementioned	ADJ
cana-1008	127	7	characteristics	characteristic	NOUN
cana-1008	127	8	,	,	PUNCT
cana-1008	127	9	a	a	DET
cana-1008	127	10	survey	survey	NOUN
cana-1008	127	11	was	be	AUX
cana-1008	127	12	developed	develop	VERB
cana-1008	127	13	and	and	CCONJ
cana-1008	127	14	administered	administer	VERB
cana-1008	127	15	.	.	PUNCT
cana-1008	128	1	the	the	DET
cana-1008	128	2	findings	finding	NOUN
cana-1008	128	3	of	of	ADP
cana-1008	128	4	the	the	DET
cana-1008	128	5	study	study	NOUN
cana-1008	128	6	led	lead	VERB
cana-1008	128	7	the	the	DET
cana-1008	128	8	researchers	researcher	NOUN
cana-1008	128	9	to	to	ADP
cana-1008	128	10	the	the	DET
cana-1008	128	11	conclusion	conclusion	NOUN
cana-1008	128	12	that	that	SCONJ
cana-1008	128	13	the	the	DET
cana-1008	128	14	c5.0	c5.0	PROPN
cana-1008	128	15	decision	decision	NOUN
cana-1008	128	16	tree	tree	NOUN
cana-1008	128	17	exhibited	exhibit	VERB
cana-1008	128	18	superior	superior	ADJ
cana-1008	128	19	performance	performance	NOUN
cana-1008	128	20	in	in	ADP
cana-1008	128	21	terms	term	NOUN
cana-1008	128	22	of	of	ADP
cana-1008	128	23	identification	identification	NOUN
cana-1008	128	24	precision	precision	NOUN
cana-1008	128	25	.	.	PUNCT
cana-1008	129	1	using	use	VERB
cana-1008	129	2	the	the	DET
cana-1008	129	3	pidd	pidd	PROPN
cana-1008	129	4	dataset	dataset	NOUN
cana-1008	129	5	,	,	PUNCT
cana-1008	129	6	abdulhadi	abdulhadi	PROPN
cana-1008	129	7	et	et	PROPN
cana-1008	129	8	al	al	PROPN
cana-1008	129	9	.	.	PUNCT
cana-1008	130	1	[	[	X
cana-1008	130	2	17	17	NUM
cana-1008	130	3	]	]	PUNCT
cana-1008	130	4	used	use	VERB
cana-1008	130	5	a	a	DET
cana-1008	130	6	number	number	NOUN
cana-1008	130	7	of	of	ADP
cana-1008	130	8	different	different	ADJ
cana-1008	130	9	machine	machine	NOUN
cana-1008	130	10	learning	learning	NOUN
cana-1008	130	11	models	model	NOUN
cana-1008	130	12	in	in	ADP
cana-1008	130	13	order	order	NOUN
cana-1008	130	14	to	to	PART
cana-1008	130	15	make	make	VERB
cana-1008	130	16	predictions	prediction	NOUN
cana-1008	130	17	about	about	ADP
cana-1008	130	18	the	the	DET
cana-1008	130	19	development	development	NOUN
cana-1008	130	20	of	of	ADP
cana-1008	130	21	diabetes	diabetes	NOUN
cana-1008	130	22	in	in	ADP
cana-1008	130	23	females	female	NOUN
cana-1008	130	24	.	.	PUNCT
cana-1008	131	1	the	the	DET
cana-1008	131	2	issue	issue	NOUN
cana-1008	131	3	of	of	ADP
cana-1008	131	4	missing	miss	VERB
cana-1008	131	5	data	datum	NOUN
cana-1008	131	6	was	be	AUX
cana-1008	131	7	handled	handle	VERB
cana-1008	131	8	by	by	ADP
cana-1008	131	9	the	the	DET
cana-1008	131	10	authors	author	NOUN
cana-1008	131	11	via	via	ADP
cana-1008	131	12	the	the	DET
cana-1008	131	13	use	use	NOUN
cana-1008	131	14	of	of	ADP
cana-1008	131	15	the	the	DET
cana-1008	131	16	mean	mean	ADJ
cana-1008	131	17	replacement	replacement	NOUN
cana-1008	131	18	strategy	strategy	NOUN
cana-1008	131	19	,	,	PUNCT
cana-1008	131	20	and	and	CCONJ
cana-1008	131	21	all	all	PRON
cana-1008	131	22	of	of	ADP
cana-1008	131	23	the	the	DET
cana-1008	131	24	attributes	attribute	NOUN
cana-1008	131	25	were	be	AUX
cana-1008	131	26	standardized	standardize	VERB
cana-1008	131	27	through	through	ADP
cana-1008	131	28	the	the	DET
cana-1008	131	29	utilization	utilization	NOUN
cana-1008	131	30	of	of	ADP
cana-1008	131	31	a	a	DET
cana-1008	131	32	standardization	standardization	NOUN
cana-1008	131	33	procedure	procedure	NOUN
cana-1008	131	34	.	.	PUNCT
cana-1008	132	1	logistic	logistic	ADJ
cana-1008	132	2	regression	regression	NOUN
cana-1008	132	3	(	(	PUNCT
cana-1008	132	4	lr	lr	NOUN
cana-1008	132	5	)	)	PUNCT
cana-1008	132	6	,	,	PUNCT
cana-1008	132	7	linear	linear	ADJ
cana-1008	132	8	discriminant	discriminant	ADJ
cana-1008	132	9	analysis	analysis	NOUN
cana-1008	132	10	(	(	PUNCT
cana-1008	132	11	lda	lda	PROPN
cana-1008	132	12	)	)	PUNCT
cana-1008	132	13	,	,	PUNCT
cana-1008	132	14	support	support	NOUN
cana-1008	132	15	vector	vector	NOUN
cana-1008	132	16	machine	machine	NOUN
cana-1008	132	17	(	(	PUNCT
cana-1008	132	18	svm	svm	PROPN
cana-1008	132	19	)	)	PUNCT
cana-1008	132	20	with	with	ADP
cana-1008	132	21	linear	linear	ADJ
cana-1008	132	22	and	and	CCONJ
cana-1008	132	23	polynomial	polynomial	ADJ
cana-1008	132	24	kernels	kernel	NOUN
cana-1008	132	25	,	,	PUNCT
cana-1008	132	26	and	and	CCONJ
cana-1008	132	27	random	random	ADJ
cana-1008	132	28	forest	forest	NOUN
cana-1008	132	29	(	(	PUNCT
cana-1008	132	30	rf	rf	NOUN
cana-1008	132	31	)	)	PUNCT
cana-1008	132	32	were	be	AUX
cana-1008	132	33	the	the	DET
cana-1008	132	34	methods	method	NOUN
cana-1008	132	35	that	that	PRON
cana-1008	132	36	were	be	AUX
cana-1008	132	37	used	use	VERB
cana-1008	132	38	in	in	ADP
cana-1008	132	39	the	the	DET
cana-1008	132	40	construction	construction	NOUN
cana-1008	132	41	of	of	ADP
cana-1008	132	42	the	the	DET
cana-1008	132	43	models	model	NOUN
cana-1008	132	44	.	.	PUNCT
cana-1008	133	1	according	accord	VERB
cana-1008	133	2	to	to	ADP
cana-1008	133	3	what	what	PRON
cana-1008	133	4	is	be	AUX
cana-1008	133	5	shown	show	VERB
cana-1008	133	6	in	in	ADP
cana-1008	133	7	the	the	DET
cana-1008	133	8	paper	paper	NOUN
cana-1008	133	9	,	,	PUNCT
cana-1008	133	10	the	the	DET
cana-1008	133	11	rf	rf	NOUN
cana-1008	133	12	model	model	NOUN
cana-1008	133	13	achieved	achieve	VERB
cana-1008	133	14	a	a	DET
cana-1008	133	15	peak	peak	NOUN
cana-1008	133	16	accuracy	accuracy	NOUN
cana-1008	133	17	score	score	NOUN
cana-1008	133	18	of	of	ADP
cana-1008	133	19	82	82	NUM
cana-1008	133	20	%	%	NOUN
cana-1008	133	21	.	.	PUNCT
cana-1008	134	1	in	in	ADP
cana-1008	134	2	order	order	NOUN
cana-1008	134	3	to	to	PART
cana-1008	134	4	accomplish	accomplish	VERB
cana-1008	134	5	accurate	accurate	ADJ
cana-1008	134	6	categorization	categorization	NOUN
cana-1008	134	7	of	of	ADP
cana-1008	134	8	diabetes	diabetes	NOUN
cana-1008	134	9	,	,	PUNCT
cana-1008	134	10	n.	n.	PROPN
cana-1008	134	11	k.	k.	PROPN
cana-1008	134	12	putri	putri	PROPN
cana-1008	134	13	,	,	PUNCT
cana-1008	134	14	z.	z.	PROPN
cana-1008	134	15	rustam	rustam	PROPN
cana-1008	134	16	,	,	PUNCT
cana-1008	134	17	and	and	CCONJ
cana-1008	134	18	d.	d.	PROPN
cana-1008	134	19	sarwinda	sarwinda	PROPN
cana-1008	134	20	used	use	VERB
cana-1008	134	21	the	the	DET
cana-1008	134	22	learning	learn	VERB
cana-1008	134	23	vector	vector	NOUN
cana-1008	134	24	quantization	quantization	NOUN
cana-1008	134	25	and	and	CCONJ
cana-1008	134	26	chi	chi	ADJ
cana-1008	134	27	-	-	PUNCT
cana-1008	134	28	square	square	ADJ
cana-1008	134	29	feature	feature	NOUN
cana-1008	134	30	selection	selection	NOUN
cana-1008	134	31	techniques	technique	NOUN
cana-1008	134	32	.	.	PUNCT
cana-1008	135	1	by	by	ADP
cana-1008	135	2	using	use	VERB
cana-1008	135	3	between	between	ADP
cana-1008	135	4	80	80	NUM
cana-1008	135	5	and	and	CCONJ
cana-1008	135	6	90	90	NUM
cana-1008	135	7	percent	percent	NOUN
cana-1008	135	8	of	of	ADP
cana-1008	135	9	the	the	DET
cana-1008	135	10	training	training	NOUN
cana-1008	135	11	data	datum	NOUN
cana-1008	135	12	,	,	PUNCT
cana-1008	135	13	they	they	PRON
cana-1008	135	14	were	be	AUX
cana-1008	135	15	able	able	ADJ
cana-1008	135	16	to	to	PART
cana-1008	135	17	achieve	achieve	VERB
cana-1008	135	18	a	a	DET
cana-1008	135	19	rate	rate	NOUN
cana-1008	135	20	of	of	ADP
cana-1008	135	21	accuracy	accuracy	NOUN
cana-1008	135	22	of	of	ADP
cana-1008	135	23	one	one	NUM
cana-1008	135	24	hundred	hundred	NUM
cana-1008	135	25	communications	communication	NOUN
cana-1008	135	26	on	on	ADP
cana-1008	135	27	applied	apply	VERB
cana-1008	135	28	nonlinear	nonlinear	ADJ
cana-1008	135	29	analysis	analysis	NOUN
cana-1008	135	30	issn	issn	NOUN
cana-1008	135	31	:	:	PUNCT
cana-1008	135	32	1074	1074	NUM
cana-1008	135	33	-	-	PUNCT
cana-1008	135	34	133x	133x	NUM
cana-1008	135	35	vol	vol	NOUN
cana-1008	135	36	31	31	NUM
cana-1008	135	37	no	no	NOUN
cana-1008	135	38	.	.	PUNCT
cana-1008	136	1	5s	5s	NUM
cana-1008	136	2	(	(	PUNCT
cana-1008	136	3	2024	2024	NUM
cana-1008	136	4	)	)	PUNCT
cana-1008	136	5	143	143	NUM
cana-1008	136	6	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-1008	136	7	percent	percent	NOUN
cana-1008	137	1	[	[	X
cana-1008	137	2	18	18	NUM
cana-1008	137	3	]	]	PUNCT
cana-1008	137	4	.	.	PUNCT
cana-1008	138	1	regarding	regard	VERB
cana-1008	138	2	the	the	DET
cana-1008	138	3	classification	classification	NOUN
cana-1008	138	4	of	of	ADP
cana-1008	138	5	cancer	cancer	NOUN
cana-1008	138	6	,	,	PUNCT
cana-1008	138	7	t.	t.	PROPN
cana-1008	138	8	nadira	nadira	PROPN
cana-1008	138	9	and	and	CCONJ
cana-1008	138	10	z.	z.	PROPN
cana-1008	138	11	rustam	rustam	PROPN
cana-1008	138	12	used	use	VERB
cana-1008	138	13	support	support	NOUN
cana-1008	138	14	vector	vector	NOUN
cana-1008	138	15	machines	machine	NOUN
cana-1008	138	16	(	(	PUNCT
cana-1008	138	17	svm	svm	PROPN
cana-1008	138	18	)	)	PUNCT
cana-1008	138	19	in	in	ADP
cana-1008	138	20	conjunction	conjunction	NOUN
cana-1008	138	21	with	with	ADP
cana-1008	138	22	feature	feature	NOUN
cana-1008	138	23	selection	selection	NOUN
cana-1008	138	24	approaches	approach	NOUN
cana-1008	138	25	.	.	PUNCT
cana-1008	139	1	as	as	SCONJ
cana-1008	139	2	can	can	AUX
cana-1008	139	3	be	be	AUX
cana-1008	139	4	seen	see	VERB
cana-1008	139	5	in	in	ADP
cana-1008	139	6	the	the	DET
cana-1008	139	7	dataset	dataset	NOUN
cana-1008	139	8	[	[	X
cana-1008	139	9	19	19	NUM
cana-1008	139	10	]	]	PUNCT
cana-1008	139	11	,	,	PUNCT
cana-1008	139	12	the	the	DET
cana-1008	139	13	accuracy	accuracy	NOUN
cana-1008	139	14	rates	rate	NOUN
cana-1008	139	15	that	that	PRON
cana-1008	139	16	were	be	AUX
cana-1008	139	17	achieved	achieve	VERB
cana-1008	139	18	for	for	ADP
cana-1008	139	19	lung	lung	NOUN
cana-1008	139	20	cancer	cancer	NOUN
cana-1008	139	21	and	and	CCONJ
cana-1008	139	22	breast	breast	NOUN
cana-1008	139	23	cancer	cancer	NOUN
cana-1008	139	24	were	be	AUX
cana-1008	139	25	respectively	respectively	ADV
cana-1008	139	26	99.9999	99.9999	NUM
cana-1008	139	27	%	%	NOUN
cana-1008	139	28	and	and	CCONJ
cana-1008	139	29	96.4286	96.4286	NUM
cana-1008	139	30	%	%	NOUN
cana-1008	139	31	.	.	PUNCT
cana-1008	140	1	a	a	DET
cana-1008	140	2	classification	classification	NOUN
cana-1008	140	3	accuracy	accuracy	NOUN
cana-1008	140	4	of	of	ADP
cana-1008	140	5	90	90	NUM
cana-1008	140	6	%	%	NOUN
cana-1008	140	7	was	be	AUX
cana-1008	140	8	achieved	achieve	VERB
cana-1008	140	9	by	by	ADP
cana-1008	140	10	arfiani	arfiani	PROPN
cana-1008	140	11	,	,	PUNCT
cana-1008	140	12	z	z	PROPN
cana-1008	140	13	rustam	rustam	PROPN
cana-1008	140	14	,	,	PUNCT
cana-1008	140	15	j	j	PROPN
cana-1008	140	16	pandelaki	pandelaki	NOUN
cana-1008	140	17	,	,	PUNCT
cana-1008	140	18	and	and	CCONJ
cana-1008	140	19	a	a	DET
cana-1008	140	20	siahaan	siahaan	NOUN
cana-1008	140	21	via	via	ADP
cana-1008	140	22	the	the	DET
cana-1008	140	23	use	use	NOUN
cana-1008	140	24	of	of	ADP
cana-1008	140	25	support	support	NOUN
cana-1008	140	26	vector	vector	NOUN
cana-1008	140	27	machines	machine	NOUN
cana-1008	140	28	(	(	PUNCT
cana-1008	140	29	svm	svm	PROPN
cana-1008	140	30	)	)	PUNCT
cana-1008	140	31	in	in	ADP
cana-1008	140	32	the	the	DET
cana-1008	140	33	categorization	categorization	NOUN
cana-1008	140	34	of	of	ADP
cana-1008	140	35	acute	acute	ADJ
cana-1008	140	36	sinusitis	sinusitis	NOUN
cana-1008	140	37	[	[	X
cana-1008	140	38	20	20	NUM
cana-1008	140	39	]	]	PUNCT
cana-1008	140	40	.	.	PUNCT
cana-1008	141	1	support	support	NOUN
cana-1008	141	2	vector	vector	NOUN
cana-1008	141	3	machines	machine	NOUN
cana-1008	141	4	(	(	PUNCT
cana-1008	141	5	svm	svm	PROPN
cana-1008	141	6	)	)	PUNCT
cana-1008	141	7	were	be	AUX
cana-1008	141	8	used	use	VERB
cana-1008	141	9	by	by	ADP
cana-1008	141	10	rustam	rustam	PROPN
cana-1008	141	11	and	and	CCONJ
cana-1008	141	12	rampisela	rampisela	VERB
cana-1008	141	13	t.	t.	PROPN
cana-1008	141	14	v.	v.	ADV
cana-1008	141	15	in	in	ADP
cana-1008	141	16	order	order	NOUN
cana-1008	141	17	to	to	PART
cana-1008	141	18	categorize	categorize	VERB
cana-1008	141	19	data	datum	NOUN
cana-1008	141	20	pertaining	pertain	VERB
cana-1008	141	21	to	to	ADP
cana-1008	141	22	schizophrenia	schizophrenia	NOUN
cana-1008	141	23	,	,	PUNCT
cana-1008	141	24	and	and	CCONJ
cana-1008	141	25	they	they	PRON
cana-1008	141	26	achieved	achieve	VERB
cana-1008	141	27	an	an	DET
cana-1008	141	28	accuracy	accuracy	NOUN
cana-1008	141	29	rate	rate	NOUN
cana-1008	141	30	rate	rate	NOUN
cana-1008	141	31	of	of	ADP
cana-1008	141	32	90.1	90.1	NUM
cana-1008	141	33	%	%	NOUN
cana-1008	142	1	[	[	X
cana-1008	142	2	21	21	NUM
cana-1008	142	3	]	]	PUNCT
cana-1008	142	4	.	.	PUNCT
cana-1008	143	1	hba1c	hba1c	NOUN
cana-1008	143	2	regression	regression	NOUN
cana-1008	143	3	models	model	NOUN
cana-1008	143	4	were	be	AUX
cana-1008	143	5	established	establish	VERB
cana-1008	143	6	in	in	ADP
cana-1008	143	7	[	[	X
cana-1008	143	8	22	22	NUM
cana-1008	143	9	]	]	PUNCT
cana-1008	143	10	,	,	PUNCT
cana-1008	143	11	which	which	PRON
cana-1008	143	12	may	may	AUX
cana-1008	143	13	be	be	AUX
cana-1008	143	14	found	find	VERB
cana-1008	143	15	here	here	ADV
cana-1008	143	16	.	.	PUNCT
cana-1008	144	1	the	the	DET
cana-1008	144	2	hba1c	hba1c	PROPN
cana-1008	144	3	test	test	NOUN
cana-1008	144	4	is	be	AUX
cana-1008	144	5	a	a	DET
cana-1008	144	6	measurement	measurement	NOUN
cana-1008	144	7	that	that	PRON
cana-1008	144	8	is	be	AUX
cana-1008	144	9	used	use	VERB
cana-1008	144	10	to	to	PART
cana-1008	144	11	determine	determine	VERB
cana-1008	144	12	the	the	DET
cana-1008	144	13	average	average	ADJ
cana-1008	144	14	concentration	concentration	NOUN
cana-1008	144	15	of	of	ADP
cana-1008	144	16	glucose	glucose	NOUN
cana-1008	144	17	in	in	ADP
cana-1008	144	18	the	the	DET
cana-1008	144	19	blood	blood	NOUN
cana-1008	144	20	over	over	ADP
cana-1008	144	21	a	a	DET
cana-1008	144	22	period	period	NOUN
cana-1008	144	23	of	of	ADP
cana-1008	144	24	two	two	NUM
cana-1008	144	25	to	to	PART
cana-1008	144	26	three	three	NUM
cana-1008	144	27	months	month	NOUN
cana-1008	144	28	,	,	PUNCT
cana-1008	144	29	as	as	SCONJ
cana-1008	144	30	stated	state	VERB
cana-1008	144	31	by	by	ADP
cana-1008	144	32	the	the	DET
cana-1008	144	33	research	research	NOUN
cana-1008	144	34	.	.	PUNCT
cana-1008	145	1	there	there	PRON
cana-1008	145	2	is	be	VERB
cana-1008	145	3	a	a	DET
cana-1008	145	4	substantial	substantial	ADJ
cana-1008	145	5	correlation	correlation	NOUN
cana-1008	145	6	between	between	ADP
cana-1008	145	7	it	it	PRON
cana-1008	145	8	and	and	CCONJ
cana-1008	145	9	diabetes	diabetes	NOUN
cana-1008	145	10	,	,	PUNCT
cana-1008	145	11	and	and	CCONJ
cana-1008	145	12	it	it	PRON
cana-1008	145	13	may	may	AUX
cana-1008	145	14	also	also	ADV
cana-1008	145	15	act	act	VERB
cana-1008	145	16	as	as	ADP
cana-1008	145	17	a	a	DET
cana-1008	145	18	warning	warning	NOUN
cana-1008	145	19	of	of	ADP
cana-1008	145	20	potential	potential	ADJ
cana-1008	145	21	future	future	ADJ
cana-1008	145	22	complications	complication	NOUN
cana-1008	145	23	.	.	PUNCT
cana-1008	146	1	the	the	DET
cana-1008	146	2	information	information	NOUN
cana-1008	146	3	that	that	PRON
cana-1008	146	4	was	be	AUX
cana-1008	146	5	used	use	VERB
cana-1008	146	6	in	in	ADP
cana-1008	146	7	this	this	DET
cana-1008	146	8	investigation	investigation	NOUN
cana-1008	146	9	was	be	AUX
cana-1008	146	10	obtained	obtain	VERB
cana-1008	146	11	from	from	ADP
cana-1008	146	12	the	the	DET
cana-1008	146	13	diabetes	diabetes	NOUN
cana-1008	146	14	research	research	NOUN
cana-1008	146	15	in	in	ADP
cana-1008	146	16	children	child	NOUN
cana-1008	146	17	network	network	NOUN
cana-1008	146	18	(	(	PUNCT
cana-1008	146	19	direcnet	direcnet	NOUN
cana-1008	146	20	)	)	PUNCT
cana-1008	146	21	studies	study	NOUN
cana-1008	146	22	.	.	PUNCT
cana-1008	147	1	these	these	DET
cana-1008	147	2	trials	trial	NOUN
cana-1008	147	3	comprised	comprise	VERB
cana-1008	147	4	170	170	NUM
cana-1008	147	5	patients	patient	NOUN
cana-1008	147	6	who	who	PRON
cana-1008	147	7	were	be	AUX
cana-1008	147	8	diagnosed	diagnose	VERB
cana-1008	147	9	with	with	ADP
cana-1008	147	10	type	type	NOUN
cana-1008	147	11	1	1	NUM
cana-1008	147	12	diabetes	diabetes	NOUN
cana-1008	147	13	mellitus	mellitus	NOUN
cana-1008	147	14	and	and	CCONJ
cana-1008	147	15	were	be	AUX
cana-1008	147	16	classified	classify	VERB
cana-1008	147	17	as	as	ADP
cana-1008	147	18	being	be	AUX
cana-1008	147	19	between	between	ADP
cana-1008	147	20	the	the	DET
cana-1008	147	21	ages	age	NOUN
cana-1008	147	22	of	of	ADP
cana-1008	147	23	4	4	NUM
cana-1008	147	24	and	and	CCONJ
cana-1008	147	25	under	under	ADP
cana-1008	147	26	10	10	NUM
cana-1008	147	27	years	year	NOUN
cana-1008	147	28	old	old	ADJ
cana-1008	147	29	.	.	PUNCT
cana-1008	148	1	in	in	ADP
cana-1008	148	2	order	order	NOUN
cana-1008	148	3	to	to	PART
cana-1008	148	4	solve	solve	VERB
cana-1008	148	5	the	the	DET
cana-1008	148	6	issue	issue	NOUN
cana-1008	148	7	of	of	ADP
cana-1008	148	8	missing	miss	VERB
cana-1008	148	9	data	datum	NOUN
cana-1008	148	10	,	,	PUNCT
cana-1008	148	11	the	the	DET
cana-1008	148	12	mean	mean	ADJ
cana-1008	148	13	substitution	substitution	NOUN
cana-1008	148	14	method	method	NOUN
cana-1008	148	15	was	be	AUX
cana-1008	148	16	used	use	VERB
cana-1008	148	17	as	as	ADP
cana-1008	148	18	a	a	DET
cana-1008	148	19	solution	solution	NOUN
cana-1008	148	20	.	.	PUNCT
cana-1008	149	1	in	in	ADP
cana-1008	149	2	addition	addition	NOUN
cana-1008	149	3	,	,	PUNCT
cana-1008	149	4	any	any	DET
cana-1008	149	5	property	property	NOUN
cana-1008	149	6	that	that	PRON
cana-1008	149	7	was	be	AUX
cana-1008	149	8	lacking	lack	VERB
cana-1008	149	9	data	datum	NOUN
cana-1008	149	10	by	by	ADP
cana-1008	149	11	more	more	ADJ
cana-1008	149	12	than	than	ADP
cana-1008	149	13	twenty	twenty	NUM
cana-1008	149	14	percent	percent	NOUN
cana-1008	149	15	was	be	AUX
cana-1008	149	16	eliminated	eliminate	VERB
cana-1008	149	17	.	.	PUNCT
cana-1008	150	1	additionally	additionally	ADV
cana-1008	150	2	,	,	PUNCT
cana-1008	150	3	the	the	DET
cana-1008	150	4	dataset	dataset	NOUN
cana-1008	150	5	was	be	AUX
cana-1008	150	6	subjected	subject	VERB
cana-1008	150	7	to	to	ADP
cana-1008	150	8	a	a	DET
cana-1008	150	9	number	number	NOUN
cana-1008	150	10	of	of	ADP
cana-1008	150	11	different	different	ADJ
cana-1008	150	12	approaches	approach	NOUN
cana-1008	150	13	for	for	ADP
cana-1008	150	14	the	the	DET
cana-1008	150	15	extraction	extraction	NOUN
cana-1008	150	16	and	and	CCONJ
cana-1008	150	17	selection	selection	NOUN
cana-1008	150	18	of	of	ADP
cana-1008	150	19	relevant	relevant	ADJ
cana-1008	150	20	features	feature	NOUN
cana-1008	150	21	.	.	PUNCT
cana-1008	151	1	a	a	DET
cana-1008	151	2	low	low	ADJ
cana-1008	151	3	mean	mean	NOUN
cana-1008	151	4	absolute	absolute	ADJ
cana-1008	151	5	error	error	NOUN
cana-1008	151	6	(	(	PUNCT
cana-1008	151	7	mae	mae	PROPN
cana-1008	151	8	)	)	PUNCT
cana-1008	151	9	of	of	ADP
cana-1008	151	10	3.39	3.39	NUM
cana-1008	151	11	mmol	mmol	NOUN
cana-1008	151	12	/	/	SYM
cana-1008	151	13	mol	mol	NOUN
cana-1008	151	14	and	and	CCONJ
cana-1008	151	15	a	a	DET
cana-1008	151	16	high	high	ADJ
cana-1008	151	17	coefficient	coefficient	NOUN
cana-1008	151	18	of	of	ADP
cana-1008	151	19	determination	determination	NOUN
cana-1008	151	20	(	(	PUNCT
cana-1008	151	21	r	r	NOUN
cana-1008	151	22	-	-	PUNCT
cana-1008	151	23	squared	square	VERB
cana-1008	151	24	)	)	PUNCT
cana-1008	151	25	score	score	NOUN
cana-1008	151	26	of	of	ADP
cana-1008	151	27	0.81	0.81	NUM
cana-1008	151	28	were	be	AUX
cana-1008	151	29	attained	attain	VERB
cana-1008	151	30	by	by	ADP
cana-1008	151	31	the	the	DET
cana-1008	151	32	ultimate	ultimate	ADJ
cana-1008	151	33	machine	machine	NOUN
cana-1008	151	34	learning	learning	NOUN
cana-1008	151	35	(	(	PUNCT
cana-1008	151	36	ml	ml	NOUN
cana-1008	151	37	)	)	PUNCT
cana-1008	151	38	model	model	NOUN
cana-1008	151	39	,	,	PUNCT
cana-1008	151	40	according	accord	VERB
cana-1008	151	41	to	to	ADP
cana-1008	151	42	the	the	DET
cana-1008	151	43	findings	finding	NOUN
cana-1008	151	44	of	of	ADP
cana-1008	151	45	the	the	DET
cana-1008	151	46	research	research	NOUN
cana-1008	151	47	.	.	PUNCT
cana-1008	152	1	this	this	DET
cana-1008	152	2	model	model	NOUN
cana-1008	152	3	used	use	VERB
cana-1008	152	4	two	two	NUM
cana-1008	152	5	ensemble	ensemble	ADJ
cana-1008	152	6	approaches	approach	NOUN
cana-1008	152	7	,	,	PUNCT
cana-1008	152	8	namely	namely	ADV
cana-1008	152	9	random	random	ADJ
cana-1008	152	10	forest	forest	NOUN
cana-1008	152	11	(	(	PUNCT
cana-1008	152	12	rf	rf	NOUN
cana-1008	152	13	)	)	PUNCT
cana-1008	152	14	and	and	CCONJ
cana-1008	152	15	extreme	extreme	ADJ
cana-1008	152	16	gradient	gradient	NOUN
cana-1008	152	17	boosting	boost	VERB
cana-1008	152	18	(	(	PUNCT
cana-1008	152	19	xgb	xgb	NUM
cana-1008	152	20	)	)	PUNCT
cana-1008	152	21	.	.	PUNCT
cana-1008	153	1	support	support	NOUN
cana-1008	153	2	vector	vector	NOUN
cana-1008	153	3	machines	machine	NOUN
cana-1008	153	4	(	(	PUNCT
cana-1008	153	5	svm	svm	PROPN
cana-1008	153	6	)	)	PUNCT
cana-1008	153	7	with	with	ADP
cana-1008	153	8	many	many	ADJ
cana-1008	153	9	kernel	kernel	NOUN
cana-1008	153	10	functions	function	NOUN
cana-1008	153	11	were	be	AUX
cana-1008	153	12	used	use	VERB
cana-1008	153	13	by	by	ADP
cana-1008	153	14	g.a	g.a	PROPN
cana-1008	153	15	.	.	PROPN
cana-1008	153	16	pethunachiyar	pethunachiyar	PROPN
cana-1008	153	17	in	in	ADP
cana-1008	153	18	order	order	NOUN
cana-1008	153	19	to	to	PART
cana-1008	153	20	classify	classify	VERB
cana-1008	153	21	diseases	disease	NOUN
cana-1008	153	22	related	relate	VERB
cana-1008	153	23	to	to	ADP
cana-1008	153	24	diabetes	diabetes	NOUN
cana-1008	153	25	.	.	PUNCT
cana-1008	154	1	the	the	DET
cana-1008	154	2	simulation	simulation	NOUN
cana-1008	154	3	model	model	NOUN
cana-1008	154	4	of	of	ADP
cana-1008	154	5	the	the	DET
cana-1008	154	6	system	system	NOUN
cana-1008	154	7	that	that	PRON
cana-1008	154	8	has	have	AUX
cana-1008	154	9	been	be	AUX
cana-1008	154	10	proposed	propose	VERB
cana-1008	154	11	is	be	AUX
cana-1008	154	12	comprised	comprise	VERB
cana-1008	154	13	of	of	ADP
cana-1008	154	14	five	five	NUM
cana-1008	154	15	phases	phase	NOUN
cana-1008	154	16	.	.	PUNCT
cana-1008	155	1	when	when	SCONJ
cana-1008	155	2	the	the	DET
cana-1008	155	3	data	datum	NOUN
cana-1008	155	4	has	have	AUX
cana-1008	155	5	been	be	AUX
cana-1008	155	6	collected	collect	VERB
cana-1008	155	7	,	,	PUNCT
cana-1008	155	8	the	the	DET
cana-1008	155	9	next	next	ADJ
cana-1008	155	10	step	step	NOUN
cana-1008	155	11	in	in	ADP
cana-1008	155	12	the	the	DET
cana-1008	155	13	selection	selection	NOUN
cana-1008	155	14	process	process	NOUN
cana-1008	155	15	is	be	AUX
cana-1008	155	16	to	to	PART
cana-1008	155	17	correct	correct	VERB
cana-1008	155	18	any	any	DET
cana-1008	155	19	faults	fault	NOUN
cana-1008	155	20	that	that	PRON
cana-1008	155	21	may	may	AUX
cana-1008	155	22	have	have	AUX
cana-1008	155	23	been	be	AUX
cana-1008	155	24	present	present	ADJ
cana-1008	155	25	,	,	PUNCT
cana-1008	155	26	such	such	ADJ
cana-1008	155	27	as	as	ADP
cana-1008	155	28	inconsistencies	inconsistency	NOUN
cana-1008	155	29	in	in	ADP
cana-1008	155	30	the	the	DET
cana-1008	155	31	data	datum	NOUN
cana-1008	155	32	,	,	PUNCT
cana-1008	155	33	numbers	number	NOUN
cana-1008	155	34	that	that	PRON
cana-1008	155	35	are	be	AUX
cana-1008	155	36	missing	miss	VERB
cana-1008	155	37	,	,	PUNCT
cana-1008	155	38	or	or	CCONJ
cana-1008	155	39	information	information	NOUN
cana-1008	155	40	that	that	PRON
cana-1008	155	41	is	be	AUX
cana-1008	155	42	erroneous	erroneous	ADJ
cana-1008	155	43	.	.	PUNCT
cana-1008	156	1	the	the	DET
cana-1008	156	2	data	datum	NOUN
cana-1008	156	3	will	will	AUX
cana-1008	156	4	be	be	AUX
cana-1008	156	5	separated	separate	VERB
cana-1008	156	6	into	into	ADP
cana-1008	156	7	two	two	NUM
cana-1008	156	8	pools	pool	NOUN
cana-1008	156	9	:	:	PUNCT
cana-1008	156	10	a	a	DET
cana-1008	156	11	training	training	NOUN
cana-1008	156	12	dataset	dataset	NOUN
cana-1008	156	13	,	,	PUNCT
cana-1008	156	14	which	which	PRON
cana-1008	156	15	will	will	AUX
cana-1008	156	16	consist	consist	VERB
cana-1008	156	17	of	of	ADP
cana-1008	156	18	seventy	seventy	NUM
cana-1008	156	19	percent	percent	NOUN
cana-1008	156	20	of	of	ADP
cana-1008	156	21	the	the	DET
cana-1008	156	22	data	datum	NOUN
cana-1008	156	23	,	,	PUNCT
cana-1008	156	24	and	and	CCONJ
cana-1008	156	25	a	a	DET
cana-1008	156	26	testing	testing	NOUN
cana-1008	156	27	dataset	dataset	NOUN
cana-1008	156	28	,	,	PUNCT
cana-1008	156	29	which	which	PRON
cana-1008	156	30	will	will	AUX
cana-1008	156	31	consist	consist	VERB
cana-1008	156	32	of	of	ADP
cana-1008	156	33	thirty	thirty	NUM
cana-1008	156	34	percent	percent	NOUN
cana-1008	156	35	of	of	ADP
cana-1008	156	36	the	the	DET
cana-1008	156	37	data	datum	NOUN
cana-1008	156	38	.	.	PUNCT
cana-1008	157	1	the	the	DET
cana-1008	157	2	technique	technique	NOUN
cana-1008	157	3	known	know	VERB
cana-1008	157	4	as	as	ADP
cana-1008	157	5	support	support	NOUN
cana-1008	157	6	vector	vector	NOUN
cana-1008	157	7	machine	machine	NOUN
cana-1008	157	8	(	(	PUNCT
cana-1008	157	9	svm	svm	PROPN
cana-1008	157	10	)	)	PUNCT
cana-1008	157	11	has	have	AUX
cana-1008	157	12	been	be	AUX
cana-1008	157	13	chosen	choose	VERB
cana-1008	157	14	because	because	SCONJ
cana-1008	157	15	of	of	ADP
cana-1008	157	16	its	its	PRON
cana-1008	157	17	capacity	capacity	NOUN
cana-1008	157	18	to	to	PART
cana-1008	157	19	produce	produce	VERB
cana-1008	157	20	accurate	accurate	ADJ
cana-1008	157	21	forecasts	forecast	NOUN
cana-1008	157	22	,	,	PUNCT
cana-1008	157	23	and	and	CCONJ
cana-1008	157	24	a	a	DET
cana-1008	157	25	model	model	NOUN
cana-1008	157	26	has	have	AUX
cana-1008	157	27	been	be	AUX
cana-1008	157	28	constructed	construct	VERB
cana-1008	157	29	in	in	ADP
cana-1008	157	30	accordance	accordance	NOUN
cana-1008	157	31	with	with	ADP
cana-1008	157	32	this	this	DET
cana-1008	157	33	particular	particular	ADJ
cana-1008	157	34	approach	approach	NOUN
cana-1008	157	35	.	.	PUNCT
cana-1008	158	1	the	the	DET
cana-1008	158	2	predictions	prediction	NOUN
cana-1008	158	3	are	be	AUX
cana-1008	158	4	generated	generate	VERB
cana-1008	158	5	by	by	ADP
cana-1008	158	6	the	the	DET
cana-1008	158	7	model	model	NOUN
cana-1008	158	8	based	base	VERB
cana-1008	158	9	on	on	ADP
cana-1008	158	10	the	the	DET
cana-1008	158	11	test	test	NOUN
cana-1008	158	12	data	datum	NOUN
cana-1008	158	13	.	.	PUNCT
cana-1008	159	1	in	in	ADP
cana-1008	159	2	this	this	DET
cana-1008	159	3	study	study	NOUN
cana-1008	159	4	,	,	PUNCT
cana-1008	159	5	support	support	VERB
cana-1008	159	6	vector	vector	NOUN
cana-1008	159	7	machines	machine	NOUN
cana-1008	159	8	(	(	PUNCT
cana-1008	159	9	svm	svm	PROPN
cana-1008	159	10	)	)	PUNCT
cana-1008	159	11	have	have	AUX
cana-1008	159	12	been	be	AUX
cana-1008	159	13	used	use	VERB
cana-1008	159	14	,	,	PUNCT
cana-1008	159	15	and	and	CCONJ
cana-1008	159	16	linear	linear	ADJ
cana-1008	159	17	,	,	PUNCT
cana-1008	159	18	polynomial	polynomial	ADJ
cana-1008	159	19	,	,	PUNCT
cana-1008	159	20	and	and	CCONJ
cana-1008	159	21	radial	radial	ADJ
cana-1008	159	22	kernel	kernel	NOUN
cana-1008	159	23	functions	function	NOUN
cana-1008	159	24	have	have	AUX
cana-1008	159	25	been	be	AUX
cana-1008	159	26	utilized	utilize	VERB
cana-1008	159	27	.	.	PUNCT
cana-1008	160	1	for	for	ADP
cana-1008	160	2	the	the	DET
cana-1008	160	3	purpose	purpose	NOUN
cana-1008	160	4	of	of	ADP
cana-1008	160	5	determining	determine	VERB
cana-1008	160	6	how	how	SCONJ
cana-1008	160	7	accurate	accurate	ADJ
cana-1008	160	8	predictions	prediction	NOUN
cana-1008	160	9	are	be	AUX
cana-1008	160	10	,	,	PUNCT
cana-1008	160	11	the	the	DET
cana-1008	160	12	confusion	confusion	NOUN
cana-1008	160	13	matrix	matrix	NOUN
cana-1008	160	14	is	be	AUX
cana-1008	160	15	used	use	VERB
cana-1008	160	16	.	.	PUNCT
cana-1008	161	1	in	in	ADP
cana-1008	161	2	order	order	NOUN
cana-1008	161	3	to	to	PART
cana-1008	161	4	analyze	analyze	VERB
cana-1008	161	5	three	three	NUM
cana-1008	161	6	different	different	ADJ
cana-1008	161	7	kernel	kernel	NOUN
cana-1008	161	8	functions	function	NOUN
cana-1008	161	9	,	,	PUNCT
cana-1008	161	10	the	the	DET
cana-1008	161	11	roc	roc	PROPN
cana-1008	161	12	curve	curve	NOUN
cana-1008	161	13	is	be	AUX
cana-1008	161	14	used	use	VERB
cana-1008	161	15	.	.	PUNCT
cana-1008	162	1	when	when	SCONJ
cana-1008	162	2	compared	compare	VERB
cana-1008	162	3	to	to	ADP
cana-1008	162	4	other	other	ADJ
cana-1008	162	5	kernels	kernel	NOUN
cana-1008	162	6	,	,	PUNCT
cana-1008	162	7	the	the	DET
cana-1008	162	8	linear	linear	ADJ
cana-1008	162	9	kernel	kernel	NOUN
cana-1008	162	10	used	use	VERB
cana-1008	162	11	in	in	ADP
cana-1008	162	12	support	support	NOUN
cana-1008	162	13	vector	vector	NOUN
cana-1008	162	14	machines	machine	NOUN
cana-1008	162	15	(	(	PUNCT
cana-1008	162	16	svm	svm	PROPN
cana-1008	162	17	)	)	PUNCT
cana-1008	162	18	offers	offer	VERB
cana-1008	162	19	a	a	DET
cana-1008	162	20	higher	high	ADJ
cana-1008	162	21	level	level	NOUN
cana-1008	162	22	of	of	ADP
cana-1008	162	23	accuracy	accuracy	NOUN
cana-1008	162	24	within	within	ADP
cana-1008	162	25	its	its	PRON
cana-1008	162	26	prediction	prediction	NOUN
cana-1008	162	27	capabilities	capability	NOUN
cana-1008	162	28	[	[	X
cana-1008	162	29	23	23	NUM
cana-1008	162	30	]	]	PUNCT
cana-1008	162	31	.	.	PUNCT
cana-1008	163	1	one	one	NUM
cana-1008	163	2	of	of	ADP
cana-1008	163	3	the	the	DET
cana-1008	163	4	most	most	ADV
cana-1008	163	5	important	important	ADJ
cana-1008	163	6	applications	application	NOUN
cana-1008	163	7	of	of	ADP
cana-1008	163	8	machine	machine	NOUN
cana-1008	163	9	learning	learning	NOUN
cana-1008	163	10	is	be	AUX
cana-1008	163	11	in	in	ADP
cana-1008	163	12	the	the	DET
cana-1008	163	13	diagnosis	diagnosis	NOUN
cana-1008	163	14	and	and	CCONJ
cana-1008	163	15	prediction	prediction	NOUN
cana-1008	163	16	of	of	ADP
cana-1008	163	17	potential	potential	ADJ
cana-1008	163	18	diseases	disease	NOUN
cana-1008	163	19	.	.	PUNCT
cana-1008	164	1	through	through	ADP
cana-1008	164	2	the	the	DET
cana-1008	164	3	development	development	NOUN
cana-1008	164	4	of	of	ADP
cana-1008	164	5	an	an	DET
cana-1008	164	6	artificial	artificial	ADJ
cana-1008	164	7	neural	neural	ADJ
cana-1008	164	8	network	network	NOUN
cana-1008	164	9	(	(	PUNCT
cana-1008	164	10	ann	ann	PROPN
cana-1008	164	11	)	)	PUNCT
cana-1008	164	12	and	and	CCONJ
cana-1008	164	13	a	a	DET
cana-1008	164	14	bayesian	bayesian	NOUN
cana-1008	164	15	network	network	NOUN
cana-1008	164	16	,	,	PUNCT
cana-1008	164	17	alade	alade	PROPN
cana-1008	164	18	et	et	PROPN
cana-1008	164	19	al	al	PROPN
cana-1008	164	20	.	.	PUNCT
cana-1008	165	1	[	[	X
cana-1008	165	2	24	24	NUM
cana-1008	165	3	]	]	PUNCT
cana-1008	165	4	presented	present	VERB
cana-1008	165	5	a	a	DET
cana-1008	165	6	method	method	NOUN
cana-1008	165	7	for	for	ADP
cana-1008	165	8	predicting	predict	VERB
cana-1008	165	9	the	the	DET
cana-1008	165	10	occurrence	occurrence	NOUN
cana-1008	165	11	of	of	ADP
cana-1008	165	12	diabetes	diabetes	NOUN
cana-1008	165	13	.	.	PUNCT
cana-1008	166	1	for	for	ADP
cana-1008	166	2	the	the	DET
cana-1008	166	3	purpose	purpose	NOUN
cana-1008	166	4	of	of	ADP
cana-1008	166	5	training	training	NOUN
cana-1008	166	6	and	and	CCONJ
cana-1008	166	7	evaluating	evaluate	VERB
cana-1008	166	8	the	the	DET
cana-1008	166	9	dataset	dataset	NOUN
cana-1008	166	10	,	,	PUNCT
cana-1008	166	11	the	the	DET
cana-1008	166	12	artificial	artificial	ADJ
cana-1008	166	13	neural	neural	ADJ
cana-1008	166	14	network	network	NOUN
cana-1008	166	15	(	(	PUNCT
cana-1008	166	16	ann	ann	PROPN
cana-1008	166	17	)	)	PUNCT
cana-1008	166	18	architecture	architecture	NOUN
cana-1008	166	19	is	be	AUX
cana-1008	166	20	comprised	comprise	VERB
cana-1008	166	21	of	of	ADP
cana-1008	166	22	four	four	NUM
cana-1008	166	23	layers	layer	NOUN
cana-1008	166	24	and	and	CCONJ
cana-1008	166	25	makes	make	VERB
cana-1008	166	26	use	use	NOUN
cana-1008	166	27	of	of	ADP
cana-1008	166	28	the	the	DET
cana-1008	166	29	back	back	ADJ
cana-1008	166	30	-	-	PUNCT
cana-1008	166	31	propagation	propagation	NOUN
cana-1008	166	32	method	method	NOUN
cana-1008	166	33	and	and	CCONJ
cana-1008	166	34	the	the	DET
cana-1008	166	35	bayesian	bayesian	ADJ
cana-1008	166	36	regulation	regulation	NOUN
cana-1008	166	37	algorithm	algorithm	NOUN
cana-1008	166	38	.	.	PUNCT
cana-1008	167	1	careful	careful	ADJ
cana-1008	167	2	training	training	NOUN
cana-1008	167	3	has	have	AUX
cana-1008	167	4	been	be	AUX
cana-1008	167	5	performed	perform	VERB
cana-1008	167	6	on	on	ADP
cana-1008	167	7	the	the	DET
cana-1008	167	8	data	datum	NOUN
cana-1008	167	9	in	in	ADP
cana-1008	167	10	order	order	NOUN
cana-1008	167	11	to	to	PART
cana-1008	167	12	ensure	ensure	VERB
cana-1008	167	13	that	that	SCONJ
cana-1008	167	14	the	the	DET
cana-1008	167	15	conclusions	conclusion	NOUN
cana-1008	167	16	on	on	ADP
cana-1008	167	17	the	the	DET
cana-1008	167	18	communications	communication	NOUN
cana-1008	167	19	on	on	ADP
cana-1008	167	20	applied	apply	VERB
cana-1008	167	21	nonlinear	nonlinear	ADJ
cana-1008	167	22	analysis	analysis	NOUN
cana-1008	167	23	issn	issn	NOUN
cana-1008	167	24	:	:	PUNCT
cana-1008	167	25	1074	1074	NUM
cana-1008	167	26	-	-	PUNCT
cana-1008	167	27	133x	133x	NUM
cana-1008	167	28	vol	vol	NOUN
cana-1008	167	29	31	31	NUM
cana-1008	167	30	no	no	NOUN
cana-1008	167	31	.	.	PUNCT
cana-1008	168	1	5s	5s	NUM
cana-1008	168	2	(	(	PUNCT
cana-1008	168	3	2024	2024	NUM
cana-1008	168	4	)	)	PUNCT
cana-1008	168	5	144	144	NUM
cana-1008	168	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1008	168	7	regression	regression	NOUN
cana-1008	168	8	graph	graph	NOUN
cana-1008	168	9	are	be	AUX
cana-1008	168	10	appropriately	appropriately	ADV
cana-1008	168	11	represented	represent	VERB
cana-1008	168	12	.	.	PUNCT
cana-1008	169	1	the	the	DET
cana-1008	169	2	use	use	NOUN
cana-1008	169	3	of	of	ADP
cana-1008	169	4	this	this	DET
cana-1008	169	5	technology	technology	NOUN
cana-1008	169	6	enables	enable	VERB
cana-1008	169	7	remote	remote	ADJ
cana-1008	169	8	diagnosis	diagnosis	NOUN
cana-1008	169	9	,	,	PUNCT
cana-1008	169	10	which	which	PRON
cana-1008	169	11	in	in	ADP
cana-1008	169	12	turn	turn	NOUN
cana-1008	169	13	makes	make	VERB
cana-1008	169	14	it	it	PRON
cana-1008	169	15	easier	easy	ADJ
cana-1008	169	16	to	to	PART
cana-1008	169	17	communicate	communicate	VERB
cana-1008	169	18	with	with	ADP
cana-1008	169	19	patients	patient	NOUN
cana-1008	169	20	without	without	ADP
cana-1008	169	21	the	the	DET
cana-1008	169	22	need	need	NOUN
cana-1008	169	23	of	of	ADP
cana-1008	169	24	being	be	AUX
cana-1008	169	25	physically	physically	ADV
cana-1008	169	26	near	near	ADJ
cana-1008	169	27	to	to	ADP
cana-1008	169	28	them	they	PRON
cana-1008	169	29	.	.	PUNCT
cana-1008	170	1	the	the	DET
cana-1008	170	2	research	research	NOUN
cana-1008	170	3	that	that	PRON
cana-1008	170	4	was	be	AUX
cana-1008	170	5	carried	carry	VERB
cana-1008	170	6	out	out	ADP
cana-1008	170	7	by	by	ADP
cana-1008	170	8	stefan	stefan	PROPN
cana-1008	170	9	ravizza	ravizza	PROPN
cana-1008	170	10	and	and	CCONJ
cana-1008	170	11	his	his	PRON
cana-1008	170	12	colleagues	colleague	NOUN
cana-1008	171	1	[	[	X
cana-1008	171	2	25	25	NUM
cana-1008	171	3	]	]	PUNCT
cana-1008	171	4	investigates	investigate	VERB
cana-1008	171	5	the	the	DET
cana-1008	171	6	use	use	NOUN
cana-1008	171	7	of	of	ADP
cana-1008	171	8	data	datum	NOUN
cana-1008	171	9	mining	mining	NOUN
cana-1008	171	10	techniques	technique	NOUN
cana-1008	171	11	in	in	ADP
cana-1008	171	12	the	the	DET
cana-1008	171	13	field	field	NOUN
cana-1008	171	14	of	of	ADP
cana-1008	171	15	healthcare	healthcare	NOUN
cana-1008	171	16	and	and	CCONJ
cana-1008	171	17	suggests	suggest	VERB
cana-1008	171	18	a	a	DET
cana-1008	171	19	model	model	NOUN
cana-1008	171	20	for	for	ADP
cana-1008	171	21	assessing	assess	VERB
cana-1008	171	22	the	the	DET
cana-1008	171	23	possible	possible	ADJ
cana-1008	171	24	dangers	danger	NOUN
cana-1008	171	25	of	of	ADP
cana-1008	171	26	illnesses	illness	NOUN
cana-1008	171	27	that	that	PRON
cana-1008	171	28	are	be	AUX
cana-1008	171	29	not	not	PART
cana-1008	171	30	appropriately	appropriately	ADV
cana-1008	171	31	treated	treat	VERB
cana-1008	171	32	.	.	PUNCT
cana-1008	172	1	the	the	DET
cana-1008	172	2	approach	approach	NOUN
cana-1008	172	3	that	that	PRON
cana-1008	172	4	they	they	PRON
cana-1008	172	5	used	use	VERB
cana-1008	172	6	in	in	ADP
cana-1008	172	7	the	the	DET
cana-1008	172	8	healthcare	healthcare	NOUN
cana-1008	172	9	industry	industry	NOUN
cana-1008	172	10	is	be	AUX
cana-1008	172	11	one	one	NUM
cana-1008	172	12	that	that	PRON
cana-1008	172	13	is	be	AUX
cana-1008	172	14	based	base	VERB
cana-1008	172	15	on	on	ADP
cana-1008	172	16	empirical	empirical	ADJ
cana-1008	172	17	evidence	evidence	NOUN
cana-1008	172	18	,	,	PUNCT
cana-1008	172	19	is	be	AUX
cana-1008	172	20	directed	direct	VERB
cana-1008	172	21	by	by	ADP
cana-1008	172	22	certain	certain	ADJ
cana-1008	172	23	characteristics	characteristic	NOUN
cana-1008	172	24	,	,	PUNCT
cana-1008	172	25	and	and	CCONJ
cana-1008	172	26	requires	require	VERB
cana-1008	172	27	the	the	DET
cana-1008	172	28	collecting	collecting	NOUN
cana-1008	172	29	of	of	ADP
cana-1008	172	30	data	datum	NOUN
cana-1008	172	31	from	from	ADP
cana-1008	172	32	live	live	ADJ
cana-1008	172	33	situations	situation	NOUN
cana-1008	172	34	.	.	PUNCT
cana-1008	173	1	within	within	ADP
cana-1008	173	2	the	the	DET
cana-1008	173	3	context	context	NOUN
cana-1008	173	4	of	of	ADP
cana-1008	173	5	the	the	DET
cana-1008	173	6	deep	deep	ADJ
cana-1008	173	7	patient	patient	NOUN
cana-1008	173	8	approach	approach	NOUN
cana-1008	173	9	,	,	PUNCT
cana-1008	173	10	this	this	DET
cana-1008	173	11	method	method	NOUN
cana-1008	173	12	is	be	AUX
cana-1008	173	13	distinct	distinct	ADJ
cana-1008	173	14	.	.	PUNCT
cana-1008	174	1	the	the	DET
cana-1008	174	2	findings	finding	NOUN
cana-1008	174	3	of	of	ADP
cana-1008	174	4	this	this	DET
cana-1008	174	5	strategy	strategy	NOUN
cana-1008	174	6	were	be	AUX
cana-1008	174	7	compared	compare	VERB
cana-1008	174	8	with	with	ADP
cana-1008	174	9	clinical	clinical	ADJ
cana-1008	174	10	data	datum	NOUN
cana-1008	174	11	utilizing	utilize	VERB
cana-1008	174	12	direct	direct	ADJ
cana-1008	174	13	algorithms	algorithm	NOUN
cana-1008	174	14	,	,	PUNCT
cana-1008	174	15	and	and	CCONJ
cana-1008	174	16	the	the	DET
cana-1008	174	17	results	result	NOUN
cana-1008	174	18	were	be	AUX
cana-1008	174	19	discussed	discuss	VERB
cana-1008	174	20	.	.	PUNCT
cana-1008	175	1	the	the	DET
cana-1008	175	2	pima	pima	PROPN
cana-1008	175	3	dataset	dataset	PROPN
cana-1008	175	4	and	and	CCONJ
cana-1008	175	5	advanced	advanced	ADJ
cana-1008	175	6	machine	machine	NOUN
cana-1008	175	7	learning	learn	VERB
cana-1008	175	8	techniques	technique	NOUN
cana-1008	175	9	were	be	AUX
cana-1008	175	10	used	use	VERB
cana-1008	175	11	in	in	ADP
cana-1008	175	12	this	this	DET
cana-1008	175	13	research	research	NOUN
cana-1008	175	14	project	project	NOUN
cana-1008	175	15	with	with	ADP
cana-1008	175	16	the	the	DET
cana-1008	175	17	intention	intention	NOUN
cana-1008	175	18	of	of	ADP
cana-1008	175	19	accurately	accurately	ADV
cana-1008	175	20	predicting	predict	VERB
cana-1008	175	21	the	the	DET
cana-1008	175	22	occurrence	occurrence	NOUN
cana-1008	175	23	of	of	ADP
cana-1008	175	24	problems	problem	NOUN
cana-1008	175	25	related	relate	VERB
cana-1008	175	26	to	to	ADP
cana-1008	175	27	diabetes	diabetes	NOUN
cana-1008	175	28	in	in	ADP
cana-1008	175	29	individuals	individual	NOUN
cana-1008	175	30	who	who	PRON
cana-1008	175	31	experience	experience	VERB
cana-1008	175	32	the	the	DET
cana-1008	175	33	condition	condition	NOUN
cana-1008	175	34	.	.	PUNCT
cana-1008	176	1	the	the	DET
cana-1008	176	2	impacts	impact	NOUN
cana-1008	176	3	of	of	ADP
cana-1008	176	4	applying	apply	VERB
cana-1008	176	5	feature	feature	NOUN
cana-1008	176	6	selection	selection	NOUN
cana-1008	176	7	to	to	ADP
cana-1008	176	8	the	the	DET
cana-1008	176	9	dataset	dataset	NOUN
cana-1008	176	10	were	be	AUX
cana-1008	176	11	investigated	investigate	VERB
cana-1008	176	12	via	via	ADP
cana-1008	176	13	a	a	DET
cana-1008	176	14	series	series	NOUN
cana-1008	176	15	of	of	ADP
cana-1008	176	16	experiments	experiment	NOUN
cana-1008	176	17	that	that	PRON
cana-1008	176	18	were	be	AUX
cana-1008	176	19	carried	carry	VERB
cana-1008	176	20	out	out	ADP
cana-1008	176	21	in	in	ADP
cana-1008	176	22	order	order	NOUN
cana-1008	176	23	to	to	PART
cana-1008	176	24	assess	assess	VERB
cana-1008	176	25	various	various	ADJ
cana-1008	176	26	data	datum	NOUN
cana-1008	176	27	imputation	imputation	NOUN
cana-1008	176	28	methods	method	NOUN
cana-1008	176	29	,	,	PUNCT
cana-1008	176	30	balancing	balance	VERB
cana-1008	176	31	procedures	procedure	NOUN
cana-1008	176	32	,	,	PUNCT
cana-1008	176	33	and	and	CCONJ
cana-1008	176	34	effects	effect	NOUN
cana-1008	176	35	.	.	PUNCT
cana-1008	177	1	3	3	X
cana-1008	177	2	.	.	NUM
cana-1008	177	3	proposed	propose	VERB
cana-1008	177	4	method	method	NOUN
cana-1008	177	5	a	a	DET
cana-1008	177	6	condensed	condense	VERB
cana-1008	177	7	review	review	NOUN
cana-1008	177	8	of	of	ADP
cana-1008	177	9	the	the	DET
cana-1008	177	10	advancements	advancement	NOUN
cana-1008	177	11	that	that	PRON
cana-1008	177	12	have	have	AUX
cana-1008	177	13	been	be	AUX
cana-1008	177	14	made	make	VERB
cana-1008	177	15	in	in	ADP
cana-1008	177	16	the	the	DET
cana-1008	177	17	use	use	NOUN
cana-1008	177	18	of	of	ADP
cana-1008	177	19	technology	technology	NOUN
cana-1008	177	20	is	be	AUX
cana-1008	177	21	provided	provide	VERB
cana-1008	177	22	in	in	ADP
cana-1008	177	23	this	this	DET
cana-1008	177	24	section	section	NOUN
cana-1008	177	25	.	.	PUNCT
cana-1008	178	1	individuals	individual	NOUN
cana-1008	178	2	who	who	PRON
cana-1008	178	3	have	have	VERB
cana-1008	178	4	diabetes	diabete	NOUN
cana-1008	178	5	are	be	AUX
cana-1008	178	6	the	the	DET
cana-1008	178	7	primary	primary	ADJ
cana-1008	178	8	recipients	recipient	NOUN
cana-1008	178	9	of	of	ADP
cana-1008	178	10	notifications	notification	NOUN
cana-1008	178	11	from	from	ADP
cana-1008	178	12	the	the	DET
cana-1008	178	13	proposed	propose	VERB
cana-1008	178	14	classifier	classifier	NOUN
cana-1008	178	15	model	model	PROPN
cana-1008	178	16	,	,	PUNCT
cana-1008	178	17	which	which	PRON
cana-1008	178	18	also	also	ADV
cana-1008	178	19	incorporates	incorporate	VERB
cana-1008	178	20	input	input	NOUN
cana-1008	178	21	into	into	ADP
cana-1008	178	22	the	the	DET
cana-1008	178	23	diabetes	diabetes	NOUN
cana-1008	178	24	dataset	dataset	VERB
cana-1008	178	25	.	.	PUNCT
cana-1008	179	1	in	in	ADP
cana-1008	179	2	the	the	DET
cana-1008	179	3	first	first	ADJ
cana-1008	179	4	step	step	NOUN
cana-1008	179	5	of	of	ADP
cana-1008	179	6	this	this	DET
cana-1008	179	7	process	process	NOUN
cana-1008	179	8	,	,	PUNCT
cana-1008	179	9	we	we	PRON
cana-1008	179	10	gather	gather	VERB
cana-1008	179	11	the	the	DET
cana-1008	179	12	contextualized	contextualized	ADJ
cana-1008	179	13	dataset	dataset	NOUN
cana-1008	179	14	of	of	ADP
cana-1008	179	15	pima	pima	PROPN
cana-1008	179	16	indian	indian	PROPN
cana-1008	179	17	diabetes	diabetes	PROPN
cana-1008	179	18	.	.	PUNCT
cana-1008	180	1	for	for	ADP
cana-1008	180	2	the	the	DET
cana-1008	180	3	purpose	purpose	NOUN
cana-1008	180	4	of	of	ADP
cana-1008	180	5	gaining	gain	VERB
cana-1008	180	6	a	a	DET
cana-1008	180	7	full	full	ADJ
cana-1008	180	8	knowledge	knowledge	NOUN
cana-1008	180	9	of	of	ADP
cana-1008	180	10	the	the	DET
cana-1008	180	11	sources	source	NOUN
cana-1008	180	12	from	from	ADP
cana-1008	180	13	which	which	PRON
cana-1008	180	14	our	our	PRON
cana-1008	180	15	data	data	NOUN
cana-1008	180	16	comes	come	VERB
cana-1008	180	17	,	,	PUNCT
cana-1008	180	18	exploratory	exploratory	ADJ
cana-1008	180	19	data	datum	NOUN
cana-1008	180	20	analysis	analysis	NOUN
cana-1008	180	21	is	be	AUX
cana-1008	180	22	carried	carry	VERB
cana-1008	180	23	out	out	ADP
cana-1008	180	24	.	.	PUNCT
cana-1008	181	1	the	the	DET
cana-1008	181	2	following	follow	VERB
cana-1008	181	3	step	step	NOUN
cana-1008	181	4	is	be	AUX
cana-1008	181	5	to	to	PART
cana-1008	181	6	preprocess	preprocess	VERB
cana-1008	181	7	our	our	PRON
cana-1008	181	8	data	datum	NOUN
cana-1008	181	9	by	by	ADP
cana-1008	181	10	purging	purge	VERB
cana-1008	181	11	our	our	PRON
cana-1008	181	12	dataset	dataset	NOUN
cana-1008	181	13	,	,	PUNCT
cana-1008	181	14	specifically	specifically	ADV
cana-1008	181	15	by	by	ADP
cana-1008	181	16	eliminating	eliminate	VERB
cana-1008	181	17	any	any	DET
cana-1008	181	18	duplicate	duplicate	NOUN
cana-1008	181	19	,	,	PUNCT
cana-1008	181	20	missing	missing	ADJ
cana-1008	181	21	,	,	PUNCT
cana-1008	181	22	or	or	CCONJ
cana-1008	181	23	anomalous	anomalous	ADJ
cana-1008	181	24	values	value	NOUN
cana-1008	181	25	that	that	PRON
cana-1008	181	26	could	could	AUX
cana-1008	181	27	be	be	AUX
cana-1008	181	28	present	present	ADJ
cana-1008	181	29	.	.	PUNCT
cana-1008	182	1	this	this	PRON
cana-1008	182	2	is	be	AUX
cana-1008	182	3	a	a	DET
cana-1008	182	4	critical	critical	ADJ
cana-1008	182	5	step	step	NOUN
cana-1008	182	6	in	in	ADP
cana-1008	182	7	the	the	DET
cana-1008	182	8	process	process	NOUN
cana-1008	182	9	.	.	PUNCT
cana-1008	183	1	after	after	ADP
cana-1008	183	2	that	that	PRON
cana-1008	183	3	,	,	PUNCT
cana-1008	183	4	we	we	PRON
cana-1008	183	5	will	will	AUX
cana-1008	183	6	choose	choose	VERB
cana-1008	183	7	the	the	DET
cana-1008	183	8	models	model	NOUN
cana-1008	183	9	that	that	PRON
cana-1008	183	10	will	will	AUX
cana-1008	183	11	be	be	AUX
cana-1008	183	12	used	use	VERB
cana-1008	183	13	to	to	PART
cana-1008	183	14	train	train	VERB
cana-1008	183	15	our	our	PRON
cana-1008	183	16	data	datum	NOUN
cana-1008	183	17	,	,	PUNCT
cana-1008	183	18	and	and	CCONJ
cana-1008	183	19	then	then	ADV
cana-1008	183	20	we	we	PRON
cana-1008	183	21	will	will	AUX
cana-1008	183	22	put	put	VERB
cana-1008	183	23	the	the	DET
cana-1008	183	24	model	model	NOUN
cana-1008	183	25	that	that	PRON
cana-1008	183	26	we	we	PRON
cana-1008	183	27	have	have	AUX
cana-1008	183	28	selected	select	VERB
cana-1008	183	29	into	into	ADP
cana-1008	183	30	action	action	NOUN
cana-1008	183	31	.	.	PUNCT
cana-1008	184	1	following	follow	VERB
cana-1008	184	2	this	this	PRON
cana-1008	184	3	,	,	PUNCT
cana-1008	184	4	the	the	DET
cana-1008	184	5	models	model	NOUN
cana-1008	184	6	will	will	AUX
cana-1008	184	7	be	be	AUX
cana-1008	184	8	evaluated	evaluate	VERB
cana-1008	184	9	and	and	CCONJ
cana-1008	184	10	compared	compare	VERB
cana-1008	184	11	based	base	VERB
cana-1008	184	12	on	on	ADP
cana-1008	184	13	a	a	DET
cana-1008	184	14	variety	variety	NOUN
cana-1008	184	15	of	of	ADP
cana-1008	184	16	performance	performance	NOUN
cana-1008	184	17	indicators	indicator	NOUN
cana-1008	184	18	,	,	PUNCT
cana-1008	184	19	such	such	ADJ
cana-1008	184	20	as	as	ADP
cana-1008	184	21	accuracy	accuracy	NOUN
cana-1008	184	22	,	,	PUNCT
cana-1008	184	23	sensitivity	sensitivity	NOUN
cana-1008	184	24	,	,	PUNCT
cana-1008	184	25	and	and	CCONJ
cana-1008	184	26	specificity	specificity	NOUN
cana-1008	184	27	amongst	amongst	ADP
cana-1008	184	28	the	the	DET
cana-1008	184	29	models	model	NOUN
cana-1008	184	30	.	.	PUNCT
cana-1008	185	1	the	the	DET
cana-1008	185	2	strategy	strategy	NOUN
cana-1008	185	3	that	that	PRON
cana-1008	185	4	is	be	AUX
cana-1008	185	5	proposed	propose	VERB
cana-1008	185	6	is	be	AUX
cana-1008	185	7	shown	show	VERB
cana-1008	185	8	in	in	ADP
cana-1008	185	9	figure	figure	NOUN
cana-1008	185	10	1	1	NUM
cana-1008	185	11	,	,	PUNCT
cana-1008	185	12	which	which	PRON
cana-1008	185	13	provides	provide	VERB
cana-1008	185	14	an	an	DET
cana-1008	185	15	overview	overview	NOUN
cana-1008	185	16	of	of	ADP
cana-1008	185	17	the	the	DET
cana-1008	185	18	stages	stage	NOUN
cana-1008	185	19	that	that	PRON
cana-1008	185	20	are	be	AUX
cana-1008	185	21	engaged	engage	VERB
cana-1008	185	22	in	in	ADP
cana-1008	185	23	the	the	DET
cana-1008	185	24	implementation	implementation	NOUN
cana-1008	185	25	process	process	NOUN
cana-1008	185	26	step	step	NOUN
cana-1008	185	27	by	by	ADP
cana-1008	185	28	step	step	NOUN
cana-1008	185	29	.	.	PUNCT
cana-1008	186	1	figure	figure	NOUN
cana-1008	186	2	1	1	NUM
cana-1008	186	3	:	:	PUNCT
cana-1008	186	4	overview	overview	NOUN
cana-1008	186	5	of	of	ADP
cana-1008	186	6	the	the	DET
cana-1008	186	7	proposed	propose	VERB
cana-1008	186	8	model	model	NOUN
cana-1008	186	9	communications	communication	NOUN
cana-1008	186	10	on	on	ADP
cana-1008	186	11	applied	apply	VERB
cana-1008	186	12	nonlinear	nonlinear	ADJ
cana-1008	186	13	analysis	analysis	NOUN
cana-1008	186	14	issn	issn	NOUN
cana-1008	186	15	:	:	PUNCT
cana-1008	186	16	1074	1074	NUM
cana-1008	186	17	-	-	PUNCT
cana-1008	186	18	133x	133x	NUM
cana-1008	186	19	vol	vol	NOUN
cana-1008	186	20	31	31	NUM
cana-1008	186	21	no	no	NOUN
cana-1008	186	22	.	.	PUNCT
cana-1008	187	1	5s	5s	NUM
cana-1008	187	2	(	(	PUNCT
cana-1008	187	3	2024	2024	NUM
cana-1008	187	4	)	)	PUNCT
cana-1008	187	5	145	145	NUM
cana-1008	187	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1008	187	7	in	in	ADP
cana-1008	187	8	the	the	DET
cana-1008	187	9	beginning	beginning	NOUN
cana-1008	187	10	,	,	PUNCT
cana-1008	187	11	the	the	DET
cana-1008	187	12	data	datum	NOUN
cana-1008	187	13	was	be	AUX
cana-1008	187	14	cleaned	clean	VERB
cana-1008	187	15	up	up	ADP
cana-1008	187	16	by	by	ADP
cana-1008	187	17	deleting	delete	VERB
cana-1008	187	18	instances	instance	NOUN
cana-1008	187	19	that	that	PRON
cana-1008	187	20	included	include	VERB
cana-1008	187	21	a	a	DET
cana-1008	187	22	substantial	substantial	ADJ
cana-1008	187	23	quantity	quantity	NOUN
cana-1008	187	24	of	of	ADP
cana-1008	187	25	values	value	NOUN
cana-1008	187	26	that	that	PRON
cana-1008	187	27	were	be	AUX
cana-1008	187	28	missing	miss	VERB
cana-1008	187	29	.	.	PUNCT
cana-1008	188	1	following	follow	VERB
cana-1008	188	2	this	this	PRON
cana-1008	188	3	,	,	PUNCT
cana-1008	188	4	mean	mean	ADJ
cana-1008	188	5	/	/	SYM
cana-1008	188	6	mode	mode	NOUN
cana-1008	188	7	imputation	imputation	NOUN
cana-1008	188	8	was	be	AUX
cana-1008	188	9	used	use	VERB
cana-1008	188	10	in	in	ADP
cana-1008	188	11	order	order	NOUN
cana-1008	188	12	to	to	PART
cana-1008	188	13	effectively	effectively	ADV
cana-1008	188	14	replace	replace	VERB
cana-1008	188	15	the	the	DET
cana-1008	188	16	data	datum	NOUN
cana-1008	188	17	that	that	PRON
cana-1008	188	18	was	be	AUX
cana-1008	188	19	absent	absent	ADJ
cana-1008	188	20	.	.	PUNCT
cana-1008	189	1	with	with	ADP
cana-1008	189	2	regard	regard	NOUN
cana-1008	189	3	to	to	ADP
cana-1008	189	4	the	the	DET
cana-1008	189	5	process	process	NOUN
cana-1008	189	6	of	of	ADP
cana-1008	189	7	imputing	impute	VERB
cana-1008	189	8	missing	miss	VERB
cana-1008	189	9	data	datum	NOUN
cana-1008	189	10	,	,	PUNCT
cana-1008	189	11	this	this	DET
cana-1008	189	12	technique	technique	NOUN
cana-1008	189	13	has	have	AUX
cana-1008	189	14	shown	show	VERB
cana-1008	189	15	excellent	excellent	ADJ
cana-1008	189	16	outcomes	outcome	NOUN
cana-1008	189	17	.	.	PUNCT
cana-1008	190	1	through	through	ADP
cana-1008	190	2	the	the	DET
cana-1008	190	3	use	use	NOUN
cana-1008	190	4	of	of	ADP
cana-1008	190	5	dummy	dummy	ADJ
cana-1008	190	6	variables	variable	NOUN
cana-1008	190	7	,	,	PUNCT
cana-1008	190	8	the	the	DET
cana-1008	190	9	categorical	categorical	ADJ
cana-1008	190	10	categories	category	NOUN
cana-1008	190	11	were	be	AUX
cana-1008	190	12	transformed	transform	VERB
cana-1008	190	13	into	into	ADP
cana-1008	190	14	numerical	numerical	ADJ
cana-1008	190	15	values	value	NOUN
cana-1008	190	16	.	.	PUNCT
cana-1008	191	1	the	the	DET
cana-1008	191	2	variables	variable	NOUN
cana-1008	191	3	were	be	AUX
cana-1008	191	4	normalized	normalize	VERB
cana-1008	191	5	to	to	ADP
cana-1008	191	6	a	a	DET
cana-1008	191	7	consistent	consistent	ADJ
cana-1008	191	8	scale	scale	NOUN
cana-1008	191	9	by	by	ADP
cana-1008	191	10	the	the	DET
cana-1008	191	11	use	use	NOUN
cana-1008	191	12	of	of	ADP
cana-1008	191	13	a	a	DET
cana-1008	191	14	feature	feature	NOUN
cana-1008	191	15	scaling	scale	VERB
cana-1008	191	16	approach	approach	NOUN
cana-1008	191	17	known	know	VERB
cana-1008	191	18	as	as	ADP
cana-1008	191	19	standardization	standardization	NOUN
cana-1008	191	20	.	.	PUNCT
cana-1008	192	1	this	this	PRON
cana-1008	192	2	was	be	AUX
cana-1008	192	3	done	do	VERB
cana-1008	192	4	in	in	ADP
cana-1008	192	5	order	order	NOUN
cana-1008	192	6	to	to	PART
cana-1008	192	7	address	address	VERB
cana-1008	192	8	the	the	DET
cana-1008	192	9	discrepancies	discrepancy	NOUN
cana-1008	192	10	in	in	ADP
cana-1008	192	11	the	the	DET
cana-1008	192	12	units	unit	NOUN
cana-1008	192	13	and	and	CCONJ
cana-1008	192	14	ranges	range	NOUN
cana-1008	192	15	of	of	ADP
cana-1008	192	16	the	the	DET
cana-1008	192	17	variables	variable	NOUN
cana-1008	192	18	.	.	PUNCT
cana-1008	193	1	following	follow	VERB
cana-1008	193	2	the	the	DET
cana-1008	193	3	process	process	NOUN
cana-1008	193	4	of	of	ADP
cana-1008	193	5	normalizing	normalize	VERB
cana-1008	193	6	all	all	PRON
cana-1008	193	7	of	of	ADP
cana-1008	193	8	the	the	DET
cana-1008	193	9	variables	variable	NOUN
cana-1008	193	10	,	,	PUNCT
cana-1008	193	11	the	the	DET
cana-1008	193	12	dimensionality	dimensionality	NOUN
cana-1008	193	13	reduction	reduction	NOUN
cana-1008	193	14	method	method	NOUN
cana-1008	193	15	known	know	VERB
cana-1008	193	16	as	as	ADP
cana-1008	193	17	principal	principal	ADJ
cana-1008	193	18	component	component	NOUN
cana-1008	193	19	analysis	analysis	NOUN
cana-1008	193	20	(	(	PUNCT
cana-1008	193	21	pca	pca	NOUN
cana-1008	193	22	)	)	PUNCT
cana-1008	193	23	was	be	AUX
cana-1008	193	24	used	use	VERB
cana-1008	193	25	.	.	PUNCT
cana-1008	194	1	these	these	DET
cana-1008	194	2	variables	variable	NOUN
cana-1008	194	3	were	be	AUX
cana-1008	194	4	connected	connect	VERB
cana-1008	194	5	to	to	ADP
cana-1008	194	6	a	a	DET
cana-1008	194	7	smaller	small	ADJ
cana-1008	194	8	set	set	NOUN
cana-1008	194	9	of	of	ADP
cana-1008	194	10	independent	independent	ADJ
cana-1008	194	11	principle	principle	ADJ
cana-1008	194	12	components	component	NOUN
cana-1008	194	13	,	,	PUNCT
cana-1008	194	14	and	and	CCONJ
cana-1008	194	15	the	the	DET
cana-1008	194	16	purpose	purpose	NOUN
cana-1008	194	17	of	of	ADP
cana-1008	194	18	this	this	DET
cana-1008	194	19	technique	technique	NOUN
cana-1008	194	20	was	be	AUX
cana-1008	194	21	to	to	PART
cana-1008	194	22	turn	turn	VERB
cana-1008	194	23	those	those	DET
cana-1008	194	24	variables	variable	NOUN
cana-1008	194	25	into	into	ADP
cana-1008	194	26	those	those	DET
cana-1008	194	27	principal	principal	ADJ
cana-1008	194	28	components	component	NOUN
cana-1008	194	29	.	.	PUNCT
cana-1008	195	1	the	the	DET
cana-1008	195	2	present	present	ADJ
cana-1008	195	3	issue	issue	NOUN
cana-1008	195	4	might	might	AUX
cana-1008	195	5	be	be	AUX
cana-1008	195	6	characterized	characterize	VERB
cana-1008	195	7	as	as	ADP
cana-1008	195	8	a	a	DET
cana-1008	195	9	multilabel	multilabel	NOUN
cana-1008	195	10	classification	classification	NOUN
cana-1008	195	11	task	task	NOUN
cana-1008	195	12	,	,	PUNCT
cana-1008	195	13	in	in	ADP
cana-1008	195	14	which	which	PRON
cana-1008	195	15	a	a	DET
cana-1008	195	16	patient	patient	NOUN
cana-1008	195	17	may	may	AUX
cana-1008	195	18	experience	experience	VERB
cana-1008	195	19	either	either	DET
cana-1008	195	20	one	one	NUM
cana-1008	195	21	of	of	ADP
cana-1008	195	22	the	the	DET
cana-1008	195	23	outcomes	outcome	NOUN
cana-1008	195	24	or	or	CCONJ
cana-1008	195	25	both	both	PRON
cana-1008	195	26	of	of	ADP
cana-1008	195	27	them	they	PRON
cana-1008	195	28	.	.	PUNCT
cana-1008	196	1	subsequently	subsequently	ADV
cana-1008	196	2	,	,	PUNCT
cana-1008	196	3	classification	classification	NOUN
cana-1008	196	4	techniques	technique	NOUN
cana-1008	196	5	such	such	ADJ
cana-1008	196	6	support	support	NOUN
cana-1008	196	7	vector	vector	NOUN
cana-1008	196	8	machines	machine	NOUN
cana-1008	196	9	(	(	PUNCT
cana-1008	196	10	svm	svm	PROPN
cana-1008	196	11	)	)	PUNCT
cana-1008	196	12	,	,	PUNCT
cana-1008	196	13	naïve	naïve	ADJ
cana-1008	196	14	bayes	bayes	PROPN
cana-1008	196	15	(	(	PUNCT
cana-1008	196	16	nb	nb	PROPN
cana-1008	196	17	)	)	PUNCT
cana-1008	196	18	,	,	PUNCT
cana-1008	196	19	random	random	ADJ
cana-1008	196	20	forest	forest	NOUN
cana-1008	196	21	,	,	PUNCT
cana-1008	196	22	and	and	CCONJ
cana-1008	196	23	decision	decision	NOUN
cana-1008	196	24	tree	tree	NOUN
cana-1008	196	25	were	be	AUX
cana-1008	196	26	used	use	VERB
cana-1008	196	27	in	in	ADP
cana-1008	196	28	order	order	NOUN
cana-1008	196	29	to	to	PART
cana-1008	196	30	forecast	forecast	VERB
cana-1008	196	31	the	the	DET
cana-1008	196	32	probability	probability	NOUN
cana-1008	196	33	of	of	ADP
cana-1008	196	34	a	a	DET
cana-1008	196	35	patient	patient	NOUN
cana-1008	196	36	's	's	PART
cana-1008	196	37	risk	risk	NOUN
cana-1008	196	38	by	by	ADP
cana-1008	196	39	using	use	VERB
cana-1008	196	40	the	the	DET
cana-1008	196	41	pima	pima	PROPN
cana-1008	196	42	dataset	dataset	PROPN
cana-1008	196	43	.	.	PUNCT
cana-1008	197	1	a	a	DET
cana-1008	197	2	number	number	NOUN
cana-1008	197	3	of	of	ADP
cana-1008	197	4	criteria	criterion	NOUN
cana-1008	197	5	,	,	PUNCT
cana-1008	197	6	such	such	ADJ
cana-1008	197	7	as	as	ADP
cana-1008	197	8	the	the	DET
cana-1008	197	9	accuracy	accuracy	NOUN
cana-1008	197	10	score	score	NOUN
cana-1008	197	11	(	(	PUNCT
cana-1008	197	12	acc	acc	PROPN
cana-1008	197	13	)	)	PUNCT
cana-1008	197	14	,	,	PUNCT
cana-1008	197	15	the	the	DET
cana-1008	197	16	confusion	confusion	NOUN
cana-1008	197	17	matrix	matrix	NOUN
cana-1008	197	18	,	,	PUNCT
cana-1008	197	19	the	the	DET
cana-1008	197	20	classification	classification	NOUN
cana-1008	197	21	report	report	NOUN
cana-1008	197	22	,	,	PUNCT
cana-1008	197	23	the	the	DET
cana-1008	197	24	roc	roc	PROPN
cana-1008	197	25	curve	curve	NOUN
cana-1008	197	26	,	,	PUNCT
cana-1008	197	27	and	and	CCONJ
cana-1008	197	28	the	the	DET
cana-1008	197	29	auc	auc	NOUN
cana-1008	197	30	score	score	NOUN
cana-1008	197	31	,	,	PUNCT
cana-1008	197	32	were	be	AUX
cana-1008	197	33	used	use	VERB
cana-1008	197	34	in	in	ADP
cana-1008	197	35	order	order	NOUN
cana-1008	197	36	to	to	PART
cana-1008	197	37	ensure	ensure	VERB
cana-1008	197	38	that	that	SCONJ
cana-1008	197	39	each	each	DET
cana-1008	197	40	algorithm	algorithm	NOUN
cana-1008	197	41	was	be	AUX
cana-1008	197	42	evaluated	evaluate	VERB
cana-1008	197	43	for	for	ADP
cana-1008	197	44	its	its	PRON
cana-1008	197	45	effectiveness	effectiveness	NOUN
cana-1008	197	46	.	.	PUNCT
cana-1008	198	1	3.1	3.1	NUM
cana-1008	198	2	pre	pre	ADJ
cana-1008	198	3	-	-	ADJ
cana-1008	198	4	processing	processing	ADJ
cana-1008	198	5	to	to	PART
cana-1008	198	6	begin	begin	VERB
cana-1008	198	7	processing	process	VERB
cana-1008	198	8	the	the	DET
cana-1008	198	9	information	information	NOUN
cana-1008	198	10	,	,	PUNCT
cana-1008	198	11	the	the	DET
cana-1008	198	12	initial	initial	ADJ
cana-1008	198	13	step	step	NOUN
cana-1008	198	14	involves	involves	AUX
cana-1008	198	15	systematically	systematically	ADV
cana-1008	198	16	deleting	delete	VERB
cana-1008	198	17	superfluous	superfluous	ADJ
cana-1008	198	18	entries	entry	NOUN
cana-1008	198	19	and	and	CCONJ
cana-1008	198	20	characteristics	characteristic	NOUN
cana-1008	198	21	using	use	VERB
cana-1008	198	22	a	a	DET
cana-1008	198	23	cleaning	clean	VERB
cana-1008	198	24	approach	approach	NOUN
cana-1008	198	25	[	[	X
cana-1008	198	26	26	26	NUM
cana-1008	198	27	]	]	PUNCT
cana-1008	198	28	.	.	PUNCT
cana-1008	199	1	initially	initially	ADV
cana-1008	199	2	,	,	PUNCT
cana-1008	199	3	the	the	DET
cana-1008	199	4	information	information	NOUN
cana-1008	199	5	contains	contain	VERB
cana-1008	199	6	several	several	ADJ
cana-1008	199	7	category	category	NOUN
cana-1008	199	8	characteristics	characteristic	NOUN
cana-1008	199	9	that	that	PRON
cana-1008	199	10	must	must	AUX
cana-1008	199	11	be	be	AUX
cana-1008	199	12	removed	remove	VERB
cana-1008	199	13	to	to	PART
cana-1008	199	14	ensure	ensure	VERB
cana-1008	199	15	anonymity	anonymity	NOUN
cana-1008	199	16	.	.	PUNCT
cana-1008	200	1	the	the	DET
cana-1008	200	2	variables	variable	NOUN
cana-1008	200	3	consist	consist	VERB
cana-1008	200	4	of	of	ADP
cana-1008	200	5	the	the	DET
cana-1008	200	6	hospital	hospital	NOUN
cana-1008	200	7	number	number	NOUN
cana-1008	200	8	,	,	PUNCT
cana-1008	200	9	the	the	DET
cana-1008	200	10	event	event	NOUN
cana-1008	200	11	date	date	NOUN
cana-1008	200	12	,	,	PUNCT
cana-1008	200	13	and	and	CCONJ
cana-1008	200	14	the	the	DET
cana-1008	200	15	occurrence	occurrence	NOUN
cana-1008	200	16	description	description	NOUN
cana-1008	200	17	.	.	PUNCT
cana-1008	201	1	moreover	moreover	ADV
cana-1008	201	2	,	,	PUNCT
cana-1008	201	3	the	the	DET
cana-1008	201	4	dataset	dataset	NOUN
cana-1008	201	5	is	be	AUX
cana-1008	201	6	deficient	deficient	ADJ
cana-1008	201	7	in	in	ADP
cana-1008	201	8	data	datum	NOUN
cana-1008	201	9	about	about	ADP
cana-1008	201	10	the	the	DET
cana-1008	201	11	specific	specific	ADJ
cana-1008	201	12	kind	kind	NOUN
cana-1008	201	13	of	of	ADP
cana-1008	201	14	diabetes	diabetes	NOUN
cana-1008	201	15	in	in	ADP
cana-1008	201	16	particular	particular	ADJ
cana-1008	201	17	patients	patient	NOUN
cana-1008	201	18	,	,	PUNCT
cana-1008	201	19	a	a	DET
cana-1008	201	20	crucial	crucial	ADJ
cana-1008	201	21	piece	piece	NOUN
cana-1008	201	22	of	of	ADP
cana-1008	201	23	information	information	NOUN
cana-1008	201	24	for	for	ADP
cana-1008	201	25	our	our	PRON
cana-1008	201	26	study	study	NOUN
cana-1008	201	27	since	since	SCONJ
cana-1008	201	28	we	we	PRON
cana-1008	201	29	examined	examine	VERB
cana-1008	201	30	difficulties	difficulty	NOUN
cana-1008	201	31	specifically	specifically	ADV
cana-1008	201	32	related	relate	VERB
cana-1008	201	33	to	to	ADP
cana-1008	201	34	diabetes	diabetes	NOUN
cana-1008	201	35	in	in	ADP
cana-1008	201	36	individuals	individual	NOUN
cana-1008	201	37	with	with	ADP
cana-1008	201	38	the	the	DET
cana-1008	201	39	condition	condition	NOUN
cana-1008	201	40	.	.	PUNCT
cana-1008	202	1	consequently	consequently	ADV
cana-1008	202	2	,	,	PUNCT
cana-1008	202	3	all	all	DET
cana-1008	202	4	twenty	twenty	NUM
cana-1008	202	5	-	-	PUNCT
cana-1008	202	6	six	six	NUM
cana-1008	202	7	occurrences	occurrence	NOUN
cana-1008	202	8	that	that	PRON
cana-1008	202	9	were	be	AUX
cana-1008	202	10	impacted	impact	VERB
cana-1008	202	11	by	by	ADP
cana-1008	202	12	this	this	DET
cana-1008	202	13	issue	issue	NOUN
cana-1008	202	14	were	be	AUX
cana-1008	202	15	eliminated	eliminate	VERB
cana-1008	202	16	.	.	PUNCT
cana-1008	203	1	subsequently	subsequently	ADV
cana-1008	203	2	,	,	PUNCT
cana-1008	203	3	a	a	DET
cana-1008	203	4	z	z	NOUN
cana-1008	203	5	-	-	PUNCT
cana-1008	203	6	score	score	NOUN
cana-1008	203	7	,	,	PUNCT
cana-1008	203	8	also	also	ADV
cana-1008	203	9	known	know	VERB
cana-1008	203	10	as	as	ADP
cana-1008	203	11	a	a	DET
cana-1008	203	12	standard	standard	ADJ
cana-1008	203	13	score	score	NOUN
cana-1008	203	14	,	,	PUNCT
cana-1008	203	15	is	be	AUX
cana-1008	203	16	used	use	VERB
cana-1008	203	17	to	to	PART
cana-1008	203	18	standardize	standardize	VERB
cana-1008	203	19	scores	score	NOUN
cana-1008	203	20	on	on	ADP
cana-1008	203	21	a	a	DET
cana-1008	203	22	uniform	uniform	ADJ
cana-1008	203	23	scale	scale	NOUN
cana-1008	203	24	.	.	PUNCT
cana-1008	204	1	to	to	PART
cana-1008	204	2	get	get	VERB
cana-1008	204	3	this	this	DET
cana-1008	204	4	measurement	measurement	NOUN
cana-1008	204	5	,	,	PUNCT
cana-1008	204	6	the	the	DET
cana-1008	204	7	variance	variance	NOUN
cana-1008	204	8	of	of	ADP
cana-1008	204	9	a	a	DET
cana-1008	204	10	score	score	NOUN
cana-1008	204	11	is	be	AUX
cana-1008	204	12	divided	divide	VERB
cana-1008	204	13	by	by	ADP
cana-1008	204	14	the	the	DET
cana-1008	204	15	standard	standard	ADJ
cana-1008	204	16	deviation	deviation	NOUN
cana-1008	204	17	of	of	ADP
cana-1008	204	18	a	a	DET
cana-1008	204	19	data	datum	NOUN
cana-1008	204	20	collection	collection	NOUN
cana-1008	204	21	.	.	PUNCT
cana-1008	205	1	the	the	DET
cana-1008	205	2	term	term	NOUN
cana-1008	205	3	"	"	PUNCT
cana-1008	205	4	standard	standard	ADJ
cana-1008	205	5	deviation	deviation	NOUN
cana-1008	205	6	"	"	PUNCT
cana-1008	205	7	is	be	AUX
cana-1008	205	8	used	use	VERB
cana-1008	205	9	to	to	PART
cana-1008	205	10	measure	measure	VERB
cana-1008	205	11	the	the	DET
cana-1008	205	12	extent	extent	NOUN
cana-1008	205	13	to	to	PART
cana-1008	205	14	which	which	PRON
cana-1008	205	15	a	a	DET
cana-1008	205	16	certain	certain	ADJ
cana-1008	205	17	data	data	NOUN
cana-1008	205	18	point	point	NOUN
cana-1008	205	19	diverges	diverge	VERB
cana-1008	205	20	from	from	ADP
cana-1008	205	21	the	the	DET
cana-1008	205	22	mean	mean	NOUN
cana-1008	205	23	.	.	PUNCT
cana-1008	206	1	this	this	DET
cana-1008	206	2	variance	variance	NOUN
cana-1008	206	3	is	be	AUX
cana-1008	206	4	expressed	express	VERB
cana-1008	206	5	in	in	ADP
cana-1008	206	6	terms	term	NOUN
cana-1008	206	7	of	of	ADP
cana-1008	206	8	the	the	DET
cana-1008	206	9	standard	standard	ADJ
cana-1008	206	10	deviation	deviation	NOUN
cana-1008	206	11	.	.	PUNCT
cana-1008	207	1	a	a	DET
cana-1008	207	2	negative	negative	ADJ
cana-1008	207	3	zscore	zscore	NOUN
cana-1008	207	4	signifies	signify	VERB
cana-1008	207	5	values	value	NOUN
cana-1008	207	6	that	that	PRON
cana-1008	207	7	are	be	AUX
cana-1008	207	8	below	below	ADP
cana-1008	207	9	the	the	DET
cana-1008	207	10	mean	mean	NOUN
cana-1008	207	11	,	,	PUNCT
cana-1008	207	12	a	a	DET
cana-1008	207	13	positive	positive	ADJ
cana-1008	207	14	z	z	NOUN
cana-1008	207	15	-	-	PUNCT
cana-1008	207	16	score	score	NOUN
cana-1008	207	17	signifies	signify	VERB
cana-1008	207	18	values	value	NOUN
cana-1008	207	19	that	that	PRON
cana-1008	207	20	are	be	AUX
cana-1008	207	21	above	above	ADP
cana-1008	207	22	the	the	DET
cana-1008	207	23	mean	mean	NOUN
cana-1008	207	24	,	,	PUNCT
cana-1008	207	25	and	and	CCONJ
cana-1008	207	26	a	a	DET
cana-1008	207	27	z	z	NOUN
cana-1008	207	28	-	-	PUNCT
cana-1008	207	29	score	score	NOUN
cana-1008	207	30	of	of	ADP
cana-1008	207	31	zero	zero	NUM
cana-1008	207	32	signifies	signifie	NOUN
cana-1008	207	33	the	the	DET
cana-1008	207	34	mean	mean	ADJ
cana-1008	207	35	value	value	NOUN
cana-1008	207	36	.	.	PUNCT
cana-1008	208	1	the	the	DET
cana-1008	208	2	value	value	NOUN
cana-1008	208	3	is	be	AUX
cana-1008	208	4	within	within	ADP
cana-1008	208	5	the	the	DET
cana-1008	208	6	range	range	NOUN
cana-1008	208	7	of	of	ADP
cana-1008	208	8	(	(	PUNCT
cana-1008	208	9	-1	-1	INTJ
cana-1008	208	10	,	,	PUNCT
cana-1008	208	11	1	1	NUM
cana-1008	208	12	)	)	PUNCT
cana-1008	208	13	,	,	PUNCT
cana-1008	208	14	with	with	ADP
cana-1008	208	15	a	a	DET
cana-1008	208	16	positive	positive	ADJ
cana-1008	208	17	z	z	NOUN
cana-1008	208	18	-	-	PUNCT
cana-1008	208	19	score	score	NOUN
cana-1008	208	20	indicating	indicate	VERB
cana-1008	208	21	values	value	NOUN
cana-1008	208	22	above	above	ADP
cana-1008	208	23	the	the	DET
cana-1008	208	24	mean	mean	NOUN
cana-1008	208	25	.	.	PUNCT
cana-1008	209	1	z	z	X
cana-1008	209	2	-	-	PUNCT
cana-1008	209	3	score	score	NOUN
cana-1008	209	4	normalization	normalization	NOUN
cana-1008	209	5	may	may	AUX
cana-1008	209	6	be	be	AUX
cana-1008	209	7	calculated	calculate	VERB
cana-1008	209	8	using	use	VERB
cana-1008	209	9	the	the	DET
cana-1008	209	10	mean	mean	NOUN
cana-1008	209	11	(	(	PUNCT
cana-1008	209	12	μ	μ	NOUN
cana-1008	209	13	)	)	PUNCT
cana-1008	209	14	and	and	CCONJ
cana-1008	209	15	standard	standard	ADJ
cana-1008	209	16	deviation	deviation	NOUN
cana-1008	209	17	(	(	PUNCT
cana-1008	209	18	σ	σ	NOUN
cana-1008	209	19	)	)	PUNCT
cana-1008	209	20	of	of	ADP
cana-1008	209	21	the	the	DET
cana-1008	209	22	characteristics	characteristic	NOUN
cana-1008	209	23	.	.	PUNCT
cana-1008	210	1	during	during	ADP
cana-1008	210	2	the	the	DET
cana-1008	210	3	calculation	calculation	NOUN
cana-1008	210	4	,	,	PUNCT
cana-1008	210	5	the	the	DET
cana-1008	210	6	feature	feature	NOUN
cana-1008	210	7	vector	vector	NOUN
cana-1008	210	8	x	x	VERB
cana-1008	210	9	is	be	AUX
cana-1008	210	10	used	use	VERB
cana-1008	210	11	as	as	ADP
cana-1008	210	12	the	the	DET
cana-1008	210	13	beginning	beginning	NOUN
cana-1008	210	14	value	value	NOUN
cana-1008	210	15	[	[	X
cana-1008	210	16	27	27	NUM
cana-1008	210	17	]	]	SYM
cana-1008	210	18	.	.	PUNCT
cana-1008	211	1	3.2	3.2	NUM
cana-1008	211	2	principal	principal	ADJ
cana-1008	211	3	component	component	NOUN
cana-1008	211	4	analysis	analysis	NOUN
cana-1008	211	5	(	(	PUNCT
cana-1008	211	6	pca	pca	NOUN
cana-1008	211	7	)	)	PUNCT
cana-1008	211	8	principal	principal	NOUN
cana-1008	211	9	component	component	NOUN
cana-1008	211	10	analysis	analysis	NOUN
cana-1008	211	11	(	(	PUNCT
cana-1008	211	12	pca	pca	NOUN
cana-1008	211	13	)	)	PUNCT
cana-1008	211	14	is	be	AUX
cana-1008	211	15	a	a	DET
cana-1008	211	16	technique	technique	NOUN
cana-1008	211	17	that	that	PRON
cana-1008	211	18	may	may	AUX
cana-1008	211	19	be	be	AUX
cana-1008	211	20	used	use	VERB
cana-1008	211	21	to	to	PART
cana-1008	211	22	decrease	decrease	VERB
cana-1008	211	23	the	the	DET
cana-1008	211	24	number	number	NOUN
cana-1008	211	25	of	of	ADP
cana-1008	211	26	variables	variable	NOUN
cana-1008	211	27	in	in	ADP
cana-1008	211	28	a	a	DET
cana-1008	211	29	dataset	dataset	NOUN
cana-1008	211	30	.	.	PUNCT
cana-1008	212	1	it	it	PRON
cana-1008	212	2	does	do	VERB
cana-1008	212	3	this	this	PRON
cana-1008	212	4	by	by	ADP
cana-1008	212	5	applying	apply	VERB
cana-1008	212	6	an	an	DET
cana-1008	212	7	orthogonal	orthogonal	ADJ
cana-1008	212	8	transformation	transformation	NOUN
cana-1008	212	9	,	,	PUNCT
cana-1008	212	10	resulting	result	VERB
cana-1008	212	11	in	in	ADP
cana-1008	212	12	a	a	DET
cana-1008	212	13	reduced	reduce	VERB
cana-1008	212	14	set	set	NOUN
cana-1008	212	15	of	of	ADP
cana-1008	212	16	variables	variable	NOUN
cana-1008	212	17	while	while	SCONJ
cana-1008	212	18	retaining	retain	VERB
cana-1008	212	19	much	much	ADJ
cana-1008	212	20	of	of	ADP
cana-1008	212	21	the	the	DET
cana-1008	212	22	original	original	ADJ
cana-1008	212	23	information	information	NOUN
cana-1008	212	24	.	.	PUNCT
cana-1008	213	1	the	the	DET
cana-1008	213	2	procedure	procedure	NOUN
cana-1008	213	3	entails	entail	VERB
cana-1008	213	4	reducing	reduce	VERB
cana-1008	213	5	the	the	DET
cana-1008	213	6	quantity	quantity	NOUN
cana-1008	213	7	of	of	ADP
cana-1008	213	8	highly	highly	ADV
cana-1008	213	9	linked	link	VERB
cana-1008	213	10	variables	variable	NOUN
cana-1008	213	11	in	in	ADP
cana-1008	213	12	the	the	DET
cana-1008	213	13	original	original	ADJ
cana-1008	213	14	dataset	dataset	NOUN
cana-1008	213	15	to	to	ADP
cana-1008	213	16	a	a	DET
cana-1008	213	17	reduced	reduce	VERB
cana-1008	213	18	number	number	NOUN
cana-1008	213	19	of	of	ADP
cana-1008	213	20	linear	linear	PROPN
cana-1008	213	21	variables	variable	NOUN
cana-1008	213	22	that	that	PRON
cana-1008	213	23	are	be	AUX
cana-1008	213	24	unconnected	unconnected	ADJ
cana-1008	213	25	to	to	ADP
cana-1008	213	26	each	each	DET
cana-1008	213	27	other	other	ADJ
cana-1008	213	28	.	.	PUNCT
cana-1008	214	1	these	these	DET
cana-1008	214	2	variables	variable	NOUN
cana-1008	214	3	are	be	AUX
cana-1008	214	4	often	often	ADV
cana-1008	214	5	known	know	VERB
cana-1008	214	6	as	as	ADP
cana-1008	214	7	principal	principal	ADJ
cana-1008	214	8	components	component	NOUN
cana-1008	214	9	.	.	PUNCT
cana-1008	215	1	subsequently	subsequently	ADV
cana-1008	215	2	,	,	PUNCT
cana-1008	215	3	these	these	DET
cana-1008	215	4	primary	primary	ADJ
cana-1008	215	5	constituents	constituent	NOUN
cana-1008	215	6	are	be	AUX
cana-1008	215	7	accountable	accountable	ADJ
cana-1008	215	8	for	for	ADP
cana-1008	215	9	the	the	DET
cana-1008	215	10	bulk	bulk	NOUN
cana-1008	215	11	of	of	ADP
cana-1008	215	12	the	the	DET
cana-1008	215	13	variability	variability	NOUN
cana-1008	215	14	seen	see	VERB
cana-1008	215	15	in	in	ADP
cana-1008	215	16	the	the	DET
cana-1008	215	17	original	original	ADJ
cana-1008	215	18	dataset	dataset	NOUN
cana-1008	216	1	[	[	X
cana-1008	216	2	28	28	NUM
cana-1008	216	3	,	,	PUNCT
cana-1008	216	4	29	29	NUM
cana-1008	216	5	]	]	PUNCT
cana-1008	216	6	.	.	PUNCT
cana-1008	217	1	principal	principal	ADJ
cana-1008	217	2	component	component	NOUN
cana-1008	217	3	analysis	analysis	NOUN
cana-1008	217	4	(	(	PUNCT
cana-1008	217	5	pca	pca	NOUN
cana-1008	217	6	)	)	PUNCT
cana-1008	217	7	is	be	AUX
cana-1008	217	8	particularly	particularly	ADV
cana-1008	217	9	advantageous	advantageous	ADJ
cana-1008	217	10	when	when	SCONJ
cana-1008	217	11	communications	communication	NOUN
cana-1008	217	12	on	on	ADP
cana-1008	217	13	applied	apply	VERB
cana-1008	217	14	nonlinear	nonlinear	ADJ
cana-1008	217	15	analysis	analysis	NOUN
cana-1008	217	16	issn	issn	NOUN
cana-1008	217	17	:	:	PUNCT
cana-1008	217	18	1074	1074	NUM
cana-1008	217	19	-	-	PUNCT
cana-1008	217	20	133x	133x	NUM
cana-1008	217	21	vol	vol	NOUN
cana-1008	217	22	31	31	NUM
cana-1008	217	23	no	no	NOUN
cana-1008	217	24	.	.	PUNCT
cana-1008	218	1	5s	5s	NUM
cana-1008	218	2	(	(	PUNCT
cana-1008	218	3	2024	2024	NUM
cana-1008	218	4	)	)	PUNCT
cana-1008	218	5	146	146	NUM
cana-1008	218	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1008	218	7	dealing	deal	VERB
cana-1008	218	8	with	with	ADP
cana-1008	218	9	data	datum	NOUN
cana-1008	218	10	that	that	PRON
cana-1008	218	11	has	have	VERB
cana-1008	218	12	three	three	NUM
cana-1008	218	13	or	or	CCONJ
cana-1008	218	14	more	more	ADJ
cana-1008	218	15	dimensions	dimension	NOUN
cana-1008	218	16	,	,	PUNCT
cana-1008	218	17	as	as	SCONJ
cana-1008	218	18	it	it	PRON
cana-1008	218	19	becomes	become	VERB
cana-1008	218	20	increasingly	increasingly	ADV
cana-1008	218	21	challenging	challenging	ADJ
cana-1008	218	22	to	to	PART
cana-1008	218	23	make	make	VERB
cana-1008	218	24	predictions	prediction	NOUN
cana-1008	218	25	based	base	VERB
cana-1008	218	26	on	on	ADP
cana-1008	218	27	such	such	DET
cana-1008	218	28	a	a	DET
cana-1008	218	29	substantial	substantial	ADJ
cana-1008	218	30	amount	amount	NOUN
cana-1008	218	31	of	of	ADP
cana-1008	218	32	information	information	NOUN
cana-1008	218	33	.	.	PUNCT
cana-1008	219	1	moreover	moreover	ADV
cana-1008	219	2	,	,	PUNCT
cana-1008	219	3	presenting	present	VERB
cana-1008	219	4	data	datum	NOUN
cana-1008	219	5	that	that	PRON
cana-1008	219	6	has	have	VERB
cana-1008	219	7	several	several	ADJ
cana-1008	219	8	dimensions	dimension	NOUN
cana-1008	219	9	is	be	AUX
cana-1008	219	10	a	a	DET
cana-1008	219	11	significant	significant	ADJ
cana-1008	219	12	challenge	challenge	NOUN
cana-1008	219	13	.	.	PUNCT
cana-1008	220	1	furthermore	furthermore	ADV
cana-1008	220	2	,	,	PUNCT
cana-1008	220	3	pca	pca	PROPN
cana-1008	220	4	has	have	VERB
cana-1008	220	5	the	the	DET
cana-1008	220	6	capability	capability	NOUN
cana-1008	220	7	to	to	PART
cana-1008	220	8	address	address	VERB
cana-1008	220	9	this	this	DET
cana-1008	220	10	problem	problem	NOUN
cana-1008	220	11	in	in	ADP
cana-1008	220	12	terms	term	NOUN
cana-1008	220	13	of	of	ADP
cana-1008	220	14	data	datum	NOUN
cana-1008	220	15	visualization	visualization	NOUN
cana-1008	220	16	.	.	PUNCT
cana-1008	221	1	the	the	DET
cana-1008	221	2	number	number	NOUN
cana-1008	221	3	of	of	ADP
cana-1008	221	4	primary	primary	ADJ
cana-1008	221	5	components	component	NOUN
cana-1008	221	6	is	be	AUX
cana-1008	221	7	determined	determine	VERB
cana-1008	221	8	by	by	ADP
cana-1008	221	9	the	the	DET
cana-1008	221	10	lower	low	ADJ
cana-1008	221	11	value	value	NOUN
cana-1008	221	12	between	between	ADP
cana-1008	221	13	the	the	DET
cana-1008	221	14	number	number	NOUN
cana-1008	221	15	of	of	ADP
cana-1008	221	16	observations	observation	NOUN
cana-1008	221	17	and	and	CCONJ
cana-1008	221	18	the	the	DET
cana-1008	221	19	number	number	NOUN
cana-1008	221	20	of	of	ADP
cana-1008	221	21	original	original	ADJ
cana-1008	221	22	features	feature	NOUN
cana-1008	221	23	.	.	PUNCT
cana-1008	222	1	initially	initially	ADV
cana-1008	222	2	,	,	PUNCT
cana-1008	222	3	the	the	DET
cana-1008	222	4	dataset	dataset	NOUN
cana-1008	222	5	consisted	consist	VERB
cana-1008	222	6	of	of	ADP
cana-1008	222	7	779	779	NUM
cana-1008	222	8	observations	observation	NOUN
cana-1008	222	9	and	and	CCONJ
cana-1008	222	10	164	164	NUM
cana-1008	222	11	relevant	relevant	ADJ
cana-1008	222	12	features	feature	NOUN
cana-1008	222	13	.	.	PUNCT
cana-1008	223	1	consequently	consequently	ADV
cana-1008	223	2	,	,	PUNCT
cana-1008	223	3	the	the	DET
cana-1008	223	4	maximum	maximum	ADJ
cana-1008	223	5	number	number	NOUN
cana-1008	223	6	of	of	ADP
cana-1008	223	7	principal	principal	ADJ
cana-1008	223	8	components	component	NOUN
cana-1008	223	9	that	that	PRON
cana-1008	223	10	could	could	AUX
cana-1008	223	11	be	be	AUX
cana-1008	223	12	used	use	VERB
cana-1008	223	13	was	be	AUX
cana-1008	223	14	164	164	NUM
cana-1008	223	15	.	.	PUNCT
cana-1008	224	1	out	out	ADP
cana-1008	224	2	of	of	ADP
cana-1008	224	3	the	the	DET
cana-1008	224	4	164	164	NUM
cana-1008	224	5	components	component	NOUN
cana-1008	224	6	,	,	PUNCT
cana-1008	224	7	a	a	DET
cana-1008	224	8	subset	subset	NOUN
cana-1008	224	9	of	of	ADP
cana-1008	224	10	fifty	fifty	NUM
cana-1008	224	11	was	be	AUX
cana-1008	224	12	chosen	choose	VERB
cana-1008	224	13	.	.	PUNCT
cana-1008	225	1	figure	figure	NOUN
cana-1008	225	2	2	2	NUM
cana-1008	225	3	clearly	clearly	ADV
cana-1008	225	4	shows	show	VERB
cana-1008	225	5	that	that	SCONJ
cana-1008	225	6	these	these	DET
cana-1008	225	7	fifty	fifty	NUM
cana-1008	225	8	components	component	NOUN
cana-1008	225	9	contributed	contribute	VERB
cana-1008	225	10	to	to	ADP
cana-1008	225	11	eighty	eighty	NUM
cana-1008	225	12	percent	percent	NOUN
cana-1008	225	13	of	of	ADP
cana-1008	225	14	the	the	DET
cana-1008	225	15	overall	overall	ADJ
cana-1008	225	16	variance	variance	NOUN
cana-1008	225	17	.	.	PUNCT
cana-1008	226	1	figure	figure	NOUN
cana-1008	226	2	2	2	NUM
cana-1008	226	3	.	.	PUNCT
cana-1008	226	4	cumulative	cumulative	ADJ
cana-1008	226	5	explained	explain	VERB
cana-1008	226	6	variance	variance	NOUN
cana-1008	226	7	against	against	ADP
cana-1008	226	8	no	no	NOUN
cana-1008	226	9	.	.	PUNCT
cana-1008	226	10	of	of	ADP
cana-1008	226	11	principal	principal	ADJ
cana-1008	226	12	components	component	NOUN
cana-1008	227	1	[	[	X
cana-1008	227	2	30	30	NUM
cana-1008	227	3	]	]	SYM
cana-1008	227	4	.	.	PUNCT
cana-1008	228	1	3.3	3.3	NUM
cana-1008	228	2	machine	machine	NOUN
cana-1008	228	3	learning	learn	VERB
cana-1008	228	4	supervised	supervised	ADJ
cana-1008	228	5	learning	learn	VERB
cana-1008	228	6	is	be	AUX
cana-1008	228	7	a	a	DET
cana-1008	228	8	versatile	versatile	ADJ
cana-1008	228	9	technique	technique	NOUN
cana-1008	228	10	that	that	PRON
cana-1008	228	11	may	may	AUX
cana-1008	228	12	be	be	AUX
cana-1008	228	13	used	use	VERB
cana-1008	228	14	to	to	ADP
cana-1008	228	15	any	any	DET
cana-1008	228	16	kind	kind	NOUN
cana-1008	228	17	of	of	ADP
cana-1008	228	18	data	datum	NOUN
cana-1008	228	19	without	without	ADP
cana-1008	228	20	limitations	limitation	NOUN
cana-1008	228	21	.	.	PUNCT
cana-1008	229	1	classification	classification	NOUN
cana-1008	229	2	is	be	AUX
cana-1008	229	3	a	a	DET
cana-1008	229	4	sequential	sequential	ADJ
cana-1008	229	5	procedure	procedure	NOUN
cana-1008	229	6	that	that	PRON
cana-1008	229	7	first	first	ADV
cana-1008	229	8	acquires	acquire	VERB
cana-1008	229	9	information	information	NOUN
cana-1008	229	10	from	from	ADP
cana-1008	229	11	the	the	DET
cana-1008	229	12	provided	provide	VERB
cana-1008	229	13	data	datum	NOUN
cana-1008	229	14	and	and	CCONJ
cana-1008	229	15	then	then	ADV
cana-1008	229	16	applies	apply	VERB
cana-1008	229	17	that	that	DET
cana-1008	229	18	knowledge	knowledge	NOUN
cana-1008	229	19	to	to	PART
cana-1008	229	20	categorize	categorize	VERB
cana-1008	229	21	new	new	ADJ
cana-1008	229	22	data	datum	NOUN
cana-1008	229	23	.	.	PUNCT
cana-1008	230	1	this	this	DET
cana-1008	230	2	technique	technique	NOUN
cana-1008	230	3	is	be	AUX
cana-1008	230	4	beneficial	beneficial	ADJ
cana-1008	230	5	for	for	ADP
cana-1008	230	6	selecting	select	VERB
cana-1008	230	7	the	the	DET
cana-1008	230	8	appropriate	appropriate	ADJ
cana-1008	230	9	class	class	NOUN
cana-1008	230	10	labels	label	NOUN
cana-1008	230	11	for	for	ADP
cana-1008	230	12	integrating	integrate	VERB
cana-1008	230	13	new	new	ADJ
cana-1008	230	14	data	datum	NOUN
cana-1008	230	15	.	.	PUNCT
cana-1008	231	1	the	the	DET
cana-1008	231	2	clustering	cluster	VERB
cana-1008	231	3	methodology	methodology	NOUN
cana-1008	231	4	enables	enable	VERB
cana-1008	231	5	the	the	DET
cana-1008	231	6	creation	creation	NOUN
cana-1008	231	7	of	of	ADP
cana-1008	231	8	diverse	diverse	ADJ
cana-1008	231	9	labels	label	NOUN
cana-1008	231	10	and	and	CCONJ
cana-1008	231	11	groupings	grouping	NOUN
cana-1008	231	12	within	within	ADP
cana-1008	231	13	the	the	DET
cana-1008	231	14	dataset	dataset	NOUN
cana-1008	231	15	,	,	PUNCT
cana-1008	231	16	relying	rely	VERB
cana-1008	231	17	on	on	ADP
cana-1008	231	18	the	the	DET
cana-1008	231	19	existing	exist	VERB
cana-1008	231	20	commonalities	commonality	NOUN
cana-1008	231	21	among	among	ADP
cana-1008	231	22	the	the	DET
cana-1008	231	23	data	data	NOUN
cana-1008	231	24	points	point	NOUN
cana-1008	231	25	.	.	PUNCT
cana-1008	232	1	classification	classification	NOUN
cana-1008	232	2	is	be	AUX
cana-1008	232	3	a	a	DET
cana-1008	232	4	technique	technique	NOUN
cana-1008	232	5	used	use	VERB
cana-1008	232	6	in	in	ADP
cana-1008	232	7	data	datum	NOUN
cana-1008	232	8	mining	mining	NOUN
cana-1008	232	9	to	to	PART
cana-1008	232	10	categorize	categorize	VERB
cana-1008	232	11	data	datum	NOUN
cana-1008	232	12	,	,	PUNCT
cana-1008	232	13	enabling	enable	VERB
cana-1008	232	14	more	more	ADV
cana-1008	232	15	precise	precise	ADJ
cana-1008	232	16	analysis	analysis	NOUN
cana-1008	232	17	and	and	CCONJ
cana-1008	232	18	predictions	prediction	NOUN
cana-1008	232	19	.	.	PUNCT
cana-1008	233	1	machine	machine	NOUN
cana-1008	233	2	learning	learn	VERB
cana-1008	233	3	[	[	X
cana-1008	233	4	31	31	NUM
cana-1008	233	5	]	]	PUNCT
cana-1008	233	6	is	be	AUX
cana-1008	233	7	a	a	DET
cana-1008	233	8	data	data	NOUN
cana-1008	233	9	mining	mining	NOUN
cana-1008	233	10	approach	approach	NOUN
cana-1008	233	11	specifically	specifically	ADV
cana-1008	233	12	designed	design	VERB
cana-1008	233	13	to	to	PART
cana-1008	233	14	analyze	analyze	VERB
cana-1008	233	15	very	very	ADV
cana-1008	233	16	large	large	ADJ
cana-1008	233	17	datasets	dataset	NOUN
cana-1008	233	18	.	.	PUNCT
cana-1008	234	1	to	to	PART
cana-1008	234	2	accurately	accurately	ADV
cana-1008	234	3	classify	classify	VERB
cana-1008	234	4	the	the	DET
cana-1008	234	5	important	important	ADJ
cana-1008	234	6	data	datum	NOUN
cana-1008	234	7	categories	category	NOUN
cana-1008	234	8	in	in	ADP
cana-1008	234	9	the	the	DET
cana-1008	234	10	data	datum	NOUN
cana-1008	234	11	collection	collection	NOUN
cana-1008	234	12	,	,	PUNCT
cana-1008	234	13	patterns	pattern	NOUN
cana-1008	234	14	are	be	AUX
cana-1008	234	15	generated	generate	VERB
cana-1008	234	16	.	.	PUNCT
cana-1008	235	1	classification	classification	NOUN
cana-1008	235	2	algorithms	algorithm	NOUN
cana-1008	235	3	provide	provide	VERB
cana-1008	235	4	predictions	prediction	NOUN
cana-1008	235	5	for	for	ADP
cana-1008	235	6	the	the	DET
cana-1008	235	7	target	target	NOUN
cana-1008	235	8	classes	class	NOUN
cana-1008	235	9	of	of	ADP
cana-1008	235	10	every	every	DET
cana-1008	235	11	occurrence	occurrence	NOUN
cana-1008	235	12	in	in	ADP
cana-1008	235	13	the	the	DET
cana-1008	235	14	given	give	VERB
cana-1008	235	15	dataset	dataset	NOUN
cana-1008	235	16	.	.	PUNCT
cana-1008	236	1	classification	classification	NOUN
cana-1008	236	2	algorithms	algorithm	NOUN
cana-1008	236	3	aim	aim	VERB
cana-1008	236	4	to	to	PART
cana-1008	236	5	examine	examine	VERB
cana-1008	236	6	data	datum	NOUN
cana-1008	236	7	and	and	CCONJ
cana-1008	236	8	establish	establish	VERB
cana-1008	236	9	connections	connection	NOUN
cana-1008	236	10	between	between	ADP
cana-1008	236	11	traits	trait	NOUN
cana-1008	236	12	to	to	PART
cana-1008	236	13	enable	enable	VERB
cana-1008	236	14	precise	precise	ADJ
cana-1008	236	15	prediction	prediction	NOUN
cana-1008	236	16	of	of	ADP
cana-1008	236	17	outcomes	outcome	NOUN
cana-1008	236	18	.	.	PUNCT
cana-1008	237	1	an	an	DET
cana-1008	237	2	analysis	analysis	NOUN
cana-1008	237	3	is	be	AUX
cana-1008	237	4	conducted	conduct	VERB
cana-1008	237	5	on	on	ADP
cana-1008	237	6	the	the	DET
cana-1008	237	7	input	input	NOUN
cana-1008	237	8	,	,	PUNCT
cana-1008	237	9	which	which	PRON
cana-1008	237	10	leads	lead	VERB
cana-1008	237	11	to	to	ADP
cana-1008	237	12	the	the	DET
cana-1008	237	13	generation	generation	NOUN
cana-1008	237	14	of	of	ADP
cana-1008	237	15	a	a	DET
cana-1008	237	16	forecast	forecast	NOUN
cana-1008	237	17	.	.	PUNCT
cana-1008	238	1	data	datum	NOUN
cana-1008	238	2	mining	mining	NOUN
cana-1008	238	3	activities	activity	NOUN
cana-1008	238	4	including	include	VERB
cana-1008	238	5	classification	classification	NOUN
cana-1008	238	6	are	be	AUX
cana-1008	238	7	often	often	ADV
cana-1008	238	8	used	use	VERB
cana-1008	238	9	in	in	ADP
cana-1008	238	10	the	the	DET
cana-1008	238	11	healthcare	healthcare	NOUN
cana-1008	238	12	sector	sector	NOUN
cana-1008	238	13	[	[	X
cana-1008	238	14	32	32	NUM
cana-1008	238	15	]	]	PUNCT
cana-1008	238	16	.	.	PUNCT
cana-1008	239	1	3.3.1	3.3.1	NUM
cana-1008	239	2	support	support	NOUN
cana-1008	239	3	vector	vector	NOUN
cana-1008	239	4	machine	machine	NOUN
cana-1008	239	5	a	a	DET
cana-1008	239	6	support	support	NOUN
cana-1008	239	7	vector	vector	NOUN
cana-1008	239	8	machine	machine	NOUN
cana-1008	239	9	(	(	PUNCT
cana-1008	239	10	svm	svm	PROPN
cana-1008	239	11	)	)	PUNCT
cana-1008	239	12	is	be	AUX
cana-1008	239	13	a	a	DET
cana-1008	239	14	linear	linear	ADJ
cana-1008	239	15	model	model	NOUN
cana-1008	239	16	that	that	PRON
cana-1008	239	17	may	may	AUX
cana-1008	239	18	be	be	AUX
cana-1008	239	19	used	use	VERB
cana-1008	239	20	to	to	PART
cana-1008	239	21	address	address	VERB
cana-1008	239	22	classification	classification	NOUN
cana-1008	239	23	and	and	CCONJ
cana-1008	239	24	regression	regression	NOUN
cana-1008	239	25	problems	problem	NOUN
cana-1008	239	26	.	.	PUNCT
cana-1008	240	1	with	with	ADP
cana-1008	240	2	this	this	DET
cana-1008	240	3	tool	tool	NOUN
cana-1008	240	4	,	,	PUNCT
cana-1008	240	5	users	user	NOUN
cana-1008	240	6	may	may	AUX
cana-1008	240	7	discover	discover	VERB
cana-1008	240	8	answers	answer	NOUN
cana-1008	240	9	to	to	ADP
cana-1008	240	10	both	both	CCONJ
cana-1008	240	11	linear	linear	ADJ
cana-1008	240	12	and	and	CCONJ
cana-1008	240	13	nonlinear	nonlinear	ADJ
cana-1008	240	14	problems	problem	NOUN
cana-1008	240	15	.	.	PUNCT
cana-1008	241	1	communications	communication	NOUN
cana-1008	241	2	on	on	ADP
cana-1008	241	3	applied	apply	VERB
cana-1008	241	4	nonlinear	nonlinear	ADJ
cana-1008	241	5	analysis	analysis	NOUN
cana-1008	241	6	issn	issn	NOUN
cana-1008	241	7	:	:	PUNCT
cana-1008	241	8	1074	1074	NUM
cana-1008	241	9	-	-	PUNCT
cana-1008	241	10	133x	133x	NUM
cana-1008	241	11	vol	vol	NOUN
cana-1008	241	12	31	31	NUM
cana-1008	241	13	no	no	NOUN
cana-1008	241	14	.	.	PUNCT
cana-1008	242	1	5s	5s	NUM
cana-1008	242	2	(	(	PUNCT
cana-1008	242	3	2024	2024	NUM
cana-1008	242	4	)	)	PUNCT
cana-1008	242	5	147	147	NUM
cana-1008	242	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1008	242	7	similar	similar	ADJ
cana-1008	242	8	to	to	ADP
cana-1008	242	9	linear	linear	PROPN
cana-1008	242	10	regression	regression	NOUN
cana-1008	242	11	,	,	PUNCT
cana-1008	242	12	it	it	PRON
cana-1008	242	13	functions	function	VERB
cana-1008	242	14	in	in	ADP
cana-1008	242	15	an	an	DET
cana-1008	242	16	analogous	analogous	ADJ
cana-1008	242	17	fashion	fashion	NOUN
cana-1008	242	18	[	[	X
cana-1008	242	19	33	33	NUM
cana-1008	242	20	]	]	PUNCT
cana-1008	242	21	.	.	PUNCT
cana-1008	243	1	the	the	DET
cana-1008	243	2	svm	svm	PROPN
cana-1008	243	3	approach	approach	NOUN
cana-1008	243	4	classifies	classify	VERB
cana-1008	243	5	new	new	ADJ
cana-1008	243	6	data	datum	NOUN
cana-1008	243	7	by	by	ADP
cana-1008	243	8	generating	generate	VERB
cana-1008	243	9	a	a	DET
cana-1008	243	10	hyperplane	hyperplane	NOUN
cana-1008	243	11	with	with	ADP
cana-1008	243	12	the	the	DET
cana-1008	243	13	largest	large	ADJ
cana-1008	243	14	possible	possible	ADJ
cana-1008	243	15	margin	margin	NOUN
cana-1008	243	16	.	.	PUNCT
cana-1008	244	1	the	the	DET
cana-1008	244	2	main	main	ADJ
cana-1008	244	3	goal	goal	NOUN
cana-1008	244	4	of	of	ADP
cana-1008	244	5	these	these	DET
cana-1008	244	6	supervised	supervised	ADJ
cana-1008	244	7	algorithms	algorithm	NOUN
cana-1008	244	8	is	be	AUX
cana-1008	244	9	to	to	PART
cana-1008	244	10	create	create	VERB
cana-1008	244	11	an	an	DET
cana-1008	244	12	unparalleled	unparalleled	ADJ
cana-1008	244	13	decision	decision	NOUN
cana-1008	244	14	boundary	boundary	NOUN
cana-1008	244	15	or	or	CCONJ
cana-1008	244	16	line	line	NOUN
cana-1008	244	17	that	that	PRON
cana-1008	244	18	can	can	AUX
cana-1008	244	19	divide	divide	VERB
cana-1008	244	20	n	n	CCONJ
cana-1008	244	21	-	-	PUNCT
cana-1008	244	22	dimensional	dimensional	ADJ
cana-1008	244	23	space	space	NOUN
cana-1008	244	24	into	into	ADP
cana-1008	244	25	distinct	distinct	ADJ
cana-1008	244	26	classes	class	NOUN
cana-1008	244	27	.	.	PUNCT
cana-1008	245	1	in	in	ADP
cana-1008	245	2	the	the	DET
cana-1008	245	3	future	future	NOUN
cana-1008	245	4	,	,	PUNCT
cana-1008	245	5	we	we	PRON
cana-1008	245	6	will	will	AUX
cana-1008	245	7	be	be	AUX
cana-1008	245	8	able	able	ADJ
cana-1008	245	9	to	to	PART
cana-1008	245	10	easily	easily	ADV
cana-1008	245	11	assign	assign	VERB
cana-1008	245	12	the	the	DET
cana-1008	245	13	new	new	ADJ
cana-1008	245	14	data	datum	NOUN
cana-1008	245	15	instance	instance	NOUN
cana-1008	245	16	to	to	ADP
cana-1008	245	17	the	the	DET
cana-1008	245	18	correct	correct	ADJ
cana-1008	245	19	group	group	NOUN
cana-1008	245	20	.	.	PUNCT
cana-1008	246	1	a	a	DET
cana-1008	246	2	hyperplane	hyperplane	NOUN
cana-1008	246	3	refers	refer	VERB
cana-1008	246	4	to	to	ADP
cana-1008	246	5	the	the	DET
cana-1008	246	6	decision	decision	NOUN
cana-1008	246	7	boundary	boundary	ADJ
cana-1008	246	8	that	that	PRON
cana-1008	246	9	can	can	AUX
cana-1008	246	10	not	not	PART
cana-1008	246	11	be	be	AUX
cana-1008	246	12	surpassed	surpass	VERB
cana-1008	246	13	.	.	PUNCT
cana-1008	247	1	multiple	multiple	ADJ
cana-1008	247	2	alternative	alternative	ADJ
cana-1008	247	3	boundaries	boundary	NOUN
cana-1008	247	4	may	may	AUX
cana-1008	247	5	be	be	AUX
cana-1008	247	6	designed	design	VERB
cana-1008	247	7	,	,	PUNCT
cana-1008	247	8	as	as	SCONJ
cana-1008	247	9	seen	see	VERB
cana-1008	247	10	in	in	ADP
cana-1008	247	11	figure	figure	NOUN
cana-1008	247	12	3	3	NUM
cana-1008	247	13	,	,	PUNCT
cana-1008	247	14	to	to	PART
cana-1008	247	15	separate	separate	VERB
cana-1008	247	16	the	the	DET
cana-1008	247	17	classes	class	NOUN
cana-1008	247	18	in	in	ADP
cana-1008	247	19	n	n	CCONJ
cana-1008	247	20	-	-	PUNCT
cana-1008	247	21	dimensional	dimensional	ADJ
cana-1008	247	22	space	space	NOUN
cana-1008	247	23	.	.	PUNCT
cana-1008	248	1	however	however	ADV
cana-1008	248	2	,	,	PUNCT
cana-1008	248	3	the	the	DET
cana-1008	248	4	primary	primary	ADJ
cana-1008	248	5	objective	objective	NOUN
cana-1008	248	6	should	should	AUX
cana-1008	248	7	be	be	AUX
cana-1008	248	8	to	to	PART
cana-1008	248	9	identify	identify	VERB
cana-1008	248	10	the	the	DET
cana-1008	248	11	ideal	ideal	ADJ
cana-1008	248	12	decision	decision	NOUN
cana-1008	248	13	boundary	boundary	ADJ
cana-1008	248	14	that	that	PRON
cana-1008	248	15	enables	enable	VERB
cana-1008	248	16	the	the	DET
cana-1008	248	17	most	most	ADV
cana-1008	248	18	efficient	efficient	ADJ
cana-1008	248	19	categorization	categorization	NOUN
cana-1008	248	20	of	of	ADP
cana-1008	248	21	the	the	DET
cana-1008	248	22	data	data	NOUN
cana-1008	248	23	points	point	NOUN
cana-1008	248	24	.	.	PUNCT
cana-1008	249	1	figure	figure	VERB
cana-1008	249	2	3	3	NUM
cana-1008	249	3	:	:	PUNCT
cana-1008	249	4	illustrating	illustrate	VERB
cana-1008	249	5	the	the	DET
cana-1008	249	6	svm	svm	ADJ
cana-1008	249	7	hyperplanes	hyperplane	NOUN
cana-1008	249	8	[	[	X
cana-1008	249	9	34	34	NUM
cana-1008	249	10	]	]	PUNCT
cana-1008	249	11	support	support	NOUN
cana-1008	249	12	vector	vector	NOUN
cana-1008	249	13	machines	machine	NOUN
cana-1008	249	14	(	(	PUNCT
cana-1008	249	15	svm	svm	PROPN
cana-1008	249	16	)	)	PUNCT
cana-1008	249	17	do	do	VERB
cana-1008	249	18	classification	classification	NOUN
cana-1008	249	19	problems	problem	NOUN
cana-1008	249	20	by	by	ADP
cana-1008	249	21	creating	create	VERB
cana-1008	249	22	hyperplanes	hyperplane	NOUN
cana-1008	249	23	in	in	ADP
cana-1008	249	24	a	a	DET
cana-1008	249	25	multidimensional	multidimensional	ADJ
cana-1008	249	26	space	space	NOUN
cana-1008	249	27	that	that	PRON
cana-1008	249	28	distinguish	distinguish	VERB
cana-1008	249	29	between	between	ADP
cana-1008	249	30	occurrences	occurrence	NOUN
cana-1008	249	31	of	of	ADP
cana-1008	249	32	distinct	distinct	ADJ
cana-1008	249	33	class	class	NOUN
cana-1008	249	34	labels	label	NOUN
cana-1008	249	35	.	.	PUNCT
cana-1008	250	1	svm	svm	PROPN
cana-1008	250	2	refers	refer	VERB
cana-1008	250	3	to	to	ADP
cana-1008	250	4	the	the	DET
cana-1008	250	5	set	set	NOUN
cana-1008	250	6	of	of	ADP
cana-1008	250	7	data	datum	NOUN
cana-1008	250	8	instances	instance	NOUN
cana-1008	250	9	that	that	PRON
cana-1008	250	10	provide	provide	VERB
cana-1008	250	11	the	the	DET
cana-1008	250	12	foundation	foundation	NOUN
cana-1008	250	13	for	for	ADP
cana-1008	250	14	the	the	DET
cana-1008	250	15	hyperplane	hyperplane	NOUN
cana-1008	250	16	.	.	PUNCT
cana-1008	251	1	the	the	DET
cana-1008	251	2	word	word	NOUN
cana-1008	251	3	"	"	PUNCT
cana-1008	251	4	margin	margin	NOUN
cana-1008	251	5	"	"	PUNCT
cana-1008	251	6	denotes	denote	VERB
cana-1008	251	7	the	the	DET
cana-1008	251	8	spatial	spatial	ADJ
cana-1008	251	9	separation	separation	NOUN
cana-1008	251	10	between	between	ADP
cana-1008	251	11	the	the	DET
cana-1008	251	12	hyperplane	hyperplane	NOUN
cana-1008	251	13	and	and	CCONJ
cana-1008	251	14	the	the	DET
cana-1008	251	15	support	support	NOUN
cana-1008	251	16	vector	vector	NOUN
cana-1008	251	17	that	that	PRON
cana-1008	251	18	is	be	AUX
cana-1008	251	19	in	in	ADP
cana-1008	251	20	closest	close	ADJ
cana-1008	251	21	proximity	proximity	NOUN
cana-1008	251	22	to	to	ADP
cana-1008	251	23	it	it	PRON
cana-1008	251	24	.	.	PUNCT
cana-1008	252	1	when	when	SCONJ
cana-1008	252	2	selecting	select	VERB
cana-1008	252	3	the	the	DET
cana-1008	252	4	optimal	optimal	ADJ
cana-1008	252	5	hyperplane	hyperplane	NOUN
cana-1008	252	6	,	,	PUNCT
cana-1008	252	7	the	the	DET
cana-1008	252	8	primary	primary	ADJ
cana-1008	252	9	factor	factor	NOUN
cana-1008	252	10	considered	consider	VERB
cana-1008	252	11	is	be	AUX
cana-1008	252	12	the	the	DET
cana-1008	252	13	maximum	maximum	ADJ
cana-1008	252	14	margin	margin	NOUN
cana-1008	252	15	,	,	PUNCT
cana-1008	252	16	which	which	PRON
cana-1008	252	17	represents	represent	VERB
cana-1008	252	18	the	the	DET
cana-1008	252	19	largest	large	ADJ
cana-1008	252	20	separation	separation	NOUN
cana-1008	252	21	distance	distance	NOUN
cana-1008	252	22	between	between	ADP
cana-1008	252	23	the	the	DET
cana-1008	252	24	two	two	NUM
cana-1008	252	25	classes	class	NOUN
cana-1008	252	26	.	.	PUNCT
cana-1008	253	1	both	both	CCONJ
cana-1008	253	2	linear	linear	ADJ
cana-1008	253	3	and	and	CCONJ
cana-1008	253	4	non	non	ADJ
cana-1008	253	5	-	-	ADJ
cana-1008	253	6	linear	linear	ADJ
cana-1008	253	7	svms	svms	NOUN
cana-1008	253	8	are	be	AUX
cana-1008	253	9	used	use	VERB
cana-1008	253	10	to	to	PART
cana-1008	253	11	classify	classify	VERB
cana-1008	253	12	data	datum	NOUN
cana-1008	253	13	points	point	NOUN
cana-1008	253	14	.	.	PUNCT
cana-1008	254	1	linear	linear	ADJ
cana-1008	254	2	svms	svms	NOUN
cana-1008	254	3	are	be	AUX
cana-1008	254	4	used	use	VERB
cana-1008	254	5	to	to	PART
cana-1008	254	6	classify	classify	VERB
cana-1008	254	7	data	data	NOUN
cana-1008	254	8	points	point	NOUN
cana-1008	254	9	by	by	ADP
cana-1008	254	10	using	use	VERB
cana-1008	254	11	a	a	DET
cana-1008	254	12	straight	straight	ADJ
cana-1008	254	13	decision	decision	NOUN
cana-1008	254	14	boundary	boundary	NOUN
cana-1008	254	15	,	,	PUNCT
cana-1008	254	16	whereas	whereas	SCONJ
cana-1008	254	17	non	non	ADJ
cana-1008	254	18	-	-	ADJ
cana-1008	254	19	linear	linear	ADJ
cana-1008	254	20	svms	svms	NOUN
cana-1008	254	21	employ	employ	VERB
cana-1008	254	22	a	a	DET
cana-1008	254	23	curved	curved	ADJ
cana-1008	254	24	decision	decision	NOUN
cana-1008	254	25	boundary	boundary	ADJ
cana-1008	254	26	to	to	PART
cana-1008	254	27	classify	classify	VERB
cana-1008	254	28	data	data	NOUN
cana-1008	254	29	points	point	NOUN
cana-1008	254	30	[	[	X
cana-1008	254	31	35	35	NUM
cana-1008	254	32	,	,	PUNCT
cana-1008	254	33	36	36	NUM
cana-1008	254	34	]	]	PUNCT
cana-1008	254	35	.	.	PUNCT
cana-1008	255	1	the	the	DET
cana-1008	255	2	data	data	NOUN
cana-1008	255	3	points	point	NOUN
cana-1008	255	4	(	(	PUNCT
cana-1008	255	5	x1	x1	PROPN
cana-1008	255	6	,	,	PUNCT
cana-1008	255	7	y1)	y1)	PROPN
cana-1008	255	8	....	....	PROPN
cana-1008	255	9	(xn	(xn	PROPN
cana-1008	255	10	,	,	PUNCT
cana-1008	255	11	yn	yn	NOUN
cana-1008	255	12	)	)	PUNCT
cana-1008	255	13	consist	consist	NOUN
cana-1008	255	14	of	of	ADP
cana-1008	255	15	real	real	ADJ
cana-1008	255	16	vectors	vector	NOUN
cana-1008	255	17	represented	represent	VERB
cana-1008	255	18	by	by	ADP
cana-1008	255	19	xi	xi	NOUN
cana-1008	255	20	and	and	CCONJ
cana-1008	255	21	binary	binary	ADJ
cana-1008	255	22	values	value	NOUN
cana-1008	255	23	represented	represent	VERB
cana-1008	255	24	by	by	ADP
cana-1008	255	25	y1	y1	NOUN
cana-1008	255	26	,	,	PUNCT
cana-1008	255	27	where	where	SCONJ
cana-1008	255	28	y1	y1	NOUN
cana-1008	255	29	takes	take	VERB
cana-1008	255	30	the	the	DET
cana-1008	255	31	value	value	NOUN
cana-1008	255	32	of	of	ADP
cana-1008	255	33	either	either	CCONJ
cana-1008	255	34	0	0	NUM
cana-1008	255	35	or	or	CCONJ
cana-1008	255	36	1	1	NUM
cana-1008	255	37	.	.	PUNCT
cana-1008	256	1	the	the	DET
cana-1008	256	2	value	value	NOUN
cana-1008	256	3	of	of	ADP
cana-1008	256	4	y1	y1	NOUN
cana-1008	256	5	indicates	indicate	VERB
cana-1008	256	6	the	the	DET
cana-1008	256	7	class	class	NOUN
cana-1008	256	8	to	to	PART
cana-1008	256	9	which	which	PRON
cana-1008	256	10	xi	xi	ADP
cana-1008	256	11	belongs	belong	VERB
cana-1008	256	12	.	.	PUNCT
cana-1008	257	1	the	the	DET
cana-1008	257	2	building	building	NOUN
cana-1008	257	3	of	of	ADP
cana-1008	257	4	a	a	DET
cana-1008	257	5	hyperplane	hyperplane	NOUN
cana-1008	257	6	is	be	AUX
cana-1008	257	7	to	to	PART
cana-1008	257	8	maximize	maximize	VERB
cana-1008	257	9	the	the	DET
cana-1008	257	10	distance	distance	NOUN
cana-1008	257	11	between	between	ADP
cana-1008	257	12	two	two	NUM
cana-1008	257	13	classes	class	NOUN
cana-1008	257	14	,	,	PUNCT
cana-1008	257	15	y	y	PROPN
cana-1008	257	16	=	=	SYM
cana-1008	257	17	0	0	NUM
cana-1008	257	18	and	and	CCONJ
cana-1008	257	19	1	1	NUM
cana-1008	257	20	.	.	X
cana-1008	258	1	its	its	PRON
cana-1008	258	2	definition	definition	NOUN
cana-1008	258	3	is	be	AUX
cana-1008	258	4	as	as	SCONJ
cana-1008	258	5	follows	follow	VERB
cana-1008	258	6	:	:	PUNCT
cana-1008	258	7	𝑚𝑎𝑥⏟𝑎	𝑚𝑎𝑥⏟𝑎	VERB
cana-1008	258	8	∑	∑	PART
cana-1008	258	9	⬚	⬚	VERB
cana-1008	258	10	𝑛	𝑛	PRON
cana-1008	258	11	𝑖=1	𝑖=1	PROPN
cana-1008	259	1	𝑎𝑖	𝑎𝑖	CCONJ
cana-1008	259	2	−	−	NUM
cana-1008	259	3	1	1	NUM
cana-1008	259	4	2	2	NUM
cana-1008	259	5	∑	∑	ADV
cana-1008	259	6	⬚	⬚	VERB
cana-1008	259	7	𝑛	𝑛	PRON
cana-1008	259	8	𝑖=1	𝑖=1	PUNCT
cana-1008	259	9	∑	∑	PUNCT
cana-1008	259	10	⬚	⬚	PROPN
cana-1008	259	11	𝑛	𝑛	DET
cana-1008	259	12	𝑗=1	𝑗=1	PROPN
cana-1008	259	13	𝑦𝑖𝑦𝑗𝐾(𝑥𝑖	𝑦𝑖𝑦𝑗𝐾(𝑥𝑖	PROPN
cana-1008	259	14	,	,	PUNCT
cana-1008	259	15	𝑥𝑗)𝑎𝑖𝑎𝑗	𝑥𝑗)𝑎𝑖𝑎𝑗	ADJ
cana-1008	259	16	(	(	PUNCT
cana-1008	259	17	1	1	X
cana-1008	259	18	)	)	PUNCT
cana-1008	259	19	subject	subject	NOUN
cana-1008	259	20	to	to	ADP
cana-1008	259	21	:	:	PUNCT
cana-1008	259	22	0	0	NUM
cana-1008	259	23	≤	≤	NUM
cana-1008	259	24	𝑎𝑖	𝑎𝑖	ADP
cana-1008	259	25	≤	≤	NUM
cana-1008	259	26	𝑐	𝑐	NOUN
cana-1008	259	27	,	,	PUNCT
cana-1008	259	28	𝑓𝑜𝑟	𝑓𝑜𝑟	NOUN
cana-1008	259	29	𝑖	𝑖	NOUN
cana-1008	259	30	=	=	SYM
cana-1008	259	31	1,2	1,2	NUM
cana-1008	259	32	,	,	PUNCT
cana-1008	259	33	…	…	PUNCT
cana-1008	259	34	𝑛	𝑛	NOUN
cana-1008	259	35	,	,	PUNCT
cana-1008	259	36	∑	∑	ADV
cana-1008	259	37	⬚	⬚	VERB
cana-1008	259	38	𝑛	𝑛	DET
cana-1008	259	39	𝑖=1	𝑖=1	PROPN
cana-1008	259	40	𝑦𝑖𝑎𝑖	𝑦𝑖𝑎𝑖	NOUN
cana-1008	259	41	=	=	SYM
cana-1008	259	42	0	0	NUM
cana-1008	259	43	(	(	PUNCT
cana-1008	259	44	2	2	NUM
cana-1008	259	45	)	)	PUNCT
cana-1008	259	46	communications	communication	NOUN
cana-1008	259	47	on	on	ADP
cana-1008	259	48	applied	apply	VERB
cana-1008	259	49	nonlinear	nonlinear	ADJ
cana-1008	259	50	analysis	analysis	NOUN
cana-1008	259	51	issn	issn	NOUN
cana-1008	259	52	:	:	PUNCT
cana-1008	259	53	1074	1074	NUM
cana-1008	259	54	-	-	PUNCT
cana-1008	259	55	133x	133x	NUM
cana-1008	259	56	vol	vol	NOUN
cana-1008	259	57	31	31	NUM
cana-1008	259	58	no	no	NOUN
cana-1008	259	59	.	.	PUNCT
cana-1008	260	1	5s	5s	NUM
cana-1008	260	2	(	(	PUNCT
cana-1008	260	3	2024	2024	NUM
cana-1008	260	4	)	)	PUNCT
cana-1008	260	5	148	148	NUM
cana-1008	260	6	https://internationalpubls.com	https://internationalpubls.com	SYM
cana-1008	260	7	3.3.2	3.3.2	NUM
cana-1008	260	8	naïve	naïve	ADJ
cana-1008	260	9	bayes	bayes	NOUN
cana-1008	260	10	(	(	PUNCT
cana-1008	260	11	nb	nb	INTJ
cana-1008	260	12	)	)	PUNCT
cana-1008	260	13	the	the	DET
cana-1008	260	14	naïve	naïve	ADJ
cana-1008	260	15	bayes	bayes	NOUN
cana-1008	260	16	method	method	NOUN
cana-1008	260	17	is	be	AUX
cana-1008	260	18	a	a	DET
cana-1008	260	19	supervised	supervised	ADJ
cana-1008	260	20	learning	learning	NOUN
cana-1008	260	21	approach	approach	NOUN
cana-1008	260	22	that	that	PRON
cana-1008	260	23	utilizes	utilize	VERB
cana-1008	260	24	bayes	bayes	PROPN
cana-1008	260	25	'	'	PART
cana-1008	260	26	theorem	theorem	NOUN
cana-1008	260	27	for	for	ADP
cana-1008	260	28	classification	classification	NOUN
cana-1008	260	29	purposes	purpose	NOUN
cana-1008	260	30	.	.	PUNCT
cana-1008	261	1	bayes	bayes	PROPN
cana-1008	261	2	'	'	PART
cana-1008	261	3	theorem	theorem	ADJ
cana-1008	261	4	utilizes	utilizes	ADJ
cana-1008	261	5	conditional	conditional	ADJ
cana-1008	261	6	probability	probability	NOUN
cana-1008	261	7	,	,	PUNCT
cana-1008	261	8	which	which	PRON
cana-1008	261	9	relies	rely	VERB
cana-1008	261	10	on	on	ADP
cana-1008	261	11	previous	previous	ADJ
cana-1008	261	12	knowledge	knowledge	NOUN
cana-1008	261	13	,	,	PUNCT
cana-1008	261	14	to	to	PART
cana-1008	261	15	assess	assess	VERB
cana-1008	261	16	the	the	DET
cana-1008	261	17	chance	chance	NOUN
cana-1008	261	18	of	of	ADP
cana-1008	261	19	a	a	DET
cana-1008	261	20	future	future	ADJ
cana-1008	261	21	event	event	NOUN
cana-1008	261	22	occurring	occur	VERB
cana-1008	261	23	.	.	PUNCT
cana-1008	262	1	bayes	bayes	PROPN
cana-1008	262	2	'	'	PART
cana-1008	262	3	theorem	theorem	NOUN
cana-1008	262	4	may	may	AUX
cana-1008	262	5	be	be	AUX
cana-1008	262	6	expressed	express	VERB
cana-1008	262	7	using	use	VERB
cana-1008	262	8	the	the	DET
cana-1008	262	9	following	follow	VERB
cana-1008	262	10	formula	formula	NOUN
cana-1008	262	11	:	:	PUNCT
cana-1008	262	12	𝑃(𝐸	𝑃(𝐸	NUM
cana-1008	262	13	)	)	PUNCT
cana-1008	262	14	=	=	SYM
cana-1008	262	15	𝑃(𝐸)∗𝑃(𝐻	𝑃(𝐸)∗𝑃(𝐻	NUM
cana-1008	262	16	)	)	PUNCT
cana-1008	262	17	𝑃(𝐸	𝑃(𝐸	NUM
cana-1008	262	18	)	)	PUNCT
cana-1008	262	19	in	in	ADP
cana-1008	262	20	this	this	DET
cana-1008	262	21	application	application	NOUN
cana-1008	262	22	,	,	PUNCT
cana-1008	262	23	the	the	DET
cana-1008	262	24	posterior	posterior	ADJ
cana-1008	262	25	probability	probability	NOUN
cana-1008	262	26	,	,	PUNCT
cana-1008	262	27	represented	represent	VERB
cana-1008	262	28	as	as	ADP
cana-1008	262	29	p(h|e	p(h|e	NUM
cana-1008	262	30	)	)	PUNCT
cana-1008	262	31	,	,	PUNCT
cana-1008	262	32	refers	refer	VERB
cana-1008	262	33	to	to	ADP
cana-1008	262	34	the	the	DET
cana-1008	262	35	likelihood	likelihood	NOUN
cana-1008	262	36	that	that	SCONJ
cana-1008	262	37	a	a	DET
cana-1008	262	38	hypothesis	hypothesis	NOUN
cana-1008	262	39	(	(	PUNCT
cana-1008	262	40	h	h	NOUN
cana-1008	262	41	)	)	PUNCT
cana-1008	262	42	is	be	AUX
cana-1008	262	43	accurate	accurate	ADJ
cana-1008	262	44	given	give	VERB
cana-1008	262	45	specific	specific	ADJ
cana-1008	262	46	evidence	evidence	NOUN
cana-1008	262	47	(	(	PUNCT
cana-1008	262	48	e	e	NOUN
cana-1008	262	49	)	)	PUNCT
cana-1008	262	50	.	.	PUNCT
cana-1008	263	1	the	the	DET
cana-1008	263	2	prior	prior	ADJ
cana-1008	263	3	probability	probability	NOUN
cana-1008	263	4	,	,	PUNCT
cana-1008	263	5	sometimes	sometimes	ADV
cana-1008	263	6	referred	refer	VERB
cana-1008	263	7	to	to	ADP
cana-1008	263	8	as	as	ADP
cana-1008	263	9	the	the	DET
cana-1008	263	10	probability	probability	NOUN
cana-1008	263	11	that	that	SCONJ
cana-1008	263	12	the	the	DET
cana-1008	263	13	hypothesis	hypothesis	NOUN
cana-1008	263	14	is	be	AUX
cana-1008	263	15	true	true	ADJ
cana-1008	263	16	,	,	PUNCT
cana-1008	263	17	is	be	AUX
cana-1008	263	18	represented	represent	VERB
cana-1008	263	19	by	by	ADP
cana-1008	263	20	the	the	DET
cana-1008	263	21	sign	sign	NOUN
cana-1008	263	22	p(h	p(h	NOUN
cana-1008	263	23	)	)	PUNCT
cana-1008	263	24	.	.	PUNCT
cana-1008	264	1	the	the	DET
cana-1008	264	2	sign	sign	NOUN
cana-1008	264	3	p(e	p(e	NOUN
cana-1008	264	4	)	)	PUNCT
cana-1008	264	5	represents	represent	VERB
cana-1008	264	6	the	the	DET
cana-1008	264	7	probability	probability	NOUN
cana-1008	264	8	of	of	ADP
cana-1008	264	9	the	the	DET
cana-1008	264	10	evidence	evidence	NOUN
cana-1008	264	11	,	,	PUNCT
cana-1008	264	12	regardless	regardless	ADV
cana-1008	264	13	of	of	ADP
cana-1008	264	14	the	the	DET
cana-1008	264	15	hypothesis	hypothesis	NOUN
cana-1008	264	16	.	.	PUNCT
cana-1008	265	1	the	the	DET
cana-1008	265	2	conditional	conditional	ADJ
cana-1008	265	3	probability	probability	NOUN
cana-1008	265	4	of	of	ADP
cana-1008	265	5	the	the	DET
cana-1008	265	6	evidence	evidence	NOUN
cana-1008	265	7	given	give	VERB
cana-1008	265	8	that	that	SCONJ
cana-1008	265	9	the	the	DET
cana-1008	265	10	hypothesis	hypothesis	NOUN
cana-1008	265	11	is	be	AUX
cana-1008	265	12	true	true	ADJ
cana-1008	265	13	is	be	AUX
cana-1008	265	14	represented	represent	VERB
cana-1008	265	15	by	by	ADP
cana-1008	265	16	the	the	DET
cana-1008	265	17	notation	notation	NOUN
cana-1008	265	18	p(e|h	p(e|h	NOUN
cana-1008	265	19	)	)	PUNCT
cana-1008	266	1	[	[	X
cana-1008	266	2	35	35	NUM
cana-1008	266	3	]	]	PUNCT
cana-1008	266	4	.	.	PUNCT
cana-1008	267	1	the	the	DET
cana-1008	267	2	naïve	naïve	ADJ
cana-1008	267	3	bayes	bayes	NOUN
cana-1008	267	4	classifier	classifier	NOUN
cana-1008	267	5	is	be	AUX
cana-1008	267	6	a	a	DET
cana-1008	267	7	classification	classification	NOUN
cana-1008	267	8	approach	approach	NOUN
cana-1008	267	9	that	that	PRON
cana-1008	267	10	assumes	assume	VERB
cana-1008	267	11	the	the	DET
cana-1008	267	12	input	input	NOUN
cana-1008	267	13	variables	variable	NOUN
cana-1008	267	14	(	(	PUNCT
cana-1008	267	15	features	feature	NOUN
cana-1008	267	16	)	)	PUNCT
cana-1008	267	17	are	be	AUX
cana-1008	267	18	independent	independent	ADJ
cana-1008	267	19	of	of	ADP
cana-1008	267	20	each	each	DET
cana-1008	267	21	other	other	ADJ
cana-1008	267	22	and	and	CCONJ
cana-1008	267	23	that	that	SCONJ
cana-1008	267	24	each	each	DET
cana-1008	267	25	feature	feature	NOUN
cana-1008	267	26	contributes	contribute	VERB
cana-1008	267	27	individually	individually	ADV
cana-1008	267	28	to	to	ADP
cana-1008	267	29	the	the	DET
cana-1008	267	30	probability	probability	NOUN
cana-1008	267	31	of	of	ADP
cana-1008	267	32	the	the	DET
cana-1008	267	33	target	target	NOUN
cana-1008	267	34	variable	variable	NOUN
cana-1008	267	35	.	.	PUNCT
cana-1008	268	1	it	it	PRON
cana-1008	268	2	may	may	AUX
cana-1008	268	3	be	be	AUX
cana-1008	268	4	inferred	infer	VERB
cana-1008	268	5	that	that	SCONJ
cana-1008	268	6	the	the	DET
cana-1008	268	7	existence	existence	NOUN
cana-1008	268	8	of	of	ADP
cana-1008	268	9	a	a	DET
cana-1008	268	10	solitary	solitary	ADJ
cana-1008	268	11	feature	feature	NOUN
cana-1008	268	12	variable	variable	NOUN
cana-1008	268	13	does	do	AUX
cana-1008	268	14	not	not	PART
cana-1008	268	15	have	have	VERB
cana-1008	268	16	any	any	DET
cana-1008	268	17	influence	influence	NOUN
cana-1008	268	18	on	on	ADP
cana-1008	268	19	the	the	DET
cana-1008	268	20	other	other	ADJ
cana-1008	268	21	feature	feature	NOUN
cana-1008	268	22	variables	variable	NOUN
cana-1008	268	23	.	.	PUNCT
cana-1008	269	1	as	as	ADP
cana-1008	269	2	a	a	DET
cana-1008	269	3	result	result	NOUN
cana-1008	269	4	,	,	PUNCT
cana-1008	269	5	it	it	PRON
cana-1008	269	6	is	be	AUX
cana-1008	269	7	often	often	ADV
cana-1008	269	8	referred	refer	VERB
cana-1008	269	9	to	to	ADP
cana-1008	269	10	as	as	ADV
cana-1008	269	11	naive	naive	ADJ
cana-1008	269	12	.	.	PUNCT
cana-1008	270	1	due	due	ADP
cana-1008	270	2	to	to	ADP
cana-1008	270	3	the	the	DET
cana-1008	270	4	interdependence	interdependence	NOUN
cana-1008	270	5	of	of	ADP
cana-1008	270	6	feature	feature	NOUN
cana-1008	270	7	variables	variable	NOUN
cana-1008	270	8	in	in	ADP
cana-1008	270	9	real	real	ADJ
cana-1008	270	10	datasets	dataset	NOUN
cana-1008	270	11	,	,	PUNCT
cana-1008	270	12	the	the	DET
cana-1008	270	13	naïve	naïve	ADJ
cana-1008	270	14	bayes	bayes	NOUN
cana-1008	270	15	classifier	classifier	NOUN
cana-1008	270	16	now	now	ADV
cana-1008	270	17	faces	face	VERB
cana-1008	270	18	this	this	DET
cana-1008	270	19	specific	specific	ADJ
cana-1008	270	20	constraint	constraint	NOUN
cana-1008	270	21	.	.	PUNCT
cana-1008	271	1	the	the	DET
cana-1008	271	2	naïve	naïve	ADJ
cana-1008	271	3	bayes	bayes	NOUN
cana-1008	271	4	classifier	classifier	NOUN
cana-1008	271	5	has	have	VERB
cana-1008	271	6	exceptional	exceptional	ADJ
cana-1008	271	7	performance	performance	NOUN
cana-1008	271	8	when	when	SCONJ
cana-1008	271	9	used	use	VERB
cana-1008	271	10	to	to	ADP
cana-1008	271	11	large	large	ADJ
cana-1008	271	12	data	datum	NOUN
cana-1008	271	13	sets	set	NOUN
cana-1008	271	14	and	and	CCONJ
cana-1008	271	15	may	may	AUX
cana-1008	271	16	even	even	ADV
cana-1008	271	17	surpass	surpass	VERB
cana-1008	271	18	more	more	ADJ
cana-1008	271	19	intricate	intricate	ADJ
cana-1008	271	20	classifiers	classifier	NOUN
cana-1008	271	21	in	in	ADP
cana-1008	271	22	some	some	DET
cana-1008	271	23	scenarios	scenario	NOUN
cana-1008	271	24	.	.	PUNCT
cana-1008	272	1	the	the	DET
cana-1008	272	2	gaussian	gaussian	ADJ
cana-1008	272	3	naïve	naïve	ADJ
cana-1008	272	4	bayes	bayes	PROPN
cana-1008	272	5	classifier	classifier	NOUN
cana-1008	272	6	,	,	PUNCT
cana-1008	272	7	a	a	DET
cana-1008	272	8	specific	specific	ADJ
cana-1008	272	9	kind	kind	NOUN
cana-1008	272	10	of	of	ADP
cana-1008	272	11	naïve	naïve	ADJ
cana-1008	272	12	bayes	bayes	NOUN
cana-1008	272	13	classifier	classifier	NOUN
cana-1008	272	14	,	,	PUNCT
cana-1008	272	15	was	be	AUX
cana-1008	272	16	used	use	VERB
cana-1008	272	17	in	in	ADP
cana-1008	272	18	this	this	DET
cana-1008	272	19	model	model	NOUN
cana-1008	272	20	.	.	PUNCT
cana-1008	273	1	the	the	DET
cana-1008	273	2	gaussian	gaussian	ADJ
cana-1008	273	3	naïve	naïve	ADJ
cana-1008	273	4	bayes	bayes	PROPN
cana-1008	273	5	classifier	classifier	PROPN
cana-1008	273	6	assumes	assume	VERB
cana-1008	273	7	that	that	SCONJ
cana-1008	273	8	the	the	DET
cana-1008	273	9	feature	feature	NOUN
cana-1008	273	10	values	value	NOUN
cana-1008	273	11	are	be	AUX
cana-1008	273	12	continuous	continuous	ADJ
cana-1008	273	13	and	and	CCONJ
cana-1008	273	14	that	that	SCONJ
cana-1008	273	15	the	the	DET
cana-1008	273	16	values	value	NOUN
cana-1008	273	17	pertaining	pertain	VERB
cana-1008	273	18	to	to	ADP
cana-1008	273	19	each	each	DET
cana-1008	273	20	class	class	NOUN
cana-1008	273	21	follow	follow	VERB
cana-1008	273	22	a	a	DET
cana-1008	273	23	normal	normal	ADJ
cana-1008	273	24	distribution	distribution	NOUN
cana-1008	273	25	[	[	X
cana-1008	273	26	36	36	NUM
cana-1008	273	27	,	,	PUNCT
cana-1008	273	28	37	37	NUM
cana-1008	273	29	]	]	PUNCT
cana-1008	273	30	.	.	PUNCT
cana-1008	274	1	an	an	DET
cana-1008	274	2	important	important	ADJ
cana-1008	274	3	characteristic	characteristic	NOUN
cana-1008	274	4	of	of	ADP
cana-1008	274	5	the	the	DET
cana-1008	274	6	naïve	naïve	ADJ
cana-1008	274	7	bayes	bayes	NOUN
cana-1008	274	8	approach	approach	NOUN
cana-1008	274	9	is	be	AUX
cana-1008	274	10	its	its	PRON
cana-1008	274	11	ability	ability	NOUN
cana-1008	274	12	to	to	PART
cana-1008	274	13	be	be	AUX
cana-1008	274	14	trained	train	VERB
cana-1008	274	15	on	on	ADP
cana-1008	274	16	a	a	DET
cana-1008	274	17	little	little	ADJ
cana-1008	274	18	dataset	dataset	NOUN
cana-1008	274	19	,	,	PUNCT
cana-1008	274	20	which	which	PRON
cana-1008	274	21	is	be	AUX
cana-1008	274	22	a	a	DET
cana-1008	274	23	notable	notable	ADJ
cana-1008	274	24	benefit	benefit	NOUN
cana-1008	274	25	.	.	PUNCT
cana-1008	275	1	this	this	DET
cana-1008	275	2	methodology	methodology	NOUN
cana-1008	275	3	is	be	AUX
cana-1008	275	4	used	use	VERB
cana-1008	275	5	for	for	ADP
cana-1008	275	6	both	both	CCONJ
cana-1008	275	7	binary	binary	ADJ
cana-1008	275	8	and	and	CCONJ
cana-1008	275	9	multiclass	multiclass	ADJ
cana-1008	275	10	classification	classification	NOUN
cana-1008	275	11	tasks	task	NOUN
cana-1008	275	12	.	.	PUNCT
cana-1008	276	1	furthermore	furthermore	ADV
cana-1008	276	2	,	,	PUNCT
cana-1008	276	3	it	it	PRON
cana-1008	276	4	is	be	AUX
cana-1008	276	5	very	very	ADV
cana-1008	276	6	efficient	efficient	ADJ
cana-1008	276	7	and	and	CCONJ
cana-1008	276	8	easily	easily	ADV
cana-1008	276	9	adaptable	adaptable	ADJ
cana-1008	276	10	to	to	ADP
cana-1008	276	11	larger	large	ADJ
cana-1008	276	12	scales	scale	NOUN
cana-1008	276	13	.	.	PUNCT
cana-1008	277	1	furthermore	furthermore	ADV
cana-1008	277	2	,	,	PUNCT
cana-1008	277	3	it	it	PRON
cana-1008	277	4	aids	aid	VERB
cana-1008	277	5	in	in	ADP
cana-1008	277	6	mitigating	mitigate	VERB
cana-1008	277	7	the	the	DET
cana-1008	277	8	challenges	challenge	NOUN
cana-1008	277	9	arising	arise	VERB
cana-1008	277	10	from	from	ADP
cana-1008	277	11	the	the	DET
cana-1008	277	12	curse	curse	NOUN
cana-1008	277	13	of	of	ADP
cana-1008	277	14	dimensionality	dimensionality	NOUN
cana-1008	277	15	to	to	ADP
cana-1008	277	16	some	some	DET
cana-1008	277	17	degree	degree	NOUN
cana-1008	277	18	.	.	PUNCT
cana-1008	278	1	however	however	ADV
cana-1008	278	2	,	,	PUNCT
cana-1008	278	3	based	base	VERB
cana-1008	278	4	on	on	ADP
cana-1008	278	5	the	the	DET
cana-1008	278	6	previous	previous	ADJ
cana-1008	278	7	statement	statement	NOUN
cana-1008	278	8	,	,	PUNCT
cana-1008	278	9	it	it	PRON
cana-1008	278	10	assumes	assume	VERB
cana-1008	278	11	without	without	ADP
cana-1008	278	12	sufficient	sufficient	ADJ
cana-1008	278	13	evidence	evidence	NOUN
cana-1008	278	14	that	that	SCONJ
cana-1008	278	15	the	the	DET
cana-1008	278	16	input	input	NOUN
cana-1008	278	17	variables	variable	NOUN
cana-1008	278	18	are	be	AUX
cana-1008	278	19	unrelated	unrelated	ADJ
cana-1008	278	20	to	to	ADP
cana-1008	278	21	one	one	NUM
cana-1008	278	22	other	other	ADJ
cana-1008	278	23	.	.	PUNCT
cana-1008	279	1	in	in	ADP
cana-1008	279	2	contrast	contrast	NOUN
cana-1008	279	3	,	,	PUNCT
cana-1008	279	4	real	real	ADJ
cana-1008	279	5	-	-	PUNCT
cana-1008	279	6	world	world	NOUN
cana-1008	279	7	datasets	dataset	NOUN
cana-1008	279	8	may	may	AUX
cana-1008	279	9	include	include	VERB
cana-1008	279	10	several	several	ADJ
cana-1008	279	11	complex	complex	ADJ
cana-1008	279	12	interactions	interaction	NOUN
cana-1008	279	13	among	among	ADP
cana-1008	279	14	the	the	DET
cana-1008	279	15	feature	feature	NOUN
cana-1008	279	16	variables	variable	NOUN
cana-1008	279	17	.	.	PUNCT
cana-1008	280	1	however	however	ADV
cana-1008	280	2	,	,	PUNCT
cana-1008	280	3	some	some	DET
cana-1008	280	4	datasets	dataset	NOUN
cana-1008	280	5	do	do	AUX
cana-1008	280	6	not	not	PART
cana-1008	280	7	follow	follow	VERB
cana-1008	280	8	this	this	DET
cana-1008	280	9	pattern	pattern	NOUN
cana-1008	280	10	.	.	PUNCT
cana-1008	281	1	3.3.3	3.3.3	NUM
cana-1008	281	2	random	random	ADJ
cana-1008	281	3	forest	forest	NOUN
cana-1008	281	4	an	an	DET
cana-1008	281	5	ensemble	ensemble	NOUN
cana-1008	281	6	of	of	ADP
cana-1008	281	7	models	model	NOUN
cana-1008	281	8	is	be	AUX
cana-1008	281	9	the	the	DET
cana-1008	281	10	most	most	ADV
cana-1008	281	11	important	important	ADJ
cana-1008	281	12	aspect	aspect	NOUN
cana-1008	281	13	of	of	ADP
cana-1008	281	14	rf	rf	NOUN
cana-1008	281	15	;	;	PUNCT
cana-1008	281	16	each	each	DET
cana-1008	281	17	model	model	NOUN
cana-1008	281	18	makes	make	VERB
cana-1008	281	19	a	a	DET
cana-1008	281	20	prediction	prediction	NOUN
cana-1008	281	21	about	about	ADP
cana-1008	281	22	the	the	DET
cana-1008	281	23	result	result	NOUN
cana-1008	281	24	,	,	PUNCT
cana-1008	281	25	and	and	CCONJ
cana-1008	281	26	in	in	ADP
cana-1008	281	27	the	the	DET
cana-1008	281	28	end	end	NOUN
cana-1008	281	29	,	,	PUNCT
cana-1008	281	30	the	the	DET
cana-1008	281	31	model	model	NOUN
cana-1008	281	32	that	that	PRON
cana-1008	281	33	has	have	VERB
cana-1008	281	34	the	the	DET
cana-1008	281	35	majority	majority	NOUN
cana-1008	281	36	of	of	ADP
cana-1008	281	37	its	its	PRON
cana-1008	281	38	predictions	prediction	NOUN
cana-1008	281	39	true	true	ADJ
cana-1008	281	40	is	be	AUX
cana-1008	281	41	the	the	DET
cana-1008	281	42	winner	winner	NOUN
cana-1008	281	43	.	.	PUNCT
cana-1008	282	1	rf	rf	PRON
cana-1008	282	2	is	be	AUX
cana-1008	282	3	a	a	DET
cana-1008	282	4	collection	collection	NOUN
cana-1008	282	5	of	of	ADP
cana-1008	282	6	models	model	NOUN
cana-1008	282	7	that	that	PRON
cana-1008	282	8	may	may	AUX
cana-1008	282	9	be	be	AUX
cana-1008	282	10	performed	perform	VERB
cana-1008	282	11	together	together	ADV
cana-1008	282	12	in	in	ADP
cana-1008	282	13	a	a	DET
cana-1008	282	14	manner	manner	NOUN
cana-1008	282	15	similar	similar	ADJ
cana-1008	282	16	to	to	ADP
cana-1008	282	17	that	that	PRON
cana-1008	282	18	of	of	ADP
cana-1008	282	19	an	an	DET
cana-1008	282	20	orchestra	orchestra	NOUN
cana-1008	282	21	[	[	X
cana-1008	282	22	33	33	NUM
cana-1008	282	23	]	]	PUNCT
cana-1008	282	24	.	.	PUNCT
cana-1008	283	1	during	during	ADP
cana-1008	283	2	the	the	DET
cana-1008	283	3	course	course	NOUN
cana-1008	283	4	of	of	ADP
cana-1008	283	5	the	the	DET
cana-1008	283	6	study	study	NOUN
cana-1008	283	7	that	that	PRON
cana-1008	283	8	has	have	AUX
cana-1008	283	9	been	be	AUX
cana-1008	283	10	conducted	conduct	VERB
cana-1008	283	11	on	on	ADP
cana-1008	283	12	the	the	DET
cana-1008	283	13	topic	topic	NOUN
cana-1008	283	14	,	,	PUNCT
cana-1008	283	15	it	it	PRON
cana-1008	283	16	has	have	AUX
cana-1008	283	17	been	be	AUX
cana-1008	283	18	investigated	investigate	VERB
cana-1008	283	19	,	,	PUNCT
cana-1008	283	20	and	and	CCONJ
cana-1008	283	21	it	it	PRON
cana-1008	283	22	has	have	AUX
cana-1008	283	23	been	be	AUX
cana-1008	283	24	proven	prove	VERB
cana-1008	283	25	to	to	PART
cana-1008	283	26	be	be	AUX
cana-1008	283	27	useful	useful	ADJ
cana-1008	283	28	in	in	ADP
cana-1008	283	29	predicting	predict	VERB
cana-1008	283	30	diabetes	diabetes	NOUN
cana-1008	283	31	.	.	PUNCT
cana-1008	284	1	there	there	PRON
cana-1008	284	2	is	be	VERB
cana-1008	284	3	a	a	DET
cana-1008	284	4	learning	learning	NOUN
cana-1008	284	5	model	model	NOUN
cana-1008	284	6	known	know	VERB
cana-1008	284	7	as	as	ADP
cana-1008	284	8	a	a	DET
cana-1008	284	9	random	random	ADJ
cana-1008	284	10	forest	forest	NOUN
cana-1008	284	11	that	that	PRON
cana-1008	284	12	is	be	AUX
cana-1008	284	13	both	both	CCONJ
cana-1008	284	14	flexible	flexible	ADJ
cana-1008	284	15	and	and	CCONJ
cana-1008	284	16	capable	capable	ADJ
cana-1008	284	17	of	of	ADP
cana-1008	284	18	solving	solve	VERB
cana-1008	284	19	issues	issue	NOUN
cana-1008	284	20	involving	involve	VERB
cana-1008	284	21	regression	regression	NOUN
cana-1008	284	22	and	and	CCONJ
cana-1008	284	23	classification	classification	NOUN
cana-1008	284	24	.	.	PUNCT
cana-1008	285	1	when	when	SCONJ
cana-1008	285	2	random	random	ADJ
cana-1008	285	3	forest	forest	NOUN
cana-1008	285	4	is	be	AUX
cana-1008	285	5	in	in	ADP
cana-1008	285	6	the	the	DET
cana-1008	285	7	training	training	NOUN
cana-1008	285	8	phase	phase	NOUN
cana-1008	285	9	,	,	PUNCT
cana-1008	285	10	it	it	PRON
cana-1008	285	11	first	first	ADV
cana-1008	285	12	creates	create	VERB
cana-1008	285	13	a	a	DET
cana-1008	285	14	huge	huge	ADJ
cana-1008	285	15	number	number	NOUN
cana-1008	285	16	of	of	ADP
cana-1008	285	17	dt	dt	PROPN
cana-1008	286	1	and	and	CCONJ
cana-1008	286	2	then	then	ADV
cana-1008	286	3	it	it	PRON
cana-1008	286	4	makes	make	VERB
cana-1008	286	5	a	a	DET
cana-1008	286	6	prediction	prediction	NOUN
cana-1008	286	7	that	that	PRON
cana-1008	286	8	is	be	AUX
cana-1008	286	9	an	an	DET
cana-1008	286	10	average	average	NOUN
cana-1008	286	11	of	of	ADP
cana-1008	286	12	all	all	PRON
cana-1008	286	13	of	of	ADP
cana-1008	286	14	the	the	DET
cana-1008	286	15	forecasts	forecast	NOUN
cana-1008	286	16	that	that	PRON
cana-1008	286	17	were	be	AUX
cana-1008	286	18	made	make	VERB
cana-1008	286	19	by	by	ADP
cana-1008	286	20	the	the	DET
cana-1008	286	21	decision	decision	NOUN
cana-1008	286	22	trees	tree	NOUN
cana-1008	286	23	.	.	PUNCT
cana-1008	287	1	both	both	DET
cana-1008	287	2	types	type	NOUN
cana-1008	287	3	of	of	ADP
cana-1008	287	4	difficulties	difficulty	NOUN
cana-1008	287	5	are	be	AUX
cana-1008	287	6	within	within	ADP
cana-1008	287	7	the	the	DET
cana-1008	287	8	capability	capability	NOUN
cana-1008	287	9	of	of	ADP
cana-1008	287	10	this	this	DET
cana-1008	287	11	model	model	NOUN
cana-1008	287	12	[	[	X
cana-1008	287	13	37	37	NUM
cana-1008	287	14	]	]	PUNCT
cana-1008	287	15	,	,	PUNCT
cana-1008	287	16	which	which	PRON
cana-1008	287	17	means	mean	VERB
cana-1008	287	18	that	that	SCONJ
cana-1008	287	19	they	they	PRON
cana-1008	287	20	are	be	AUX
cana-1008	287	21	applicable	applicable	ADJ
cana-1008	287	22	.	.	PUNCT
cana-1008	288	1	when	when	SCONJ
cana-1008	288	2	dealing	deal	VERB
cana-1008	288	3	with	with	ADP
cana-1008	288	4	classification	classification	NOUN
cana-1008	288	5	issues	issue	NOUN
cana-1008	288	6	,	,	PUNCT
cana-1008	288	7	the	the	DET
cana-1008	288	8	goal	goal	NOUN
cana-1008	288	9	variable	variable	NOUN
cana-1008	288	10	is	be	AUX
cana-1008	288	11	categorical	categorical	ADJ
cana-1008	288	12	,	,	PUNCT
cana-1008	288	13	but	but	CCONJ
cana-1008	288	14	when	when	SCONJ
cana-1008	288	15	dealing	deal	VERB
cana-1008	288	16	with	with	ADP
cana-1008	288	17	regression	regression	NOUN
cana-1008	288	18	,	,	PUNCT
cana-1008	288	19	it	it	PRON
cana-1008	288	20	communications	communication	VERB
cana-1008	288	21	on	on	ADP
cana-1008	288	22	applied	apply	VERB
cana-1008	288	23	nonlinear	nonlinear	ADJ
cana-1008	288	24	analysis	analysis	NOUN
cana-1008	288	25	issn	issn	NOUN
cana-1008	288	26	:	:	PUNCT
cana-1008	288	27	1074	1074	NUM
cana-1008	288	28	-	-	PUNCT
cana-1008	288	29	133x	133x	NUM
cana-1008	288	30	vol	vol	NOUN
cana-1008	288	31	31	31	NUM
cana-1008	288	32	no	no	NOUN
cana-1008	288	33	.	.	PUNCT
cana-1008	289	1	5s	5s	NUM
cana-1008	289	2	(	(	PUNCT
cana-1008	289	3	2024	2024	NUM
cana-1008	289	4	)	)	PUNCT
cana-1008	289	5	149	149	NUM
cana-1008	289	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1008	289	7	is	be	AUX
cana-1008	289	8	continuous	continuous	ADJ
cana-1008	289	9	.	.	PUNCT
cana-1008	290	1	during	during	ADP
cana-1008	290	2	regression	regression	NOUN
cana-1008	290	3	,	,	PUNCT
cana-1008	290	4	continuous	continuous	ADJ
cana-1008	290	5	variables	variable	NOUN
cana-1008	290	6	are	be	AUX
cana-1008	290	7	used	use	VERB
cana-1008	290	8	.	.	PUNCT
cana-1008	291	1	eda	eda	PROPN
cana-1008	291	2	methods	method	NOUN
cana-1008	291	3	are	be	AUX
cana-1008	291	4	applied	apply	VERB
cana-1008	291	5	in	in	ADP
cana-1008	291	6	order	order	NOUN
cana-1008	291	7	to	to	PART
cana-1008	291	8	accomplish	accomplish	VERB
cana-1008	291	9	the	the	DET
cana-1008	291	10	goal	goal	NOUN
cana-1008	291	11	of	of	ADP
cana-1008	291	12	achieving	achieve	VERB
cana-1008	291	13	a	a	DET
cana-1008	291	14	high	high	ADJ
cana-1008	291	15	degree	degree	NOUN
cana-1008	291	16	of	of	ADP
cana-1008	291	17	accuracy	accuracy	NOUN
cana-1008	291	18	via	via	ADP
cana-1008	291	19	the	the	DET
cana-1008	291	20	use	use	NOUN
cana-1008	291	21	of	of	ADP
cana-1008	291	22	random	random	ADJ
cana-1008	291	23	forest	forest	NOUN
cana-1008	291	24	.	.	PUNCT
cana-1008	292	1	a	a	DET
cana-1008	292	2	more	more	ADV
cana-1008	292	3	robust	robust	ADJ
cana-1008	292	4	model	model	NOUN
cana-1008	292	5	is	be	AUX
cana-1008	292	6	produced	produce	VERB
cana-1008	292	7	as	as	ADP
cana-1008	292	8	a	a	DET
cana-1008	292	9	consequence	consequence	NOUN
cana-1008	292	10	of	of	ADP
cana-1008	292	11	the	the	DET
cana-1008	292	12	combination	combination	NOUN
cana-1008	292	13	of	of	ADP
cana-1008	292	14	a	a	DET
cana-1008	292	15	large	large	ADJ
cana-1008	292	16	number	number	NOUN
cana-1008	292	17	of	of	ADP
cana-1008	292	18	models	model	NOUN
cana-1008	292	19	that	that	PRON
cana-1008	292	20	are	be	AUX
cana-1008	292	21	relatively	relatively	ADV
cana-1008	292	22	weak	weak	ADJ
cana-1008	292	23	.	.	PUNCT
cana-1008	293	1	the	the	DET
cana-1008	293	2	method	method	NOUN
cana-1008	293	3	that	that	PRON
cana-1008	293	4	is	be	AUX
cana-1008	293	5	often	often	ADV
cana-1008	293	6	referred	refer	VERB
cana-1008	293	7	to	to	ADP
cana-1008	293	8	as	as	ADP
cana-1008	293	9	"	"	PUNCT
cana-1008	293	10	random	random	ADJ
cana-1008	293	11	forest	forest	NOUN
cana-1008	293	12	"	"	PUNCT
cana-1008	293	13	is	be	AUX
cana-1008	293	14	notable	notable	ADJ
cana-1008	293	15	for	for	ADP
cana-1008	293	16	its	its	PRON
cana-1008	293	17	ability	ability	NOUN
cana-1008	293	18	to	to	PART
cana-1008	293	19	analyze	analyze	VERB
cana-1008	293	20	big	big	ADJ
cana-1008	293	21	datasets	dataset	NOUN
cana-1008	293	22	that	that	PRON
cana-1008	293	23	include	include	VERB
cana-1008	293	24	a	a	DET
cana-1008	293	25	significant	significant	ADJ
cana-1008	293	26	amount	amount	NOUN
cana-1008	293	27	of	of	ADP
cana-1008	293	28	geographical	geographical	ADJ
cana-1008	293	29	information	information	NOUN
cana-1008	293	30	.	.	PUNCT
cana-1008	294	1	the	the	DET
cana-1008	294	2	dimensionality	dimensionality	NOUN
cana-1008	294	3	reduction	reduction	NOUN
cana-1008	294	4	capability	capability	NOUN
cana-1008	294	5	of	of	ADP
cana-1008	294	6	the	the	DET
cana-1008	294	7	model	model	NOUN
cana-1008	294	8	is	be	AUX
cana-1008	294	9	the	the	DET
cana-1008	294	10	capacity	capacity	NOUN
cana-1008	294	11	of	of	ADP
cana-1008	294	12	the	the	DET
cana-1008	294	13	model	model	NOUN
cana-1008	294	14	to	to	PART
cana-1008	294	15	analyze	analyze	VERB
cana-1008	294	16	a	a	DET
cana-1008	294	17	large	large	ADJ
cana-1008	294	18	number	number	NOUN
cana-1008	294	19	of	of	ADP
cana-1008	294	20	input	input	NOUN
cana-1008	294	21	variables	variable	NOUN
cana-1008	294	22	and	and	CCONJ
cana-1008	294	23	identify	identify	VERB
cana-1008	294	24	which	which	DET
cana-1008	294	25	ones	one	NOUN
cana-1008	294	26	are	be	AUX
cana-1008	294	27	the	the	DET
cana-1008	294	28	most	most	ADV
cana-1008	294	29	important	important	ADJ
cana-1008	294	30	.	.	PUNCT
cana-1008	295	1	this	this	DET
cana-1008	295	2	capability	capability	NOUN
cana-1008	295	3	includes	include	VERB
cana-1008	295	4	the	the	DET
cana-1008	295	5	ability	ability	NOUN
cana-1008	295	6	to	to	PART
cana-1008	295	7	decide	decide	VERB
cana-1008	295	8	which	which	PRON
cana-1008	295	9	variables	variable	NOUN
cana-1008	295	10	are	be	AUX
cana-1008	295	11	the	the	DET
cana-1008	295	12	most	most	ADV
cana-1008	295	13	significant	significant	ADJ
cana-1008	295	14	.	.	PUNCT
cana-1008	296	1	it	it	PRON
cana-1008	296	2	is	be	AUX
cana-1008	296	3	a	a	DET
cana-1008	296	4	valuable	valuable	ADJ
cana-1008	296	5	feature	feature	NOUN
cana-1008	296	6	of	of	ADP
cana-1008	296	7	the	the	DET
cana-1008	296	8	model	model	NOUN
cana-1008	296	9	because	because	SCONJ
cana-1008	296	10	it	it	PRON
cana-1008	296	11	shows	show	VERB
cana-1008	296	12	the	the	DET
cana-1008	296	13	significance	significance	NOUN
cana-1008	296	14	of	of	ADP
cana-1008	296	15	variables	variable	NOUN
cana-1008	296	16	even	even	ADV
cana-1008	296	17	when	when	SCONJ
cana-1008	296	18	dealing	deal	VERB
cana-1008	296	19	with	with	ADP
cana-1008	296	20	a	a	DET
cana-1008	296	21	random	random	ADJ
cana-1008	296	22	dataset	dataset	NOUN
cana-1008	296	23	.	.	PUNCT
cana-1008	297	1	this	this	PRON
cana-1008	297	2	is	be	AUX
cana-1008	297	3	one	one	NUM
cana-1008	297	4	of	of	ADP
cana-1008	297	5	the	the	DET
cana-1008	297	6	model	model	NOUN
cana-1008	297	7	's	's	PART
cana-1008	297	8	characteristics	characteristic	NOUN
cana-1008	297	9	.	.	PUNCT
cana-1008	298	1	in	in	ADP
cana-1008	298	2	contrast	contrast	NOUN
cana-1008	298	3	to	to	ADP
cana-1008	298	4	the	the	DET
cana-1008	298	5	traditional	traditional	ADJ
cana-1008	298	6	model	model	NOUN
cana-1008	298	7	,	,	PUNCT
cana-1008	298	8	which	which	PRON
cana-1008	298	9	employs	employ	VERB
cana-1008	298	10	a	a	DET
cana-1008	298	11	voting	voting	NOUN
cana-1008	298	12	strategy	strategy	NOUN
cana-1008	298	13	that	that	PRON
cana-1008	298	14	is	be	AUX
cana-1008	298	15	equivalent	equivalent	ADJ
cana-1008	298	16	to	to	ADP
cana-1008	298	17	one	one	NUM
cana-1008	298	18	another	another	DET
cana-1008	298	19	,	,	PUNCT
cana-1008	298	20	the	the	DET
cana-1008	298	21	"	"	PUNCT
cana-1008	298	22	adaptive	adaptive	ADJ
cana-1008	298	23	random	random	ADJ
cana-1008	298	24	forest	forest	NOUN
cana-1008	298	25	"	"	PUNCT
cana-1008	298	26	(	(	PUNCT
cana-1008	298	27	arf	arf	NOUN
cana-1008	298	28	)	)	PUNCT
cana-1008	298	29	model	model	NOUN
cana-1008	298	30	is	be	AUX
cana-1008	298	31	the	the	DET
cana-1008	298	32	one	one	NOUN
cana-1008	298	33	that	that	PRON
cana-1008	298	34	accomplishes	accomplish	VERB
cana-1008	298	35	the	the	DET
cana-1008	298	36	greatest	great	ADJ
cana-1008	298	37	results	result	NOUN
cana-1008	298	38	when	when	SCONJ
cana-1008	298	39	an	an	DET
cana-1008	298	40	uneven	uneven	ADJ
cana-1008	298	41	voting	voting	NOUN
cana-1008	298	42	strategy	strategy	NOUN
cana-1008	298	43	is	be	AUX
cana-1008	298	44	used	use	VERB
cana-1008	298	45	[	[	PUNCT
cana-1008	298	46	38	38	NUM
cana-1008	298	47	]	]	PUNCT
cana-1008	298	48	.	.	PUNCT
cana-1008	299	1	figure	figure	VERB
cana-1008	299	2	4	4	NUM
cana-1008	299	3	:	:	PUNCT
cana-1008	299	4	random	random	ADJ
cana-1008	299	5	forest	forest	NOUN
cana-1008	299	6	structure	structure	NOUN
cana-1008	300	1	[	[	X
cana-1008	300	2	38	38	NUM
cana-1008	300	3	]	]	PUNCT
cana-1008	300	4	.	.	PUNCT
cana-1008	301	1	3.3.4	3.3.4	NUM
cana-1008	301	2	decision	decision	NOUN
cana-1008	301	3	tree	tree	NOUN
cana-1008	301	4	a	a	DET
cana-1008	301	5	decision	decision	NOUN
cana-1008	301	6	tree	tree	NOUN
cana-1008	301	7	method	method	NOUN
cana-1008	301	8	,	,	PUNCT
cana-1008	301	9	often	often	ADV
cana-1008	301	10	known	know	VERB
cana-1008	301	11	as	as	ADP
cana-1008	301	12	a	a	DET
cana-1008	301	13	dt	dt	NOUN
cana-1008	301	14	classifier	classifier	NOUN
cana-1008	301	15	,	,	PUNCT
cana-1008	301	16	is	be	AUX
cana-1008	301	17	a	a	DET
cana-1008	301	18	kind	kind	NOUN
cana-1008	301	19	of	of	ADP
cana-1008	301	20	decision	decision	NOUN
cana-1008	301	21	-	-	PUNCT
cana-1008	301	22	making	make	VERB
cana-1008	301	23	assistance	assistance	NOUN
cana-1008	301	24	.	.	PUNCT
cana-1008	302	1	this	this	DET
cana-1008	302	2	approach	approach	NOUN
cana-1008	302	3	is	be	AUX
cana-1008	302	4	implemented	implement	VERB
cana-1008	302	5	using	use	VERB
cana-1008	302	6	a	a	DET
cana-1008	302	7	hierarchical	hierarchical	ADJ
cana-1008	302	8	structure	structure	NOUN
cana-1008	302	9	resembling	resemble	VERB
cana-1008	302	10	a	a	DET
cana-1008	302	11	tree	tree	NOUN
cana-1008	302	12	,	,	PUNCT
cana-1008	302	13	and	and	CCONJ
cana-1008	302	14	it	it	PRON
cana-1008	302	15	is	be	AUX
cana-1008	302	16	built	build	VERB
cana-1008	302	17	by	by	ADP
cana-1008	302	18	including	include	VERB
cana-1008	302	19	certain	certain	ADJ
cana-1008	302	20	input	input	NOUN
cana-1008	302	21	properties	property	NOUN
cana-1008	302	22	[	[	X
cana-1008	302	23	36	36	NUM
cana-1008	302	24	]	]	PUNCT
cana-1008	302	25	.	.	PUNCT
cana-1008	303	1	the	the	DET
cana-1008	303	2	primary	primary	ADJ
cana-1008	303	3	goal	goal	NOUN
cana-1008	303	4	of	of	ADP
cana-1008	303	5	this	this	DET
cana-1008	303	6	classifier	classifier	NOUN
cana-1008	303	7	is	be	AUX
cana-1008	303	8	to	to	PART
cana-1008	303	9	construct	construct	VERB
cana-1008	303	10	a	a	DET
cana-1008	303	11	model	model	NOUN
cana-1008	303	12	that	that	PRON
cana-1008	303	13	can	can	AUX
cana-1008	303	14	make	make	VERB
cana-1008	303	15	predictions	prediction	NOUN
cana-1008	303	16	about	about	ADP
cana-1008	303	17	the	the	DET
cana-1008	303	18	targeted	target	VERB
cana-1008	303	19	variables	variable	NOUN
cana-1008	303	20	based	base	VERB
cana-1008	303	21	on	on	ADP
cana-1008	303	22	a	a	DET
cana-1008	303	23	range	range	NOUN
cana-1008	303	24	of	of	ADP
cana-1008	303	25	input	input	NOUN
cana-1008	303	26	attributes	attribute	NOUN
cana-1008	303	27	.	.	PUNCT
cana-1008	304	1	this	this	DET
cana-1008	304	2	classifier	classifier	NOUN
cana-1008	304	3	is	be	AUX
cana-1008	304	4	suitable	suitable	ADJ
cana-1008	304	5	for	for	ADP
cana-1008	304	6	a	a	DET
cana-1008	304	7	wide	wide	ADJ
cana-1008	304	8	variety	variety	NOUN
cana-1008	304	9	of	of	ADP
cana-1008	304	10	applications	application	NOUN
cana-1008	304	11	[	[	X
cana-1008	304	12	37	37	NUM
cana-1008	304	13	]	]	PUNCT
cana-1008	304	14	.	.	PUNCT
cana-1008	305	1	the	the	DET
cana-1008	305	2	reason	reason	NOUN
cana-1008	305	3	for	for	ADP
cana-1008	305	4	this	this	PRON
cana-1008	305	5	is	be	AUX
cana-1008	305	6	because	because	SCONJ
cana-1008	305	7	it	it	PRON
cana-1008	305	8	is	be	AUX
cana-1008	305	9	quite	quite	ADV
cana-1008	305	10	simple	simple	ADJ
cana-1008	305	11	to	to	PART
cana-1008	305	12	construct	construct	VERB
cana-1008	305	13	decision	decision	NOUN
cana-1008	305	14	rules	rule	NOUN
cana-1008	305	15	based	base	VERB
cana-1008	305	16	on	on	ADP
cana-1008	305	17	a	a	DET
cana-1008	305	18	given	give	VERB
cana-1008	305	19	set	set	NOUN
cana-1008	305	20	of	of	ADP
cana-1008	305	21	input	input	NOUN
cana-1008	305	22	data	datum	NOUN
cana-1008	305	23	.	.	PUNCT
cana-1008	306	1	the	the	DET
cana-1008	306	2	dt	dt	PROPN
cana-1008	306	3	methodology	methodology	NOUN
cana-1008	306	4	is	be	AUX
cana-1008	306	5	a	a	DET
cana-1008	306	6	nonparametric	nonparametric	NOUN
cana-1008	306	7	supervised	supervise	VERB
cana-1008	306	8	learning	learning	NOUN
cana-1008	306	9	method	method	NOUN
cana-1008	306	10	that	that	PRON
cana-1008	306	11	may	may	AUX
cana-1008	306	12	be	be	AUX
cana-1008	306	13	used	use	VERB
cana-1008	306	14	to	to	PART
cana-1008	306	15	address	address	VERB
cana-1008	306	16	difficulties	difficulty	NOUN
cana-1008	306	17	like	like	ADP
cana-1008	306	18	as	as	ADP
cana-1008	306	19	regression	regression	NOUN
cana-1008	306	20	and	and	CCONJ
cana-1008	306	21	classification	classification	NOUN
cana-1008	306	22	,	,	PUNCT
cana-1008	306	23	when	when	SCONJ
cana-1008	306	24	implemented	implement	VERB
cana-1008	306	25	correctly	correctly	ADV
cana-1008	306	26	.	.	PUNCT
cana-1008	307	1	figure	figure	VERB
cana-1008	307	2	4	4	NUM
cana-1008	307	3	presents	present	VERB
cana-1008	307	4	a	a	DET
cana-1008	307	5	representation	representation	NOUN
cana-1008	307	6	of	of	ADP
cana-1008	307	7	a	a	DET
cana-1008	307	8	structure	structure	NOUN
cana-1008	307	9	,	,	PUNCT
cana-1008	307	10	demonstrating	demonstrate	VERB
cana-1008	307	11	the	the	DET
cana-1008	307	12	possible	possible	ADJ
cana-1008	307	13	interpretation	interpretation	NOUN
cana-1008	307	14	of	of	ADP
cana-1008	307	15	the	the	DET
cana-1008	307	16	dt	dt	PROPN
cana-1008	307	17	model	model	NOUN
cana-1008	307	18	.	.	PUNCT
cana-1008	308	1	this	this	DET
cana-1008	308	2	specific	specific	ADJ
cana-1008	308	3	paradigm	paradigm	NOUN
cana-1008	308	4	consists	consist	VERB
cana-1008	308	5	of	of	ADP
cana-1008	308	6	three	three	NUM
cana-1008	308	7	nodes	node	NOUN
cana-1008	308	8	:	:	PUNCT
cana-1008	308	9	the	the	DET
cana-1008	308	10	root	root	NOUN
cana-1008	308	11	node	node	PROPN
cana-1008	308	12	,	,	PUNCT
cana-1008	308	13	the	the	DET
cana-1008	308	14	division	division	NOUN
cana-1008	308	15	node	node	NOUN
cana-1008	308	16	,	,	PUNCT
cana-1008	308	17	and	and	CCONJ
cana-1008	308	18	the	the	DET
cana-1008	308	19	leaf	leaf	NOUN
cana-1008	308	20	node	node	NOUN
cana-1008	308	21	.	.	PUNCT
cana-1008	309	1	each	each	DET
cana-1008	309	2	internal	internal	ADJ
cana-1008	309	3	node	node	NOUN
cana-1008	309	4	functions	function	NOUN
cana-1008	309	5	as	as	ADP
cana-1008	309	6	a	a	DET
cana-1008	309	7	test	test	NOUN
cana-1008	309	8	that	that	PRON
cana-1008	309	9	is	be	AUX
cana-1008	309	10	conducted	conduct	VERB
cana-1008	309	11	on	on	ADP
cana-1008	309	12	a	a	DET
cana-1008	309	13	certain	certain	ADJ
cana-1008	309	14	communications	communication	NOUN
cana-1008	309	15	on	on	ADP
cana-1008	309	16	applied	apply	VERB
cana-1008	309	17	nonlinear	nonlinear	ADJ
cana-1008	309	18	analysis	analysis	NOUN
cana-1008	309	19	issn	issn	NOUN
cana-1008	309	20	:	:	PUNCT
cana-1008	309	21	1074	1074	NUM
cana-1008	309	22	-	-	PUNCT
cana-1008	309	23	133x	133x	NUM
cana-1008	309	24	vol	vol	NOUN
cana-1008	309	25	31	31	NUM
cana-1008	309	26	no	no	NOUN
cana-1008	309	27	.	.	PUNCT
cana-1008	310	1	5s	5s	NUM
cana-1008	310	2	(	(	PUNCT
cana-1008	310	3	2024	2024	NUM
cana-1008	310	4	)	)	PUNCT
cana-1008	310	5	150	150	NUM
cana-1008	310	6	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-1008	310	7	attribute	attribute	NOUN
cana-1008	310	8	.	.	PUNCT
cana-1008	311	1	this	this	DET
cana-1008	311	2	test	test	NOUN
cana-1008	311	3	yields	yield	VERB
cana-1008	311	4	the	the	DET
cana-1008	311	5	outcomes	outcome	NOUN
cana-1008	311	6	for	for	ADP
cana-1008	311	7	every	every	DET
cana-1008	311	8	division	division	NOUN
cana-1008	311	9	,	,	PUNCT
cana-1008	311	10	and	and	CCONJ
cana-1008	311	11	each	each	DET
cana-1008	311	12	individual	individual	ADJ
cana-1008	311	13	leaf	leaf	NOUN
cana-1008	311	14	node	node	NOUN
cana-1008	311	15	retains	retain	VERB
cana-1008	311	16	the	the	DET
cana-1008	311	17	class	class	NOUN
cana-1008	311	18	label	label	NOUN
cana-1008	311	19	of	of	ADP
cana-1008	311	20	its	its	PRON
cana-1008	311	21	parent	parent	NOUN
cana-1008	311	22	.	.	PUNCT
cana-1008	312	1	figure	figure	VERB
cana-1008	312	2	5	5	NUM
cana-1008	312	3	:	:	PUNCT
cana-1008	312	4	decision	decision	NOUN
cana-1008	312	5	tree	tree	NOUN
cana-1008	312	6	structure	structure	NOUN
cana-1008	313	1	[	[	X
cana-1008	313	2	39	39	NUM
cana-1008	313	3	]	]	PUNCT
cana-1008	313	4	.	.	PUNCT
cana-1008	314	1	each	each	DET
cana-1008	314	2	division	division	NOUN
cana-1008	314	3	corresponds	correspond	VERB
cana-1008	314	4	to	to	ADP
cana-1008	314	5	the	the	DET
cana-1008	314	6	outcome	outcome	NOUN
cana-1008	314	7	of	of	ADP
cana-1008	314	8	that	that	DET
cana-1008	314	9	exam	exam	NOUN
cana-1008	314	10	.	.	PUNCT
cana-1008	315	1	the	the	DET
cana-1008	315	2	root	root	NOUN
cana-1008	315	3	node	node	NOUN
cana-1008	315	4	marks	mark	VERB
cana-1008	315	5	the	the	DET
cana-1008	315	6	beginning	beginning	NOUN
cana-1008	315	7	of	of	ADP
cana-1008	315	8	the	the	DET
cana-1008	315	9	tree	tree	NOUN
cana-1008	315	10	construction	construction	NOUN
cana-1008	315	11	process	process	NOUN
cana-1008	315	12	.	.	PUNCT
cana-1008	316	1	initially	initially	ADV
cana-1008	316	2	,	,	PUNCT
cana-1008	316	3	an	an	DET
cana-1008	316	4	attribute	attribute	NOUN
cana-1008	316	5	is	be	AUX
cana-1008	316	6	selected	select	VERB
cana-1008	316	7	to	to	PART
cana-1008	316	8	be	be	AUX
cana-1008	316	9	positioned	position	VERB
cana-1008	316	10	at	at	ADP
cana-1008	316	11	the	the	DET
cana-1008	316	12	root	root	NOUN
cana-1008	316	13	node	node	NOUN
cana-1008	316	14	,	,	PUNCT
cana-1008	316	15	where	where	SCONJ
cana-1008	316	16	it	it	PRON
cana-1008	316	17	will	will	AUX
cana-1008	316	18	persist	persist	VERB
cana-1008	316	19	during	during	ADP
cana-1008	316	20	the	the	DET
cana-1008	316	21	whole	whole	ADJ
cana-1008	316	22	method	method	NOUN
cana-1008	316	23	as	as	ADP
cana-1008	316	24	the	the	DET
cana-1008	316	25	initial	initial	ADJ
cana-1008	316	26	point	point	NOUN
cana-1008	316	27	.	.	PUNCT
cana-1008	317	1	after	after	ADP
cana-1008	317	2	completing	complete	VERB
cana-1008	317	3	the	the	DET
cana-1008	317	4	division	division	NOUN
cana-1008	317	5	,	,	PUNCT
cana-1008	317	6	each	each	PRON
cana-1008	317	7	of	of	ADP
cana-1008	317	8	the	the	DET
cana-1008	317	9	potential	potential	ADJ
cana-1008	317	10	values	value	NOUN
cana-1008	317	11	is	be	AUX
cana-1008	317	12	submitted	submit	VERB
cana-1008	317	13	to	to	ADP
cana-1008	317	14	it	it	PRON
cana-1008	317	15	.	.	PUNCT
cana-1008	318	1	as	as	ADP
cana-1008	318	2	a	a	DET
cana-1008	318	3	result	result	NOUN
cana-1008	318	4	,	,	PUNCT
cana-1008	318	5	the	the	DET
cana-1008	318	6	dataset	dataset	NOUN
cana-1008	318	7	is	be	AUX
cana-1008	318	8	divided	divide	VERB
cana-1008	318	9	into	into	ADP
cana-1008	318	10	subgroups	subgroup	NOUN
cana-1008	318	11	,	,	PUNCT
cana-1008	318	12	with	with	ADP
cana-1008	318	13	each	each	DET
cana-1008	318	14	subgroup	subgroup	NOUN
cana-1008	318	15	representing	represent	VERB
cana-1008	318	16	one	one	NUM
cana-1008	318	17	of	of	ADP
cana-1008	318	18	the	the	DET
cana-1008	318	19	several	several	ADJ
cana-1008	318	20	possible	possible	ADJ
cana-1008	318	21	values	value	NOUN
cana-1008	318	22	found	find	VERB
cana-1008	318	23	in	in	ADP
cana-1008	318	24	the	the	DET
cana-1008	318	25	property	property	NOUN
cana-1008	318	26	.	.	PUNCT
cana-1008	319	1	the	the	DET
cana-1008	319	2	tree	tree	NOUN
cana-1008	319	3	operation	operation	NOUN
cana-1008	319	4	is	be	AUX
cana-1008	319	5	executed	execute	VERB
cana-1008	319	6	recursively	recursively	ADV
cana-1008	319	7	for	for	ADP
cana-1008	319	8	each	each	DET
cana-1008	319	9	division	division	NOUN
cana-1008	319	10	,	,	PUNCT
cana-1008	319	11	considering	consider	VERB
cana-1008	319	12	only	only	ADJ
cana-1008	319	13	instances	instance	NOUN
cana-1008	319	14	that	that	PRON
cana-1008	319	15	have	have	AUX
cana-1008	319	16	reached	reach	VERB
cana-1008	319	17	the	the	DET
cana-1008	319	18	branch	branch	NOUN
cana-1008	319	19	.	.	PUNCT
cana-1008	320	1	when	when	SCONJ
cana-1008	320	2	all	all	DET
cana-1008	320	3	instances	instance	NOUN
cana-1008	320	4	on	on	ADP
cana-1008	320	5	a	a	DET
cana-1008	320	6	node	node	NOUN
cana-1008	320	7	are	be	AUX
cana-1008	320	8	categorized	categorize	VERB
cana-1008	320	9	uniformly	uniformly	ADV
cana-1008	320	10	,	,	PUNCT
cana-1008	320	11	it	it	PRON
cana-1008	320	12	becomes	become	VERB
cana-1008	320	13	straightforward	straightforward	ADJ
cana-1008	320	14	to	to	PART
cana-1008	320	15	halt	halt	VERB
cana-1008	320	16	the	the	DET
cana-1008	320	17	progress	progress	NOUN
cana-1008	320	18	of	of	ADP
cana-1008	320	19	the	the	DET
cana-1008	320	20	tree	tree	NOUN
cana-1008	320	21	.	.	PUNCT
cana-1008	321	1	when	when	SCONJ
cana-1008	321	2	attempting	attempt	VERB
cana-1008	321	3	to	to	PART
cana-1008	321	4	determine	determine	VERB
cana-1008	321	5	the	the	DET
cana-1008	321	6	optimum	optimum	ADJ
cana-1008	321	7	tree	tree	NOUN
cana-1008	321	8	partition	partition	NOUN
cana-1008	321	9	,	,	PUNCT
cana-1008	321	10	it	it	PRON
cana-1008	321	11	is	be	AUX
cana-1008	321	12	customary	customary	ADJ
cana-1008	321	13	to	to	PART
cana-1008	321	14	utilize	utilize	VERB
cana-1008	321	15	either	either	CCONJ
cana-1008	321	16	entropy	entropy	NOUN
cana-1008	321	17	or	or	CCONJ
cana-1008	321	18	classification	classification	NOUN
cana-1008	321	19	error	error	NOUN
cana-1008	321	20	as	as	ADP
cana-1008	321	21	the	the	DET
cana-1008	321	22	preferred	preferred	ADJ
cana-1008	321	23	metrics	metric	NOUN
cana-1008	321	24	[	[	X
cana-1008	321	25	33	33	NUM
cana-1008	321	26	]	]	PUNCT
cana-1008	321	27	.	.	PUNCT
cana-1008	322	1	3.4	3.4	NUM
cana-1008	322	2	model	model	NOUN
cana-1008	322	3	training	training	NOUN
cana-1008	322	4	during	during	ADP
cana-1008	322	5	the	the	DET
cana-1008	322	6	model	model	NOUN
cana-1008	322	7	training	training	NOUN
cana-1008	322	8	phase	phase	NOUN
cana-1008	322	9	of	of	ADP
cana-1008	322	10	our	our	PRON
cana-1008	322	11	technique	technique	NOUN
cana-1008	322	12	,	,	PUNCT
cana-1008	322	13	which	which	PRON
cana-1008	322	14	is	be	AUX
cana-1008	322	15	centered	center	VERB
cana-1008	322	16	on	on	ADP
cana-1008	322	17	the	the	DET
cana-1008	322	18	construction	construction	NOUN
cana-1008	322	19	of	of	ADP
cana-1008	322	20	predictive	predictive	ADJ
cana-1008	322	21	models	model	NOUN
cana-1008	322	22	for	for	ADP
cana-1008	322	23	diabetes	diabetes	NOUN
cana-1008	322	24	diagnosis	diagnosis	NOUN
cana-1008	322	25	,	,	PUNCT
cana-1008	322	26	we	we	PRON
cana-1008	322	27	make	make	VERB
cana-1008	322	28	significant	significant	ADJ
cana-1008	322	29	progress	progress	NOUN
cana-1008	322	30	.	.	PUNCT
cana-1008	323	1	additionally	additionally	ADV
cana-1008	323	2	,	,	PUNCT
cana-1008	323	3	in	in	ADP
cana-1008	323	4	order	order	NOUN
cana-1008	323	5	to	to	PART
cana-1008	323	6	construct	construct	VERB
cana-1008	323	7	reliable	reliable	ADJ
cana-1008	323	8	prediction	prediction	NOUN
cana-1008	323	9	models	model	NOUN
cana-1008	323	10	,	,	PUNCT
cana-1008	323	11	we	we	PRON
cana-1008	323	12	have	have	AUX
cana-1008	323	13	integrated	integrate	VERB
cana-1008	323	14	a	a	DET
cana-1008	323	15	number	number	NOUN
cana-1008	323	16	of	of	ADP
cana-1008	323	17	different	different	ADJ
cana-1008	323	18	machine	machine	NOUN
cana-1008	323	19	learning	learn	VERB
cana-1008	323	20	algorithms	algorithm	NOUN
cana-1008	323	21	,	,	PUNCT
cana-1008	323	22	as	as	SCONJ
cana-1008	323	23	was	be	AUX
cana-1008	323	24	mentioned	mention	VERB
cana-1008	323	25	before	before	ADV
cana-1008	323	26	.	.	PUNCT
cana-1008	324	1	the	the	DET
cana-1008	324	2	method	method	NOUN
cana-1008	324	3	starts	start	VERB
cana-1008	324	4	with	with	ADP
cana-1008	324	5	the	the	DET
cana-1008	324	6	importing	importing	NOUN
cana-1008	324	7	of	of	ADP
cana-1008	324	8	the	the	DET
cana-1008	324	9	diabetic	diabetic	ADJ
cana-1008	324	10	dataset	dataset	NOUN
cana-1008	324	11	and	and	CCONJ
cana-1008	324	12	any	any	DET
cana-1008	324	13	relevant	relevant	ADJ
cana-1008	324	14	libraries	library	NOUN
cana-1008	324	15	required	require	VERB
cana-1008	324	16	for	for	ADP
cana-1008	324	17	the	the	DET
cana-1008	324	18	process	process	NOUN
cana-1008	324	19	.	.	PUNCT
cana-1008	325	1	after	after	ADP
cana-1008	325	2	this	this	PRON
cana-1008	325	3	,	,	PUNCT
cana-1008	325	4	the	the	DET
cana-1008	325	5	stages	stage	NOUN
cana-1008	325	6	of	of	ADP
cana-1008	325	7	data	datum	NOUN
cana-1008	325	8	preparation	preparation	NOUN
cana-1008	325	9	are	be	AUX
cana-1008	325	10	carried	carry	VERB
cana-1008	325	11	out	out	ADP
cana-1008	325	12	,	,	PUNCT
cana-1008	325	13	which	which	PRON
cana-1008	325	14	include	include	VERB
cana-1008	325	15	the	the	DET
cana-1008	325	16	elimination	elimination	NOUN
cana-1008	325	17	of	of	ADP
cana-1008	325	18	any	any	DET
cana-1008	325	19	missing	miss	VERB
cana-1008	325	20	data	datum	NOUN
cana-1008	325	21	and	and	CCONJ
cana-1008	325	22	the	the	DET
cana-1008	325	23	normalization	normalization	NOUN
cana-1008	325	24	of	of	ADP
cana-1008	325	25	the	the	DET
cana-1008	325	26	data	datum	NOUN
cana-1008	325	27	in	in	ADP
cana-1008	325	28	order	order	NOUN
cana-1008	325	29	to	to	PART
cana-1008	325	30	guarantee	guarantee	VERB
cana-1008	325	31	the	the	DET
cana-1008	325	32	quality	quality	NOUN
cana-1008	325	33	and	and	CCONJ
cana-1008	325	34	consistency	consistency	NOUN
cana-1008	325	35	of	of	ADP
cana-1008	325	36	the	the	DET
cana-1008	325	37	data	datum	NOUN
cana-1008	325	38	.	.	PUNCT
cana-1008	326	1	in	in	ADP
cana-1008	326	2	addition	addition	NOUN
cana-1008	326	3	,	,	PUNCT
cana-1008	326	4	we	we	PRON
cana-1008	326	5	carry	carry	VERB
cana-1008	326	6	out	out	ADP
cana-1008	326	7	two	two	NUM
cana-1008	326	8	percentage	percentage	NOUN
cana-1008	326	9	splits	split	NOUN
cana-1008	326	10	:	:	PUNCT
cana-1008	326	11	the	the	DET
cana-1008	326	12	first	first	ADJ
cana-1008	326	13	separates	separate	VERB
cana-1008	326	14	the	the	DET
cana-1008	326	15	dataset	dataset	NOUN
cana-1008	326	16	into	into	ADP
cana-1008	326	17	forty	forty	NUM
cana-1008	326	18	percent	percent	NOUN
cana-1008	326	19	for	for	ADP
cana-1008	326	20	training	training	NOUN
cana-1008	326	21	and	and	CCONJ
cana-1008	326	22	twenty	twenty	NUM
cana-1008	326	23	percent	percent	NOUN
cana-1008	326	24	for	for	ADP
cana-1008	326	25	testing	testing	NOUN
cana-1008	326	26	,	,	PUNCT
cana-1008	326	27	and	and	CCONJ
cana-1008	326	28	the	the	DET
cana-1008	326	29	second	second	ADJ
cana-1008	326	30	splits	split	VERB
cana-1008	326	31	it	it	PRON
cana-1008	326	32	into	into	ADP
cana-1008	326	33	seventy	seventy	NUM
cana-1008	326	34	percent	percent	NOUN
cana-1008	326	35	for	for	ADP
cana-1008	326	36	training	training	NOUN
cana-1008	326	37	and	and	CCONJ
cana-1008	326	38	thirty	thirty	NUM
cana-1008	326	39	percent	percent	NOUN
cana-1008	326	40	for	for	ADP
cana-1008	326	41	testing	testing	NOUN
cana-1008	326	42	.	.	PUNCT
cana-1008	327	1	principal	principal	ADJ
cana-1008	327	2	component	component	NOUN
cana-1008	327	3	analysis	analysis	NOUN
cana-1008	327	4	(	(	PUNCT
cana-1008	327	5	pca	pca	NOUN
cana-1008	327	6	)	)	PUNCT
cana-1008	327	7	is	be	AUX
cana-1008	327	8	then	then	ADV
cana-1008	327	9	used	use	VERB
cana-1008	327	10	for	for	ADP
cana-1008	327	11	the	the	DET
cana-1008	327	12	purpose	purpose	NOUN
cana-1008	327	13	of	of	ADP
cana-1008	327	14	feature	feature	NOUN
cana-1008	327	15	selection	selection	NOUN
cana-1008	327	16	,	,	PUNCT
cana-1008	327	17	which	which	PRON
cana-1008	327	18	ultimately	ultimately	ADV
cana-1008	327	19	results	result	VERB
cana-1008	327	20	in	in	ADP
cana-1008	327	21	an	an	DET
cana-1008	327	22	increase	increase	NOUN
cana-1008	327	23	in	in	ADP
cana-1008	327	24	the	the	DET
cana-1008	327	25	effectiveness	effectiveness	NOUN
cana-1008	327	26	of	of	ADP
cana-1008	327	27	our	our	PRON
cana-1008	327	28	models	model	NOUN
cana-1008	327	29	.	.	PUNCT
cana-1008	328	1	as	as	SCONJ
cana-1008	328	2	we	we	PRON
cana-1008	328	3	go	go	VERB
cana-1008	328	4	further	far	ADV
cana-1008	328	5	,	,	PUNCT
cana-1008	328	6	a	a	DET
cana-1008	328	7	variety	variety	NOUN
cana-1008	328	8	of	of	ADP
cana-1008	328	9	machine	machine	NOUN
cana-1008	328	10	learning	learning	NOUN
cana-1008	328	11	algorithms	algorithm	NOUN
cana-1008	328	12	,	,	PUNCT
cana-1008	328	13	such	such	ADJ
cana-1008	328	14	as	as	ADP
cana-1008	328	15	support	support	NOUN
cana-1008	328	16	vector	vector	NOUN
cana-1008	328	17	machine	machine	NOUN
cana-1008	328	18	,	,	PUNCT
cana-1008	328	19	decision	decision	NOUN
cana-1008	328	20	tree	tree	NOUN
cana-1008	328	21	,	,	PUNCT
cana-1008	328	22	random	random	ADJ
cana-1008	328	23	forest	forest	NOUN
cana-1008	328	24	,	,	PUNCT
cana-1008	328	25	and	and	CCONJ
cana-1008	328	26	naïve	naïve	ADJ
cana-1008	328	27	bayes	bayes	NOUN
cana-1008	328	28	(	(	PUNCT
cana-1008	328	29	nb	nb	PROPN
cana-1008	328	30	)	)	PUNCT
cana-1008	328	31	,	,	PUNCT
cana-1008	328	32	are	be	AUX
cana-1008	328	33	chosen	choose	VERB
cana-1008	328	34	for	for	ADP
cana-1008	328	35	the	the	DET
cana-1008	328	36	purpose	purpose	NOUN
cana-1008	328	37	of	of	ADP
cana-1008	328	38	developing	develop	VERB
cana-1008	328	39	models	model	NOUN
cana-1008	328	40	.	.	PUNCT
cana-1008	329	1	following	follow	VERB
cana-1008	329	2	the	the	DET
cana-1008	329	3	construction	construction	NOUN
cana-1008	329	4	of	of	ADP
cana-1008	329	5	classifier	classifier	NOUN
cana-1008	329	6	models	model	NOUN
cana-1008	329	7	for	for	ADP
cana-1008	329	8	each	each	DET
cana-1008	329	9	method	method	NOUN
cana-1008	329	10	based	base	VERB
cana-1008	329	11	on	on	ADP
cana-1008	329	12	the	the	DET
cana-1008	329	13	training	training	NOUN
cana-1008	329	14	set	set	NOUN
cana-1008	329	15	,	,	PUNCT
cana-1008	329	16	the	the	DET
cana-1008	329	17	models	model	NOUN
cana-1008	329	18	are	be	AUX
cana-1008	329	19	next	next	ADV
cana-1008	329	20	subjected	subject	VERB
cana-1008	329	21	to	to	ADP
cana-1008	329	22	rigorous	rigorous	ADJ
cana-1008	329	23	testing	testing	NOUN
cana-1008	329	24	on	on	ADP
cana-1008	329	25	the	the	DET
cana-1008	329	26	test	test	NOUN
cana-1008	329	27	set	set	VERB
cana-1008	329	28	in	in	ADP
cana-1008	329	29	order	order	NOUN
cana-1008	329	30	to	to	PART
cana-1008	329	31	evaluate	evaluate	VERB
cana-1008	329	32	their	their	PRON
cana-1008	329	33	performance	performance	NOUN
cana-1008	329	34	.	.	PUNCT
cana-1008	330	1	with	with	ADP
cana-1008	330	2	the	the	DET
cana-1008	330	3	purpose	purpose	NOUN
cana-1008	330	4	of	of	ADP
cana-1008	330	5	determining	determine	VERB
cana-1008	330	6	the	the	DET
cana-1008	330	7	effectiveness	effectiveness	NOUN
cana-1008	330	8	of	of	ADP
cana-1008	330	9	each	each	DET
cana-1008	330	10	classifier	classifier	NOUN
cana-1008	330	11	,	,	PUNCT
cana-1008	330	12	a	a	DET
cana-1008	330	13	comparative	comparative	ADJ
cana-1008	330	14	analysis	analysis	NOUN
cana-1008	330	15	of	of	ADP
cana-1008	330	16	the	the	DET
cana-1008	330	17	outcomes	outcome	NOUN
cana-1008	330	18	of	of	ADP
cana-1008	330	19	the	the	DET
cana-1008	330	20	experiments	experiment	NOUN
cana-1008	330	21	is	be	AUX
cana-1008	330	22	carried	carry	VERB
cana-1008	330	23	out	out	ADP
cana-1008	330	24	.	.	PUNCT
cana-1008	331	1	finally	finally	ADV
cana-1008	331	2	,	,	PUNCT
cana-1008	331	3	after	after	ADP
cana-1008	331	4	doing	do	VERB
cana-1008	331	5	an	an	DET
cana-1008	331	6	in	in	ADP
cana-1008	331	7	-	-	PUNCT
cana-1008	331	8	depth	depth	NOUN
cana-1008	331	9	investigation	investigation	NOUN
cana-1008	331	10	communications	communication	NOUN
cana-1008	331	11	on	on	ADP
cana-1008	331	12	applied	apply	VERB
cana-1008	331	13	nonlinear	nonlinear	ADJ
cana-1008	331	14	analysis	analysis	NOUN
cana-1008	331	15	issn	issn	NOUN
cana-1008	331	16	:	:	PUNCT
cana-1008	331	17	1074	1074	NUM
cana-1008	331	18	-	-	PUNCT
cana-1008	331	19	133x	133x	NUM
cana-1008	331	20	vol	vol	NOUN
cana-1008	331	21	31	31	NUM
cana-1008	331	22	no	no	NOUN
cana-1008	331	23	.	.	PUNCT
cana-1008	332	1	5s	5s	NUM
cana-1008	332	2	(	(	PUNCT
cana-1008	332	3	2024	2024	NUM
cana-1008	332	4	)	)	PUNCT
cana-1008	332	5	151	151	NUM
cana-1008	332	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1008	332	7	based	base	VERB
cana-1008	332	8	on	on	ADP
cana-1008	332	9	a	a	DET
cana-1008	332	10	variety	variety	NOUN
cana-1008	332	11	of	of	ADP
cana-1008	332	12	performance	performance	NOUN
cana-1008	332	13	measures	measure	NOUN
cana-1008	332	14	,	,	PUNCT
cana-1008	332	15	we	we	PRON
cana-1008	332	16	have	have	AUX
cana-1008	332	17	arrived	arrive	VERB
cana-1008	332	18	at	at	ADP
cana-1008	332	19	the	the	DET
cana-1008	332	20	conclusion	conclusion	NOUN
cana-1008	332	21	that	that	SCONJ
cana-1008	332	22	the	the	DET
cana-1008	332	23	algorithm	algorithm	NOUN
cana-1008	332	24	that	that	PRON
cana-1008	332	25	performs	perform	VERB
cana-1008	332	26	the	the	DET
cana-1008	332	27	best	good	ADJ
cana-1008	332	28	for	for	ADP
cana-1008	332	29	diabetes	diabetes	NOUN
cana-1008	332	30	prediction	prediction	NOUN
cana-1008	332	31	is	be	AUX
cana-1008	332	32	.	.	PUNCT
cana-1008	333	1	the	the	DET
cana-1008	333	2	use	use	NOUN
cana-1008	333	3	of	of	ADP
cana-1008	333	4	this	this	DET
cana-1008	333	5	all	all	ADV
cana-1008	333	6	-	-	PUNCT
cana-1008	333	7	encompassing	encompass	VERB
cana-1008	333	8	methodology	methodology	NOUN
cana-1008	333	9	guarantees	guarantee	VERB
cana-1008	333	10	the	the	DET
cana-1008	333	11	selection	selection	NOUN
cana-1008	333	12	of	of	ADP
cana-1008	333	13	the	the	DET
cana-1008	333	14	most	most	ADV
cana-1008	333	15	suitable	suitable	ADJ
cana-1008	333	16	model	model	NOUN
cana-1008	333	17	for	for	ADP
cana-1008	333	18	the	the	DET
cana-1008	333	19	precise	precise	ADJ
cana-1008	333	20	and	and	CCONJ
cana-1008	333	21	dependable	dependable	ADJ
cana-1008	333	22	identification	identification	NOUN
cana-1008	333	23	of	of	ADP
cana-1008	333	24	diabetes	diabetes	NOUN
cana-1008	333	25	.	.	PUNCT
cana-1008	334	1	4	4	X
cana-1008	334	2	.	.	X
cana-1008	334	3	experimental	experimental	ADJ
cana-1008	334	4	result	result	NOUN
cana-1008	334	5	the	the	DET
cana-1008	334	6	results	result	NOUN
cana-1008	334	7	of	of	ADP
cana-1008	334	8	the	the	DET
cana-1008	334	9	experiments	experiment	NOUN
cana-1008	334	10	conducted	conduct	VERB
cana-1008	334	11	on	on	ADP
cana-1008	334	12	the	the	DET
cana-1008	334	13	suggested	suggest	VERB
cana-1008	334	14	models	model	NOUN
cana-1008	334	15	are	be	AUX
cana-1008	334	16	presented	present	VERB
cana-1008	334	17	in	in	ADP
cana-1008	334	18	this	this	DET
cana-1008	334	19	section	section	NOUN
cana-1008	334	20	.	.	PUNCT
cana-1008	335	1	in	in	ADP
cana-1008	335	2	order	order	NOUN
cana-1008	335	3	to	to	PART
cana-1008	335	4	construct	construct	VERB
cana-1008	335	5	the	the	DET
cana-1008	335	6	prediction	prediction	NOUN
cana-1008	335	7	models	model	NOUN
cana-1008	335	8	,	,	PUNCT
cana-1008	335	9	the	the	DET
cana-1008	335	10	svm	svm	PROPN
cana-1008	335	11	,	,	PUNCT
cana-1008	335	12	rf	rf	ADJ
cana-1008	335	13	,	,	PUNCT
cana-1008	335	14	dt	dt	X
cana-1008	335	15	,	,	PUNCT
cana-1008	335	16	and	and	CCONJ
cana-1008	335	17	nb	nb	PROPN
cana-1008	335	18	algorithms	algorithm	NOUN
cana-1008	335	19	were	be	AUX
cana-1008	335	20	applied	apply	VERB
cana-1008	335	21	.	.	PUNCT
cana-1008	336	1	the	the	DET
cana-1008	336	2	performance	performance	NOUN
cana-1008	336	3	of	of	ADP
cana-1008	336	4	these	these	DET
cana-1008	336	5	models	model	NOUN
cana-1008	336	6	was	be	AUX
cana-1008	336	7	assessed	assess	VERB
cana-1008	336	8	by	by	ADP
cana-1008	336	9	using	use	VERB
cana-1008	336	10	the	the	DET
cana-1008	336	11	appropriate	appropriate	ADJ
cana-1008	336	12	metrics	metric	NOUN
cana-1008	336	13	,	,	PUNCT
cana-1008	336	14	which	which	PRON
cana-1008	336	15	included	include	VERB
cana-1008	336	16	accuracy	accuracy	NOUN
cana-1008	336	17	,	,	PUNCT
cana-1008	336	18	precision	precision	NOUN
cana-1008	336	19	,	,	PUNCT
cana-1008	336	20	recall	recall	NOUN
cana-1008	336	21	,	,	PUNCT
cana-1008	336	22	and	and	CCONJ
cana-1008	336	23	f1	f1	NOUN
cana-1008	336	24	-	-	PUNCT
cana-1008	336	25	score	score	NOUN
cana-1008	336	26	.	.	PUNCT
cana-1008	337	1	for	for	ADP
cana-1008	337	2	the	the	DET
cana-1008	337	3	purposes	purpose	NOUN
cana-1008	337	4	of	of	ADP
cana-1008	337	5	training	training	NOUN
cana-1008	337	6	and	and	CCONJ
cana-1008	337	7	testing	testing	NOUN
cana-1008	337	8	,	,	PUNCT
cana-1008	337	9	we	we	PRON
cana-1008	337	10	used	use	VERB
cana-1008	337	11	a	a	DET
cana-1008	337	12	splitting	splitting	NOUN
cana-1008	337	13	data	datum	NOUN
cana-1008	337	14	distribution	distribution	NOUN
cana-1008	337	15	,	,	PUNCT
cana-1008	337	16	and	and	CCONJ
cana-1008	337	17	we	we	PRON
cana-1008	337	18	utilized	utilize	VERB
cana-1008	337	19	two	two	NUM
cana-1008	337	20	different	different	ADJ
cana-1008	337	21	splitting	splitting	NOUN
cana-1008	337	22	ratios	ratio	NOUN
cana-1008	337	23	.	.	PUNCT
cana-1008	338	1	there	there	PRON
cana-1008	338	2	is	be	VERB
cana-1008	338	3	a	a	DET
cana-1008	338	4	70	70	NUM
cana-1008	338	5	%	%	NOUN
cana-1008	338	6	training	training	NOUN
cana-1008	338	7	and	and	CCONJ
cana-1008	338	8	30	30	NUM
cana-1008	338	9	%	%	NOUN
cana-1008	338	10	testing	testing	NOUN
cana-1008	338	11	ratio	ratio	NOUN
cana-1008	338	12	,	,	PUNCT
cana-1008	338	13	and	and	CCONJ
cana-1008	338	14	80	80	NUM
cana-1008	338	15	%	%	NOUN
cana-1008	338	16	training	training	NOUN
cana-1008	338	17	and	and	CCONJ
cana-1008	338	18	20	20	NUM
cana-1008	338	19	%	%	NOUN
cana-1008	338	20	testing	testing	NOUN
cana-1008	338	21	.	.	PUNCT
cana-1008	339	1	by	by	ADP
cana-1008	339	2	training	training	NOUN
cana-1008	339	3	and	and	CCONJ
cana-1008	339	4	testing	test	VERB
cana-1008	339	5	a	a	DET
cana-1008	339	6	model	model	NOUN
cana-1008	339	7	,	,	PUNCT
cana-1008	339	8	one	one	PRON
cana-1008	339	9	may	may	AUX
cana-1008	339	10	increase	increase	VERB
cana-1008	339	11	the	the	DET
cana-1008	339	12	likelihood	likelihood	NOUN
cana-1008	339	13	of	of	ADP
cana-1008	339	14	attaining	attain	VERB
cana-1008	339	15	a	a	DET
cana-1008	339	16	successful	successful	ADJ
cana-1008	339	17	outcome	outcome	NOUN
cana-1008	339	18	with	with	ADP
cana-1008	339	19	the	the	DET
cana-1008	339	20	prediction	prediction	NOUN
cana-1008	339	21	.	.	PUNCT
cana-1008	340	1	for	for	ADP
cana-1008	340	2	the	the	DET
cana-1008	340	3	purpose	purpose	NOUN
cana-1008	340	4	of	of	ADP
cana-1008	340	5	training	training	NOUN
cana-1008	340	6	algorithms	algorithm	NOUN
cana-1008	340	7	that	that	PRON
cana-1008	340	8	are	be	AUX
cana-1008	340	9	capable	capable	ADJ
cana-1008	340	10	of	of	ADP
cana-1008	340	11	learning	learn	VERB
cana-1008	340	12	and	and	CCONJ
cana-1008	340	13	making	make	VERB
cana-1008	340	14	predictions	prediction	NOUN
cana-1008	340	15	,	,	PUNCT
cana-1008	340	16	the	the	DET
cana-1008	340	17	training	training	NOUN
cana-1008	340	18	dataset	dataset	NOUN
cana-1008	340	19	is	be	AUX
cana-1008	340	20	a	a	DET
cana-1008	340	21	collection	collection	NOUN
cana-1008	340	22	of	of	ADP
cana-1008	340	23	learning	learn	VERB
cana-1008	340	24	sets	set	NOUN
cana-1008	340	25	that	that	PRON
cana-1008	340	26	are	be	AUX
cana-1008	340	27	necessary	necessary	ADJ
cana-1008	340	28	.	.	PUNCT
cana-1008	341	1	it	it	PRON
cana-1008	341	2	is	be	AUX
cana-1008	341	3	primarily	primarily	ADV
cana-1008	341	4	for	for	ADP
cana-1008	341	5	the	the	DET
cana-1008	341	6	purpose	purpose	NOUN
cana-1008	341	7	of	of	ADP
cana-1008	341	8	evaluating	evaluate	VERB
cana-1008	341	9	the	the	DET
cana-1008	341	10	effectiveness	effectiveness	NOUN
cana-1008	341	11	of	of	ADP
cana-1008	341	12	the	the	DET
cana-1008	341	13	chosen	choose	VERB
cana-1008	341	14	classifiers	classifier	NOUN
cana-1008	341	15	that	that	SCONJ
cana-1008	341	16	the	the	DET
cana-1008	341	17	test	test	NOUN
cana-1008	341	18	set	set	NOUN
cana-1008	341	19	is	be	AUX
cana-1008	341	20	used	use	VERB
cana-1008	341	21	.	.	PUNCT
cana-1008	342	1	the	the	DET
cana-1008	342	2	sole	sole	ADJ
cana-1008	342	3	reason	reason	NOUN
cana-1008	342	4	it	it	PRON
cana-1008	342	5	is	be	AUX
cana-1008	342	6	included	include	VERB
cana-1008	342	7	is	be	AUX
cana-1008	342	8	for	for	ADP
cana-1008	342	9	the	the	DET
cana-1008	342	10	purpose	purpose	NOUN
cana-1008	342	11	of	of	ADP
cana-1008	342	12	testing	test	VERB
cana-1008	342	13	the	the	DET
cana-1008	342	14	classifiers	classifier	NOUN
cana-1008	342	15	.	.	PUNCT
cana-1008	343	1	the	the	DET
cana-1008	343	2	accuracy	accuracy	NOUN
cana-1008	343	3	that	that	PRON
cana-1008	343	4	is	be	AUX
cana-1008	343	5	anticipated	anticipate	VERB
cana-1008	343	6	to	to	PART
cana-1008	343	7	be	be	AUX
cana-1008	343	8	achieved	achieve	VERB
cana-1008	343	9	by	by	ADP
cana-1008	343	10	a	a	DET
cana-1008	343	11	model	model	NOUN
cana-1008	343	12	is	be	AUX
cana-1008	343	13	improved	improve	VERB
cana-1008	343	14	if	if	SCONJ
cana-1008	343	15	it	it	PRON
cana-1008	343	16	performs	perform	VERB
cana-1008	343	17	better	well	ADV
cana-1008	343	18	in	in	ADP
cana-1008	343	19	both	both	DET
cana-1008	343	20	datasets	dataset	NOUN
cana-1008	343	21	.	.	PUNCT
cana-1008	344	1	4.1	4.1	NUM
cana-1008	344	2	dataset	dataset	VERB
cana-1008	344	3	the	the	DET
cana-1008	344	4	dataset	dataset	NOUN
cana-1008	344	5	resulting	result	VERB
cana-1008	344	6	from	from	ADP
cana-1008	344	7	the	the	DET
cana-1008	344	8	pima	pima	PROPN
cana-1008	344	9	indian	indian	PROPN
cana-1008	344	10	diabetes	diabetes	NOUN
cana-1008	344	11	study	study	NOUN
cana-1008	344	12	is	be	AUX
cana-1008	344	13	named	name	VERB
cana-1008	344	14	"	"	PUNCT
cana-1008	344	15	diabetes.csv	diabetes.csv	PUNCT
cana-1008	344	16	"	"	PUNCT
cana-1008	344	17	.	.	PUNCT
cana-1008	345	1	this	this	DET
cana-1008	345	2	dataset	dataset	NOUN
cana-1008	345	3	was	be	AUX
cana-1008	345	4	generated	generate	VERB
cana-1008	345	5	from	from	ADP
cana-1008	345	6	diabetes	diabetes	NOUN
cana-1008	345	7	.	.	PUNCT
cana-1008	346	1	diabetes	diabetes	NOUN
cana-1008	346	2	may	may	AUX
cana-1008	346	3	be	be	AUX
cana-1008	346	4	recognized	recognize	VERB
cana-1008	346	5	by	by	ADP
cana-1008	346	6	its	its	PRON
cana-1008	346	7	eight	eight	NUM
cana-1008	346	8	distinctive	distinctive	ADJ
cana-1008	346	9	characteristics	characteristic	NOUN
cana-1008	346	10	,	,	PUNCT
cana-1008	346	11	which	which	PRON
cana-1008	346	12	function	function	VERB
cana-1008	346	13	as	as	ADP
cana-1008	346	14	indicators	indicator	NOUN
cana-1008	346	15	.	.	PUNCT
cana-1008	347	1	these	these	DET
cana-1008	347	2	findings	finding	NOUN
cana-1008	347	3	are	be	AUX
cana-1008	347	4	derived	derive	VERB
cana-1008	347	5	from	from	ADP
cana-1008	347	6	a	a	DET
cana-1008	347	7	dataset	dataset	NOUN
cana-1008	347	8	of	of	ADP
cana-1008	347	9	768	768	NUM
cana-1008	347	10	cases	case	NOUN
cana-1008	347	11	and	and	CCONJ
cana-1008	347	12	are	be	AUX
cana-1008	347	13	used	use	VERB
cana-1008	347	14	to	to	PART
cana-1008	347	15	ascertain	ascertain	VERB
cana-1008	347	16	the	the	DET
cana-1008	347	17	presence	presence	NOUN
cana-1008	347	18	or	or	CCONJ
cana-1008	347	19	absence	absence	NOUN
cana-1008	347	20	of	of	ADP
cana-1008	347	21	diabetes	diabetes	NOUN
cana-1008	347	22	in	in	ADP
cana-1008	347	23	patients	patient	NOUN
cana-1008	347	24	.	.	PUNCT
cana-1008	348	1	regarding	regard	VERB
cana-1008	348	2	patients	patient	NOUN
cana-1008	348	3	,	,	PUNCT
cana-1008	348	4	this	this	DET
cana-1008	348	5	norm	norm	NOUN
cana-1008	348	6	might	might	AUX
cana-1008	348	7	manifest	manifest	VERB
cana-1008	348	8	in	in	ADP
cana-1008	348	9	several	several	ADJ
cana-1008	348	10	ways	way	NOUN
cana-1008	348	11	.	.	PUNCT
cana-1008	349	1	the	the	DET
cana-1008	349	2	table	table	NOUN
cana-1008	349	3	below	below	ADP
cana-1008	349	4	displays	display	VERB
cana-1008	349	5	the	the	DET
cana-1008	349	6	indicators	indicator	NOUN
cana-1008	349	7	of	of	ADP
cana-1008	349	8	the	the	DET
cana-1008	349	9	physical	physical	ADJ
cana-1008	349	10	features	feature	NOUN
cana-1008	349	11	of	of	ADP
cana-1008	349	12	the	the	DET
cana-1008	349	13	data	datum	NOUN
cana-1008	349	14	set	set	VERB
cana-1008	349	15	.	.	PUNCT
cana-1008	350	1	table	table	NOUN
cana-1008	350	2	i	i	PRON
cana-1008	350	3	is	be	AUX
cana-1008	350	4	a	a	DET
cana-1008	350	5	succinct	succinct	ADJ
cana-1008	350	6	explanation	explanation	NOUN
cana-1008	350	7	of	of	ADP
cana-1008	350	8	the	the	DET
cana-1008	350	9	eight	eight	NUM
cana-1008	350	10	attributes	attribute	NOUN
cana-1008	350	11	that	that	PRON
cana-1008	350	12	constitute	constitute	VERB
cana-1008	350	13	the	the	DET
cana-1008	350	14	diabetes	diabetes	NOUN
cana-1008	350	15	dataset	dataset	VERB
cana-1008	350	16	[	[	X
cana-1008	350	17	14	14	NUM
cana-1008	350	18	]	]	PUNCT
cana-1008	350	19	.	.	PUNCT
cana-1008	351	1	the	the	DET
cana-1008	351	2	research	research	NOUN
cana-1008	351	3	investigation	investigation	NOUN
cana-1008	351	4	has	have	AUX
cana-1008	351	5	confirmed	confirm	VERB
cana-1008	351	6	that	that	SCONJ
cana-1008	351	7	the	the	DET
cana-1008	351	8	diabetic	diabetic	ADJ
cana-1008	351	9	database	database	NOUN
cana-1008	351	10	file	file	NOUN
cana-1008	351	11	,	,	PUNCT
cana-1008	351	12	which	which	PRON
cana-1008	351	13	was	be	AUX
cana-1008	351	14	helped	help	VERB
cana-1008	351	15	by	by	ADP
cana-1008	351	16	pandas	panda	NOUN
cana-1008	351	17	,	,	PUNCT
cana-1008	351	18	has	have	AUX
cana-1008	351	19	been	be	AUX
cana-1008	351	20	accessed	access	VERB
cana-1008	351	21	.	.	PUNCT
cana-1008	352	1	the	the	DET
cana-1008	352	2	database	database	NOUN
cana-1008	352	3	consists	consist	VERB
cana-1008	352	4	of	of	ADP
cana-1008	352	5	768	768	NUM
cana-1008	352	6	entries	entry	NOUN
cana-1008	352	7	,	,	PUNCT
cana-1008	352	8	each	each	PRON
cana-1008	352	9	containing	contain	VERB
cana-1008	352	10	eight	eight	NUM
cana-1008	352	11	medical	medical	ADJ
cana-1008	352	12	prediction	prediction	NOUN
cana-1008	352	13	parameters	parameter	NOUN
cana-1008	352	14	as	as	ADP
cana-1008	352	15	input	input	NOUN
cana-1008	352	16	,	,	PUNCT
cana-1008	352	17	and	and	CCONJ
cana-1008	352	18	one	one	NUM
cana-1008	352	19	target	target	NOUN
cana-1008	352	20	variable	variable	ADJ
cana-1008	352	21	output	output	NOUN
cana-1008	352	22	.	.	PUNCT
cana-1008	353	1	the	the	DET
cana-1008	353	2	target	target	NOUN
cana-1008	353	3	variable	variable	NOUN
cana-1008	353	4	represents	represent	VERB
cana-1008	353	5	the	the	DET
cana-1008	353	6	presence	presence	NOUN
cana-1008	353	7	of	of	ADP
cana-1008	353	8	diabetes	diabetes	NOUN
cana-1008	353	9	,	,	PUNCT
cana-1008	353	10	with	with	ADP
cana-1008	353	11	a	a	DET
cana-1008	353	12	value	value	NOUN
cana-1008	353	13	of	of	ADP
cana-1008	353	14	one	one	NUM
cana-1008	353	15	indicating	indicate	VERB
cana-1008	353	16	"	"	PUNCT
cana-1008	353	17	yes	yes	INTJ
cana-1008	353	18	"	"	PUNCT
cana-1008	353	19	and	and	CCONJ
cana-1008	353	20	zero	zero	NUM
cana-1008	353	21	indicating	indicate	VERB
cana-1008	353	22	"	"	PUNCT
cana-1008	353	23	no	no	INTJ
cana-1008	353	24	"	"	PUNCT
cana-1008	353	25	.	.	PUNCT
cana-1008	354	1	it	it	PRON
cana-1008	354	2	has	have	AUX
cana-1008	354	3	been	be	AUX
cana-1008	354	4	noted	note	VERB
cana-1008	354	5	that	that	SCONJ
cana-1008	354	6	among	among	ADP
cana-1008	354	7	a	a	DET
cana-1008	354	8	group	group	NOUN
cana-1008	354	9	of	of	ADP
cana-1008	354	10	768	768	NUM
cana-1008	354	11	pima	pima	PROPN
cana-1008	354	12	indian	indian	ADJ
cana-1008	354	13	women	woman	NOUN
cana-1008	354	14	,	,	PUNCT
cana-1008	354	15	65.1	65.1	NUM
cana-1008	354	16	%	%	NOUN
cana-1008	354	17	of	of	ADP
cana-1008	354	18	them	they	PRON
cana-1008	354	19	had	have	AUX
cana-1008	354	20	not	not	PART
cana-1008	354	21	received	receive	VERB
cana-1008	354	22	a	a	DET
cana-1008	354	23	diagnosis	diagnosis	NOUN
cana-1008	354	24	of	of	ADP
cana-1008	354	25	diabetes	diabetes	NOUN
cana-1008	354	26	.	.	PUNCT
cana-1008	355	1	the	the	DET
cana-1008	355	2	data	datum	NOUN
cana-1008	355	3	is	be	AUX
cana-1008	355	4	shown	show	VERB
cana-1008	355	5	in	in	ADP
cana-1008	355	6	figure	figure	NOUN
cana-1008	355	7	4	4	NUM
cana-1008	355	8	,	,	PUNCT
cana-1008	355	9	illustrating	illustrate	VERB
cana-1008	355	10	this	this	DET
cana-1008	355	11	discovery	discovery	NOUN
cana-1008	355	12	.	.	PUNCT
cana-1008	356	1	on	on	ADP
cana-1008	356	2	the	the	DET
cana-1008	356	3	other	other	ADJ
cana-1008	356	4	hand	hand	NOUN
cana-1008	356	5	,	,	PUNCT
cana-1008	356	6	a	a	DET
cana-1008	356	7	total	total	NOUN
cana-1008	356	8	of	of	ADP
cana-1008	356	9	34.90	34.90	NUM
cana-1008	356	10	percent	percent	NOUN
cana-1008	356	11	of	of	ADP
cana-1008	356	12	the	the	DET
cana-1008	356	13	768	768	NUM
cana-1008	356	14	pima	pima	PROPN
cana-1008	356	15	indian	indian	ADJ
cana-1008	356	16	women	woman	NOUN
cana-1008	356	17	were	be	AUX
cana-1008	356	18	found	find	VERB
cana-1008	356	19	to	to	PART
cana-1008	356	20	have	have	AUX
cana-1008	356	21	been	be	AUX
cana-1008	356	22	diagnosed	diagnose	VERB
cana-1008	356	23	with	with	ADP
cana-1008	356	24	diabetes	diabetes	NOUN
cana-1008	356	25	[	[	X
cana-1008	356	26	29	29	NUM
cana-1008	356	27	]	]	PUNCT
cana-1008	356	28	.	.	PUNCT
cana-1008	357	1	communications	communication	NOUN
cana-1008	357	2	on	on	ADP
cana-1008	357	3	applied	apply	VERB
cana-1008	357	4	nonlinear	nonlinear	ADJ
cana-1008	357	5	analysis	analysis	NOUN
cana-1008	357	6	issn	issn	NOUN
cana-1008	357	7	:	:	PUNCT
cana-1008	357	8	1074	1074	NUM
cana-1008	357	9	-	-	PUNCT
cana-1008	357	10	133x	133x	NUM
cana-1008	357	11	vol	vol	NOUN
cana-1008	357	12	31	31	NUM
cana-1008	357	13	no	no	NOUN
cana-1008	357	14	.	.	PUNCT
cana-1008	358	1	5s	5s	NUM
cana-1008	358	2	(	(	PUNCT
cana-1008	358	3	2024	2024	NUM
cana-1008	358	4	)	)	PUNCT
cana-1008	358	5	152	152	NUM
cana-1008	358	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1008	358	7	table	table	NOUN
cana-1008	358	8	1	1	NUM
cana-1008	358	9	:	:	PUNCT
cana-1008	358	10	attribute	attribute	NOUN
cana-1008	358	11	of	of	ADP
cana-1008	358	12	dataset	dataset	VERB
cana-1008	358	13	4.2	4.2	NUM
cana-1008	358	14	results	result	NOUN
cana-1008	358	15	and	and	CCONJ
cana-1008	358	16	discussion	discussion	NOUN
cana-1008	358	17	it	it	PRON
cana-1008	358	18	is	be	AUX
cana-1008	358	19	possible	possible	ADJ
cana-1008	358	20	to	to	PART
cana-1008	358	21	examine	examine	VERB
cana-1008	358	22	a	a	DET
cana-1008	358	23	variety	variety	NOUN
cana-1008	358	24	of	of	ADP
cana-1008	358	25	performance	performance	NOUN
cana-1008	358	26	indicators	indicator	NOUN
cana-1008	358	27	,	,	PUNCT
cana-1008	358	28	such	such	ADJ
cana-1008	358	29	as	as	ADP
cana-1008	358	30	accuracy	accuracy	NOUN
cana-1008	358	31	,	,	PUNCT
cana-1008	358	32	sensitivity	sensitivity	NOUN
cana-1008	358	33	,	,	PUNCT
cana-1008	358	34	specificity	specificity	NOUN
cana-1008	358	35	,	,	PUNCT
cana-1008	358	36	and	and	CCONJ
cana-1008	358	37	error	error	NOUN
cana-1008	358	38	rate	rate	NOUN
cana-1008	358	39	.	.	PUNCT
cana-1008	359	1	accuracy	accuracy	NOUN
cana-1008	359	2	may	may	AUX
cana-1008	359	3	be	be	AUX
cana-1008	359	4	defined	define	VERB
cana-1008	359	5	as	as	ADP
cana-1008	359	6	the	the	DET
cana-1008	359	7	correlation	correlation	NOUN
cana-1008	359	8	between	between	ADP
cana-1008	359	9	the	the	DET
cana-1008	359	10	total	total	ADJ
cana-1008	359	11	number	number	NOUN
cana-1008	359	12	of	of	ADP
cana-1008	359	13	predictions	prediction	NOUN
cana-1008	359	14	and	and	CCONJ
cana-1008	359	15	the	the	DET
cana-1008	359	16	fraction	fraction	NOUN
cana-1008	359	17	of	of	ADP
cana-1008	359	18	correct	correct	ADJ
cana-1008	359	19	forecasts	forecast	NOUN
cana-1008	359	20	.	.	PUNCT
cana-1008	360	1	it	it	PRON
cana-1008	360	2	is	be	AUX
cana-1008	360	3	possible	possible	ADJ
cana-1008	360	4	to	to	PART
cana-1008	360	5	define	define	VERB
cana-1008	360	6	sensitivity	sensitivity	NOUN
cana-1008	360	7	as	as	ADP
cana-1008	360	8	the	the	DET
cana-1008	360	9	percentage	percentage	NOUN
cana-1008	360	10	of	of	ADP
cana-1008	360	11	positive	positive	ADJ
cana-1008	360	12	samples	sample	NOUN
cana-1008	360	13	that	that	PRON
cana-1008	360	14	are	be	AUX
cana-1008	360	15	found	find	VERB
cana-1008	360	16	to	to	PART
cana-1008	360	17	be	be	AUX
cana-1008	360	18	positive	positive	ADJ
cana-1008	360	19	after	after	ADP
cana-1008	360	20	testing	test	VERB
cana-1008	360	21	.	.	PUNCT
cana-1008	361	1	the	the	DET
cana-1008	361	2	term	term	NOUN
cana-1008	361	3	"	"	PUNCT
cana-1008	361	4	true	true	ADJ
cana-1008	361	5	positive	positive	ADJ
cana-1008	361	6	rate	rate	NOUN
cana-1008	361	7	"	"	PUNCT
cana-1008	361	8	is	be	AUX
cana-1008	361	9	another	another	DET
cana-1008	361	10	name	name	NOUN
cana-1008	361	11	for	for	ADP
cana-1008	361	12	this	this	DET
cana-1008	361	13	proportion	proportion	NOUN
cana-1008	361	14	.	.	PUNCT
cana-1008	362	1	specificity	specificity	NOUN
cana-1008	362	2	may	may	AUX
cana-1008	362	3	be	be	AUX
cana-1008	362	4	defined	define	VERB
cana-1008	362	5	as	as	ADP
cana-1008	362	6	the	the	DET
cana-1008	362	7	percentage	percentage	NOUN
cana-1008	362	8	of	of	ADP
cana-1008	362	9	samples	sample	NOUN
cana-1008	362	10	that	that	PRON
cana-1008	362	11	are	be	AUX
cana-1008	362	12	negative	negative	ADJ
cana-1008	362	13	that	that	PRON
cana-1008	362	14	are	be	AUX
cana-1008	362	15	successfully	successfully	ADV
cana-1008	362	16	tested	test	VERB
cana-1008	362	17	negative	negative	ADJ
cana-1008	362	18	.	.	PUNCT
cana-1008	363	1	additional	additional	ADJ
cana-1008	363	2	names	name	NOUN
cana-1008	363	3	for	for	ADP
cana-1008	363	4	this	this	DET
cana-1008	363	5	kind	kind	NOUN
cana-1008	363	6	of	of	ADP
cana-1008	363	7	rate	rate	NOUN
cana-1008	363	8	include	include	VERB
cana-1008	363	9	a	a	DET
cana-1008	363	10	real	real	ADJ
cana-1008	363	11	negative	negative	ADJ
cana-1008	363	12	rate	rate	NOUN
cana-1008	363	13	.	.	PUNCT
cana-1008	364	1	it	it	PRON
cana-1008	364	2	is	be	AUX
cana-1008	364	3	possible	possible	ADJ
cana-1008	364	4	to	to	PART
cana-1008	364	5	get	get	VERB
cana-1008	364	6	the	the	DET
cana-1008	364	7	formula	formula	NOUN
cana-1008	364	8	in	in	ADP
cana-1008	364	9	equation	equation	NOUN
cana-1008	364	10	(	(	PUNCT
cana-1008	364	11	3	3	NUM
cana-1008	364	12	)	)	PUNCT
cana-1008	364	13	.	.	PUNCT
cana-1008	365	1	𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦	𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦	PROPN
cana-1008	365	2	=	=	SYM
cana-1008	365	3	𝑇𝑁+𝑇𝑃	𝑇𝑁+𝑇𝑃	PROPN
cana-1008	365	4	𝑇𝑁+𝑇𝑃+𝐹𝑁+𝐹𝑃	𝑇𝑁+𝑇𝑃+𝐹𝑁+𝐹𝑃	NUM
cana-1008	365	5	(	(	PUNCT
cana-1008	365	6	3	3	NUM
cana-1008	365	7	)	)	PUNCT
cana-1008	365	8	𝑅𝑒𝑐𝑎𝑙𝑙	𝑅𝑒𝑐𝑎𝑙𝑙	PROPN
cana-1008	365	9	=	=	SYM
cana-1008	365	10	𝑇𝑃	𝑇𝑃	NOUN
cana-1008	365	11	𝑇𝑃+𝐹𝑁	𝑇𝑃+𝐹𝑁	NOUN
cana-1008	365	12	(	(	PUNCT
cana-1008	365	13	4	4	NUM
cana-1008	365	14	)	)	PUNCT
cana-1008	365	15	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛	PROPN
cana-1008	365	16	=	=	SYM
cana-1008	365	17	𝑇𝑃	𝑇𝑃	PROPN
cana-1008	365	18	𝑇𝑃+𝐹𝑃	𝑇𝑃+𝐹𝑃	NUM
cana-1008	365	19	(	(	PUNCT
cana-1008	365	20	5	5	NUM
cana-1008	365	21	)	)	PUNCT
cana-1008	365	22	𝐹1	𝐹1	NOUN
cana-1008	365	23	−	−	PROPN
cana-1008	365	24	𝑆𝑐𝑜𝑟𝑒	𝑆𝑐𝑜𝑟𝑒	PROPN
cana-1008	365	25	=	=	NOUN
cana-1008	365	26	2𝑇𝑃	2𝑇𝑃	NUM
cana-1008	365	27	2𝑇𝑃	2𝑇𝑃	NUM
cana-1008	365	28	+	+	NOUN
cana-1008	365	29	𝐹𝑃+𝐹𝑁	𝐹𝑃+𝐹𝑁	NOUN
cana-1008	365	30	(	(	PUNCT
cana-1008	365	31	6	6	X
cana-1008	365	32	)	)	PUNCT
cana-1008	365	33	the	the	DET
cana-1008	365	34	findings	finding	NOUN
cana-1008	365	35	of	of	ADP
cana-1008	365	36	the	the	DET
cana-1008	365	37	performance	performance	NOUN
cana-1008	365	38	assessment	assessment	NOUN
cana-1008	365	39	of	of	ADP
cana-1008	365	40	this	this	DET
cana-1008	365	41	research	research	NOUN
cana-1008	365	42	are	be	AUX
cana-1008	365	43	shown	show	VERB
cana-1008	365	44	in	in	ADP
cana-1008	365	45	this	this	DET
cana-1008	365	46	part	part	NOUN
cana-1008	365	47	.	.	PUNCT
cana-1008	366	1	the	the	DET
cana-1008	366	2	evaluation	evaluation	NOUN
cana-1008	366	3	is	be	AUX
cana-1008	366	4	based	base	VERB
cana-1008	366	5	on	on	ADP
cana-1008	366	6	the	the	DET
cana-1008	366	7	accuracy	accuracy	NOUN
cana-1008	366	8	,	,	PUNCT
cana-1008	366	9	sensitivity	sensitivity	NOUN
cana-1008	366	10	,	,	PUNCT
cana-1008	366	11	and	and	CCONJ
cana-1008	366	12	specificity	specificity	NOUN
cana-1008	366	13	of	of	ADP
cana-1008	366	14	various	various	ADJ
cana-1008	366	15	matrices	matrix	NOUN
cana-1008	366	16	using	use	VERB
cana-1008	366	17	the	the	DET
cana-1008	366	18	support	support	NOUN
cana-1008	366	19	vector	vector	NOUN
cana-1008	366	20	machine	machine	NOUN
cana-1008	366	21	(	(	PUNCT
cana-1008	366	22	svm	svm	ADJ
cana-1008	366	23	)	)	PUNCT
cana-1008	366	24	classifier	classifier	NOUN
cana-1008	366	25	.	.	PUNCT
cana-1008	367	1	the	the	DET
cana-1008	367	2	assessment	assessment	NOUN
cana-1008	367	3	was	be	AUX
cana-1008	367	4	carried	carry	VERB
cana-1008	367	5	out	out	ADP
cana-1008	367	6	in	in	ADP
cana-1008	367	7	two	two	NUM
cana-1008	367	8	stages	stage	NOUN
cana-1008	367	9	:	:	PUNCT
cana-1008	367	10	first	first	ADV
cana-1008	367	11	,	,	PUNCT
cana-1008	367	12	before	before	ADP
cana-1008	367	13	being	be	AUX
cana-1008	367	14	submitted	submit	VERB
cana-1008	367	15	to	to	ADP
cana-1008	367	16	pca	pca	PROPN
cana-1008	367	17	,	,	PUNCT
cana-1008	367	18	and	and	CCONJ
cana-1008	367	19	then	then	ADV
cana-1008	367	20	again	again	ADV
cana-1008	367	21	after	after	ADP
cana-1008	367	22	being	be	AUX
cana-1008	367	23	submitted	submit	VERB
cana-1008	367	24	to	to	ADP
cana-1008	367	25	pca	pca	PROPN
cana-1008	367	26	.	.	PUNCT
cana-1008	368	1	following	follow	VERB
cana-1008	368	2	the	the	DET
cana-1008	368	3	construction	construction	NOUN
cana-1008	368	4	of	of	ADP
cana-1008	368	5	the	the	DET
cana-1008	368	6	piam	piam	NOUN
cana-1008	368	7	diabetes	diabetes	NOUN
cana-1008	368	8	dataset	dataset	VERB
cana-1008	368	9	,	,	PUNCT
cana-1008	368	10	the	the	DET
cana-1008	368	11	findings	finding	NOUN
cana-1008	368	12	that	that	PRON
cana-1008	368	13	were	be	AUX
cana-1008	368	14	achieved	achieve	VERB
cana-1008	368	15	for	for	ADP
cana-1008	368	16	all	all	DET
cana-1008	368	17	classifiers	classifier	NOUN
cana-1008	368	18	are	be	AUX
cana-1008	368	19	shown	show	VERB
cana-1008	368	20	in	in	ADP
cana-1008	368	21	the	the	DET
cana-1008	368	22	tables	table	NOUN
cana-1008	368	23	that	that	PRON
cana-1008	368	24	are	be	AUX
cana-1008	368	25	located	locate	VERB
cana-1008	368	26	below	below	ADV
cana-1008	368	27	(	(	PUNCT
cana-1008	368	28	tables	table	NOUN
cana-1008	368	29	2	2	NUM
cana-1008	368	30	and	and	CCONJ
cana-1008	368	31	3	3	NUM
cana-1008	368	32	)	)	PUNCT
cana-1008	368	33	.	.	PUNCT
cana-1008	369	1	according	accord	VERB
cana-1008	369	2	to	to	ADP
cana-1008	369	3	the	the	DET
cana-1008	369	4	classification	classification	NOUN
cana-1008	369	5	results	result	NOUN
cana-1008	369	6	that	that	PRON
cana-1008	369	7	were	be	AUX
cana-1008	369	8	obtained	obtain	VERB
cana-1008	369	9	,	,	PUNCT
cana-1008	369	10	table	table	NOUN
cana-1008	369	11	2	2	NUM
cana-1008	369	12	displays	display	VERB
cana-1008	369	13	the	the	DET
cana-1008	369	14	results	result	NOUN
cana-1008	369	15	of	of	ADP
cana-1008	369	16	70	70	NUM
cana-1008	369	17	%	%	NOUN
cana-1008	369	18	training	training	NOUN
cana-1008	369	19	and	and	CCONJ
cana-1008	369	20	30	30	NUM
cana-1008	369	21	%	%	NOUN
cana-1008	369	22	testing	testing	NOUN
cana-1008	369	23	.	.	PUNCT
cana-1008	370	1	on	on	ADP
cana-1008	370	2	the	the	DET
cana-1008	370	3	other	other	ADJ
cana-1008	370	4	hand	hand	NOUN
cana-1008	370	5	,	,	PUNCT
cana-1008	370	6	table	table	NOUN
cana-1008	370	7	3	3	NUM
cana-1008	370	8	displays	display	VERB
cana-1008	370	9	the	the	DET
cana-1008	370	10	results	result	NOUN
cana-1008	370	11	of	of	ADP
cana-1008	370	12	80	80	NUM
cana-1008	370	13	%	%	NOUN
cana-1008	370	14	training	training	NOUN
cana-1008	370	15	and	and	CCONJ
cana-1008	370	16	20	20	NUM
cana-1008	370	17	%	%	NOUN
cana-1008	370	18	testing	testing	NOUN
cana-1008	370	19	.	.	PUNCT
cana-1008	371	1	the	the	DET
cana-1008	371	2	training	training	NOUN
cana-1008	371	3	and	and	CCONJ
cana-1008	371	4	testing	testing	NOUN
cana-1008	371	5	split	split	NOUN
cana-1008	371	6	were	be	AUX
cana-1008	371	7	determined	determine	VERB
cana-1008	371	8	to	to	PART
cana-1008	371	9	be	be	AUX
cana-1008	371	10	thirty	thirty	NUM
cana-1008	371	11	percent	percent	NOUN
cana-1008	371	12	,	,	PUNCT
cana-1008	371	13	regardless	regardless	ADV
cana-1008	371	14	of	of	ADP
cana-1008	371	15	whether	whether	SCONJ
cana-1008	371	16	pca	pca	PROPN
cana-1008	371	17	was	be	AUX
cana-1008	371	18	used	use	VERB
cana-1008	371	19	or	or	CCONJ
cana-1008	371	20	not	not	PART
cana-1008	371	21	.	.	PUNCT
cana-1008	372	1	the	the	DET
cana-1008	372	2	assessment	assessment	NOUN
cana-1008	372	3	method	method	NOUN
cana-1008	372	4	utilizes	utilize	VERB
cana-1008	372	5	techniques	technique	NOUN
cana-1008	372	6	such	such	ADJ
cana-1008	372	7	as	as	ADP
cana-1008	372	8	svm	svm	ADJ
cana-1008	372	9	,	,	PUNCT
cana-1008	372	10	nb	nb	INTJ
cana-1008	372	11	,	,	PUNCT
cana-1008	372	12	rf	rf	ADJ
cana-1008	372	13	,	,	PUNCT
cana-1008	372	14	and	and	CCONJ
cana-1008	372	15	dt	dt	PROPN
cana-1008	372	16	.	.	PUNCT
cana-1008	372	17	accuracy	accuracy	NOUN
cana-1008	372	18	,	,	PUNCT
cana-1008	372	19	precision	precision	NOUN
cana-1008	372	20	,	,	PUNCT
cana-1008	372	21	recall	recall	NOUN
cana-1008	372	22	,	,	PUNCT
cana-1008	372	23	f1	f1	NOUN
cana-1008	372	24	-	-	PUNCT
cana-1008	372	25	score	score	NOUN
cana-1008	372	26	,	,	PUNCT
cana-1008	372	27	and	and	CCONJ
cana-1008	372	28	auc	auc	NOUN
cana-1008	372	29	are	be	AUX
cana-1008	372	30	measures	measure	NOUN
cana-1008	372	31	used	use	VERB
cana-1008	372	32	to	to	PART
cana-1008	372	33	assess	assess	VERB
cana-1008	372	34	the	the	DET
cana-1008	372	35	success	success	NOUN
cana-1008	372	36	of	of	ADP
cana-1008	372	37	any	any	DET
cana-1008	372	38	procedure	procedure	NOUN
cana-1008	372	39	.	.	PUNCT
cana-1008	373	1	the	the	DET
cana-1008	373	2	study	study	NOUN
cana-1008	373	3	of	of	ADP
cana-1008	373	4	table	table	NOUN
cana-1008	373	5	2	2	NUM
cana-1008	373	6	reveals	reveal	VERB
cana-1008	373	7	that	that	SCONJ
cana-1008	373	8	random	random	ADJ
cana-1008	373	9	forest	forest	NOUN
cana-1008	373	10	consistently	consistently	ADV
cana-1008	373	11	achieves	achieve	VERB
cana-1008	373	12	exceptional	exceptional	ADJ
cana-1008	373	13	performance	performance	NOUN
cana-1008	373	14	across	across	ADP
cana-1008	373	15	all	all	DET
cana-1008	373	16	metrics	metric	NOUN
cana-1008	373	17	,	,	PUNCT
cana-1008	373	18	including	include	VERB
cana-1008	373	19	accuracy	accuracy	NOUN
cana-1008	373	20	,	,	PUNCT
cana-1008	373	21	precision	precision	NOUN
cana-1008	373	22	,	,	PUNCT
cana-1008	373	23	recall	recall	NOUN
cana-1008	373	24	,	,	PUNCT
cana-1008	373	25	and	and	CCONJ
cana-1008	373	26	auc	auc	NOUN
cana-1008	373	27	,	,	PUNCT
cana-1008	373	28	independent	independent	ADJ
cana-1008	373	29	of	of	ADP
cana-1008	373	30	the	the	DET
cana-1008	373	31	use	use	NOUN
cana-1008	373	32	of	of	ADP
cana-1008	373	33	pca	pca	PROPN
cana-1008	373	34	.	.	PUNCT
cana-1008	374	1	svm	svm	PROPN
cana-1008	374	2	has	have	VERB
cana-1008	374	3	exceptional	exceptional	ADJ
cana-1008	374	4	performance	performance	NOUN
cana-1008	374	5	,	,	PUNCT
cana-1008	374	6	particularly	particularly	ADV
cana-1008	374	7	when	when	SCONJ
cana-1008	374	8	combined	combine	VERB
cana-1008	374	9	with	with	ADP
cana-1008	374	10	pca	pca	PROPN
cana-1008	374	11	,	,	PUNCT
cana-1008	374	12	resulting	result	VERB
cana-1008	374	13	in	in	ADP
cana-1008	374	14	outstanding	outstanding	ADJ
cana-1008	374	15	accuracy	accuracy	NOUN
cana-1008	374	16	communications	communication	NOUN
cana-1008	374	17	on	on	ADP
cana-1008	374	18	applied	apply	VERB
cana-1008	374	19	nonlinear	nonlinear	ADJ
cana-1008	374	20	analysis	analysis	NOUN
cana-1008	374	21	issn	issn	NOUN
cana-1008	374	22	:	:	PUNCT
cana-1008	374	23	1074	1074	NUM
cana-1008	374	24	-	-	PUNCT
cana-1008	374	25	133x	133x	NUM
cana-1008	374	26	vol	vol	NOUN
cana-1008	374	27	31	31	NUM
cana-1008	374	28	no	no	NOUN
cana-1008	374	29	.	.	PUNCT
cana-1008	375	1	5s	5s	NUM
cana-1008	375	2	(	(	PUNCT
cana-1008	375	3	2024	2024	NUM
cana-1008	375	4	)	)	PUNCT
cana-1008	375	5	153	153	NUM
cana-1008	375	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1008	375	7	and	and	CCONJ
cana-1008	375	8	auc	auc	NOUN
cana-1008	375	9	.	.	PUNCT
cana-1008	376	1	when	when	SCONJ
cana-1008	376	2	compared	compare	VERB
cana-1008	376	3	to	to	ADP
cana-1008	376	4	other	other	ADJ
cana-1008	376	5	algorithms	algorithm	NOUN
cana-1008	376	6	,	,	PUNCT
cana-1008	376	7	naive	naive	ADJ
cana-1008	376	8	bayes	bayes	NOUN
cana-1008	376	9	demonstrates	demonstrate	VERB
cana-1008	376	10	a	a	DET
cana-1008	376	11	somewhat	somewhat	ADV
cana-1008	376	12	stable	stable	ADJ
cana-1008	376	13	performance	performance	NOUN
cana-1008	376	14	,	,	PUNCT
cana-1008	376	15	while	while	SCONJ
cana-1008	376	16	decision	decision	NOUN
cana-1008	376	17	trees	tree	NOUN
cana-1008	376	18	tend	tend	VERB
cana-1008	376	19	to	to	PART
cana-1008	376	20	exhibit	exhibit	VERB
cana-1008	376	21	poorer	poor	ADJ
cana-1008	376	22	accuracy	accuracy	NOUN
cana-1008	376	23	and	and	CCONJ
cana-1008	376	24	auc	auc	NOUN
cana-1008	376	25	.	.	PUNCT
cana-1008	377	1	table	table	NOUN
cana-1008	377	2	2	2	NUM
cana-1008	377	3	:	:	PUNCT
cana-1008	377	4	all	all	DET
cana-1008	377	5	model	model	NOUN
cana-1008	377	6	performance	performance	NOUN
cana-1008	377	7	for	for	ADP
cana-1008	377	8	the	the	DET
cana-1008	377	9	70	70	NUM
cana-1008	377	10	%	%	NOUN
cana-1008	377	11	and	and	CCONJ
cana-1008	377	12	30	30	NUM
cana-1008	377	13	%	%	NOUN
cana-1008	377	14	of	of	ADP
cana-1008	377	15	training	training	NOUN
cana-1008	377	16	and	and	CCONJ
cana-1008	377	17	testing	testing	NOUN
cana-1008	377	18	ratio	ratio	NOUN
cana-1008	377	19	.	.	PUNCT
cana-1008	378	1	algorithm	algorithm	PROPN
cana-1008	378	2	accuracy	accuracy	PROPN
cana-1008	378	3	precision	precision	NOUN
cana-1008	378	4	recall	recall	VERB
cana-1008	378	5	f1	f1	NOUN
cana-1008	378	6	-	-	PUNCT
cana-1008	378	7	score	score	NOUN
cana-1008	378	8	auc	auc	NOUN
cana-1008	378	9	without	without	ADP
cana-1008	378	10	using	use	VERB
cana-1008	378	11	pca	pca	NOUN
cana-1008	378	12	svm	svm	NOUN
cana-1008	378	13	76.32	76.32	NUM
cana-1008	378	14	72.53	72.53	NUM
cana-1008	378	15	77.65	77.65	NUM
cana-1008	378	16	70.91	70.91	NUM
cana-1008	378	17	85.49	85.49	NUM
cana-1008	378	18	nb	nb	PROPN
cana-1008	378	19	75.42	75.42	NUM
cana-1008	378	20	71.76	71.76	NUM
cana-1008	378	21	78.16	78.16	NUM
cana-1008	378	22	75.55	75.55	NUM
cana-1008	378	23	87.63	87.63	NUM
cana-1008	378	24	rf	rf	NUM
cana-1008	378	25	82.42	82.42	NUM
cana-1008	378	26	85.33	85.33	NUM
cana-1008	378	27	74.62	74.62	NUM
cana-1008	378	28	77.38	77.38	NUM
cana-1008	378	29	79.32	79.32	NUM
cana-1008	378	30	dt	dt	PROPN
cana-1008	378	31	73.57	73.57	NUM
cana-1008	378	32	68.55	68.55	NUM
cana-1008	378	33	71.43	71.43	NUM
cana-1008	378	34	70.87	70.87	NUM
cana-1008	378	35	74.65	74.65	NUM
cana-1008	378	36	without	without	ADP
cana-1008	378	37	using	use	VERB
cana-1008	378	38	pca	pca	NOUN
cana-1008	378	39	svm	svm	NOUN
cana-1008	378	40	85.54	85.54	NUM
cana-1008	378	41	87.33	87.33	NUM
cana-1008	378	42	86.47	86.47	NUM
cana-1008	378	43	89.12	89.12	NUM
cana-1008	378	44	91.13	91.13	NUM
cana-1008	378	45	nb	nb	PROPN
cana-1008	378	46	84.54	84.54	NUM
cana-1008	378	47	88.55	88.55	NUM
cana-1008	378	48	86.42	86.42	NUM
cana-1008	378	49	84.76	84.76	NUM
cana-1008	378	50	88.55	88.55	NUM
cana-1008	378	51	rf	rf	VERB
cana-1008	378	52	88.76	88.76	NUM
cana-1008	378	53	87.33	87.33	NUM
cana-1008	378	54	89.13	89.13	NUM
cana-1008	378	55	90.22	90.22	NUM
cana-1008	378	56	92.43	92.43	NUM
cana-1008	378	57	dt	dt	NOUN
cana-1008	378	58	80.38	80.38	NUM
cana-1008	378	59	74.32	74.32	NUM
cana-1008	378	60	74.73	74.73	NUM
cana-1008	378	61	74.30	74.30	NUM
cana-1008	378	62	76.77	76.77	NUM
cana-1008	378	63	with	with	ADP
cana-1008	378	64	an	an	DET
cana-1008	378	65	allocation	allocation	NOUN
cana-1008	378	66	of	of	ADP
cana-1008	378	67	80	80	NUM
cana-1008	378	68	%	%	NOUN
cana-1008	378	69	for	for	ADP
cana-1008	378	70	training	training	NOUN
cana-1008	378	71	and	and	CCONJ
cana-1008	378	72	20	20	NUM
cana-1008	378	73	%	%	NOUN
cana-1008	378	74	for	for	ADP
cana-1008	378	75	testing	testing	NOUN
cana-1008	378	76	,	,	PUNCT
cana-1008	378	77	table	table	NOUN
cana-1008	378	78	3	3	NUM
cana-1008	378	79	shows	show	VERB
cana-1008	378	80	performance	performance	NOUN
cana-1008	378	81	metrics	metric	NOUN
cana-1008	378	82	that	that	PRON
cana-1008	378	83	are	be	AUX
cana-1008	378	84	equivalent	equivalent	ADJ
cana-1008	378	85	to	to	ADP
cana-1008	378	86	those	those	PRON
cana-1008	378	87	shown	show	VERB
cana-1008	378	88	in	in	ADP
cana-1008	378	89	table	table	NOUN
cana-1008	378	90	2	2	NUM
cana-1008	378	91	.	.	PUNCT
cana-1008	379	1	the	the	DET
cana-1008	379	2	svm	svm	PROPN
cana-1008	379	3	,	,	PUNCT
cana-1008	379	4	nb	nb	INTJ
cana-1008	379	5	,	,	PUNCT
cana-1008	379	6	rf	rf	ADJ
cana-1008	379	7	,	,	PUNCT
cana-1008	379	8	and	and	CCONJ
cana-1008	379	9	dt	dt	ADP
cana-1008	379	10	algorithms	algorithm	NOUN
cana-1008	379	11	are	be	AUX
cana-1008	379	12	evaluated	evaluate	VERB
cana-1008	379	13	whether	whether	SCONJ
cana-1008	379	14	or	or	CCONJ
cana-1008	379	15	not	not	PART
cana-1008	379	16	they	they	PRON
cana-1008	379	17	make	make	VERB
cana-1008	379	18	use	use	NOUN
cana-1008	379	19	of	of	ADP
cana-1008	379	20	principal	principal	ADJ
cana-1008	379	21	component	component	NOUN
cana-1008	379	22	analysis	analysis	NOUN
cana-1008	379	23	(	(	PUNCT
cana-1008	379	24	pca	pca	NOUN
cana-1008	379	25	)	)	PUNCT
cana-1008	379	26	.	.	PUNCT
cana-1008	380	1	upon	upon	SCONJ
cana-1008	380	2	closer	close	ADJ
cana-1008	380	3	inspection	inspection	NOUN
cana-1008	380	4	,	,	PUNCT
cana-1008	380	5	table	table	NOUN
cana-1008	380	6	3	3	NUM
cana-1008	380	7	exhibits	exhibit	VERB
cana-1008	380	8	patterns	pattern	NOUN
cana-1008	380	9	that	that	PRON
cana-1008	380	10	are	be	AUX
cana-1008	380	11	comparable	comparable	ADJ
cana-1008	380	12	to	to	ADP
cana-1008	380	13	those	those	PRON
cana-1008	380	14	seen	see	VERB
cana-1008	380	15	in	in	ADP
cana-1008	380	16	table	table	NOUN
cana-1008	380	17	2	2	NUM
cana-1008	380	18	,	,	PUNCT
cana-1008	380	19	which	which	PRON
cana-1008	380	20	indicates	indicate	VERB
cana-1008	380	21	that	that	SCONJ
cana-1008	380	22	random	random	ADJ
cana-1008	380	23	forest	forest	NOUN
cana-1008	380	24	consistently	consistently	ADV
cana-1008	380	25	achieves	achieve	VERB
cana-1008	380	26	good	good	ADJ
cana-1008	380	27	performance	performance	NOUN
cana-1008	380	28	across	across	ADP
cana-1008	380	29	all	all	DET
cana-1008	380	30	criteria	criterion	NOUN
cana-1008	380	31	.	.	PUNCT
cana-1008	381	1	performance	performance	NOUN
cana-1008	381	2	that	that	PRON
cana-1008	381	3	is	be	AUX
cana-1008	381	4	constantly	constantly	ADV
cana-1008	381	5	resilient	resilient	ADJ
cana-1008	381	6	is	be	AUX
cana-1008	381	7	shown	show	VERB
cana-1008	381	8	by	by	ADP
cana-1008	381	9	the	the	DET
cana-1008	381	10	support	support	NOUN
cana-1008	381	11	vector	vector	NOUN
cana-1008	381	12	machine	machine	NOUN
cana-1008	381	13	technique	technique	NOUN
cana-1008	381	14	,	,	PUNCT
cana-1008	381	15	especially	especially	ADV
cana-1008	381	16	when	when	SCONJ
cana-1008	381	17	it	it	PRON
cana-1008	381	18	is	be	AUX
cana-1008	381	19	used	use	VERB
cana-1008	381	20	in	in	ADP
cana-1008	381	21	conjunction	conjunction	NOUN
cana-1008	381	22	with	with	ADP
cana-1008	381	23	principal	principal	ADJ
cana-1008	381	24	component	component	NOUN
cana-1008	381	25	analysis	analysis	NOUN
cana-1008	381	26	(	(	PUNCT
cana-1008	381	27	pca	pca	NOUN
cana-1008	381	28	)	)	PUNCT
cana-1008	381	29	.	.	PUNCT
cana-1008	382	1	decision	decision	NOUN
cana-1008	382	2	trees	tree	NOUN
cana-1008	382	3	have	have	VERB
cana-1008	382	4	a	a	DET
cana-1008	382	5	little	little	ADJ
cana-1008	382	6	lower	low	ADJ
cana-1008	382	7	accuracy	accuracy	NOUN
cana-1008	382	8	and	and	CCONJ
cana-1008	382	9	area	area	NOUN
cana-1008	382	10	under	under	ADP
cana-1008	382	11	the	the	DET
cana-1008	382	12	curve	curve	NOUN
cana-1008	382	13	(	(	PUNCT
cana-1008	382	14	auc	auc	NOUN
cana-1008	382	15	)	)	PUNCT
cana-1008	382	16	,	,	PUNCT
cana-1008	382	17	but	but	CCONJ
cana-1008	382	18	naive	naive	ADJ
cana-1008	382	19	bayes	bayes	NOUN
cana-1008	382	20	continually	continually	ADV
cana-1008	382	21	maintains	maintain	VERB
cana-1008	382	22	a	a	DET
cana-1008	382	23	high	high	ADJ
cana-1008	382	24	level	level	NOUN
cana-1008	382	25	of	of	ADP
cana-1008	382	26	performance	performance	NOUN
cana-1008	382	27	.	.	PUNCT
cana-1008	383	1	when	when	SCONJ
cana-1008	383	2	the	the	DET
cana-1008	383	3	data	datum	NOUN
cana-1008	383	4	produced	produce	VERB
cana-1008	383	5	from	from	ADP
cana-1008	383	6	both	both	DET
cana-1008	383	7	tables	table	NOUN
cana-1008	383	8	are	be	AUX
cana-1008	383	9	compared	compare	VERB
cana-1008	383	10	,	,	PUNCT
cana-1008	383	11	it	it	PRON
cana-1008	383	12	is	be	AUX
cana-1008	383	13	evident	evident	ADJ
cana-1008	383	14	that	that	SCONJ
cana-1008	383	15	the	the	DET
cana-1008	383	16	random	random	ADJ
cana-1008	383	17	forest	forest	NOUN
cana-1008	383	18	method	method	NOUN
cana-1008	383	19	constantly	constantly	ADV
cana-1008	383	20	produces	produce	VERB
cana-1008	383	21	greater	great	ADJ
cana-1008	383	22	performance	performance	NOUN
cana-1008	383	23	when	when	SCONJ
cana-1008	383	24	compared	compare	VERB
cana-1008	383	25	to	to	ADP
cana-1008	383	26	other	other	ADJ
cana-1008	383	27	algorithms	algorithm	NOUN
cana-1008	383	28	.	.	PUNCT
cana-1008	384	1	this	this	PRON
cana-1008	384	2	is	be	AUX
cana-1008	384	3	the	the	DET
cana-1008	384	4	case	case	NOUN
cana-1008	384	5	regardless	regardless	ADV
cana-1008	384	6	of	of	ADP
cana-1008	384	7	the	the	DET
cana-1008	384	8	training	training	NOUN
cana-1008	384	9	and	and	CCONJ
cana-1008	384	10	testing	testing	NOUN
cana-1008	384	11	ratios	ratio	NOUN
cana-1008	384	12	that	that	PRON
cana-1008	384	13	are	be	AUX
cana-1008	384	14	used	use	VERB
cana-1008	384	15	.	.	PUNCT
cana-1008	385	1	it	it	PRON
cana-1008	385	2	is	be	AUX
cana-1008	385	3	clear	clear	ADJ
cana-1008	385	4	that	that	SCONJ
cana-1008	385	5	lowering	lower	VERB
cana-1008	385	6	the	the	DET
cana-1008	385	7	number	number	NOUN
cana-1008	385	8	of	of	ADP
cana-1008	385	9	dimensions	dimension	NOUN
cana-1008	385	10	is	be	AUX
cana-1008	385	11	an	an	DET
cana-1008	385	12	effective	effective	ADJ
cana-1008	385	13	method	method	NOUN
cana-1008	385	14	for	for	ADP
cana-1008	385	15	enhancing	enhance	VERB
cana-1008	385	16	classification	classification	NOUN
cana-1008	385	17	abilities	ability	NOUN
cana-1008	385	18	,	,	PUNCT
cana-1008	385	19	as	as	SCONJ
cana-1008	385	20	shown	show	VERB
cana-1008	385	21	by	by	ADP
cana-1008	385	22	the	the	DET
cana-1008	385	23	strong	strong	ADJ
cana-1008	385	24	performance	performance	NOUN
cana-1008	385	25	of	of	ADP
cana-1008	385	26	the	the	DET
cana-1008	385	27	support	support	NOUN
cana-1008	385	28	vector	vector	NOUN
cana-1008	385	29	machine	machine	NOUN
cana-1008	385	30	algorithm	algorithm	NOUN
cana-1008	385	31	,	,	PUNCT
cana-1008	385	32	particularly	particularly	ADV
cana-1008	385	33	when	when	SCONJ
cana-1008	385	34	principal	principal	ADJ
cana-1008	385	35	component	component	NOUN
cana-1008	385	36	analysis	analysis	NOUN
cana-1008	385	37	(	(	PUNCT
cana-1008	385	38	pca	pca	NOUN
cana-1008	385	39	)	)	PUNCT
cana-1008	385	40	is	be	AUX
cana-1008	385	41	used	use	VERB
cana-1008	385	42	.	.	PUNCT
cana-1008	386	1	while	while	SCONJ
cana-1008	386	2	decision	decision	NOUN
cana-1008	386	3	trees	tree	NOUN
cana-1008	386	4	have	have	VERB
cana-1008	386	5	intermediate	intermediate	ADJ
cana-1008	386	6	performance	performance	NOUN
cana-1008	386	7	,	,	PUNCT
cana-1008	386	8	they	they	PRON
cana-1008	386	9	often	often	ADV
cana-1008	386	10	have	have	VERB
cana-1008	386	11	lower	low	ADJ
cana-1008	386	12	accuracy	accuracy	NOUN
cana-1008	386	13	and	and	CCONJ
cana-1008	386	14	area	area	NOUN
cana-1008	386	15	under	under	ADP
cana-1008	386	16	the	the	DET
cana-1008	386	17	curve	curve	NOUN
cana-1008	386	18	(	(	PUNCT
cana-1008	386	19	auc	auc	NOUN
cana-1008	386	20	)	)	PUNCT
cana-1008	386	21	,	,	PUNCT
cana-1008	386	22	naive	naive	ADJ
cana-1008	386	23	bayes	bayes	NOUN
cana-1008	386	24	is	be	AUX
cana-1008	386	25	reliable	reliable	ADJ
cana-1008	386	26	and	and	CCONJ
cana-1008	386	27	reliable	reliable	ADJ
cana-1008	386	28	all	all	DET
cana-1008	386	29	the	the	DET
cana-1008	386	30	time	time	NOUN
cana-1008	386	31	,	,	PUNCT
cana-1008	386	32	and	and	CCONJ
cana-1008	386	33	its	its	PRON
cana-1008	386	34	performance	performance	NOUN
cana-1008	386	35	is	be	AUX
cana-1008	386	36	constant	constant	ADJ
cana-1008	386	37	.	.	PUNCT
cana-1008	387	1	the	the	DET
cana-1008	387	2	purpose	purpose	NOUN
cana-1008	387	3	of	of	ADP
cana-1008	387	4	these	these	DET
cana-1008	387	5	insights	insight	NOUN
cana-1008	387	6	is	be	AUX
cana-1008	387	7	to	to	PART
cana-1008	387	8	give	give	VERB
cana-1008	387	9	useful	useful	ADJ
cana-1008	387	10	guidance	guidance	NOUN
cana-1008	387	11	in	in	ADP
cana-1008	387	12	selecting	select	VERB
cana-1008	387	13	the	the	DET
cana-1008	387	14	best	well	ADV
cana-1008	387	15	appropriate	appropriate	ADJ
cana-1008	387	16	machine	machine	NOUN
cana-1008	387	17	learning	learn	VERB
cana-1008	387	18	algorithm	algorithm	NOUN
cana-1008	387	19	based	base	VERB
cana-1008	387	20	on	on	ADP
cana-1008	387	21	the	the	DET
cana-1008	387	22	specific	specific	ADJ
cana-1008	387	23	aims	aim	NOUN
cana-1008	387	24	and	and	CCONJ
cana-1008	387	25	features	feature	NOUN
cana-1008	387	26	of	of	ADP
cana-1008	387	27	the	the	DET
cana-1008	387	28	dataset	dataset	NOUN
cana-1008	387	29	.	.	PUNCT
cana-1008	388	1	in	in	ADP
cana-1008	388	2	addition	addition	NOUN
cana-1008	388	3	to	to	ADP
cana-1008	388	4	taking	take	VERB
cana-1008	388	5	into	into	ADP
cana-1008	388	6	consideration	consideration	NOUN
cana-1008	388	7	the	the	DET
cana-1008	388	8	potential	potential	ADJ
cana-1008	388	9	impact	impact	NOUN
cana-1008	388	10	of	of	ADP
cana-1008	388	11	pca	pca	NOUN
cana-1008	388	12	,	,	PUNCT
cana-1008	388	13	they	they	PRON
cana-1008	388	14	take	take	VERB
cana-1008	388	15	into	into	ADP
cana-1008	388	16	account	account	NOUN
cana-1008	388	17	a	a	DET
cana-1008	388	18	variety	variety	NOUN
cana-1008	388	19	of	of	ADP
cana-1008	388	20	training	training	NOUN
cana-1008	388	21	and	and	CCONJ
cana-1008	388	22	testing	testing	NOUN
cana-1008	388	23	ratios	ratio	NOUN
cana-1008	388	24	.	.	PUNCT
cana-1008	389	1	table	table	NOUN
cana-1008	389	2	3	3	NUM
cana-1008	389	3	:	:	PUNCT
cana-1008	389	4	all	all	DET
cana-1008	389	5	model	model	NOUN
cana-1008	389	6	performance	performance	NOUN
cana-1008	389	7	for	for	ADP
cana-1008	389	8	the	the	DET
cana-1008	389	9	80	80	NUM
cana-1008	389	10	%	%	NOUN
cana-1008	389	11	and	and	CCONJ
cana-1008	389	12	20	20	NUM
cana-1008	389	13	%	%	NOUN
cana-1008	389	14	of	of	ADP
cana-1008	389	15	training	training	NOUN
cana-1008	389	16	and	and	CCONJ
cana-1008	389	17	testing	testing	NOUN
cana-1008	389	18	ratio	ratio	NOUN
cana-1008	389	19	.	.	PUNCT
cana-1008	390	1	algorithm	algorithm	PROPN
cana-1008	390	2	accuracy	accuracy	PROPN
cana-1008	390	3	precision	precision	NOUN
cana-1008	390	4	recall	recall	VERB
cana-1008	390	5	f1	f1	NOUN
cana-1008	390	6	-	-	PUNCT
cana-1008	390	7	score	score	NOUN
cana-1008	390	8	auc	auc	NOUN
cana-1008	390	9	without	without	ADP
cana-1008	390	10	using	use	VERB
cana-1008	390	11	pca	pca	NOUN
cana-1008	390	12	svm	svm	NOUN
cana-1008	390	13	79.02	79.02	NUM
cana-1008	390	14	74.43	74.43	NUM
cana-1008	390	15	78.03	78.03	NUM
cana-1008	390	16	71.33	71.33	NUM
cana-1008	390	17	87.22	87.22	NUM
cana-1008	390	18	nb	nb	PROPN
cana-1008	390	19	78.18	78.18	NUM
cana-1008	390	20	73.43	73.43	NUM
cana-1008	390	21	78.92	78.92	NUM
cana-1008	390	22	76.53	76.53	NUM
cana-1008	390	23	87.87	87.87	NUM
cana-1008	390	24	rf	rf	NUM
cana-1008	390	25	83.65	83.65	NUM
cana-1008	390	26	86.98	86.98	NUM
cana-1008	390	27	75.65	75.65	NUM
cana-1008	390	28	80.02	80.02	NUM
cana-1008	390	29	77.94	77.94	NUM
cana-1008	390	30	dt	dt	X
cana-1008	390	31	72.55	72.55	NUM
cana-1008	390	32	71.12	71.12	NUM
cana-1008	390	33	73.04	73.04	NUM
cana-1008	390	34	72.01	72.01	NUM
cana-1008	390	35	80.82	80.82	NUM
cana-1008	390	36	without	without	ADP
cana-1008	390	37	using	use	VERB
cana-1008	390	38	pca	pca	NOUN
cana-1008	390	39	svm	svm	NOUN
cana-1008	390	40	86.08	86.08	NUM
cana-1008	390	41	88.88	88.88	NUM
cana-1008	390	42	86.90	86.90	NUM
cana-1008	390	43	88.65	88.65	NUM
cana-1008	390	44	92.91	92.91	NUM
cana-1008	390	45	communications	communication	NOUN
cana-1008	390	46	on	on	ADP
cana-1008	390	47	applied	apply	VERB
cana-1008	390	48	nonlinear	nonlinear	ADJ
cana-1008	390	49	analysis	analysis	NOUN
cana-1008	390	50	issn	issn	NOUN
cana-1008	390	51	:	:	PUNCT
cana-1008	390	52	1074	1074	NUM
cana-1008	390	53	-	-	PUNCT
cana-1008	390	54	133x	133x	NUM
cana-1008	390	55	vol	vol	NOUN
cana-1008	390	56	31	31	NUM
cana-1008	390	57	no	no	NOUN
cana-1008	390	58	.	.	PUNCT
cana-1008	391	1	5s	5s	NUM
cana-1008	391	2	(	(	PUNCT
cana-1008	391	3	2024	2024	NUM
cana-1008	391	4	)	)	PUNCT
cana-1008	391	5	154	154	NUM
cana-1008	391	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1008	391	7	nb	nb	NOUN
cana-1008	391	8	89.54	89.54	NUM
cana-1008	391	9	88.23	88.23	NUM
cana-1008	391	10	85.93	85.93	NUM
cana-1008	391	11	85.83	85.83	NUM
cana-1008	391	12	92.33	92.33	NUM
cana-1008	391	13	rf	rf	NUM
cana-1008	391	14	89.86	89.86	NUM
cana-1008	391	15	89.18	89.18	NUM
cana-1008	391	16	89.77	89.77	NUM
cana-1008	391	17	89.91	89.91	NUM
cana-1008	391	18	93.72	93.72	NUM
cana-1008	391	19	dt	dt	NOUN
cana-1008	391	20	82.02	82.02	NUM
cana-1008	391	21	84.65	84.65	NUM
cana-1008	391	22	73.91	73.91	NUM
cana-1008	391	23	73.92	73.92	NUM
cana-1008	391	24	87.55	87.55	NUM
cana-1008	391	25	the	the	DET
cana-1008	391	26	current	current	ADJ
cana-1008	391	27	analysis	analysis	NOUN
cana-1008	391	28	evaluated	evaluate	VERB
cana-1008	391	29	the	the	DET
cana-1008	391	30	accuracy	accuracy	NOUN
cana-1008	391	31	of	of	ADP
cana-1008	391	32	the	the	DET
cana-1008	391	33	machine	machine	NOUN
cana-1008	391	34	learning	learning	NOUN
cana-1008	391	35	technique	technique	NOUN
cana-1008	391	36	with	with	ADP
cana-1008	391	37	the	the	DET
cana-1008	391	38	accuracy	accuracy	NOUN
cana-1008	391	39	of	of	ADP
cana-1008	391	40	methods	method	NOUN
cana-1008	391	41	utilized	utilize	VERB
cana-1008	391	42	in	in	ADP
cana-1008	391	43	three	three	NUM
cana-1008	391	44	prior	prior	ADJ
cana-1008	391	45	investigations	investigation	NOUN
cana-1008	391	46	,	,	PUNCT
cana-1008	391	47	each	each	PRON
cana-1008	391	48	using	use	VERB
cana-1008	391	49	a	a	DET
cana-1008	391	50	different	different	ADJ
cana-1008	391	51	methodology	methodology	NOUN
cana-1008	391	52	.	.	PUNCT
cana-1008	392	1	the	the	DET
cana-1008	392	2	results	result	NOUN
cana-1008	392	3	are	be	AUX
cana-1008	392	4	shown	show	VERB
cana-1008	392	5	in	in	ADP
cana-1008	392	6	table	table	NOUN
cana-1008	392	7	4	4	NUM
cana-1008	392	8	.	.	PUNCT
cana-1008	393	1	the	the	DET
cana-1008	393	2	reference	reference	NOUN
cana-1008	393	3	articles	article	NOUN
cana-1008	393	4	[	[	X
cana-1008	393	5	40	40	NUM
cana-1008	393	6	,	,	PUNCT
cana-1008	393	7	41	41	NUM
cana-1008	393	8	]	]	PUNCT
cana-1008	393	9	and	and	CCONJ
cana-1008	393	10	[	[	X
cana-1008	393	11	42	42	NUM
cana-1008	393	12	]	]	PUNCT
cana-1008	393	13	used	use	VERB
cana-1008	393	14	the	the	DET
cana-1008	393	15	levenberg	levenberg	PROPN
cana-1008	393	16	-	-	PUNCT
cana-1008	393	17	marquardt	marquardt	PROPN
cana-1008	393	18	methodology	methodology	PROPN
cana-1008	393	19	,	,	PUNCT
cana-1008	393	20	the	the	DET
cana-1008	393	21	genetic	genetic	ADJ
cana-1008	393	22	algorithm	algorithm	NOUN
cana-1008	393	23	with	with	ADP
cana-1008	393	24	radial	radial	ADJ
cana-1008	393	25	basis	basis	NOUN
cana-1008	393	26	function	function	NOUN
cana-1008	393	27	neural	neural	ADJ
cana-1008	393	28	network	network	NOUN
cana-1008	393	29	(	(	PUNCT
cana-1008	393	30	ga_rbf	ga_rbf	PROPN
cana-1008	393	31	nn	nn	PROPN
cana-1008	393	32	)	)	PUNCT
cana-1008	393	33	,	,	PUNCT
cana-1008	393	34	and	and	CCONJ
cana-1008	393	35	the	the	DET
cana-1008	393	36	modified	modify	VERB
cana-1008	393	37	particle	particle	NOUN
cana-1008	393	38	swarm	swarm	NOUN
cana-1008	393	39	optimization	optimization	NOUN
cana-1008	393	40	neural	neural	ADJ
cana-1008	393	41	network	network	NOUN
cana-1008	393	42	(	(	PUNCT
cana-1008	393	43	mpso	mpso	NOUN
cana-1008	393	44	-	-	PUNCT
cana-1008	393	45	nn	nn	NOUN
cana-1008	393	46	)	)	PUNCT
cana-1008	393	47	approaches	approach	NOUN
cana-1008	393	48	,	,	PUNCT
cana-1008	393	49	respectively	respectively	ADV
cana-1008	393	50	.	.	PUNCT
cana-1008	393	51	table	table	NOUN
cana-1008	393	52	4	4	NUM
cana-1008	393	53	:	:	PUNCT
cana-1008	393	54	comparison	comparison	NOUN
cana-1008	393	55	between	between	ADP
cana-1008	393	56	this	this	DET
cana-1008	393	57	study	study	NOUN
cana-1008	393	58	and	and	CCONJ
cana-1008	393	59	previous	previous	ADJ
cana-1008	393	60	studies	study	NOUN
cana-1008	393	61	the	the	DET
cana-1008	393	62	research	research	NOUN
cana-1008	393	63	used	use	VERB
cana-1008	393	64	the	the	DET
cana-1008	393	65	random	random	ADJ
cana-1008	393	66	forest	forest	NOUN
cana-1008	393	67	(	(	PUNCT
cana-1008	393	68	rf	rf	NOUN
cana-1008	393	69	)	)	PUNCT
cana-1008	393	70	classification	classification	NOUN
cana-1008	393	71	algorithm	algorithm	NOUN
cana-1008	393	72	,	,	PUNCT
cana-1008	393	73	which	which	PRON
cana-1008	393	74	differs	differ	VERB
cana-1008	393	75	from	from	ADP
cana-1008	393	76	the	the	DET
cana-1008	393	77	approach	approach	NOUN
cana-1008	393	78	utilized	utilize	VERB
cana-1008	393	79	in	in	ADP
cana-1008	393	80	the	the	DET
cana-1008	393	81	prior	prior	ADJ
cana-1008	393	82	study	study	NOUN
cana-1008	393	83	.	.	PUNCT
cana-1008	394	1	the	the	DET
cana-1008	394	2	accuracy	accuracy	NOUN
cana-1008	394	3	results	result	NOUN
cana-1008	394	4	indicate	indicate	VERB
cana-1008	394	5	that	that	SCONJ
cana-1008	394	6	the	the	DET
cana-1008	394	7	rf	rf	ADJ
cana-1008	394	8	strategy	strategy	NOUN
cana-1008	394	9	used	use	VERB
cana-1008	394	10	in	in	ADP
cana-1008	394	11	the	the	DET
cana-1008	394	12	current	current	ADJ
cana-1008	394	13	study	study	NOUN
cana-1008	394	14	achieved	achieve	VERB
cana-1008	394	15	a	a	DET
cana-1008	394	16	much	much	ADV
cana-1008	394	17	higher	high	ADJ
cana-1008	394	18	accuracy	accuracy	NOUN
cana-1008	394	19	rate	rate	NOUN
cana-1008	394	20	of	of	ADP
cana-1008	394	21	89.86	89.86	NUM
cana-1008	394	22	%	%	NOUN
cana-1008	394	23	compared	compare	VERB
cana-1008	394	24	to	to	ADP
cana-1008	394	25	the	the	DET
cana-1008	394	26	methodology	methodology	NOUN
cana-1008	394	27	used	use	VERB
cana-1008	394	28	in	in	ADP
cana-1008	394	29	previous	previous	ADJ
cana-1008	394	30	investigations	investigation	NOUN
cana-1008	394	31	.	.	PUNCT
cana-1008	395	1	the	the	DET
cana-1008	395	2	reported	report	VERB
cana-1008	395	3	accuracy	accuracy	NOUN
cana-1008	395	4	in	in	ADP
cana-1008	395	5	this	this	DET
cana-1008	395	6	study	study	NOUN
cana-1008	395	7	surpassed	surpass	VERB
cana-1008	395	8	that	that	PRON
cana-1008	395	9	of	of	ADP
cana-1008	395	10	the	the	DET
cana-1008	395	11	levenberg	levenberg	PROPN
cana-1008	395	12	-	-	PUNCT
cana-1008	395	13	marquardt	marquardt	PROPN
cana-1008	395	14	method	method	NOUN
cana-1008	395	15	82	82	NUM
cana-1008	395	16	%	%	NOUN
cana-1008	395	17	accuracy	accuracy	NOUN
cana-1008	395	18	in	in	ADP
cana-1008	395	19	[	[	X
cana-1008	395	20	40	40	NUM
cana-1008	395	21	]	]	PUNCT
cana-1008	395	22	,	,	PUNCT
cana-1008	395	23	the	the	DET
cana-1008	395	24	ga_rbf	ga_rbf	PROPN
cana-1008	395	25	nn	nn	PROPN
cana-1008	395	26	approach	approach	NOUN
cana-1008	395	27	77.4	77.4	NUM
cana-1008	395	28	%	%	NOUN
cana-1008	395	29	accuracy	accuracy	NOUN
cana-1008	395	30	in	in	ADP
cana-1008	395	31	[	[	X
cana-1008	395	32	41	41	NUM
cana-1008	395	33	]	]	PUNCT
cana-1008	395	34	,	,	PUNCT
cana-1008	395	35	and	and	CCONJ
cana-1008	395	36	the	the	DET
cana-1008	395	37	mpso	mpso	PROPN
cana-1008	395	38	-	-	PUNCT
cana-1008	395	39	nn	nn	NOUN
cana-1008	395	40	methodology	methodology	NOUN
cana-1008	395	41	81.8	81.8	NUM
cana-1008	395	42	%	%	NOUN
cana-1008	395	43	accuracy	accuracy	NOUN
cana-1008	395	44	in	in	ADP
cana-1008	395	45	[	[	X
cana-1008	395	46	42	42	NUM
cana-1008	395	47	]	]	PUNCT
cana-1008	395	48	.	.	PUNCT
cana-1008	396	1	5	5	X
cana-1008	396	2	.	.	X
cana-1008	396	3	conclusion	conclusion	VERB
cana-1008	396	4	the	the	DET
cana-1008	396	5	primary	primary	ADJ
cana-1008	396	6	objective	objective	NOUN
cana-1008	396	7	of	of	ADP
cana-1008	396	8	our	our	PRON
cana-1008	396	9	work	work	NOUN
cana-1008	396	10	was	be	AUX
cana-1008	396	11	to	to	PART
cana-1008	396	12	construct	construct	VERB
cana-1008	396	13	appropriate	appropriate	ADJ
cana-1008	396	14	categorization	categorization	NOUN
cana-1008	396	15	models	model	NOUN
cana-1008	396	16	to	to	PART
cana-1008	396	17	assist	assist	VERB
cana-1008	396	18	in	in	ADP
cana-1008	396	19	the	the	DET
cana-1008	396	20	timely	timely	ADJ
cana-1008	396	21	identification	identification	NOUN
cana-1008	396	22	of	of	ADP
cana-1008	396	23	diabetes	diabetes	NOUN
cana-1008	396	24	.	.	PUNCT
cana-1008	397	1	although	although	SCONJ
cana-1008	397	2	the	the	DET
cana-1008	397	3	pima	pima	PROPN
cana-1008	397	4	indian	indian	PROPN
cana-1008	397	5	diabetes	diabetes	NOUN
cana-1008	397	6	dataset	dataset	NOUN
cana-1008	397	7	included	include	VERB
cana-1008	397	8	several	several	ADJ
cana-1008	397	9	missing	miss	VERB
cana-1008	397	10	variables	variable	NOUN
cana-1008	397	11	,	,	PUNCT
cana-1008	397	12	we	we	PRON
cana-1008	397	13	successfully	successfully	ADV
cana-1008	397	14	addressed	address	VERB
cana-1008	397	15	these	these	DET
cana-1008	397	16	problems	problem	NOUN
cana-1008	397	17	.	.	PUNCT
cana-1008	398	1	pca	pca	PROPN
cana-1008	398	2	was	be	AUX
cana-1008	398	3	used	use	VERB
cana-1008	398	4	to	to	PART
cana-1008	398	5	select	select	VERB
cana-1008	398	6	features	feature	NOUN
cana-1008	398	7	and	and	CCONJ
cana-1008	398	8	eliminate	eliminate	VERB
cana-1008	398	9	outliers	outlier	NOUN
cana-1008	398	10	in	in	ADP
cana-1008	398	11	our	our	PRON
cana-1008	398	12	data	data	NOUN
cana-1008	398	13	processing	processing	NOUN
cana-1008	398	14	procedures	procedure	NOUN
cana-1008	398	15	,	,	PUNCT
cana-1008	398	16	resulting	result	VERB
cana-1008	398	17	in	in	ADP
cana-1008	398	18	improved	improved	ADJ
cana-1008	398	19	models	model	NOUN
cana-1008	398	20	.	.	PUNCT
cana-1008	399	1	by	by	ADP
cana-1008	399	2	combining	combine	VERB
cana-1008	399	3	these	these	DET
cana-1008	399	4	techniques	technique	NOUN
cana-1008	399	5	with	with	ADP
cana-1008	399	6	classifiers	classifier	NOUN
cana-1008	399	7	such	such	ADJ
cana-1008	399	8	as	as	ADP
cana-1008	399	9	support	support	NOUN
cana-1008	399	10	vector	vector	NOUN
cana-1008	399	11	machine	machine	NOUN
cana-1008	399	12	,	,	PUNCT
cana-1008	399	13	random	random	ADJ
cana-1008	399	14	forest	forest	NOUN
cana-1008	399	15	,	,	PUNCT
cana-1008	399	16	naïve	naïve	ADJ
cana-1008	399	17	bayes	bayes	NOUN
cana-1008	399	18	,	,	PUNCT
cana-1008	399	19	and	and	CCONJ
cana-1008	399	20	decision	decision	NOUN
cana-1008	399	21	tree	tree	NOUN
cana-1008	399	22	,	,	PUNCT
cana-1008	399	23	we	we	PRON
cana-1008	399	24	attained	attain	VERB
cana-1008	399	25	a	a	DET
cana-1008	399	26	remarkable	remarkable	ADJ
cana-1008	399	27	accuracy	accuracy	NOUN
cana-1008	399	28	rate	rate	NOUN
cana-1008	399	29	of	of	ADP
cana-1008	399	30	89.86	89.86	NUM
cana-1008	399	31	%	%	NOUN
cana-1008	399	32	.	.	PUNCT
cana-1008	400	1	the	the	DET
cana-1008	400	2	training	training	NOUN
cana-1008	400	3	phase	phase	NOUN
cana-1008	400	4	was	be	AUX
cana-1008	400	5	deemed	deem	VERB
cana-1008	400	6	effective	effective	ADJ
cana-1008	400	7	as	as	SCONJ
cana-1008	400	8	it	it	PRON
cana-1008	400	9	used	use	VERB
cana-1008	400	10	80	80	NUM
cana-1008	400	11	%	%	NOUN
cana-1008	400	12	of	of	ADP
cana-1008	400	13	the	the	DET
cana-1008	400	14	dataset	dataset	NOUN
cana-1008	400	15	.	.	PUNCT
cana-1008	401	1	this	this	PRON
cana-1008	401	2	unequivocally	unequivocally	ADV
cana-1008	401	3	demonstrates	demonstrate	VERB
cana-1008	401	4	that	that	SCONJ
cana-1008	401	5	our	our	PRON
cana-1008	401	6	methodology	methodology	NOUN
cana-1008	401	7	reliably	reliably	ADV
cana-1008	401	8	identifies	identify	VERB
cana-1008	401	9	instances	instance	NOUN
cana-1008	401	10	of	of	ADP
cana-1008	401	11	diabetes	diabetes	NOUN
cana-1008	401	12	.	.	PUNCT
cana-1008	402	1	enhancing	enhance	VERB
cana-1008	402	2	the	the	DET
cana-1008	402	3	diagnosis	diagnosis	NOUN
cana-1008	402	4	of	of	ADP
cana-1008	402	5	diabetes	diabetes	NOUN
cana-1008	402	6	is	be	AUX
cana-1008	402	7	essential	essential	ADJ
cana-1008	402	8	for	for	ADP
cana-1008	402	9	improving	improve	VERB
cana-1008	402	10	disease	disease	NOUN
cana-1008	402	11	management	management	NOUN
cana-1008	402	12	and	and	CCONJ
cana-1008	402	13	patient	patient	ADJ
cana-1008	402	14	outcomes	outcome	NOUN
cana-1008	402	15	.	.	PUNCT
cana-1008	403	1	our	our	PRON
cana-1008	403	2	research	research	NOUN
cana-1008	403	3	demonstrates	demonstrate	VERB
cana-1008	403	4	that	that	SCONJ
cana-1008	403	5	the	the	DET
cana-1008	403	6	use	use	NOUN
cana-1008	403	7	of	of	ADP
cana-1008	403	8	sophisticated	sophisticated	ADJ
cana-1008	403	9	machine	machine	NOUN
cana-1008	403	10	learning	learning	NOUN
cana-1008	403	11	techniques	technique	NOUN
cana-1008	403	12	is	be	AUX
cana-1008	403	13	necessary	necessary	ADJ
cana-1008	403	14	to	to	PART
cana-1008	403	15	achieve	achieve	VERB
cana-1008	403	16	this	this	DET
cana-1008	403	17	objective	objective	NOUN
cana-1008	403	18	.	.	PUNCT
cana-1008	404	1	references	reference	NOUN
cana-1008	404	2	[	[	X
cana-1008	404	3	1	1	NUM
cana-1008	404	4	]	]	PUNCT
cana-1008	404	5	a.	a.	PROPN
cana-1008	404	6	salih	salih	PROPN
cana-1008	404	7	,	,	PUNCT
cana-1008	404	8	s.	s.	PROPN
cana-1008	404	9	t.	t.	PROPN
cana-1008	404	10	zeebaree	zeebaree	PROPN
cana-1008	404	11	,	,	PUNCT
cana-1008	404	12	s.	s.	PROPN
cana-1008	404	13	ameen	ameen	PROPN
cana-1008	404	14	,	,	PUNCT
cana-1008	404	15	a.	a.	NOUN
cana-1008	404	16	alkhyyat	alkhyyat	ADJ
cana-1008	404	17	,	,	PUNCT
cana-1008	404	18	and	and	CCONJ
cana-1008	404	19	h.	h.	PROPN
cana-1008	404	20	m.	m.	PROPN
cana-1008	404	21	shukur	shukur	PROPN
cana-1008	404	22	,	,	PUNCT
cana-1008	404	23	"	"	PUNCT
cana-1008	404	24	a	a	DET
cana-1008	404	25	survey	survey	NOUN
cana-1008	404	26	on	on	ADP
cana-1008	404	27	the	the	DET
cana-1008	404	28	role	role	NOUN
cana-1008	404	29	of	of	ADP
cana-1008	404	30	artificial	artificial	ADJ
cana-1008	404	31	intelligence	intelligence	NOUN
cana-1008	404	32	,	,	PUNCT
cana-1008	404	33	machine	machine	NOUN
cana-1008	404	34	learning	learning	NOUN
cana-1008	404	35	and	and	CCONJ
cana-1008	404	36	deep	deep	ADJ
cana-1008	404	37	learning	learning	NOUN
cana-1008	404	38	for	for	ADP
cana-1008	404	39	cybersecurity	cybersecurity	NOUN
cana-1008	404	40	attack	attack	NOUN
cana-1008	404	41	detection	detection	NOUN
cana-1008	404	42	,	,	PUNCT
cana-1008	404	43	"	"	PUNCT
cana-1008	404	44	in	in	ADP
cana-1008	404	45	2021	2021	NUM
cana-1008	404	46	7th	7th	ADJ
cana-1008	404	47	international	international	ADJ
cana-1008	404	48	engineering	engineering	NOUN
cana-1008	404	49	conference	conference	NOUN
cana-1008	404	50	“	"	PUNCT
cana-1008	404	51	research	research	NOUN
cana-1008	404	52	&	&	CCONJ
cana-1008	404	53	innovation	innovation	NOUN
cana-1008	404	54	amid	amid	ADP
cana-1008	404	55	global	global	ADJ
cana-1008	404	56	pandemic"(iec	pandemic"(iec	NOUN
cana-1008	404	57	)	)	PUNCT
cana-1008	404	58	,	,	PUNCT
cana-1008	404	59	2021	2021	NUM
cana-1008	404	60	:	:	PUNCT
cana-1008	404	61	ieee	ieee	NOUN
cana-1008	404	62	,	,	PUNCT
cana-1008	404	63	pp	pp	ADJ
cana-1008	404	64	.	.	PUNCT
cana-1008	405	1	61	61	NUM
cana-1008	405	2	-	-	SYM
cana-1008	405	3	66	66	NUM
cana-1008	405	4	.	.	PUNCT
cana-1008	406	1	reference	reference	NOUN
cana-1008	406	2	method	method	NOUN
cana-1008	406	3	accuracy	accuracy	NOUN
cana-1008	406	4	[	[	X
cana-1008	406	5	33	33	NUM
cana-1008	406	6	]	]	X
cana-1008	406	7	levenberg	levenberg	PROPN
cana-1008	406	8	-	-	PUNCT
cana-1008	406	9	marquardt	marquardt	PROPN
cana-1008	407	1	82	82	NUM
cana-1008	407	2	%	%	NOUN
cana-1008	407	3	[	[	X
cana-1008	407	4	34	34	NUM
cana-1008	407	5	]	]	X
cana-1008	407	6	ga_rbf	ga_rbf	PROPN
cana-1008	408	1	nn	nn	ADP
cana-1008	408	2	77.4	77.4	NUM
cana-1008	408	3	%	%	NOUN
cana-1008	408	4	[	[	X
cana-1008	408	5	35	35	NUM
cana-1008	408	6	]	]	X
cana-1008	408	7	mpso	mpso	PROPN
cana-1008	408	8	-	-	PUNCT
cana-1008	408	9	nn	nn	PROPN
cana-1008	408	10	81.8	81.8	NUM
cana-1008	408	11	%	%	NOUN
cana-1008	408	12	this	this	DET
cana-1008	408	13	study	study	NOUN
cana-1008	408	14	rf	rf	VERB
cana-1008	408	15	89.86	89.86	NUM
cana-1008	408	16	%	%	NOUN
cana-1008	408	17	communications	communication	NOUN
cana-1008	408	18	on	on	ADP
cana-1008	408	19	applied	apply	VERB
cana-1008	408	20	nonlinear	nonlinear	ADJ
cana-1008	408	21	analysis	analysis	NOUN
cana-1008	408	22	issn	issn	NOUN
cana-1008	408	23	:	:	PUNCT
cana-1008	408	24	1074	1074	NUM
cana-1008	408	25	-	-	PUNCT
cana-1008	408	26	133x	133x	NUM
cana-1008	408	27	vol	vol	NOUN
cana-1008	408	28	31	31	NUM
cana-1008	408	29	no	no	NOUN
cana-1008	408	30	.	.	PUNCT
cana-1008	409	1	5s	5s	NUM
cana-1008	409	2	(	(	PUNCT
cana-1008	409	3	2024	2024	NUM
cana-1008	409	4	)	)	PUNCT
cana-1008	409	5	155	155	NUM
cana-1008	409	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1008	410	1	[	[	X
cana-1008	410	2	2	2	X
cana-1008	410	3	]	]	PUNCT
cana-1008	410	4	d.	d.	PROPN
cana-1008	410	5	a.	a.	PROPN
cana-1008	410	6	hasan	hasan	PROPN
cana-1008	410	7	,	,	PUNCT
cana-1008	410	8	s.	s.	PROPN
cana-1008	410	9	r.	r.	PROPN
cana-1008	410	10	zeebaree	zeebaree	PROPN
cana-1008	410	11	,	,	PUNCT
cana-1008	410	12	m.	m.	NOUN
cana-1008	410	13	a.	a.	NOUN
cana-1008	410	14	sadeeq	sadeeq	PROPN
cana-1008	410	15	,	,	PUNCT
cana-1008	410	16	h.	h.	PROPN
cana-1008	410	17	m.	m.	PROPN
cana-1008	410	18	shukur	shukur	PROPN
cana-1008	410	19	,	,	PUNCT
cana-1008	410	20	r.	r.	PROPN
cana-1008	410	21	r.	r.	PROPN
cana-1008	410	22	zebari	zebari	PROPN
cana-1008	410	23	,	,	PUNCT
cana-1008	410	24	and	and	CCONJ
cana-1008	410	25	a.	a.	PROPN
cana-1008	410	26	h.	h.	PROPN
cana-1008	410	27	alkhayyat	alkhayyat	PROPN
cana-1008	410	28	,	,	PUNCT
cana-1008	410	29	"	"	PUNCT
cana-1008	410	30	machine	machine	NOUN
cana-1008	410	31	learningbased	learningbase	VERB
cana-1008	410	32	diabetic	diabetic	ADJ
cana-1008	410	33	retinopathy	retinopathy	ADJ
cana-1008	410	34	early	early	ADJ
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cana-1008	410	36	and	and	CCONJ
cana-1008	410	37	classification	classification	NOUN
cana-1008	410	38	systems	system	NOUN
cana-1008	410	39	-	-	PUNCT
cana-1008	410	40	a	a	DET
cana-1008	410	41	survey	survey	NOUN
cana-1008	410	42	,	,	PUNCT
cana-1008	410	43	"	"	PUNCT
cana-1008	410	44	in	in	ADP
cana-1008	410	45	2021	2021	NUM
cana-1008	410	46	1st	1st	NOUN
cana-1008	410	47	babylon	babylon	PROPN
cana-1008	410	48	international	international	ADJ
cana-1008	410	49	conference	conference	NOUN
cana-1008	410	50	on	on	ADP
cana-1008	410	51	information	information	NOUN
cana-1008	410	52	technology	technology	NOUN
cana-1008	410	53	and	and	CCONJ
cana-1008	410	54	science	science	NOUN
cana-1008	410	55	(	(	PUNCT
cana-1008	410	56	bicits	bicit	NOUN
cana-1008	410	57	)	)	PUNCT
cana-1008	410	58	,	,	PUNCT
cana-1008	410	59	2021	2021	NUM
cana-1008	410	60	:	:	PUNCT
cana-1008	410	61	ieee	ieee	NOUN
cana-1008	410	62	,	,	PUNCT
cana-1008	410	63	pp	pp	ADJ
cana-1008	410	64	.	.	PUNCT
cana-1008	411	1	16	16	NUM
cana-1008	411	2	-	-	SYM
cana-1008	411	3	21	21	NUM
cana-1008	411	4	.	.	PUNCT
cana-1008	412	1	[	[	X
cana-1008	412	2	3	3	X
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cana-1008	412	20	learning	learning	NOUN
cana-1008	412	21	technique	technique	NOUN
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cana-1008	412	23	"	"	PUNCT
cana-1008	412	24	in	in	ADP
cana-1008	412	25	2021	2021	NUM
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cana-1008	412	35	(	(	PUNCT
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cana-1008	412	40	:	:	PUNCT
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cana-1008	413	2	-	-	SYM
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cana-1008	414	2	4	4	NUM
cana-1008	414	3	]	]	X
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cana-1008	414	41	,	,	PUNCT
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cana-1008	414	43	.	.	PUNCT
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cana-1008	415	3	]	]	PUNCT
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cana-1008	415	36	convolutional	convolutional	ADJ
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cana-1008	415	40	:	:	PUNCT
cana-1008	415	41	a	a	DET
cana-1008	415	42	review	review	NOUN
cana-1008	415	43	,	,	PUNCT
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cana-1008	416	2	-	-	SYM
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cana-1008	418	2	-	-	SYM
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cana-1008	420	2	-	-	SYM
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cana-1008	424	36	(	(	PUNCT
cana-1008	424	37	iccubea	iccubea	NOUN
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cana-1008	424	39	,	,	PUNCT
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cana-1008	424	41	:	:	PUNCT
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cana-1008	424	45	.	.	PUNCT
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cana-1008	426	21	:	:	PUNCT
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cana-1008	427	2	-	-	SYM
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cana-1008	428	22	science	science	NOUN
cana-1008	428	23	,	,	PUNCT
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cana-1008	428	29	.	.	PUNCT
cana-1008	428	30	1578	1578	NUM
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cana-1008	428	32	1585	1585	NUM
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cana-1008	428	35	.	.	PUNCT
cana-1008	429	1	[	[	X
cana-1008	429	2	12	12	NUM
cana-1008	429	3	]	]	PUNCT
cana-1008	429	4	w.	w.	PROPN
cana-1008	429	5	h.	h.	PROPN
cana-1008	429	6	organization	organization	PROPN
cana-1008	429	7	,	,	PUNCT
cana-1008	429	8	world	world	PROPN
cana-1008	429	9	health	health	PROPN
cana-1008	429	10	statistics	statistic	NOUN
cana-1008	429	11	2016	2016	NUM
cana-1008	430	1	[	[	X
cana-1008	430	2	op	op	X
cana-1008	430	3	]	]	X
cana-1008	430	4	:	:	PUNCT
cana-1008	430	5	monitoring	monitor	VERB
cana-1008	430	6	health	health	NOUN
cana-1008	430	7	for	for	ADP
cana-1008	430	8	the	the	DET
cana-1008	430	9	sustainable	sustainable	ADJ
cana-1008	430	10	development	development	NOUN
cana-1008	430	11	goals	goal	NOUN
cana-1008	430	12	(	(	PUNCT
cana-1008	430	13	sdgs	sdgs	ADJ
cana-1008	430	14	)	)	PUNCT
cana-1008	430	15	.	.	PUNCT
cana-1008	431	1	world	world	PROPN
cana-1008	431	2	health	health	PROPN
cana-1008	431	3	organization	organization	NOUN
cana-1008	431	4	,	,	PUNCT
cana-1008	431	5	2016	2016	NUM
cana-1008	431	6	.	.	PUNCT
cana-1008	432	1	[	[	X
cana-1008	432	2	13	13	NUM
cana-1008	432	3	]	]	PUNCT
cana-1008	432	4	m.	m.	NOUN
cana-1008	432	5	franciosi	franciosi	INTJ
cana-1008	432	6	et	et	PROPN
cana-1008	432	7	al	al	PROPN
cana-1008	432	8	.	.	PROPN
cana-1008	432	9	,	,	PUNCT
cana-1008	432	10	"	"	PUNCT
cana-1008	432	11	use	use	NOUN
cana-1008	432	12	of	of	ADP
cana-1008	432	13	the	the	DET
cana-1008	432	14	diabetes	diabetes	NOUN
cana-1008	432	15	risk	risk	NOUN
cana-1008	432	16	score	score	NOUN
cana-1008	432	17	for	for	ADP
cana-1008	432	18	opportunistic	opportunistic	ADJ
cana-1008	432	19	screening	screening	NOUN
cana-1008	432	20	of	of	ADP
cana-1008	432	21	undiagnosed	undiagnosed	ADJ
cana-1008	432	22	diabetes	diabetes	NOUN
cana-1008	432	23	and	and	CCONJ
cana-1008	432	24	impaired	impaired	ADJ
cana-1008	432	25	glucose	glucose	NOUN
cana-1008	432	26	tolerance	tolerance	NOUN
cana-1008	432	27	:	:	PUNCT
cana-1008	432	28	the	the	DET
cana-1008	432	29	igloo	igloo	PROPN
cana-1008	432	30	(	(	PUNCT
cana-1008	432	31	impaired	impaired	ADJ
cana-1008	432	32	glucose	glucose	NOUN
cana-1008	432	33	tolerance	tolerance	NOUN
cana-1008	432	34	and	and	CCONJ
cana-1008	432	35	long	long	ADJ
cana-1008	432	36	-	-	PUNCT
cana-1008	432	37	term	term	NOUN
cana-1008	432	38	outcomes	outcome	NOUN
cana-1008	432	39	observational	observational	ADJ
cana-1008	432	40	)	)	PUNCT
cana-1008	432	41	study	study	NOUN
cana-1008	432	42	,	,	PUNCT
cana-1008	432	43	"	"	PUNCT
cana-1008	432	44	diabetes	diabetes	NOUN
cana-1008	432	45	care	care	NOUN
cana-1008	432	46	,	,	PUNCT
cana-1008	432	47	vol	vol	NOUN
cana-1008	432	48	.	.	PROPN
cana-1008	432	49	28	28	NUM
cana-1008	432	50	,	,	PUNCT
cana-1008	432	51	no	no	INTJ
cana-1008	432	52	.	.	NOUN
cana-1008	432	53	5	5	NUM
cana-1008	432	54	,	,	PUNCT
cana-1008	432	55	pp	pp	ADJ
cana-1008	432	56	.	.	PUNCT
cana-1008	432	57	1187	1187	NUM
cana-1008	432	58	-	-	SYM
cana-1008	432	59	1194	1194	NUM
cana-1008	432	60	,	,	PUNCT
cana-1008	432	61	2005	2005	NUM
cana-1008	432	62	.	.	PUNCT
cana-1008	433	1	[	[	X
cana-1008	433	2	14	14	NUM
cana-1008	433	3	]	]	X
cana-1008	433	4	s.	s.	PROPN
cana-1008	433	5	palaniappan	palaniappan	PROPN
cana-1008	433	6	and	and	CCONJ
cana-1008	433	7	r.	r.	PROPN
cana-1008	433	8	awang	awang	PROPN
cana-1008	433	9	,	,	PUNCT
cana-1008	433	10	"	"	PUNCT
cana-1008	433	11	intelligent	intelligent	ADJ
cana-1008	433	12	heart	heart	NOUN
cana-1008	433	13	disease	disease	NOUN
cana-1008	433	14	prediction	prediction	NOUN
cana-1008	433	15	system	system	NOUN
cana-1008	433	16	using	use	VERB
cana-1008	433	17	data	datum	NOUN
cana-1008	433	18	mining	mining	NOUN
cana-1008	433	19	techniques	technique	NOUN
cana-1008	433	20	,	,	PUNCT
cana-1008	433	21	"	"	PUNCT
cana-1008	433	22	in	in	ADP
cana-1008	433	23	2008	2008	NUM
cana-1008	433	24	ieee	ieee	NOUN
cana-1008	433	25	/	/	SYM
cana-1008	433	26	acs	acs	PROPN
cana-1008	433	27	international	international	ADJ
cana-1008	433	28	conference	conference	NOUN
cana-1008	433	29	on	on	ADP
cana-1008	433	30	computer	computer	NOUN
cana-1008	433	31	systems	system	NOUN
cana-1008	433	32	and	and	CCONJ
cana-1008	433	33	applications	application	NOUN
cana-1008	433	34	,	,	PUNCT
cana-1008	433	35	2008	2008	NUM
cana-1008	433	36	:	:	PUNCT
cana-1008	433	37	ieee	ieee	NOUN
cana-1008	433	38	,	,	PUNCT
cana-1008	433	39	pp	pp	ADJ
cana-1008	433	40	.	.	PUNCT
cana-1008	433	41	108	108	NUM
cana-1008	433	42	-	-	SYM
cana-1008	433	43	115	115	NUM
cana-1008	433	44	.	.	PUNCT
cana-1008	434	1	[	[	X
cana-1008	434	2	15	15	NUM
cana-1008	434	3	]	]	X
cana-1008	434	4	c.-l	c.-l	NOUN
cana-1008	434	5	.	.	PUNCT
cana-1008	435	1	huang	huang	PROPN
cana-1008	435	2	,	,	PUNCT
cana-1008	435	3	m.-c	m.-c	PROPN
cana-1008	435	4	.	.	PUNCT
cana-1008	436	1	chen	chen	PROPN
cana-1008	436	2	,	,	PUNCT
cana-1008	436	3	and	and	CCONJ
cana-1008	436	4	c.-j	c.-j	PROPN
cana-1008	436	5	.	.	PUNCT
cana-1008	437	1	wang	wang	PROPN
cana-1008	437	2	,	,	PUNCT
cana-1008	437	3	"	"	PUNCT
cana-1008	437	4	credit	credit	NOUN
cana-1008	437	5	scoring	scoring	NOUN
cana-1008	437	6	with	with	ADP
cana-1008	437	7	a	a	DET
cana-1008	437	8	data	data	NOUN
cana-1008	437	9	mining	mining	NOUN
cana-1008	437	10	approach	approach	NOUN
cana-1008	437	11	based	base	VERB
cana-1008	437	12	on	on	ADP
cana-1008	437	13	support	support	NOUN
cana-1008	437	14	vector	vector	NOUN
cana-1008	437	15	machines	machine	NOUN
cana-1008	437	16	,	,	PUNCT
cana-1008	437	17	"	"	PUNCT
cana-1008	437	18	expert	expert	NOUN
cana-1008	437	19	systems	system	NOUN
cana-1008	437	20	with	with	ADP
cana-1008	437	21	applications	application	NOUN
cana-1008	437	22	,	,	PUNCT
cana-1008	437	23	vol	vol	NOUN
cana-1008	437	24	.	.	PROPN
cana-1008	437	25	33	33	NUM
cana-1008	437	26	,	,	PUNCT
cana-1008	437	27	no	no	INTJ
cana-1008	437	28	.	.	NOUN
cana-1008	437	29	4	4	NUM
cana-1008	437	30	,	,	PUNCT
cana-1008	437	31	pp	pp	ADJ
cana-1008	437	32	.	.	PUNCT
cana-1008	437	33	847	847	NUM
cana-1008	437	34	-	-	SYM
cana-1008	437	35	856	856	NUM
cana-1008	437	36	,	,	PUNCT
cana-1008	437	37	2007	2007	NUM
cana-1008	437	38	.	.	PUNCT
cana-1008	438	1	[	[	X
cana-1008	438	2	16	16	NUM
cana-1008	438	3	]	]	X
cana-1008	438	4	x.-h	x.-h	NOUN
cana-1008	438	5	.	.	PUNCT
cana-1008	439	1	meng	meng	PROPN
cana-1008	439	2	,	,	PUNCT
cana-1008	439	3	y.-x	y.-x	PROPN
cana-1008	439	4	.	.	PUNCT
cana-1008	440	1	huang	huang	PROPN
cana-1008	440	2	,	,	PUNCT
cana-1008	440	3	d.-p	d.-p	PROPN
cana-1008	440	4	.	.	PUNCT
cana-1008	441	1	rao	rao	PROPN
cana-1008	441	2	,	,	PUNCT
cana-1008	441	3	q.	q.	PROPN
cana-1008	441	4	zhang	zhang	PROPN
cana-1008	441	5	,	,	PUNCT
cana-1008	441	6	and	and	CCONJ
cana-1008	441	7	q.	q.	PROPN
cana-1008	441	8	liu	liu	PROPN
cana-1008	441	9	,	,	PUNCT
cana-1008	441	10	"	"	PUNCT
cana-1008	441	11	comparison	comparison	NOUN
cana-1008	441	12	of	of	ADP
cana-1008	441	13	three	three	NUM
cana-1008	441	14	data	datum	NOUN
cana-1008	441	15	mining	mining	NOUN
cana-1008	441	16	models	model	NOUN
cana-1008	441	17	for	for	ADP
cana-1008	441	18	predicting	predict	VERB
cana-1008	441	19	diabetes	diabetes	NOUN
cana-1008	441	20	or	or	CCONJ
cana-1008	441	21	prediabetes	prediabete	NOUN
cana-1008	441	22	by	by	ADP
cana-1008	441	23	risk	risk	NOUN
cana-1008	441	24	factors	factor	NOUN
cana-1008	441	25	,	,	PUNCT
cana-1008	441	26	"	"	PUNCT
cana-1008	441	27	the	the	DET
cana-1008	441	28	kaohsiung	kaohsiung	PROPN
cana-1008	441	29	journal	journal	PROPN
cana-1008	441	30	of	of	ADP
cana-1008	441	31	medical	medical	ADJ
cana-1008	441	32	sciences	science	NOUN
cana-1008	441	33	,	,	PUNCT
cana-1008	441	34	vol	vol	NOUN
cana-1008	441	35	.	.	PROPN
cana-1008	441	36	29	29	NUM
cana-1008	441	37	,	,	PUNCT
cana-1008	441	38	no	no	INTJ
cana-1008	441	39	.	.	NOUN
cana-1008	441	40	2	2	NUM
cana-1008	441	41	,	,	PUNCT
cana-1008	441	42	pp	pp	ADJ
cana-1008	441	43	.	.	PUNCT
cana-1008	442	1	93	93	NUM
cana-1008	442	2	-	-	SYM
cana-1008	442	3	99	99	NUM
cana-1008	442	4	,	,	PUNCT
cana-1008	442	5	2013	2013	NUM
cana-1008	442	6	.	.	PUNCT
cana-1008	443	1	[	[	X
cana-1008	443	2	17	17	NUM
cana-1008	443	3	]	]	X
cana-1008	443	4	n.	n.	NOUN
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cana-1008	443	6	and	and	CCONJ
cana-1008	443	7	a.	a.	PROPN
cana-1008	443	8	al	al	PROPN
cana-1008	443	9	-	-	PUNCT
cana-1008	443	10	mousa	mousa	PROPN
cana-1008	443	11	,	,	PUNCT
cana-1008	443	12	"	"	PUNCT
cana-1008	443	13	diabetes	diabetes	NOUN
cana-1008	443	14	detection	detection	NOUN
cana-1008	443	15	using	use	VERB
cana-1008	443	16	machine	machine	NOUN
cana-1008	443	17	learning	learn	VERB
cana-1008	443	18	classification	classification	NOUN
cana-1008	443	19	methods	method	NOUN
cana-1008	443	20	,	,	PUNCT
cana-1008	443	21	"	"	PUNCT
cana-1008	443	22	in	in	ADP
cana-1008	443	23	2021	2021	NUM
cana-1008	443	24	international	international	ADJ
cana-1008	443	25	conference	conference	NOUN
cana-1008	443	26	on	on	ADP
cana-1008	443	27	information	information	NOUN
cana-1008	443	28	technology	technology	NOUN
cana-1008	443	29	(	(	PUNCT
cana-1008	443	30	icit	icit	PROPN
cana-1008	443	31	)	)	PUNCT
cana-1008	443	32	,	,	PUNCT
cana-1008	443	33	2021	2021	NUM
cana-1008	443	34	:	:	PUNCT
cana-1008	443	35	ieee	ieee	NOUN
cana-1008	443	36	,	,	PUNCT
cana-1008	443	37	pp	pp	ADJ
cana-1008	443	38	.	.	PUNCT
cana-1008	444	1	350	350	NUM
cana-1008	444	2	-	-	SYM
cana-1008	444	3	354	354	NUM
cana-1008	444	4	.	.	PUNCT
cana-1008	445	1	[	[	X
cana-1008	445	2	18	18	NUM
cana-1008	445	3	]	]	X
cana-1008	445	4	n.	n.	PROPN
cana-1008	445	5	k.	k.	PROPN
cana-1008	445	6	putri	putri	PROPN
cana-1008	445	7	,	,	PUNCT
cana-1008	445	8	z.	z.	PROPN
cana-1008	445	9	rustam	rustam	PROPN
cana-1008	445	10	,	,	PUNCT
cana-1008	445	11	and	and	CCONJ
cana-1008	445	12	d.	d.	PROPN
cana-1008	445	13	sarwinda	sarwinda	PROPN
cana-1008	445	14	,	,	PUNCT
cana-1008	445	15	"	"	PUNCT
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cana-1008	445	17	vector	vector	NOUN
cana-1008	445	18	quantization	quantization	NOUN
cana-1008	445	19	for	for	ADP
cana-1008	445	20	diabetes	diabetes	NOUN
cana-1008	445	21	data	datum	NOUN
cana-1008	445	22	classification	classification	NOUN
cana-1008	445	23	with	with	ADP
cana-1008	445	24	chisquare	chisquare	NOUN
cana-1008	445	25	feature	feature	NOUN
cana-1008	445	26	selection	selection	NOUN
cana-1008	445	27	,	,	PUNCT
cana-1008	445	28	"	"	PUNCT
cana-1008	445	29	in	in	ADP
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cana-1008	445	31	conference	conference	NOUN
cana-1008	445	32	series	series	NOUN
cana-1008	445	33	:	:	PUNCT
cana-1008	445	34	materials	material	NOUN
cana-1008	445	35	science	science	NOUN
cana-1008	445	36	and	and	CCONJ
cana-1008	445	37	engineering	engineering	NOUN
cana-1008	445	38	,	,	PUNCT
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cana-1008	445	40	,	,	PUNCT
cana-1008	445	41	vol	vol	NOUN
cana-1008	445	42	.	.	PROPN
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cana-1008	446	2	,	,	PUNCT
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cana-1008	446	5	5	5	NUM
cana-1008	446	6	:	:	PUNCT
cana-1008	446	7	iop	iop	NOUN
cana-1008	446	8	publishing	publishing	NOUN
cana-1008	446	9	,	,	PUNCT
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cana-1008	446	11	052059	052059	NUM
cana-1008	446	12	.	.	PUNCT
cana-1008	447	1	[	[	X
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cana-1008	447	3	]	]	X
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cana-1008	447	8	rustam	rustam	PROPN
cana-1008	447	9	,	,	PUNCT
cana-1008	447	10	"	"	PUNCT
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cana-1008	447	13	cancer	cancer	NOUN
cana-1008	447	14	data	datum	NOUN
cana-1008	447	15	using	use	VERB
cana-1008	447	16	support	support	NOUN
cana-1008	447	17	vector	vector	NOUN
cana-1008	447	18	machines	machine	NOUN
cana-1008	447	19	with	with	ADP
cana-1008	447	20	features	feature	NOUN
cana-1008	447	21	selection	selection	NOUN
cana-1008	447	22	method	method	NOUN
cana-1008	447	23	based	base	VERB
cana-1008	447	24	on	on	ADP
cana-1008	447	25	global	global	ADJ
cana-1008	447	26	artificial	artificial	ADJ
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cana-1008	447	28	colony	colony	NOUN
cana-1008	447	29	,	,	PUNCT
cana-1008	447	30	"	"	PUNCT
cana-1008	447	31	in	in	ADP
cana-1008	447	32	aip	aip	PROPN
cana-1008	447	33	conference	conference	NOUN
cana-1008	447	34	proceedings	proceeding	NOUN
cana-1008	447	35	,	,	PUNCT
cana-1008	447	36	2018	2018	NUM
cana-1008	447	37	,	,	PUNCT
cana-1008	447	38	vol	vol	NOUN
cana-1008	447	39	.	.	PUNCT
cana-1008	447	40	2023	2023	NUM
cana-1008	447	41	,	,	PUNCT
cana-1008	447	42	no	no	INTJ
cana-1008	447	43	.	.	NOUN
cana-1008	447	44	1	1	NUM
cana-1008	447	45	:	:	PUNCT
cana-1008	447	46	aip	aip	PROPN
cana-1008	447	47	publishing	publishing	NOUN
cana-1008	447	48	.	.	PUNCT
cana-1008	448	1	[	[	X
cana-1008	448	2	20	20	NUM
cana-1008	448	3	]	]	PUNCT
cana-1008	448	4	z.	z.	PROPN
cana-1008	448	5	rustam	rustam	PROPN
cana-1008	448	6	,	,	PUNCT
cana-1008	448	7	j.	j.	PROPN
cana-1008	448	8	pandelaki	pandelaki	PROPN
cana-1008	448	9	,	,	PUNCT
cana-1008	448	10	and	and	CCONJ
cana-1008	448	11	a.	a.	NOUN
cana-1008	448	12	siahaan	siahaan	PROPN
cana-1008	448	13	,	,	PUNCT
cana-1008	448	14	"	"	PUNCT
cana-1008	448	15	kernel	kernel	PROPN
cana-1008	448	16	spherical	spherical	ADJ
cana-1008	448	17	k	k	NOUN
cana-1008	448	18	-	-	PUNCT
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cana-1008	448	20	and	and	CCONJ
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cana-1008	448	22	vector	vector	NOUN
cana-1008	448	23	machine	machine	NOUN
cana-1008	448	24	for	for	ADP
cana-1008	448	25	acute	acute	ADJ
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cana-1008	448	27	classification	classification	NOUN
cana-1008	448	28	,	,	PUNCT
cana-1008	448	29	"	"	PUNCT
cana-1008	448	30	in	in	ADP
cana-1008	448	31	iop	iop	PROPN
cana-1008	448	32	conference	conference	NOUN
cana-1008	448	33	series	series	NOUN
cana-1008	448	34	:	:	PUNCT
cana-1008	448	35	materials	material	NOUN
cana-1008	448	36	science	science	NOUN
cana-1008	448	37	and	and	CCONJ
cana-1008	448	38	engineering	engineering	NOUN
cana-1008	448	39	,	,	PUNCT
cana-1008	448	40	2019	2019	NUM
cana-1008	448	41	,	,	PUNCT
cana-1008	448	42	vol	vol	NOUN
cana-1008	448	43	.	.	PROPN
cana-1008	449	1	546	546	NUM
cana-1008	449	2	,	,	PUNCT
cana-1008	449	3	no	no	INTJ
cana-1008	449	4	.	.	NOUN
cana-1008	449	5	5	5	NUM
cana-1008	449	6	:	:	PUNCT
cana-1008	449	7	iop	iop	NOUN
cana-1008	449	8	publishing	publishing	NOUN
cana-1008	449	9	,	,	PUNCT
cana-1008	449	10	p.	p.	NOUN
cana-1008	449	11	052011	052011	NUM
cana-1008	449	12	.	.	PUNCT
cana-1008	450	1	[	[	X
cana-1008	450	2	21	21	NUM
cana-1008	450	3	]	]	PUNCT
cana-1008	450	4	t.	t.	PROPN
cana-1008	450	5	v.	v.	PROPN
cana-1008	450	6	rampisela	rampisela	PROPN
cana-1008	450	7	and	and	CCONJ
cana-1008	450	8	z.	z.	PROPN
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cana-1008	450	17	support	support	NOUN
cana-1008	450	18	vector	vector	NOUN
cana-1008	450	19	machine	machine	NOUN
cana-1008	450	20	(	(	PUNCT
cana-1008	450	21	svm	svm	PROPN
cana-1008	450	22	)	)	PUNCT
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cana-1008	450	24	"	"	PUNCT
cana-1008	450	25	in	in	ADP
cana-1008	450	26	journal	journal	NOUN
cana-1008	450	27	of	of	ADP
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cana-1008	450	29	:	:	PUNCT
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cana-1008	450	32	,	,	PUNCT
cana-1008	450	33	2018	2018	NUM
cana-1008	450	34	,	,	PUNCT
cana-1008	450	35	vol	vol	NOUN
cana-1008	450	36	.	.	PUNCT
cana-1008	450	37	1108	1108	NUM
cana-1008	450	38	:	:	PUNCT
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cana-1008	450	41	,	,	PUNCT
cana-1008	450	42	p.	p.	NOUN
cana-1008	450	43	012044	012044	NUM
cana-1008	450	44	.	.	PUNCT
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cana-1008	451	2	22	22	NUM
cana-1008	451	3	]	]	PUNCT
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cana-1008	451	5	s.	s.	PROPN
cana-1008	451	6	islam	islam	PROPN
cana-1008	451	7	,	,	PUNCT
cana-1008	451	8	m.	m.	PROPN
cana-1008	451	9	k.	k.	PROPN
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cana-1008	451	12	and	and	CCONJ
cana-1008	451	13	s.	s.	PROPN
cana-1008	451	14	b.	b.	PROPN
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cana-1008	451	16	,	,	PUNCT
cana-1008	451	17	"	"	PUNCT
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cana-1008	451	20	of	of	ADP
cana-1008	451	21	hemoglobin	hemoglobin	NOUN
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cana-1008	451	23	:	:	PUNCT
cana-1008	451	24	a	a	DET
cana-1008	451	25	novel	novel	ADJ
cana-1008	451	26	framework	framework	NOUN
cana-1008	451	27	for	for	ADP
cana-1008	451	28	better	well	ADJ
cana-1008	451	29	diabetes	diabetes	NOUN
cana-1008	451	30	management	management	NOUN
cana-1008	451	31	,	,	PUNCT
cana-1008	451	32	"	"	PUNCT
cana-1008	451	33	in	in	ADP
cana-1008	451	34	2020	2020	NUM
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cana-1008	451	37	series	series	NOUN
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cana-1008	451	39	computational	computational	ADJ
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cana-1008	451	41	(	(	PUNCT
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cana-1008	451	43	)	)	PUNCT
cana-1008	451	44	,	,	PUNCT
cana-1008	451	45	2020	2020	NUM
cana-1008	451	46	:	:	PUNCT
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cana-1008	451	48	,	,	PUNCT
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cana-1008	451	50	.	.	PUNCT
cana-1008	451	51	542547	542547	NUM
cana-1008	451	52	.	.	PUNCT
cana-1008	452	1	communications	communication	NOUN
cana-1008	452	2	on	on	ADP
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cana-1008	452	6	issn	issn	NOUN
cana-1008	452	7	:	:	PUNCT
cana-1008	452	8	1074	1074	NUM
cana-1008	452	9	-	-	PUNCT
cana-1008	452	10	133x	133x	NUM
cana-1008	452	11	vol	vol	NOUN
cana-1008	452	12	31	31	NUM
cana-1008	452	13	no	no	NOUN
cana-1008	452	14	.	.	PUNCT
cana-1008	453	1	5s	5s	NUM
cana-1008	453	2	(	(	PUNCT
cana-1008	453	3	2024	2024	NUM
cana-1008	453	4	)	)	PUNCT
cana-1008	453	5	156	156	NUM
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cana-1008	454	1	[	[	X
cana-1008	454	2	23	23	NUM
cana-1008	454	3	]	]	X
cana-1008	454	4	g.	g.	PROPN
cana-1008	454	5	pethunachiyar	pethunachiyar	PROPN
cana-1008	454	6	,	,	PUNCT
cana-1008	454	7	"	"	PUNCT
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cana-1008	454	10	diabetes	diabetes	NOUN
cana-1008	454	11	patients	patient	NOUN
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cana-1008	454	13	kernel	kernel	PROPN
cana-1008	454	14	based	base	VERB
cana-1008	454	15	support	support	NOUN
cana-1008	454	16	vector	vector	NOUN
cana-1008	454	17	machines	machine	NOUN
cana-1008	454	18	,	,	PUNCT
cana-1008	454	19	"	"	PUNCT
cana-1008	454	20	in	in	ADP
cana-1008	454	21	2020	2020	NUM
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cana-1008	454	23	conference	conference	NOUN
cana-1008	454	24	on	on	ADP
cana-1008	454	25	computer	computer	NOUN
cana-1008	454	26	communication	communication	NOUN
cana-1008	454	27	and	and	CCONJ
cana-1008	454	28	informatics	informatic	NOUN
cana-1008	454	29	(	(	PUNCT
cana-1008	454	30	iccci	iccci	NOUN
cana-1008	454	31	)	)	PUNCT
cana-1008	454	32	,	,	PUNCT
cana-1008	454	33	2020	2020	NUM
cana-1008	454	34	:	:	PUNCT
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cana-1008	454	36	,	,	PUNCT
cana-1008	454	37	pp	pp	ADJ
cana-1008	454	38	.	.	PUNCT
cana-1008	455	1	1	1	NUM
cana-1008	455	2	-	-	SYM
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cana-1008	455	4	.	.	PUNCT
cana-1008	456	1	[	[	X
cana-1008	456	2	24	24	NUM
cana-1008	456	3	]	]	X
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cana-1008	456	5	m.	m.	PROPN
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cana-1008	456	7	,	,	PUNCT
cana-1008	456	8	o.	o.	PROPN
cana-1008	456	9	y.	y.	PROPN
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cana-1008	456	11	,	,	PUNCT
cana-1008	456	12	s.	s.	PROPN
cana-1008	456	13	misra	misra	PROPN
cana-1008	456	14	,	,	PUNCT
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cana-1008	456	19	r.	r.	PROPN
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cana-1008	456	22	"	"	PUNCT
cana-1008	456	23	a	a	DET
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cana-1008	456	32	of	of	ADP
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cana-1008	456	35	,	,	PUNCT
cana-1008	456	36	"	"	PUNCT
cana-1008	456	37	in	in	ADP
cana-1008	456	38	information	information	NOUN
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cana-1008	456	40	science	science	NOUN
cana-1008	456	41	,	,	PUNCT
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cana-1008	456	43	:	:	PUNCT
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cana-1008	457	2	-	-	SYM
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cana-1008	457	4	.	.	PUNCT
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cana-1008	458	3	]	]	X
cana-1008	458	4	s.	s.	PROPN
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cana-1008	458	19	in	in	ADP
cana-1008	458	20	patients	patient	NOUN
cana-1008	458	21	with	with	ADP
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cana-1008	458	23	using	use	VERB
cana-1008	458	24	real	real	ADJ
cana-1008	458	25	-	-	PUNCT
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cana-1008	458	27	data	datum	NOUN
cana-1008	458	28	,	,	PUNCT
cana-1008	458	29	"	"	PUNCT
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cana-1008	458	32	,	,	PUNCT
cana-1008	458	33	vol	vol	NOUN
cana-1008	458	34	.	.	PROPN
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cana-1008	458	36	,	,	PUNCT
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cana-1008	458	38	.	.	NOUN
cana-1008	458	39	1	1	NUM
cana-1008	458	40	,	,	PUNCT
cana-1008	458	41	pp	pp	ADJ
cana-1008	458	42	.	.	PUNCT
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cana-1008	459	2	-	-	SYM
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cana-1008	459	4	,	,	PUNCT
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cana-1008	459	6	.	.	PUNCT
cana-1008	460	1	[	[	X
cana-1008	460	2	26	26	NUM
cana-1008	460	3	]	]	X
cana-1008	460	4	r.	r.	PROPN
cana-1008	460	5	r.	r.	PROPN
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cana-1008	460	7	and	and	CCONJ
cana-1008	460	8	r.	r.	PROPN
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cana-1008	460	12	"	"	PUNCT
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cana-1008	460	19	knowledge	knowledge	NOUN
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cana-1008	460	22	,	,	PUNCT
cana-1008	460	23	"	"	PUNCT
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cana-1008	460	27	,	,	PUNCT
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cana-1008	460	29	.	.	PROPN
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cana-1008	460	37	.	.	PUNCT
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cana-1008	461	2	-	-	SYM
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cana-1008	461	4	,	,	PUNCT
cana-1008	461	5	2021	2021	NUM
cana-1008	461	6	.	.	PUNCT
cana-1008	462	1	[	[	X
cana-1008	462	2	27	27	NUM
cana-1008	462	3	]	]	X
cana-1008	462	4	m.	m.	NOUN
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cana-1008	462	6	and	and	CCONJ
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cana-1008	462	10	"	"	PUNCT
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cana-1008	462	21	,	,	PUNCT
cana-1008	462	22	"	"	PUNCT
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cana-1008	462	25	of	of	ADP
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cana-1008	462	29	,	,	PUNCT
cana-1008	462	30	vol	vol	NOUN
cana-1008	462	31	.	.	PROPN
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cana-1008	462	33	,	,	PUNCT
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cana-1008	462	36	22	22	NUM
cana-1008	462	37	,	,	PUNCT
cana-1008	462	38	pp	pp	ADJ
cana-1008	462	39	.	.	PUNCT
cana-1008	463	1	1	1	NUM
cana-1008	463	2	-	-	SYM
cana-1008	463	3	9	9	NUM
cana-1008	463	4	,	,	PUNCT
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cana-1008	463	6	.	.	PUNCT
cana-1008	464	1	[	[	X
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cana-1008	464	3	]	]	X
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cana-1008	464	5	t.	t.	PROPN
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cana-1008	464	8	j.	j.	PROPN
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cana-1008	464	11	"	"	PUNCT
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cana-1008	464	22	"	"	PUNCT
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cana-1008	464	25	of	of	ADP
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cana-1008	464	29	a	a	DET
cana-1008	464	30	:	:	PUNCT
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cana-1008	464	32	,	,	PUNCT
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cana-1008	464	37	,	,	PUNCT
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cana-1008	464	44	2065	2065	NUM
cana-1008	464	45	,	,	PUNCT
cana-1008	464	46	p.	p.	NOUN
cana-1008	464	47	20150202	20150202	NUM
cana-1008	464	48	,	,	PUNCT
cana-1008	464	49	2016	2016	NUM
cana-1008	464	50	.	.	PUNCT
cana-1008	465	1	[	[	X
cana-1008	465	2	29	29	NUM
cana-1008	465	3	]	]	PUNCT
cana-1008	465	4	a.	a.	PROPN
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cana-1008	465	6	b.	b.	PROPN
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cana-1008	465	12	"	"	PUNCT
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cana-1008	465	28	"	"	PUNCT
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cana-1008	466	2	.	.	PUNCT
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cana-1008	467	2	.	.	PUNCT
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cana-1008	468	2	.	.	PROPN
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cana-1008	468	5	.	.	PROPN
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cana-1008	469	2	,	,	PUNCT
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cana-1008	469	8	.	.	PUNCT
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cana-1008	470	2	-	-	SYM
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cana-1008	470	4	,	,	PUNCT
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cana-1008	471	3	]	]	PUNCT
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cana-1008	471	19	"	"	PUNCT
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cana-1008	476	2	-	-	SYM
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cana-1008	477	17	"	"	PUNCT
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cana-1008	477	29	,	,	PUNCT
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cana-1008	477	40	engineering	engineering	NOUN
cana-1008	477	41	(	(	PUNCT
cana-1008	477	42	csase	csase	NOUN
cana-1008	477	43	)	)	PUNCT
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cana-1008	477	45	2022	2022	NUM
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cana-1008	478	2	-	-	SYM
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cana-1008	479	21	"	"	PUNCT
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cana-1008	481	2	-	-	SYM
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cana-1008	481	4	,	,	PUNCT
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cana-1008	482	3	]	]	PUNCT
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cana-1008	483	2	-	-	SYM
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cana-1008	484	37	"	"	PUNCT
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cana-1008	487	2	-	-	SYM
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cana-1008	489	2	-	-	SYM
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cana-1008	490	11	study	study	NOUN
cana-1008	490	12	support	support	NOUN
cana-1008	490	13	vector	vector	NOUN
cana-1008	490	14	machines	machine	NOUN
cana-1008	490	15	and	and	CCONJ
cana-1008	490	16	k	k	ADJ
cana-1008	490	17	-	-	ADJ
cana-1008	490	18	mean	mean	ADJ
cana-1008	490	19	algorithms	algorithm	NOUN
cana-1008	490	20	for	for	ADP
cana-1008	490	21	diabetes	diabetes	NOUN
cana-1008	490	22	dataset	dataset	VERB
cana-1008	490	23	,	,	PUNCT
cana-1008	490	24	"	"	PUNCT
cana-1008	490	25	academic	academic	ADJ
cana-1008	490	26	journal	journal	NOUN
cana-1008	490	27	of	of	ADP
cana-1008	490	28	research	research	NOUN
cana-1008	490	29	and	and	CCONJ
cana-1008	490	30	scientific	scientific	ADJ
cana-1008	490	31	publishing|	publishing|	NOUN
cana-1008	490	32	vol	vol	NOUN
cana-1008	490	33	,	,	PUNCT
cana-1008	490	34	vol	vol	NOUN
cana-1008	490	35	.	.	PROPN
cana-1008	491	1	2	2	NUM
cana-1008	491	2	,	,	PUNCT
cana-1008	491	3	no	no	INTJ
cana-1008	491	4	.	.	NOUN
cana-1008	491	5	14	14	NUM
cana-1008	491	6	,	,	PUNCT
cana-1008	491	7	2020	2020	NUM
cana-1008	491	8	.	.	PUNCT
cana-1008	492	1	[	[	X
cana-1008	492	2	41	41	NUM
cana-1008	492	3	]	]	X
cana-1008	492	4	d.	d.	PROPN
cana-1008	492	5	k.	k.	PROPN
cana-1008	492	6	choubey	choubey	PROPN
cana-1008	492	7	and	and	CCONJ
cana-1008	492	8	s.	s.	PROPN
cana-1008	492	9	paul	paul	PROPN
cana-1008	492	10	,	,	PUNCT
cana-1008	492	11	"	"	PUNCT
cana-1008	492	12	ga_rbf	ga_rbf	PROPN
cana-1008	492	13	nn	nn	PROPN
cana-1008	492	14	:	:	PUNCT
cana-1008	492	15	a	a	DET
cana-1008	492	16	classification	classification	NOUN
cana-1008	492	17	system	system	NOUN
cana-1008	492	18	for	for	ADP
cana-1008	492	19	diabetes	diabetes	NOUN
cana-1008	492	20	,	,	PUNCT
cana-1008	492	21	"	"	PUNCT
cana-1008	492	22	international	international	ADJ
cana-1008	492	23	journal	journal	NOUN
cana-1008	492	24	of	of	ADP
cana-1008	492	25	biomedical	biomedical	ADJ
cana-1008	492	26	engineering	engineering	NOUN
cana-1008	492	27	and	and	CCONJ
cana-1008	492	28	technology	technology	NOUN
cana-1008	492	29	,	,	PUNCT
cana-1008	492	30	vol	vol	NOUN
cana-1008	492	31	.	.	PROPN
cana-1008	492	32	23	23	NUM
cana-1008	492	33	,	,	PUNCT
cana-1008	492	34	no	no	INTJ
cana-1008	492	35	.	.	NOUN
cana-1008	492	36	1	1	NUM
cana-1008	492	37	,	,	PUNCT
cana-1008	492	38	pp	pp	ADJ
cana-1008	492	39	.	.	PUNCT
cana-1008	493	1	71	71	NUM
cana-1008	493	2	-	-	SYM
cana-1008	493	3	93	93	NUM
cana-1008	493	4	,	,	PUNCT
cana-1008	493	5	2017	2017	NUM
cana-1008	493	6	.	.	PUNCT
cana-1008	494	1	[	[	X
cana-1008	494	2	42	42	NUM
cana-1008	494	3	]	]	PUNCT
cana-1008	494	4	k.	k.	PROPN
cana-1008	494	5	ateeq	ateeq	ADV
cana-1008	494	6	and	and	CCONJ
cana-1008	494	7	g.	g.	PROPN
cana-1008	494	8	ganapathy	ganapathy	PROPN
cana-1008	494	9	,	,	PUNCT
cana-1008	494	10	"	"	PUNCT
cana-1008	494	11	the	the	DET
cana-1008	494	12	novel	novel	ADJ
cana-1008	494	13	hybrid	hybrid	ADJ
cana-1008	494	14	modified	modify	VERB
cana-1008	494	15	particle	particle	NOUN
cana-1008	494	16	swarm	swarm	NOUN
cana-1008	494	17	optimization	optimization	NOUN
cana-1008	494	18	–	–	PUNCT
cana-1008	494	19	neural	neural	ADJ
cana-1008	494	20	network	network	NOUN
cana-1008	494	21	(	(	PUNCT
cana-1008	494	22	mpsonn	mpsonn	NOUN
cana-1008	494	23	)	)	PUNCT
cana-1008	494	24	algorithm	algorithm	NOUN
cana-1008	494	25	for	for	ADP
cana-1008	494	26	classifying	classify	VERB
cana-1008	494	27	the	the	DET
cana-1008	494	28	diabetes	diabetes	NOUN
cana-1008	494	29	,	,	PUNCT
cana-1008	494	30	"	"	PUNCT
cana-1008	494	31	international	international	ADJ
cana-1008	494	32	journal	journal	NOUN
cana-1008	494	33	of	of	ADP
cana-1008	494	34	computational	computational	ADJ
cana-1008	494	35	intelligence	intelligence	NOUN
cana-1008	494	36	research	research	NOUN
cana-1008	494	37	,	,	PUNCT
cana-1008	494	38	vol	vol	NOUN
cana-1008	494	39	.	.	PROPN
cana-1008	494	40	13	13	NUM
cana-1008	494	41	,	,	PUNCT
cana-1008	494	42	no	no	INTJ
cana-1008	494	43	.	.	NOUN
cana-1008	494	44	4	4	NUM
cana-1008	494	45	,	,	PUNCT
cana-1008	494	46	pp	pp	ADJ
cana-1008	494	47	.	.	PUNCT
cana-1008	495	1	595	595	NUM
cana-1008	495	2	-	-	SYM
cana-1008	495	3	614	614	NUM
cana-1008	495	4	,	,	PUNCT
cana-1008	495	5	2017	2017	NUM
cana-1008	495	6	.	.	PUNCT