id	sid	tid	token	lemma	pos
cana-4522	1	1	communications	communication	NOUN
cana-4522	1	2	on	on	ADP
cana-4522	1	3	applied	apply	VERB
cana-4522	1	4	nonlinear	nonlinear	ADJ
cana-4522	1	5	analysis	analysis	NOUN
cana-4522	1	6	issn	issn	NOUN
cana-4522	1	7	:	:	PUNCT
cana-4522	1	8	1074	1074	NUM
cana-4522	1	9	-	-	PUNCT
cana-4522	1	10	133x	133x	NUM
cana-4522	1	11	vol	vol	NOUN
cana-4522	1	12	32	32	NUM
cana-4522	1	13	no	no	NOUN
cana-4522	1	14	.	.	PUNCT
cana-4522	2	1	9s	9s	NUM
cana-4522	2	2	(	(	PUNCT
cana-4522	2	3	2025	2025	NUM
cana-4522	2	4	)	)	PUNCT
cana-4522	2	5	2348	2348	NUM
cana-4522	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4522	3	1	a	a	DET
cana-4522	3	2	machine	machine	NOUN
cana-4522	3	3	learning	learn	VERB
cana-4522	3	4	approach	approach	NOUN
cana-4522	3	5	using	use	VERB
cana-4522	3	6	feature	feature	NOUN
cana-4522	3	7	selection	selection	NOUN
cana-4522	3	8	and	and	CCONJ
cana-4522	3	9	scaling	scaling	NOUN
cana-4522	3	10	with	with	ADP
cana-4522	3	11	hyper	hyper	ADJ
cana-4522	3	12	parameter	parameter	NOUN
cana-4522	3	13	tuning	tuning	NOUN
cana-4522	3	14	method	method	NOUN
cana-4522	3	15	for	for	ADP
cana-4522	3	16	early	early	ADJ
cana-4522	3	17	prediction	prediction	NOUN
cana-4522	3	18	of	of	ADP
cana-4522	3	19	diabetes	diabetes	NOUN
cana-4522	3	20	1jyoti	1jyoti	NUM
cana-4522	3	21	n.	n.	NOUN
cana-4522	3	22	dangat	dangat	NOUN
cana-4522	3	23	(	(	PUNCT
cana-4522	3	24	shendage	shendage	NOUN
cana-4522	3	25	)	)	PUNCT
cana-4522	3	26	,	,	PUNCT
cana-4522	3	27	2dr	2dr	X
cana-4522	3	28	.	.	PUNCT
cana-4522	4	1	santosh	santosh	PROPN
cana-4522	4	2	p.	p.	PROPN
cana-4522	4	3	shrikhande	shrikhande	PROPN
cana-4522	4	4	,	,	PUNCT
cana-4522	4	5	3dr	3dr	PROPN
cana-4522	4	6	vijaya	vijaya	PROPN
cana-4522	4	7	kumbhar	kumbhar	NOUN
cana-4522	5	1	1school	1school	NUM
cana-4522	5	2	of	of	ADP
cana-4522	5	3	computer	computer	NOUN
cana-4522	5	4	studies	study	NOUN
cana-4522	5	5	,	,	PUNCT
cana-4522	5	6	sri	sri	PROPN
cana-4522	5	7	balaji	balaji	PROPN
cana-4522	5	8	university	university	PROPN
cana-4522	5	9	,	,	PUNCT
cana-4522	5	10	pune	pune	NOUN
cana-4522	5	11	(	(	PUNCT
cana-4522	5	12	sbup	sbup	PROPN
cana-4522	5	13	)	)	PUNCT
cana-4522	5	14	(	(	PUNCT
cana-4522	5	15	ms	ms	NOUN
cana-4522	5	16	)	)	PUNCT
cana-4522	5	17	email	email	NOUN
cana-4522	5	18	:	:	PUNCT
cana-4522	5	19	dangatjyoti@gmail.com	dangatjyoti@gmail.com	X
cana-4522	6	1	school	school	NOUN
cana-4522	6	2	of	of	ADP
cana-4522	6	3	technology	technology	NOUN
cana-4522	6	4	2s.r.t.m	2s.r.t.m	PROPN
cana-4522	6	5	.	.	PUNCT
cana-4522	6	6	university	university	PROPN
cana-4522	6	7	nanded	nanded	PROPN
cana-4522	6	8	,	,	PUNCT
cana-4522	6	9	sub	sub	NOUN
cana-4522	6	10	-	-	NOUN
cana-4522	6	11	campus	campus	NOUN
cana-4522	6	12	,	,	PUNCT
cana-4522	6	13	latur	latur	PROPN
cana-4522	6	14	(	(	PUNCT
cana-4522	6	15	ms	ms	NOUN
cana-4522	6	16	)	)	PUNCT
cana-4522	6	17	email	email	NOUN
cana-4522	6	18	:	:	PUNCT
cana-4522	6	19	santoshshrikhande@gmail.com	santoshshrikhande@gmail.com	X
cana-4522	6	20	3school	3school	NUM
cana-4522	6	21	of	of	ADP
cana-4522	6	22	computer	computer	NOUN
cana-4522	6	23	studies	study	NOUN
cana-4522	6	24	,	,	PUNCT
cana-4522	6	25	sri	sri	PROPN
cana-4522	6	26	balaji	balaji	PROPN
cana-4522	6	27	university	university	PROPN
cana-4522	6	28	,	,	PUNCT
cana-4522	6	29	pune	pune	NOUN
cana-4522	6	30	(	(	PUNCT
cana-4522	6	31	sbup	sbup	PROPN
cana-4522	6	32	)	)	PUNCT
cana-4522	6	33	(	(	PUNCT
cana-4522	6	34	ms	ms	NOUN
cana-4522	6	35	)	)	PUNCT
cana-4522	6	36	email	email	NOUN
cana-4522	6	37	:	:	PUNCT
cana-4522	6	38	veejeya.kumbhar@gmail.com	veejeya.kumbhar@gmail.com	X
cana-4522	6	39	article	article	NOUN
cana-4522	6	40	history	history	NOUN
cana-4522	6	41	:	:	PUNCT
cana-4522	6	42	received	receive	VERB
cana-4522	6	43	:	:	PUNCT
cana-4522	6	44	12	12	NUM
cana-4522	6	45	-	-	SYM
cana-4522	6	46	01	01	NUM
cana-4522	6	47	-	-	PUNCT
cana-4522	6	48	2025	2025	NUM
cana-4522	6	49	revised	revise	VERB
cana-4522	6	50	:	:	PUNCT
cana-4522	6	51	15	15	NUM
cana-4522	6	52	-	-	NUM
cana-4522	6	53	02	02	NUM
cana-4522	6	54	-	-	PUNCT
cana-4522	6	55	2025	2025	NUM
cana-4522	6	56	accepted	accept	VERB
cana-4522	6	57	:	:	PUNCT
cana-4522	6	58	01	01	NUM
cana-4522	6	59	-	-	SYM
cana-4522	6	60	03	03	NUM
cana-4522	6	61	-	-	PUNCT
cana-4522	6	62	2025	2025	NUM
cana-4522	6	63	abstract	abstract	NOUN
cana-4522	6	64	:	:	PUNCT
cana-4522	6	65	introduction	introduction	NOUN
cana-4522	6	66	:	:	PUNCT
cana-4522	6	67	diabetes	diabetes	NOUN
cana-4522	6	68	mellitus	mellitus	NOUN
cana-4522	6	69	is	be	AUX
cana-4522	6	70	an	an	DET
cana-4522	6	71	enduring	endure	VERB
cana-4522	6	72	condition	condition	NOUN
cana-4522	6	73	characterized	characterize	VERB
cana-4522	6	74	by	by	ADP
cana-4522	6	75	raised	raise	VERB
cana-4522	6	76	blood	blood	NOUN
cana-4522	6	77	glucose	glucose	NOUN
cana-4522	6	78	levels	level	NOUN
cana-4522	6	79	and	and	CCONJ
cana-4522	6	80	has	have	AUX
cana-4522	6	81	become	become	VERB
cana-4522	6	82	a	a	DET
cana-4522	6	83	significant	significant	ADJ
cana-4522	6	84	global	global	ADJ
cana-4522	6	85	health	health	NOUN
cana-4522	6	86	concern	concern	NOUN
cana-4522	6	87	.	.	PUNCT
cana-4522	7	1	early	early	ADJ
cana-4522	7	2	and	and	CCONJ
cana-4522	7	3	accurate	accurate	ADJ
cana-4522	7	4	diagnosis	diagnosis	NOUN
cana-4522	7	5	is	be	AUX
cana-4522	7	6	crucial	crucial	ADJ
cana-4522	7	7	and	and	CCONJ
cana-4522	7	8	that	that	PRON
cana-4522	7	9	can	can	AUX
cana-4522	7	10	help	help	VERB
cana-4522	7	11	to	to	PART
cana-4522	7	12	prevent	prevent	VERB
cana-4522	7	13	or	or	CCONJ
cana-4522	7	14	delay	delay	NOUN
cana-4522	7	15	of	of	ADP
cana-4522	7	16	problems	problem	NOUN
cana-4522	7	17	like	like	ADP
cana-4522	7	18	cardiovascular	cardiovascular	ADJ
cana-4522	7	19	diseases	disease	NOUN
cana-4522	7	20	,	,	PUNCT
cana-4522	7	21	kidney	kidney	NOUN
cana-4522	7	22	complications	complication	NOUN
cana-4522	7	23	,	,	PUNCT
cana-4522	7	24	nerve	nerve	NOUN
cana-4522	7	25	impairment	impairment	NOUN
cana-4522	7	26	,	,	PUNCT
cana-4522	7	27	and	and	CCONJ
cana-4522	7	28	vision	vision	NOUN
cana-4522	7	29	diminishing	diminish	VERB
cana-4522	7	30	due	due	ADP
cana-4522	7	31	to	to	ADP
cana-4522	7	32	diabetes	diabetes	NOUN
cana-4522	7	33	.	.	PUNCT
cana-4522	8	1	diabetes	diabetes	NOUN
cana-4522	8	2	is	be	AUX
cana-4522	8	3	a	a	DET
cana-4522	8	4	chronic	chronic	ADJ
cana-4522	8	5	metabolic	metabolic	NOUN
cana-4522	8	6	disorder	disorder	NOUN
cana-4522	8	7	that	that	PRON
cana-4522	8	8	affects	affect	VERB
cana-4522	8	9	millions	million	NOUN
cana-4522	8	10	of	of	ADP
cana-4522	8	11	people	people	NOUN
cana-4522	8	12	worldwide	worldwide	ADV
cana-4522	8	13	,	,	PUNCT
cana-4522	8	14	leading	lead	VERB
cana-4522	8	15	to	to	ADP
cana-4522	8	16	severe	severe	ADJ
cana-4522	8	17	health	health	NOUN
cana-4522	8	18	complications	complication	NOUN
cana-4522	8	19	if	if	SCONJ
cana-4522	8	20	not	not	PART
cana-4522	8	21	diagnosed	diagnose	VERB
cana-4522	8	22	and	and	CCONJ
cana-4522	8	23	managed	manage	VERB
cana-4522	8	24	in	in	ADP
cana-4522	8	25	its	its	PRON
cana-4522	8	26	early	early	ADJ
cana-4522	8	27	stages	stage	NOUN
cana-4522	8	28	.	.	PUNCT
cana-4522	9	1	early	early	ADJ
cana-4522	9	2	prediction	prediction	NOUN
cana-4522	9	3	of	of	ADP
cana-4522	9	4	diabetes	diabetes	NOUN
cana-4522	9	5	is	be	AUX
cana-4522	9	6	crucial	crucial	ADJ
cana-4522	9	7	for	for	ADP
cana-4522	9	8	timely	timely	ADJ
cana-4522	9	9	intervention	intervention	NOUN
cana-4522	9	10	and	and	CCONJ
cana-4522	9	11	personalized	personalized	ADJ
cana-4522	9	12	treatment	treatment	NOUN
cana-4522	9	13	,	,	PUNCT
cana-4522	9	14	reducing	reduce	VERB
cana-4522	9	15	the	the	DET
cana-4522	9	16	risk	risk	NOUN
cana-4522	9	17	of	of	ADP
cana-4522	9	18	long	long	ADJ
cana-4522	9	19	-	-	PUNCT
cana-4522	9	20	term	term	NOUN
cana-4522	9	21	complications	complication	NOUN
cana-4522	9	22	.	.	PUNCT
cana-4522	10	1	traditional	traditional	ADJ
cana-4522	10	2	diagnostic	diagnostic	ADJ
cana-4522	10	3	approaches	approach	NOUN
cana-4522	10	4	rely	rely	VERB
cana-4522	10	5	on	on	ADP
cana-4522	10	6	clinical	clinical	ADJ
cana-4522	10	7	tests	test	NOUN
cana-4522	10	8	,	,	PUNCT
cana-4522	10	9	which	which	PRON
cana-4522	10	10	may	may	AUX
cana-4522	10	11	not	not	PART
cana-4522	10	12	always	always	ADV
cana-4522	10	13	be	be	AUX
cana-4522	10	14	efficient	efficient	ADJ
cana-4522	10	15	in	in	ADP
cana-4522	10	16	identifying	identify	VERB
cana-4522	10	17	high	high	ADJ
cana-4522	10	18	-	-	PUNCT
cana-4522	10	19	risk	risk	NOUN
cana-4522	10	20	individuals	individual	NOUN
cana-4522	10	21	before	before	ADP
cana-4522	10	22	the	the	DET
cana-4522	10	23	onset	onset	NOUN
cana-4522	10	24	of	of	ADP
cana-4522	10	25	the	the	DET
cana-4522	10	26	disease	disease	NOUN
cana-4522	10	27	.	.	PUNCT
cana-4522	11	1	with	with	ADP
cana-4522	11	2	advancements	advancement	NOUN
cana-4522	11	3	in	in	ADP
cana-4522	11	4	artificial	artificial	ADJ
cana-4522	11	5	intelligence	intelligence	NOUN
cana-4522	11	6	(	(	PUNCT
cana-4522	11	7	ai	ai	NOUN
cana-4522	11	8	)	)	PUNCT
cana-4522	11	9	and	and	CCONJ
cana-4522	11	10	machine	machine	NOUN
cana-4522	11	11	learning	learning	NOUN
cana-4522	11	12	(	(	PUNCT
cana-4522	11	13	ml	ml	NOUN
cana-4522	11	14	)	)	PUNCT
cana-4522	11	15	,	,	PUNCT
cana-4522	11	16	predictive	predictive	ADJ
cana-4522	11	17	models	model	NOUN
cana-4522	11	18	have	have	AUX
cana-4522	11	19	gained	gain	VERB
cana-4522	11	20	prominence	prominence	NOUN
cana-4522	11	21	in	in	ADP
cana-4522	11	22	healthcare	healthcare	PROPN
cana-4522	11	23	applications	application	NOUN
cana-4522	11	24	,	,	PUNCT
cana-4522	11	25	offering	offer	VERB
cana-4522	11	26	improved	improved	ADJ
cana-4522	11	27	accuracy	accuracy	NOUN
cana-4522	11	28	and	and	CCONJ
cana-4522	11	29	efficiency	efficiency	NOUN
cana-4522	11	30	in	in	ADP
cana-4522	11	31	disease	disease	NOUN
cana-4522	11	32	diagnosis	diagnosis	NOUN
cana-4522	11	33	.	.	PUNCT
cana-4522	12	1	however	however	ADV
cana-4522	12	2	,	,	PUNCT
cana-4522	12	3	the	the	DET
cana-4522	12	4	performance	performance	NOUN
cana-4522	12	5	of	of	ADP
cana-4522	12	6	these	these	DET
cana-4522	12	7	models	model	NOUN
cana-4522	12	8	heavily	heavily	ADV
cana-4522	12	9	depends	depend	VERB
cana-4522	12	10	on	on	ADP
cana-4522	12	11	the	the	DET
cana-4522	12	12	quality	quality	NOUN
cana-4522	12	13	of	of	ADP
cana-4522	12	14	input	input	NOUN
cana-4522	12	15	features	feature	NOUN
cana-4522	12	16	,	,	PUNCT
cana-4522	12	17	data	datum	NOUN
cana-4522	12	18	preprocessing	preprocessing	NOUN
cana-4522	12	19	techniques	technique	NOUN
cana-4522	12	20	,	,	PUNCT
cana-4522	12	21	and	and	CCONJ
cana-4522	12	22	hyperparameter	hyperparameter	NOUN
cana-4522	12	23	tuning	tune	VERB
cana-4522	12	24	strategies	strategy	NOUN
cana-4522	12	25	.	.	PUNCT
cana-4522	13	1	objectives	objective	NOUN
cana-4522	13	2	:	:	PUNCT
cana-4522	13	3	the	the	DET
cana-4522	13	4	main	main	ADJ
cana-4522	13	5	objective	objective	NOUN
cana-4522	13	6	of	of	ADP
cana-4522	13	7	this	this	DET
cana-4522	13	8	research	research	NOUN
cana-4522	13	9	work	work	NOUN
cana-4522	13	10	is	be	AUX
cana-4522	13	11	to	to	PART
cana-4522	13	12	predict	predict	VERB
cana-4522	13	13	diabetes	diabetes	NOUN
cana-4522	13	14	at	at	ADP
cana-4522	13	15	an	an	DET
cana-4522	13	16	early	early	ADJ
cana-4522	13	17	stage	stage	NOUN
cana-4522	13	18	so	so	SCONJ
cana-4522	13	19	that	that	SCONJ
cana-4522	13	20	any	any	DET
cana-4522	13	21	severe	severe	ADJ
cana-4522	13	22	complications	complication	NOUN
cana-4522	13	23	may	may	AUX
cana-4522	13	24	avoid	avoid	VERB
cana-4522	13	25	.	.	PUNCT
cana-4522	14	1	methods	method	NOUN
cana-4522	14	2	:	:	PUNCT
cana-4522	14	3	the	the	DET
cana-4522	14	4	most	most	ADV
cana-4522	14	5	significant	significant	ADJ
cana-4522	14	6	and	and	CCONJ
cana-4522	14	7	robust	robust	ADJ
cana-4522	14	8	features	feature	NOUN
cana-4522	14	9	of	of	ADP
cana-4522	14	10	the	the	DET
cana-4522	14	11	dataset	dataset	NOUN
cana-4522	14	12	are	be	AUX
cana-4522	14	13	chosen	choose	VERB
cana-4522	14	14	using	use	VERB
cana-4522	14	15	the	the	DET
cana-4522	14	16	attribute	attribute	NOUN
cana-4522	14	17	selection	selection	NOUN
cana-4522	14	18	tool	tool	NOUN
cana-4522	14	19	and	and	CCONJ
cana-4522	14	20	correlation	correlation	NOUN
cana-4522	14	21	attribute	attribute	NOUN
cana-4522	14	22	estimation	estimation	NOUN
cana-4522	14	23	method	method	NOUN
cana-4522	14	24	by	by	ADP
cana-4522	14	25	using	use	VERB
cana-4522	14	26	the	the	DET
cana-4522	14	27	weka	weka	PROPN
cana-4522	14	28	software	software	PROPN
cana-4522	14	29	tool	tool	PROPN
cana-4522	14	30	.	.	PUNCT
cana-4522	15	1	then	then	ADV
cana-4522	15	2	,	,	PUNCT
cana-4522	15	3	features	feature	NOUN
cana-4522	15	4	form	form	VERB
cana-4522	15	5	the	the	DET
cana-4522	15	6	dataset	dataset	NOUN
cana-4522	15	7	are	be	AUX
cana-4522	15	8	scaled	scale	VERB
cana-4522	15	9	using	use	VERB
cana-4522	15	10	the	the	DET
cana-4522	15	11	standardization	standardization	NOUN
cana-4522	15	12	feature	feature	NOUN
cana-4522	15	13	scaling	scale	VERB
cana-4522	15	14	technique	technique	NOUN
cana-4522	15	15	and	and	CCONJ
cana-4522	15	16	different	different	ADJ
cana-4522	15	17	ml	ml	NOUN
cana-4522	15	18	classification	classification	NOUN
cana-4522	15	19	algorithms	algorithm	NOUN
cana-4522	15	20	such	such	ADJ
cana-4522	15	21	as	as	ADP
cana-4522	15	22	lr	lr	PROPN
cana-4522	15	23	,	,	PUNCT
cana-4522	15	24	knn	knn	PROPN
cana-4522	15	25	,	,	PUNCT
cana-4522	15	26	naïve	naïve	ADJ
cana-4522	15	27	bayes	bayes	NOUN
cana-4522	15	28	,	,	PUNCT
cana-4522	15	29	support	support	VERB
cana-4522	15	30	vector	vector	NOUN
cana-4522	15	31	machine	machine	NOUN
cana-4522	15	32	,	,	PUNCT
cana-4522	15	33	decision	decision	NOUN
cana-4522	15	34	tree	tree	NOUN
cana-4522	15	35	and	and	CCONJ
cana-4522	15	36	random	random	ADJ
cana-4522	15	37	forest	forest	NOUN
cana-4522	15	38	are	be	AUX
cana-4522	15	39	used	use	VERB
cana-4522	15	40	for	for	ADP
cana-4522	15	41	experimenting	experiment	VERB
cana-4522	15	42	with	with	ADP
cana-4522	15	43	the	the	DET
cana-4522	15	44	above	above	ADJ
cana-4522	15	45	machine	machine	NOUN
cana-4522	15	46	algorithm	algorithm	NOUN
cana-4522	15	47	on	on	ADP
cana-4522	15	48	the	the	DET
cana-4522	15	49	pima	pima	PROPN
cana-4522	15	50	indian	indian	PROPN
cana-4522	15	51	diabetes	diabetes	NOUN
cana-4522	15	52	dataset	dataset	VERB
cana-4522	15	53	in	in	ADP
cana-4522	15	54	pythonin	pythonin	PROPN
cana-4522	15	55	the	the	DET
cana-4522	15	56	preprocessing	preprocessing	NOUN
cana-4522	15	57	method	method	NOUN
cana-4522	15	58	,	,	PUNCT
cana-4522	15	59	identification	identification	NOUN
cana-4522	15	60	and	and	CCONJ
cana-4522	15	61	removal	removal	NOUN
cana-4522	15	62	of	of	ADP
cana-4522	15	63	null	null	ADJ
cana-4522	15	64	and	and	CCONJ
cana-4522	15	65	duplicate	duplicate	ADJ
cana-4522	15	66	values	value	NOUN
cana-4522	15	67	have	have	AUX
cana-4522	15	68	been	be	AUX
cana-4522	15	69	replaced	replace	VERB
cana-4522	15	70	with	with	ADP
cana-4522	15	71	the	the	DET
cana-4522	15	72	mean	mean	ADJ
cana-4522	15	73	values	value	NOUN
cana-4522	15	74	.	.	PUNCT
cana-4522	16	1	results	result	NOUN
cana-4522	16	2	:	:	PUNCT
cana-4522	16	3	by	by	ADP
cana-4522	16	4	applying	apply	VERB
cana-4522	16	5	six	six	NUM
cana-4522	16	6	different	different	ADJ
cana-4522	16	7	machine	machine	NOUN
cana-4522	16	8	learning	learn	VERB
cana-4522	16	9	algorithms	algorithm	NOUN
cana-4522	16	10	on	on	ADP
cana-4522	16	11	pima	pima	PROPN
cana-4522	16	12	indian	indian	PROPN
cana-4522	16	13	diabetes	diabetes	NOUN
cana-4522	16	14	dataset	dataset	VERB
cana-4522	16	15	have	have	AUX
cana-4522	16	16	shown	show	VERB
cana-4522	16	17	that	that	SCONJ
cana-4522	16	18	the	the	DET
cana-4522	16	19	k	k	NOUN
cana-4522	16	20	-	-	PUNCT
cana-4522	16	21	nearest	near	ADJ
cana-4522	16	22	neighbor	neighbor	NOUN
cana-4522	16	23	and	and	CCONJ
cana-4522	16	24	naïve	naïve	ADJ
cana-4522	16	25	bayes	baye	NOUN
cana-4522	16	26	both	both	DET
cana-4522	16	27	classifiers	classifier	NOUN
cana-4522	16	28	reported	report	VERB
cana-4522	16	29	the	the	DET
cana-4522	16	30	maximum	maximum	ADJ
cana-4522	16	31	prediction	prediction	NOUN
cana-4522	16	32	accuracy	accuracy	NOUN
cana-4522	16	33	of	of	ADP
cana-4522	16	34	81.82	81.82	NUM
cana-4522	16	35	%	%	NOUN
cana-4522	16	36	,	,	PUNCT
cana-4522	16	37	followed	follow	VERB
cana-4522	16	38	by	by	ADP
cana-4522	16	39	lr	lr	NOUN
cana-4522	16	40	,	,	PUNCT
cana-4522	16	41	svm	svm	ADJ
cana-4522	16	42	,	,	PUNCT
cana-4522	16	43	and	and	CCONJ
cana-4522	16	44	rf	rf	VERB
cana-4522	16	45	with	with	ADP
cana-4522	16	46	accuracies	accuracy	NOUN
cana-4522	16	47	of	of	ADP
cana-4522	16	48	79.87	79.87	NUM
cana-4522	16	49	%	%	NOUN
cana-4522	16	50	,	,	PUNCT
cana-4522	16	51	79.22	79.22	NUM
cana-4522	16	52	%	%	NOUN
cana-4522	16	53	,	,	PUNCT
cana-4522	16	54	and	and	CCONJ
cana-4522	16	55	77.27	77.27	NUM
cana-4522	16	56	%	%	NOUN
cana-4522	16	57	respectively	respectively	ADV
cana-4522	16	58	.	.	PUNCT
cana-4522	17	1	conclusions	conclusion	NOUN
cana-4522	17	2	:	:	PUNCT
cana-4522	17	3	by	by	ADP
cana-4522	17	4	appropriate	appropriate	ADJ
cana-4522	17	5	feature	feature	NOUN
cana-4522	17	6	selection	selection	NOUN
cana-4522	17	7	and	and	CCONJ
cana-4522	17	8	by	by	ADP
cana-4522	17	9	hyperparameter	hyperparameter	NOUN
cana-4522	17	10	tunning	tun	VERB
cana-4522	17	11	increase	increase	NOUN
cana-4522	17	12	the	the	DET
cana-4522	17	13	diabetes	diabetes	NOUN
cana-4522	17	14	prediction	prediction	NOUN
cana-4522	17	15	accuracy	accuracy	NOUN
cana-4522	17	16	at	at	ADP
cana-4522	17	17	an	an	DET
cana-4522	17	18	early	early	ADJ
cana-4522	17	19	stage	stage	NOUN
cana-4522	17	20	.	.	PUNCT
cana-4522	18	1	keywords	keyword	NOUN
cana-4522	18	2	:	:	PUNCT
cana-4522	18	3	diabetes	diabetes	NOUN
cana-4522	18	4	prediction	prediction	NOUN
cana-4522	18	5	,	,	PUNCT
cana-4522	18	6	feature	feature	NOUN
cana-4522	18	7	selection	selection	NOUN
cana-4522	18	8	,	,	PUNCT
cana-4522	18	9	features	feature	VERB
cana-4522	18	10	scaling	scaling	NOUN
cana-4522	18	11	,	,	PUNCT
cana-4522	18	12	hyper	hyper	ADJ
cana-4522	18	13	parameter	parameter	NOUN
cana-4522	18	14	tuning	tuning	NOUN
cana-4522	18	15	,	,	PUNCT
cana-4522	18	16	machine	machine	NOUN
cana-4522	18	17	learning	learning	NOUN
cana-4522	18	18	algorithms	algorithm	NOUN
cana-4522	18	19	.	.	PUNCT
cana-4522	19	1	mailto:dangatjyoti@gmail.com	mailto:dangatjyoti@gmail.com	X
cana-4522	19	2	mailto:santoshshrikhande@gmail.com	mailto:santoshshrikhande@gmail.com	X
cana-4522	20	1	communications	communication	NOUN
cana-4522	20	2	on	on	ADP
cana-4522	20	3	applied	apply	VERB
cana-4522	20	4	nonlinear	nonlinear	ADJ
cana-4522	20	5	analysis	analysis	NOUN
cana-4522	20	6	issn	issn	NOUN
cana-4522	20	7	:	:	PUNCT
cana-4522	20	8	1074	1074	NUM
cana-4522	20	9	-	-	PUNCT
cana-4522	20	10	133x	133x	NUM
cana-4522	20	11	vol	vol	NOUN
cana-4522	20	12	32	32	NUM
cana-4522	20	13	no	no	NOUN
cana-4522	20	14	.	.	PUNCT
cana-4522	21	1	9s	9s	NUM
cana-4522	21	2	(	(	PUNCT
cana-4522	21	3	2025	2025	NUM
cana-4522	21	4	)	)	PUNCT
cana-4522	21	5	2349	2349	NUM
cana-4522	21	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4522	21	7	1	1	X
cana-4522	21	8	.	.	PUNCT
cana-4522	22	1	introduction	introduction	NOUN
cana-4522	22	2	diabetes	diabetes	NOUN
cana-4522	22	3	mellitus	mellitus	NOUN
cana-4522	22	4	or	or	CCONJ
cana-4522	22	5	simply	simply	ADV
cana-4522	22	6	diabetes	diabetes	NOUN
cana-4522	22	7	is	be	AUX
cana-4522	22	8	one	one	NUM
cana-4522	22	9	of	of	ADP
cana-4522	22	10	the	the	DET
cana-4522	22	11	most	most	ADV
cana-4522	22	12	widespread	widespread	ADJ
cana-4522	22	13	diseases	disease	NOUN
cana-4522	22	14	in	in	ADP
cana-4522	22	15	the	the	DET
cana-4522	22	16	world	world	NOUN
cana-4522	22	17	with	with	ADP
cana-4522	22	18	the	the	DET
cana-4522	22	19	high	high	ADJ
cana-4522	22	20	mortality	mortality	NOUN
cana-4522	22	21	rate	rate	NOUN
cana-4522	22	22	.	.	PUNCT
cana-4522	23	1	approximately	approximately	ADV
cana-4522	23	2	830	830	NUM
cana-4522	23	3	million	million	NUM
cana-4522	23	4	people	people	NOUN
cana-4522	23	5	global	global	ADJ
cana-4522	23	6	have	have	VERB
cana-4522	23	7	diabetes	diabetes	NOUN
cana-4522	23	8	disease	disease	NOUN
cana-4522	23	9	,	,	PUNCT
cana-4522	23	10	with	with	ADP
cana-4522	23	11	the	the	DET
cana-4522	23	12	majority	majority	NOUN
cana-4522	23	13	living	live	VERB
cana-4522	23	14	in	in	ADP
cana-4522	23	15	the	the	DET
cana-4522	23	16	low	low	ADJ
cana-4522	23	17	and	and	CCONJ
cana-4522	23	18	middle	middle	ADJ
cana-4522	23	19	-	-	PUNCT
cana-4522	23	20	income	income	NOUN
cana-4522	23	21	countries	country	NOUN
cana-4522	23	22	[	[	X
cana-4522	23	23	1	1	NUM
cana-4522	23	24	]	]	PUNCT
cana-4522	23	25	,	,	PUNCT
cana-4522	23	26	[	[	X
cana-4522	23	27	2	2	NUM
cana-4522	23	28	]	]	PUNCT
cana-4522	23	29	.	.	PUNCT
cana-4522	24	1	each	each	DET
cana-4522	24	2	year	year	NOUN
cana-4522	24	3	,	,	PUNCT
cana-4522	24	4	diabetes	diabetes	NOUN
cana-4522	24	5	is	be	AUX
cana-4522	24	6	directly	directly	ADV
cana-4522	24	7	responsible	responsible	ADJ
cana-4522	24	8	for	for	ADP
cana-4522	24	9	the	the	DET
cana-4522	24	10	millions	million	NOUN
cana-4522	24	11	of	of	ADP
cana-4522	24	12	deaths	death	NOUN
cana-4522	24	13	worldwide	worldwide	ADV
cana-4522	24	14	.	.	PUNCT
cana-4522	25	1	as	as	ADP
cana-4522	25	2	per	per	ADP
cana-4522	25	3	the	the	DET
cana-4522	25	4	international	international	ADJ
cana-4522	25	5	diabetes	diabetes	PROPN
cana-4522	25	6	federation	federation	PROPN
cana-4522	25	7	(	(	PUNCT
cana-4522	25	8	idf	idf	PROPN
cana-4522	25	9	)	)	PUNCT
cana-4522	25	10	projections	projection	NOUN
cana-4522	25	11	indicates	indicate	VERB
cana-4522	25	12	a	a	DET
cana-4522	25	13	concerning	concern	VERB
cana-4522	25	14	growth	growth	NOUN
cana-4522	25	15	in	in	ADP
cana-4522	25	16	the	the	DET
cana-4522	25	17	diabetes	diabetes	NOUN
cana-4522	25	18	and	and	CCONJ
cana-4522	25	19	approximately	approximately	ADV
cana-4522	25	20	1	1	NUM
cana-4522	25	21	in	in	ADP
cana-4522	25	22	8	8	NUM
cana-4522	25	23	adults	adult	NOUN
cana-4522	25	24	will	will	AUX
cana-4522	25	25	be	be	AUX
cana-4522	25	26	living	live	VERB
cana-4522	25	27	with	with	ADP
cana-4522	25	28	diabetes	diabetes	NOUN
cana-4522	25	29	by	by	ADP
cana-4522	25	30	2045	2045	NUM
cana-4522	25	31	[	[	X
cana-4522	25	32	3	3	NUM
cana-4522	25	33	]	]	PUNCT
cana-4522	25	34	.	.	PUNCT
cana-4522	26	1	diabetes	diabetes	NOUN
cana-4522	26	2	is	be	AUX
cana-4522	26	3	the	the	DET
cana-4522	26	4	condition	condition	NOUN
cana-4522	26	5	considered	consider	VERB
cana-4522	26	6	by	by	ADP
cana-4522	26	7	unusually	unusually	ADV
cana-4522	26	8	maximum	maximum	ADJ
cana-4522	26	9	level	level	NOUN
cana-4522	26	10	of	of	ADP
cana-4522	26	11	glucose	glucose	NOUN
cana-4522	26	12	in	in	ADP
cana-4522	26	13	the	the	DET
cana-4522	26	14	blood	blood	NOUN
cana-4522	26	15	because	because	SCONJ
cana-4522	26	16	human	human	ADJ
cana-4522	26	17	body	body	NOUN
cana-4522	26	18	does	do	AUX
cana-4522	26	19	not	not	PART
cana-4522	26	20	properly	properly	ADV
cana-4522	26	21	process	process	VERB
cana-4522	26	22	the	the	DET
cana-4522	26	23	food	food	NOUN
cana-4522	26	24	for	for	ADP
cana-4522	26	25	making	make	VERB
cana-4522	26	26	energy	energy	NOUN
cana-4522	26	27	.	.	PUNCT
cana-4522	27	1	diabetic	diabetic	ADJ
cana-4522	27	2	human	human	ADJ
cana-4522	27	3	body	body	NOUN
cana-4522	27	4	does	do	AUX
cana-4522	27	5	not	not	PART
cana-4522	27	6	produce	produce	VERB
cana-4522	27	7	enough	enough	ADJ
cana-4522	27	8	amount	amount	NOUN
cana-4522	27	9	of	of	ADP
cana-4522	27	10	insulin	insulin	NOUN
cana-4522	27	11	from	from	ADP
cana-4522	27	12	the	the	DET
cana-4522	27	13	pancreas	pancrea	NOUN
cana-4522	27	14	and	and	CCONJ
cana-4522	27	15	therefore	therefore	ADV
cana-4522	27	16	glucose	glucose	NOUN
cana-4522	27	17	can	can	AUX
cana-4522	27	18	not	not	PART
cana-4522	27	19	be	be	AUX
cana-4522	27	20	converted	convert	VERB
cana-4522	27	21	into	into	ADP
cana-4522	27	22	energy	energy	NOUN
cana-4522	27	23	.	.	PUNCT
cana-4522	28	1	the	the	DET
cana-4522	28	2	general	general	ADJ
cana-4522	28	3	symptoms	symptom	NOUN
cana-4522	28	4	of	of	ADP
cana-4522	28	5	diabetes	diabetes	NOUN
cana-4522	28	6	are	be	AUX
cana-4522	28	7	recurrent	recurrent	ADJ
cana-4522	28	8	urination	urination	NOUN
cana-4522	28	9	,	,	PUNCT
cana-4522	28	10	weight	weight	NOUN
cana-4522	28	11	loss	loss	NOUN
cana-4522	28	12	,	,	PUNCT
cana-4522	28	13	rise	rise	VERB
cana-4522	28	14	in	in	ADP
cana-4522	28	15	thirst	thirst	NOUN
cana-4522	28	16	,	,	PUNCT
cana-4522	28	17	growth	growth	NOUN
cana-4522	28	18	in	in	ADP
cana-4522	28	19	hunger	hunger	NOUN
cana-4522	28	20	,	,	PUNCT
cana-4522	28	21	slowly	slowly	ADV
cana-4522	28	22	recovery	recovery	NOUN
cana-4522	28	23	of	of	ADP
cana-4522	28	24	injuries	injury	NOUN
cana-4522	28	25	and	and	CCONJ
cana-4522	28	26	dizziness	dizziness	NOUN
cana-4522	28	27	etc	etc	X
cana-4522	28	28	.	.	PUNCT
cana-4522	29	1	[	[	X
cana-4522	29	2	4	4	NUM
cana-4522	29	3	]	]	PUNCT
cana-4522	29	4	,	,	PUNCT
cana-4522	29	5	[	[	X
cana-4522	29	6	5	5	NUM
cana-4522	29	7	]	]	PUNCT
cana-4522	29	8	.	.	PUNCT
cana-4522	30	1	if	if	SCONJ
cana-4522	30	2	diabetes	diabetes	NOUN
cana-4522	30	3	is	be	AUX
cana-4522	30	4	not	not	PART
cana-4522	30	5	undergone	undergo	VERB
cana-4522	30	6	proper	proper	ADJ
cana-4522	30	7	treatments	treatment	NOUN
cana-4522	30	8	then	then	ADV
cana-4522	30	9	it	it	PRON
cana-4522	30	10	effects	effect	VERB
cana-4522	30	11	on	on	ADP
cana-4522	30	12	other	other	ADJ
cana-4522	30	13	part	part	NOUN
cana-4522	30	14	of	of	ADP
cana-4522	30	15	human	human	ADJ
cana-4522	30	16	body	body	NOUN
cana-4522	30	17	such	such	ADJ
cana-4522	30	18	as	as	ADP
cana-4522	30	19	cardiovascular	cardiovascular	ADJ
cana-4522	30	20	disease	disease	NOUN
cana-4522	30	21	i.e.	i.e.	X
cana-4522	30	22	heart	heart	NOUN
cana-4522	30	23	attacks	attack	NOUN
cana-4522	30	24	,	,	PUNCT
cana-4522	30	25	kidney	kidney	NOUN
cana-4522	30	26	diseases	disease	NOUN
cana-4522	30	27	,	,	PUNCT
cana-4522	30	28	blindness	blindness	NOUN
cana-4522	30	29	,	,	PUNCT
cana-4522	30	30	nerve	nerve	NOUN
cana-4522	30	31	impairment	impairment	NOUN
cana-4522	30	32	etc	etc	X
cana-4522	30	33	.	.	PUNCT
cana-4522	31	1	[	[	X
cana-4522	31	2	6	6	NUM
cana-4522	31	3	]	]	PUNCT
cana-4522	31	4	.	.	PUNCT
cana-4522	32	1	hence	hence	ADV
cana-4522	32	2	,	,	PUNCT
cana-4522	32	3	initial	initial	ADJ
cana-4522	32	4	-	-	PUNCT
cana-4522	32	5	stage	stage	NOUN
cana-4522	32	6	diagnosis	diagnosis	NOUN
cana-4522	32	7	of	of	ADP
cana-4522	32	8	diabetes	diabetes	NOUN
cana-4522	32	9	is	be	AUX
cana-4522	32	10	essential	essential	ADJ
cana-4522	32	11	,	,	PUNCT
cana-4522	32	12	as	as	SCONJ
cana-4522	32	13	several	several	ADJ
cana-4522	32	14	cases	case	NOUN
cana-4522	32	15	become	become	VERB
cana-4522	32	16	severe	severe	ADJ
cana-4522	32	17	due	due	ADP
cana-4522	32	18	to	to	ADP
cana-4522	32	19	the	the	DET
cana-4522	32	20	delayed	delay	VERB
cana-4522	32	21	detection	detection	NOUN
cana-4522	32	22	and	and	CCONJ
cana-4522	32	23	medical	medical	ADJ
cana-4522	32	24	treatments	treatment	NOUN
cana-4522	32	25	.	.	PUNCT
cana-4522	33	1	now	now	ADV
cana-4522	33	2	a	a	DET
cana-4522	33	3	day	day	NOUN
cana-4522	33	4	,	,	PUNCT
cana-4522	33	5	machine	machine	NOUN
cana-4522	33	6	learning	learn	VERB
cana-4522	33	7	algorithm	algorithm	PROPN
cana-4522	33	8	based	base	VERB
cana-4522	33	9	approaches	approach	NOUN
cana-4522	33	10	have	have	AUX
cana-4522	33	11	gained	gain	VERB
cana-4522	33	12	prominence	prominence	NOUN
cana-4522	33	13	for	for	ADP
cana-4522	33	14	their	their	PRON
cana-4522	33	15	effectiveness	effectiveness	NOUN
cana-4522	33	16	in	in	ADP
cana-4522	33	17	early	early	ADJ
cana-4522	33	18	prediction	prediction	NOUN
cana-4522	33	19	of	of	ADP
cana-4522	33	20	diabetes	diabetes	NOUN
cana-4522	33	21	.	.	PUNCT
cana-4522	34	1	therefore	therefore	ADV
cana-4522	34	2	,	,	PUNCT
cana-4522	34	3	machine	machine	NOUN
cana-4522	34	4	learning	learn	VERB
cana-4522	34	5	algorithms	algorithm	NOUN
cana-4522	34	6	are	be	AUX
cana-4522	34	7	mostly	mostly	ADV
cana-4522	34	8	used	use	VERB
cana-4522	34	9	recently	recently	ADV
cana-4522	34	10	for	for	ADP
cana-4522	34	11	early	early	ADJ
cana-4522	34	12	prediction	prediction	NOUN
cana-4522	34	13	of	of	ADP
cana-4522	34	14	diabetes	diabetes	NOUN
cana-4522	34	15	so	so	SCONJ
cana-4522	34	16	that	that	SCONJ
cana-4522	34	17	treatments	treatment	NOUN
cana-4522	34	18	can	can	AUX
cana-4522	34	19	be	be	AUX
cana-4522	34	20	taken	take	VERB
cana-4522	34	21	quickly	quickly	ADV
cana-4522	34	22	and	and	CCONJ
cana-4522	34	23	avoid	avoid	VERB
cana-4522	34	24	further	further	ADJ
cana-4522	34	25	complications	complication	NOUN
cana-4522	34	26	.	.	PUNCT
cana-4522	35	1	the	the	DET
cana-4522	35	2	proposed	propose	VERB
cana-4522	35	3	research	research	NOUN
cana-4522	35	4	work	work	NOUN
cana-4522	35	5	methodology	methodology	NOUN
cana-4522	35	6	for	for	ADP
cana-4522	35	7	early	early	ADJ
cana-4522	35	8	prediction	prediction	NOUN
cana-4522	35	9	of	of	ADP
cana-4522	35	10	the	the	DET
cana-4522	35	11	diabetes	diabetes	NOUN
cana-4522	35	12	disease	disease	NOUN
cana-4522	35	13	using	use	VERB
cana-4522	35	14	different	different	ADJ
cana-4522	35	15	machine	machine	NOUN
cana-4522	35	16	learning	learn	VERB
cana-4522	35	17	algorithms	algorithm	NOUN
cana-4522	35	18	with	with	ADP
cana-4522	35	19	feature	feature	NOUN
cana-4522	35	20	selection	selection	NOUN
cana-4522	35	21	,	,	PUNCT
cana-4522	35	22	feature	feature	VERB
cana-4522	35	23	scaling	scaling	NOUN
cana-4522	35	24	and	and	CCONJ
cana-4522	35	25	hyper	hyper	ADJ
cana-4522	35	26	parameter	parameter	NOUN
cana-4522	35	27	tuning	tune	VERB
cana-4522	35	28	techniques	technique	NOUN
cana-4522	35	29	on	on	ADP
cana-4522	35	30	the	the	DET
cana-4522	35	31	indian	indian	ADJ
cana-4522	35	32	diabetes	diabetes	PROPN
cana-4522	35	33	pima	pima	PROPN
cana-4522	35	34	dataset	dataset	PROPN
cana-4522	35	35	.	.	PUNCT
cana-4522	36	1	the	the	DET
cana-4522	36	2	main	main	ADJ
cana-4522	36	3	contributions	contribution	NOUN
cana-4522	36	4	of	of	ADP
cana-4522	36	5	this	this	DET
cana-4522	36	6	proposed	propose	VERB
cana-4522	36	7	methodology	methodology	NOUN
cana-4522	36	8	are	be	AUX
cana-4522	36	9	:	:	PUNCT
cana-4522	36	10	(	(	PUNCT
cana-4522	36	11	i	i	NOUN
cana-4522	36	12	)	)	PUNCT
cana-4522	36	13	select	select	VERB
cana-4522	36	14	the	the	DET
cana-4522	36	15	robust	robust	ADJ
cana-4522	36	16	and	and	CCONJ
cana-4522	36	17	significant	significant	ADJ
cana-4522	36	18	feature	feature	NOUN
cana-4522	36	19	from	from	ADP
cana-4522	36	20	the	the	DET
cana-4522	36	21	diabetes	diabetes	NOUN
cana-4522	36	22	dataset	dataset	VERB
cana-4522	36	23	using	use	VERB
cana-4522	36	24	attribute	attribute	NOUN
cana-4522	36	25	selection	selection	NOUN
cana-4522	36	26	tool	tool	NOUN
cana-4522	36	27	and	and	CCONJ
cana-4522	36	28	correlation	correlation	NOUN
cana-4522	36	29	attribute	attribute	NOUN
cana-4522	36	30	estimation	estimation	NOUN
cana-4522	36	31	method	method	NOUN
cana-4522	36	32	in	in	ADP
cana-4522	36	33	the	the	DET
cana-4522	36	34	weka	weka	PROPN
cana-4522	36	35	.	.	PUNCT
cana-4522	37	1	(	(	PUNCT
cana-4522	37	2	ii	ii	NOUN
cana-4522	37	3	)	)	PUNCT
cana-4522	37	4	apply	apply	VERB
cana-4522	37	5	the	the	DET
cana-4522	37	6	standard	standard	ADJ
cana-4522	37	7	scalar	scalar	ADJ
cana-4522	37	8	features	feature	NOUN
cana-4522	37	9	scaling	scale	VERB
cana-4522	37	10	techniques	technique	NOUN
cana-4522	37	11	for	for	ADP
cana-4522	37	12	scaling	scale	VERB
cana-4522	37	13	the	the	DET
cana-4522	37	14	significant	significant	ADJ
cana-4522	37	15	features	feature	NOUN
cana-4522	37	16	(	(	PUNCT
cana-4522	37	17	iii	iii	NOUN
cana-4522	37	18	)	)	PUNCT
cana-4522	37	19	then	then	ADV
cana-4522	37	20	,	,	PUNCT
cana-4522	37	21	different	different	ADJ
cana-4522	37	22	machine	machine	NOUN
cana-4522	37	23	learning	learn	VERB
cana-4522	37	24	algorithms	algorithm	NOUN
cana-4522	37	25	with	with	ADP
cana-4522	37	26	hyper	hyper	ADJ
cana-4522	37	27	parameter	parameter	NOUN
cana-4522	37	28	tuning	tune	VERB
cana-4522	37	29	techniques	technique	NOUN
cana-4522	37	30	are	be	AUX
cana-4522	37	31	used	use	VERB
cana-4522	37	32	for	for	ADP
cana-4522	37	33	classification	classification	NOUN
cana-4522	37	34	of	of	ADP
cana-4522	37	35	the	the	DET
cana-4522	37	36	diabetes	diabetes	NOUN
cana-4522	37	37	dataset	dataset	VERB
cana-4522	37	38	and	and	CCONJ
cana-4522	37	39	improving	improve	VERB
cana-4522	37	40	the	the	DET
cana-4522	37	41	diabetes	diabetes	NOUN
cana-4522	37	42	forecast	forecast	NOUN
cana-4522	37	43	results	result	NOUN
cana-4522	37	44	.	.	PUNCT
cana-4522	38	1	the	the	DET
cana-4522	38	2	performance	performance	NOUN
cana-4522	38	3	of	of	ADP
cana-4522	38	4	the	the	DET
cana-4522	38	5	proposed	propose	VERB
cana-4522	38	6	methodology	methodology	NOUN
cana-4522	38	7	is	be	AUX
cana-4522	38	8	verified	verify	VERB
cana-4522	38	9	using	use	VERB
cana-4522	38	10	perdition	perdition	NOUN
cana-4522	38	11	accuracy	accuracy	NOUN
cana-4522	38	12	percentage	percentage	NOUN
cana-4522	38	13	,	,	PUNCT
cana-4522	38	14	confusion	confusion	NOUN
cana-4522	38	15	matrix	matrix	NOUN
cana-4522	38	16	,	,	PUNCT
cana-4522	38	17	precision	precision	NOUN
cana-4522	38	18	,	,	PUNCT
cana-4522	38	19	recall	recall	NOUN
cana-4522	38	20	,	,	PUNCT
cana-4522	38	21	and	and	CCONJ
cana-4522	38	22	f1	f1	NOUN
cana-4522	38	23	score	score	NOUN
cana-4522	38	24	measures	measure	NOUN
cana-4522	38	25	.	.	PUNCT
cana-4522	39	1	2	2	X
cana-4522	39	2	.	.	X
cana-4522	39	3	objectives	objective	VERB
cana-4522	39	4	the	the	DET
cana-4522	39	5	primary	primary	ADJ
cana-4522	39	6	objectives	objective	NOUN
cana-4522	39	7	of	of	ADP
cana-4522	39	8	this	this	DET
cana-4522	39	9	study	study	NOUN
cana-4522	39	10	are	be	AUX
cana-4522	39	11	as	as	SCONJ
cana-4522	39	12	follows	follow	VERB
cana-4522	39	13	:	:	PUNCT
cana-4522	39	14	1	1	X
cana-4522	39	15	.	.	X
cana-4522	39	16	develop	develop	VERB
cana-4522	39	17	an	an	DET
cana-4522	39	18	early	early	ADJ
cana-4522	39	19	prediction	prediction	NOUN
cana-4522	39	20	model	model	NOUN
cana-4522	39	21	for	for	ADP
cana-4522	39	22	diabetes	diabetes	NOUN
cana-4522	39	23	–	–	PUNCT
cana-4522	39	24	utilize	utilize	VERB
cana-4522	39	25	machine	machine	NOUN
cana-4522	39	26	learning	learn	VERB
cana-4522	39	27	techniques	technique	NOUN
cana-4522	39	28	to	to	PART
cana-4522	39	29	create	create	VERB
cana-4522	39	30	an	an	DET
cana-4522	39	31	efficient	efficient	ADJ
cana-4522	39	32	and	and	CCONJ
cana-4522	39	33	accurate	accurate	ADJ
cana-4522	39	34	model	model	NOUN
cana-4522	39	35	for	for	ADP
cana-4522	39	36	the	the	DET
cana-4522	39	37	early	early	ADJ
cana-4522	39	38	detection	detection	NOUN
cana-4522	39	39	of	of	ADP
cana-4522	39	40	diabetes	diabetes	NOUN
cana-4522	39	41	.	.	PUNCT
cana-4522	40	1	2	2	X
cana-4522	40	2	.	.	X
cana-4522	40	3	implement	implement	VERB
cana-4522	40	4	feature	feature	NOUN
cana-4522	40	5	selection	selection	NOUN
cana-4522	40	6	techniques	technique	NOUN
cana-4522	40	7	–	–	PUNCT
cana-4522	40	8	identify	identify	VERB
cana-4522	40	9	the	the	DET
cana-4522	40	10	most	most	ADV
cana-4522	40	11	relevant	relevant	ADJ
cana-4522	40	12	features	feature	NOUN
cana-4522	40	13	that	that	PRON
cana-4522	40	14	contribute	contribute	VERB
cana-4522	40	15	to	to	ADP
cana-4522	40	16	diabetes	diabetes	NOUN
cana-4522	40	17	prediction	prediction	NOUN
cana-4522	40	18	,	,	PUNCT
cana-4522	40	19	reducing	reduce	VERB
cana-4522	40	20	computational	computational	ADJ
cana-4522	40	21	complexity	complexity	NOUN
cana-4522	40	22	and	and	CCONJ
cana-4522	40	23	improving	improve	VERB
cana-4522	40	24	model	model	NOUN
cana-4522	40	25	interpretability	interpretability	NOUN
cana-4522	40	26	.	.	PUNCT
cana-4522	41	1	3	3	X
cana-4522	41	2	.	.	X
cana-4522	41	3	apply	apply	VERB
cana-4522	41	4	data	datum	NOUN
cana-4522	41	5	scaling	scale	VERB
cana-4522	41	6	methods	method	NOUN
cana-4522	41	7	–	–	PUNCT
cana-4522	41	8	standardize	standardize	VERB
cana-4522	41	9	and	and	CCONJ
cana-4522	41	10	normalize	normalize	VERB
cana-4522	41	11	data	datum	NOUN
cana-4522	41	12	to	to	PART
cana-4522	41	13	enhance	enhance	VERB
cana-4522	41	14	model	model	NOUN
cana-4522	41	15	performance	performance	NOUN
cana-4522	41	16	by	by	ADP
cana-4522	41	17	ensuring	ensure	VERB
cana-4522	41	18	consistent	consistent	ADJ
cana-4522	41	19	feature	feature	NOUN
cana-4522	41	20	contribution	contribution	NOUN
cana-4522	41	21	and	and	CCONJ
cana-4522	41	22	reducing	reduce	VERB
cana-4522	41	23	biases	bias	NOUN
cana-4522	41	24	.	.	PUNCT
cana-4522	42	1	4	4	X
cana-4522	42	2	.	.	X
cana-4522	42	3	optimize	optimize	NOUN
cana-4522	42	4	model	model	NOUN
cana-4522	42	5	performance	performance	NOUN
cana-4522	42	6	using	use	VERB
cana-4522	42	7	hyperparameter	hyperparameter	NOUN
cana-4522	42	8	tuning	tuning	NOUN
cana-4522	42	9	–	–	PUNCT
cana-4522	42	10	fine	fine	ADJ
cana-4522	42	11	-	-	PUNCT
cana-4522	42	12	tune	tune	NOUN
cana-4522	42	13	machine	machine	NOUN
cana-4522	42	14	learning	learn	VERB
cana-4522	42	15	algorithms	algorithm	NOUN
cana-4522	42	16	to	to	PART
cana-4522	42	17	achieve	achieve	VERB
cana-4522	42	18	the	the	DET
cana-4522	42	19	best	good	ADJ
cana-4522	42	20	predictive	predictive	ADJ
cana-4522	42	21	accuracy	accuracy	NOUN
cana-4522	42	22	and	and	CCONJ
cana-4522	42	23	generalization	generalization	NOUN
cana-4522	42	24	.	.	PUNCT
cana-4522	43	1	5	5	X
cana-4522	43	2	.	.	X
cana-4522	43	3	compare	compare	NOUN
cana-4522	43	4	machine	machine	NOUN
cana-4522	43	5	learning	learn	VERB
cana-4522	43	6	algorithms	algorithm	NOUN
cana-4522	43	7	–	–	PUNCT
cana-4522	43	8	to	to	PART
cana-4522	43	9	determine	determine	VERB
cana-4522	43	10	the	the	DET
cana-4522	43	11	most	most	ADV
cana-4522	43	12	effective	effective	ADJ
cana-4522	43	13	approach	approach	NOUN
cana-4522	43	14	,	,	PUNCT
cana-4522	43	15	evaluate	evaluate	VERB
cana-4522	43	16	and	and	CCONJ
cana-4522	43	17	compare	compare	VERB
cana-4522	43	18	different	different	ADJ
cana-4522	43	19	ml	ml	NOUN
cana-4522	43	20	models	model	NOUN
cana-4522	43	21	,	,	PUNCT
cana-4522	43	22	such	such	ADJ
cana-4522	43	23	as	as	ADP
cana-4522	43	24	decision	decision	NOUN
cana-4522	43	25	trees	tree	NOUN
cana-4522	43	26	,	,	PUNCT
cana-4522	43	27	support	support	NOUN
cana-4522	43	28	vector	vector	NOUN
cana-4522	43	29	machines	machine	NOUN
cana-4522	43	30	,	,	PUNCT
cana-4522	43	31	random	random	ADJ
cana-4522	43	32	forest	forest	NOUN
cana-4522	43	33	,	,	PUNCT
cana-4522	43	34	and	and	CCONJ
cana-4522	43	35	deep	deep	ADJ
cana-4522	43	36	learning	learning	NOUN
cana-4522	43	37	models	model	NOUN
cana-4522	43	38	.	.	PUNCT
cana-4522	44	1	6	6	X
cana-4522	44	2	.	.	X
cana-4522	44	3	enhance	enhance	VERB
cana-4522	44	4	predictive	predictive	ADJ
cana-4522	44	5	accuracy	accuracy	NOUN
cana-4522	44	6	and	and	CCONJ
cana-4522	44	7	efficiency	efficiency	NOUN
cana-4522	44	8	–	–	PUNCT
cana-4522	44	9	improve	improve	VERB
cana-4522	44	10	the	the	DET
cana-4522	44	11	overall	overall	ADJ
cana-4522	44	12	performance	performance	NOUN
cana-4522	44	13	of	of	ADP
cana-4522	44	14	diabetes	diabetes	NOUN
cana-4522	44	15	prediction	prediction	NOUN
cana-4522	44	16	models	model	NOUN
cana-4522	44	17	by	by	ADP
cana-4522	44	18	integrating	integrate	VERB
cana-4522	44	19	advanced	advanced	ADJ
cana-4522	44	20	data	datum	NOUN
cana-4522	44	21	preprocessing	preprocessing	NOUN
cana-4522	44	22	and	and	CCONJ
cana-4522	44	23	optimization	optimization	NOUN
cana-4522	44	24	techniques	technique	NOUN
cana-4522	44	25	.	.	PUNCT
cana-4522	45	1	communications	communication	NOUN
cana-4522	45	2	on	on	ADP
cana-4522	45	3	applied	apply	VERB
cana-4522	45	4	nonlinear	nonlinear	ADJ
cana-4522	45	5	analysis	analysis	NOUN
cana-4522	45	6	issn	issn	NOUN
cana-4522	45	7	:	:	PUNCT
cana-4522	45	8	1074	1074	NUM
cana-4522	45	9	-	-	PUNCT
cana-4522	45	10	133x	133x	NUM
cana-4522	45	11	vol	vol	NOUN
cana-4522	45	12	32	32	NUM
cana-4522	45	13	no	no	NOUN
cana-4522	45	14	.	.	PUNCT
cana-4522	46	1	9s	9s	NUM
cana-4522	46	2	(	(	PUNCT
cana-4522	46	3	2025	2025	NUM
cana-4522	46	4	)	)	PUNCT
cana-4522	46	5	2350	2350	NUM
cana-4522	46	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4522	46	7	3	3	X
cana-4522	46	8	.	.	PUNCT
cana-4522	46	9	methods	method	NOUN
cana-4522	46	10	in	in	ADP
cana-4522	46	11	the	the	DET
cana-4522	46	12	proposed	propose	VERB
cana-4522	46	13	research	research	NOUN
cana-4522	46	14	work	work	NOUN
cana-4522	46	15	,	,	PUNCT
cana-4522	46	16	a	a	DET
cana-4522	46	17	machine	machine	NOUN
cana-4522	46	18	learning	learn	VERB
cana-4522	46	19	approach	approach	NOUN
cana-4522	46	20	is	be	AUX
cana-4522	46	21	developed	develop	VERB
cana-4522	46	22	with	with	ADP
cana-4522	46	23	different	different	ADJ
cana-4522	46	24	preprocessing	preprocessing	NOUN
cana-4522	46	25	techniques	technique	NOUN
cana-4522	46	26	for	for	ADP
cana-4522	46	27	significant	significant	ADJ
cana-4522	46	28	feature	feature	NOUN
cana-4522	46	29	selection	selection	NOUN
cana-4522	46	30	,	,	PUNCT
cana-4522	46	31	features	feature	VERB
cana-4522	46	32	scaling	scaling	NOUN
cana-4522	46	33	and	and	CCONJ
cana-4522	46	34	hyper	hyper	ADJ
cana-4522	46	35	parameter	parameter	NOUN
cana-4522	46	36	tuning	tuning	NOUN
cana-4522	46	37	method	method	NOUN
cana-4522	46	38	for	for	ADP
cana-4522	46	39	improving	improve	VERB
cana-4522	46	40	classifier	classifier	NOUN
cana-4522	46	41	performance	performance	NOUN
cana-4522	46	42	.	.	PUNCT
cana-4522	47	1	the	the	DET
cana-4522	47	2	significant	significant	ADJ
cana-4522	47	3	features	feature	NOUN
cana-4522	47	4	from	from	ADP
cana-4522	47	5	the	the	DET
cana-4522	47	6	dataset	dataset	NOUN
cana-4522	47	7	play	play	VERB
cana-4522	47	8	an	an	DET
cana-4522	47	9	important	important	ADJ
cana-4522	47	10	role	role	NOUN
cana-4522	47	11	in	in	ADP
cana-4522	47	12	the	the	DET
cana-4522	47	13	accurate	accurate	ADJ
cana-4522	47	14	prediction	prediction	NOUN
cana-4522	47	15	of	of	ADP
cana-4522	47	16	the	the	DET
cana-4522	47	17	diabetes	diabetes	NOUN
cana-4522	47	18	disease	disease	NOUN
cana-4522	47	19	.	.	PUNCT
cana-4522	48	1	therefore	therefore	ADV
cana-4522	48	2	,	,	PUNCT
cana-4522	48	3	most	most	ADV
cana-4522	48	4	significant	significant	ADJ
cana-4522	48	5	features	feature	NOUN
cana-4522	48	6	from	from	ADP
cana-4522	48	7	the	the	DET
cana-4522	48	8	database	database	NOUN
cana-4522	48	9	are	be	AUX
cana-4522	48	10	selected	select	VERB
cana-4522	48	11	using	use	VERB
cana-4522	48	12	attribute	attribute	NOUN
cana-4522	48	13	selection	selection	NOUN
cana-4522	48	14	tool	tool	NOUN
cana-4522	48	15	and	and	CCONJ
cana-4522	48	16	correlation	correlation	NOUN
cana-4522	48	17	attribute	attribute	NOUN
cana-4522	48	18	estimation	estimation	NOUN
cana-4522	48	19	method	method	NOUN
cana-4522	48	20	in	in	ADP
cana-4522	48	21	the	the	DET
cana-4522	48	22	weka	weka	PROPN
cana-4522	48	23	software	software	PROPN
cana-4522	48	24	tool	tool	PROPN
cana-4522	48	25	.	.	PUNCT
cana-4522	49	1	then	then	ADV
cana-4522	49	2	,	,	PUNCT
cana-4522	49	3	selected	select	VERB
cana-4522	49	4	significant	significant	ADJ
cana-4522	49	5	features	feature	NOUN
cana-4522	49	6	form	form	VERB
cana-4522	49	7	the	the	DET
cana-4522	49	8	database	database	NOUN
cana-4522	49	9	are	be	AUX
cana-4522	49	10	scaled	scale	VERB
cana-4522	49	11	using	use	VERB
cana-4522	49	12	standardization	standardization	NOUN
cana-4522	49	13	feature	feature	NOUN
cana-4522	49	14	scaling	scale	VERB
cana-4522	49	15	technique	technique	NOUN
cana-4522	49	16	.	.	PUNCT
cana-4522	50	1	lastly	lastly	ADV
cana-4522	50	2	,	,	PUNCT
cana-4522	50	3	the	the	DET
cana-4522	50	4	different	different	ADJ
cana-4522	50	5	ml	ml	NOUN
cana-4522	50	6	algorithms	algorithm	NOUN
cana-4522	50	7	such	such	ADJ
cana-4522	50	8	as	as	ADP
cana-4522	50	9	lr	lr	PROPN
cana-4522	50	10	,	,	PUNCT
cana-4522	50	11	knn	knn	PROPN
cana-4522	50	12	,	,	PUNCT
cana-4522	50	13	nb	nb	PROPN
cana-4522	50	14	,	,	PUNCT
cana-4522	50	15	svm	svm	PROPN
cana-4522	50	16	,	,	PUNCT
cana-4522	50	17	dt	dt	PUNCT
cana-4522	50	18	and	and	CCONJ
cana-4522	50	19	rf	rf	NOUN
cana-4522	50	20	algorithms	algorithm	NOUN
cana-4522	50	21	and	and	CCONJ
cana-4522	50	22	hyper	hyper	ADJ
cana-4522	50	23	parameter	parameter	NOUN
cana-4522	50	24	tuning	tuning	NOUN
cana-4522	50	25	technique	technique	NOUN
cana-4522	50	26	are	be	AUX
cana-4522	50	27	used	use	VERB
cana-4522	50	28	for	for	ADP
cana-4522	50	29	classification	classification	NOUN
cana-4522	50	30	of	of	ADP
cana-4522	50	31	dataset	dataset	NOUN
cana-4522	50	32	into	into	ADP
cana-4522	50	33	diabetes	diabetes	NOUN
cana-4522	50	34	classes	class	NOUN
cana-4522	50	35	.	.	PUNCT
cana-4522	51	1	the	the	DET
cana-4522	51	2	python	python	PROPN
cana-4522	51	3	jupiter	jupiter	PROPN
cana-4522	51	4	notebook	notebook	NOUN
cana-4522	51	5	is	be	AUX
cana-4522	51	6	used	use	VERB
cana-4522	51	7	for	for	ADP
cana-4522	51	8	implementing	implement	VERB
cana-4522	51	9	the	the	DET
cana-4522	51	10	code	code	NOUN
cana-4522	51	11	of	of	ADP
cana-4522	51	12	classification	classification	NOUN
cana-4522	51	13	algorithms	algorithm	NOUN
cana-4522	51	14	of	of	ADP
cana-4522	51	15	proposed	propose	VERB
cana-4522	51	16	method	method	NOUN
cana-4522	51	17	.	.	PUNCT
cana-4522	52	1	overall	overall	ADJ
cana-4522	52	2	work	work	NOUN
cana-4522	52	3	flow	flow	NOUN
cana-4522	52	4	of	of	ADP
cana-4522	52	5	the	the	DET
cana-4522	52	6	proposed	propose	VERB
cana-4522	52	7	research	research	NOUN
cana-4522	52	8	ml	ml	ADP
cana-4522	52	9	model	model	NOUN
cana-4522	52	10	using	use	VERB
cana-4522	52	11	preprocessing	preprocessing	NOUN
cana-4522	52	12	,	,	PUNCT
cana-4522	52	13	feature	feature	NOUN
cana-4522	52	14	selection	selection	NOUN
cana-4522	52	15	,	,	PUNCT
cana-4522	52	16	feature	feature	NOUN
cana-4522	52	17	scaling	scaling	NOUN
cana-4522	52	18	,	,	PUNCT
cana-4522	52	19	hyperparameter	hyperparameter	NOUN
cana-4522	52	20	tuning	tuning	NOUN
cana-4522	52	21	method	method	NOUN
cana-4522	52	22	is	be	AUX
cana-4522	52	23	shown	show	VERB
cana-4522	52	24	in	in	ADP
cana-4522	52	25	the	the	DET
cana-4522	52	26	following	follow	VERB
cana-4522	52	27	figure-1	figure-1	PROPN
cana-4522	52	28	.	.	PUNCT
cana-4522	53	1	figure	figure	NOUN
cana-4522	53	2	1	1	NUM
cana-4522	53	3	.	.	PUNCT
cana-4522	53	4	proposed	propose	VERB
cana-4522	53	5	machine	machine	NOUN
cana-4522	53	6	learning	learning	NOUN
cana-4522	53	7	model	model	NOUN
cana-4522	53	8	for	for	SCONJ
cana-4522	53	9	diabetes	diabetes	NOUN
cana-4522	53	10	prediction	prediction	NOUN
cana-4522	53	11	a.	a.	NOUN
cana-4522	53	12	data	datum	NOUN
cana-4522	53	13	set	set	VERB
cana-4522	53	14	selection	selection	NOUN
cana-4522	53	15	the	the	DET
cana-4522	53	16	proposed	propose	VERB
cana-4522	53	17	approach	approach	NOUN
cana-4522	53	18	using	use	VERB
cana-4522	53	19	machine	machine	NOUN
cana-4522	53	20	learning	learn	VERB
cana-4522	53	21	algorithms	algorithm	NOUN
cana-4522	53	22	utilizes	utilize	VERB
cana-4522	53	23	the	the	DET
cana-4522	53	24	pima	pima	PROPN
cana-4522	53	25	indian	indian	PROPN
cana-4522	53	26	diabetes	diabetes	NOUN
cana-4522	53	27	dataset	dataset	VERB
cana-4522	53	28	,	,	PUNCT
cana-4522	53	29	obtained	obtain	VERB
cana-4522	53	30	from	from	ADP
cana-4522	53	31	uci	uci	PROPN
cana-4522	53	32	machine	machine	NOUN
cana-4522	53	33	learning	learn	VERB
cana-4522	53	34	repository	repository	NOUN
cana-4522	54	1	[	[	X
cana-4522	54	2	22	22	NUM
cana-4522	54	3	]	]	PUNCT
cana-4522	54	4	.	.	PUNCT
cana-4522	55	1	this	this	DET
cana-4522	55	2	dataset	dataset	NOUN
cana-4522	55	3	contains	contain	VERB
cana-4522	55	4	768	768	NUM
cana-4522	55	5	records	record	NOUN
cana-4522	55	6	,	,	PUNCT
cana-4522	55	7	including	include	VERB
cana-4522	55	8	268	268	NUM
cana-4522	55	9	individuals	individual	NOUN
cana-4522	55	10	detected	detect	VERB
cana-4522	55	11	with	with	ADP
cana-4522	55	12	diabetes	diabetes	NOUN
cana-4522	55	13	and	and	CCONJ
cana-4522	55	14	500	500	NUM
cana-4522	55	15	with	with	ADP
cana-4522	55	16	no	no	DET
cana-4522	55	17	diabetes	diabetes	NOUN
cana-4522	55	18	condition	condition	NOUN
cana-4522	55	19	.	.	PUNCT
cana-4522	56	1	there	there	PRON
cana-4522	56	2	are	be	VERB
cana-4522	56	3	nine	nine	NUM
cana-4522	56	4	attributes	attribute	NOUN
cana-4522	56	5	available	available	ADJ
cana-4522	56	6	for	for	SCONJ
cana-4522	56	7	every	every	DET
cana-4522	56	8	individual	individual	NOUN
cana-4522	56	9	related	relate	VERB
cana-4522	56	10	to	to	ADP
cana-4522	56	11	their	their	PRON
cana-4522	56	12	health	health	NOUN
cana-4522	56	13	such	such	ADJ
cana-4522	56	14	as	as	ADP
cana-4522	56	15	pregnancy	pregnancy	NOUN
cana-4522	56	16	,	,	PUNCT
cana-4522	56	17	bmi	bmi	NOUN
cana-4522	56	18	,	,	PUNCT
cana-4522	56	19	insulin	insulin	NOUN
cana-4522	56	20	level	level	NOUN
cana-4522	56	21	,	,	PUNCT
cana-4522	56	22	age	age	NOUN
cana-4522	56	23	,	,	PUNCT
cana-4522	56	24	blood	blood	NOUN
cana-4522	56	25	pressure	pressure	NOUN
cana-4522	56	26	,	,	PUNCT
cana-4522	56	27	skin	skin	NOUN
cana-4522	56	28	thickness	thickness	NOUN
cana-4522	56	29	,	,	PUNCT
cana-4522	56	30	glucose	glucose	NOUN
cana-4522	56	31	,	,	PUNCT
cana-4522	56	32	diabetes	diabete	VERB
cana-4522	56	33	pedigree	pedigree	ADJ
cana-4522	56	34	function	function	NOUN
cana-4522	56	35	,	,	PUNCT
cana-4522	56	36	and	and	CCONJ
cana-4522	56	37	outcome	outcome	NOUN
cana-4522	56	38	.	.	PUNCT
cana-4522	57	1	the	the	DET
cana-4522	57	2	"	"	PUNCT
cana-4522	57	3	outcome	outcome	NOUN
cana-4522	57	4	"	"	PUNCT
cana-4522	57	5	attribute	attribute	NOUN
cana-4522	57	6	has	have	VERB
cana-4522	57	7	two	two	NUM
cana-4522	57	8	values	value	NOUN
cana-4522	57	9	:	:	PUNCT
cana-4522	57	10	"	"	PUNCT
cana-4522	57	11	0	0	NUM
cana-4522	57	12	"	"	PUNCT
cana-4522	57	13	for	for	ADP
cana-4522	57	14	non	non	NOUN
cana-4522	57	15	-	-	NOUN
cana-4522	57	16	diabetes	diabetes	NOUN
cana-4522	57	17	and	and	CCONJ
cana-4522	57	18	"	"	PUNCT
cana-4522	57	19	1	1	NUM
cana-4522	57	20	"	"	PUNCT
cana-4522	57	21	for	for	ADP
cana-4522	57	22	diabetes	diabetes	NOUN
cana-4522	57	23	.	.	PUNCT
cana-4522	58	1	the	the	DET
cana-4522	58	2	following	follow	VERB
cana-4522	58	3	table	table	NOUN
cana-4522	58	4	1	1	NUM
cana-4522	58	5	shows	show	VERB
cana-4522	58	6	the	the	DET
cana-4522	58	7	feature	feature	NOUN
cana-4522	58	8	from	from	ADP
cana-4522	58	9	the	the	DET
cana-4522	58	10	diabetes	diabetes	NOUN
cana-4522	58	11	dataset	dataset	VERB
cana-4522	58	12	.	.	PUNCT
cana-4522	59	1	table	table	NOUN
cana-4522	59	2	1	1	NUM
cana-4522	59	3	.	.	PUNCT
cana-4522	60	1	pima	pima	PROPN
cana-4522	60	2	indian	indian	PROPN
cana-4522	60	3	diabetes	diabetes	PROPN
cana-4522	60	4	data	datum	NOUN
cana-4522	60	5	set	set	VERB
cana-4522	60	6	with	with	ADP
cana-4522	60	7	all	all	DET
cana-4522	60	8	nine	nine	NUM
cana-4522	60	9	features	feature	NOUN
cana-4522	60	10	sr	sr	PROPN
cana-4522	60	11	.	.	PUNCT
cana-4522	61	1	no	no	INTJ
cana-4522	61	2	.	.	PUNCT
cana-4522	61	3	feature	feature	NOUN
cana-4522	61	4	/	/	SYM
cana-4522	61	5	attribute	attribute	NOUN
cana-4522	61	6	data	datum	NOUN
cana-4522	61	7	type	type	NOUN
cana-4522	61	8	description	description	NOUN
cana-4522	61	9	1	1	NUM
cana-4522	61	10	pregnancies	pregnancy	NOUN
cana-4522	61	11	integer	integer	NOUN
cana-4522	61	12	number	number	NOUN
cana-4522	61	13	of	of	ADP
cana-4522	61	14	times	time	NOUN
cana-4522	61	15	pregnancies	pregnancy	NOUN
cana-4522	61	16	2	2	NUM
cana-4522	61	17	glucose	glucose	NOUN
cana-4522	61	18	integer	integer	NOUN
cana-4522	61	19	glucose	glucose	NOUN
cana-4522	61	20	level	level	NOUN
cana-4522	61	21	in	in	ADP
cana-4522	61	22	blood	blood	NOUN
cana-4522	61	23	3	3	NUM
cana-4522	61	24	blood	blood	NOUN
cana-4522	61	25	pressure	pressure	NOUN
cana-4522	61	26	integer	integer	NOUN
cana-4522	61	27	diastolic	diastolic	NOUN
cana-4522	61	28	blood	blood	NOUN
cana-4522	61	29	pressure	pressure	NOUN
cana-4522	61	30	measurement	measurement	NOUN
cana-4522	61	31	communications	communication	NOUN
cana-4522	61	32	on	on	ADP
cana-4522	61	33	applied	apply	VERB
cana-4522	61	34	nonlinear	nonlinear	ADJ
cana-4522	61	35	analysis	analysis	NOUN
cana-4522	61	36	issn	issn	NOUN
cana-4522	61	37	:	:	PUNCT
cana-4522	61	38	1074	1074	NUM
cana-4522	61	39	-	-	PUNCT
cana-4522	61	40	133x	133x	NUM
cana-4522	61	41	vol	vol	NOUN
cana-4522	61	42	32	32	NUM
cana-4522	61	43	no	no	NOUN
cana-4522	61	44	.	.	PUNCT
cana-4522	62	1	9s	9s	NUM
cana-4522	62	2	(	(	PUNCT
cana-4522	62	3	2025	2025	NUM
cana-4522	62	4	)	)	PUNCT
cana-4522	62	5	2351	2351	NUM
cana-4522	62	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4522	62	7	4	4	NUM
cana-4522	62	8	skin	skin	NOUN
cana-4522	62	9	thickness	thickness	NOUN
cana-4522	62	10	integer	integer	NOUN
cana-4522	62	11	triceps	tricep	NOUN
cana-4522	62	12	skin	skin	NOUN
cana-4522	62	13	thickness	thickness	NOUN
cana-4522	62	14	5	5	NUM
cana-4522	62	15	insulin	insulin	NOUN
cana-4522	62	16	integer	integer	NOUN
cana-4522	62	17	2	2	NUM
cana-4522	62	18	hour	hour	NOUN
cana-4522	62	19	serum	serum	NOUN
cana-4522	62	20	insulin	insulin	NOUN
cana-4522	62	21	level	level	NOUN
cana-4522	62	22	in	in	ADP
cana-4522	62	23	blood	blood	NOUN
cana-4522	62	24	6	6	NUM
cana-4522	62	25	bmi	bmi	NOUN
cana-4522	62	26	float	float	NOUN
cana-4522	62	27	body	body	NOUN
cana-4522	62	28	mass	mass	PROPN
cana-4522	62	29	index	index	NOUN
cana-4522	62	30	7	7	NUM
cana-4522	62	31	diabetes	diabetes	NOUN
cana-4522	62	32	pedigree	pedigree	ADJ
cana-4522	62	33	function	function	PROPN
cana-4522	62	34	float	float	NOUN
cana-4522	62	35	diabetes	diabetes	NOUN
cana-4522	62	36	percentage	percentage	NOUN
cana-4522	62	37	8	8	NUM
cana-4522	62	38	age	age	NOUN
cana-4522	62	39	integer	integer	NOUN
cana-4522	62	40	patients	patient	NOUN
cana-4522	62	41	age	age	VERB
cana-4522	62	42	9	9	NUM
cana-4522	62	43	outcome	outcome	NOUN
cana-4522	62	44	integer	integer	NOUN
cana-4522	62	45	class	class	NOUN
cana-4522	62	46	0	0	NUM
cana-4522	62	47	-	-	PUNCT
cana-4522	62	48	for	for	ADP
cana-4522	62	49	negative	negative	ADJ
cana-4522	62	50	,	,	PUNCT
cana-4522	62	51	1	1	NUM
cana-4522	62	52	-	-	PUNCT
cana-4522	62	53	for	for	ADP
cana-4522	62	54	positive	positive	ADJ
cana-4522	62	55	b.	b.	PROPN
cana-4522	62	56	pre	pre	NOUN
cana-4522	62	57	-	-	VERB
cana-4522	62	58	processing	process	VERB
cana-4522	62	59	the	the	DET
cana-4522	62	60	pre	pre	ADJ
cana-4522	62	61	-	-	ADJ
cana-4522	62	62	processing	processing	ADJ
cana-4522	62	63	techniques	technique	NOUN
cana-4522	62	64	play	play	VERB
cana-4522	62	65	a	a	DET
cana-4522	62	66	vital	vital	ADJ
cana-4522	62	67	job	job	NOUN
cana-4522	62	68	in	in	ADP
cana-4522	62	69	transforming	transform	VERB
cana-4522	62	70	dataset	dataset	NOUN
cana-4522	62	71	into	into	ADP
cana-4522	62	72	different	different	ADJ
cana-4522	62	73	forms	form	NOUN
cana-4522	62	74	for	for	ADP
cana-4522	62	75	better	well	ADJ
cana-4522	62	76	prediction	prediction	NOUN
cana-4522	62	77	accuracy	accuracy	NOUN
cana-4522	62	78	in	in	ADP
cana-4522	62	79	the	the	DET
cana-4522	62	80	diabetes	diabetes	NOUN
cana-4522	62	81	forecast	forecast	NOUN
cana-4522	62	82	using	use	VERB
cana-4522	62	83	different	different	ADJ
cana-4522	62	84	classification	classification	NOUN
cana-4522	62	85	algorithms	algorithm	NOUN
cana-4522	62	86	.	.	PUNCT
cana-4522	63	1	following	follow	VERB
cana-4522	63	2	task	task	NOUN
cana-4522	63	3	are	be	AUX
cana-4522	63	4	achieved	achieve	VERB
cana-4522	63	5	in	in	ADP
cana-4522	63	6	the	the	DET
cana-4522	63	7	pre	pre	NOUN
cana-4522	63	8	-	-	NOUN
cana-4522	63	9	processing	processing	NOUN
cana-4522	63	10	of	of	ADP
cana-4522	63	11	the	the	DET
cana-4522	63	12	pima	pima	PROPN
cana-4522	63	13	diabetes	diabetes	NOUN
cana-4522	63	14	set	set	VERB
cana-4522	63	15	of	of	ADP
cana-4522	63	16	data	datum	NOUN
cana-4522	63	17	for	for	ADP
cana-4522	63	18	the	the	DET
cana-4522	63	19	proposed	propose	VERB
cana-4522	63	20	research	research	NOUN
cana-4522	63	21	work	work	NOUN
cana-4522	63	22	.	.	PUNCT
cana-4522	64	1	i.replacing	i.replace	VERB
cana-4522	64	2	duplicate	duplicate	NOUN
cana-4522	64	3	and	and	CCONJ
cana-4522	64	4	null	null	ADJ
cana-4522	64	5	values	value	NOUN
cana-4522	64	6	the	the	DET
cana-4522	64	7	duplicate	duplicate	ADJ
cana-4522	64	8	and	and	CCONJ
cana-4522	64	9	null	null	ADJ
cana-4522	64	10	values	value	NOUN
cana-4522	64	11	from	from	ADP
cana-4522	64	12	the	the	DET
cana-4522	64	13	diabetes	diabetes	NOUN
cana-4522	64	14	dataset	dataset	VERB
cana-4522	64	15	are	be	AUX
cana-4522	64	16	initially	initially	ADV
cana-4522	64	17	identified	identify	VERB
cana-4522	64	18	and	and	CCONJ
cana-4522	64	19	replaced	replace	VERB
cana-4522	64	20	with	with	ADP
cana-4522	64	21	the	the	DET
cana-4522	64	22	mean	mean	ADJ
cana-4522	64	23	values	value	NOUN
cana-4522	64	24	in	in	ADP
cana-4522	64	25	the	the	DET
cana-4522	64	26	python	python	NOUN
cana-4522	64	27	using	use	VERB
cana-4522	64	28	the	the	DET
cana-4522	64	29	functions	function	NOUN
cana-4522	64	30	available	available	ADJ
cana-4522	64	31	for	for	ADP
cana-4522	64	32	the	the	DET
cana-4522	64	33	entire	entire	ADJ
cana-4522	64	34	dataset	dataset	NOUN
cana-4522	64	35	.	.	PUNCT
cana-4522	65	1	ii	ii	PROPN
cana-4522	65	2	.	.	PROPN
cana-4522	65	3	feature	feature	NOUN
cana-4522	65	4	selection	selection	NOUN
cana-4522	65	5	the	the	DET
cana-4522	65	6	pearson	pearson	PROPN
cana-4522	65	7	’s	’s	PART
cana-4522	65	8	correlation	correlation	NOUN
cana-4522	65	9	matrix	matrix	NOUN
cana-4522	65	10	and	and	CCONJ
cana-4522	65	11	weka	weka	PROPN
cana-4522	65	12	software	software	NOUN
cana-4522	65	13	tool	tool	NOUN
cana-4522	65	14	is	be	AUX
cana-4522	65	15	been	be	AUX
cana-4522	65	16	used	use	VERB
cana-4522	65	17	for	for	ADP
cana-4522	65	18	the	the	DET
cana-4522	65	19	significant	significant	ADJ
cana-4522	65	20	feature	feature	NOUN
cana-4522	65	21	selection	selection	NOUN
cana-4522	65	22	from	from	ADP
cana-4522	65	23	the	the	DET
cana-4522	65	24	diabetes	diabetes	NOUN
cana-4522	65	25	dataset	dataset	VERB
cana-4522	65	26	.	.	PUNCT
cana-4522	66	1	the	the	DET
cana-4522	66	2	correlation	correlation	NOUN
cana-4522	66	3	coefficient	coefficient	NOUN
cana-4522	66	4	is	be	AUX
cana-4522	66	5	considered	consider	VERB
cana-4522	66	6	in	in	ADP
cana-4522	66	7	the	the	DET
cana-4522	66	8	proposed	propose	VERB
cana-4522	66	9	model	model	NOUN
cana-4522	66	10	that	that	PRON
cana-4522	66	11	relates	relate	VERB
cana-4522	66	12	with	with	ADP
cana-4522	66	13	input	input	NOUN
cana-4522	66	14	and	and	CCONJ
cana-4522	66	15	output	output	NOUN
cana-4522	66	16	features	feature	NOUN
cana-4522	66	17	.	.	PUNCT
cana-4522	67	1	by	by	ADP
cana-4522	67	2	applying	apply	VERB
cana-4522	67	3	feature	feature	NOUN
cana-4522	67	4	selection	selection	NOUN
cana-4522	67	5	technique	technique	NOUN
cana-4522	67	6	on	on	ADP
cana-4522	67	7	dataset	dataset	NOUN
cana-4522	67	8	,	,	PUNCT
cana-4522	67	9	three	three	NUM
cana-4522	67	10	features	feature	NOUN
cana-4522	67	11	i.e.	i.e.	X
cana-4522	67	12	skin	skin	NOUN
cana-4522	67	13	thickness	thickness	NOUN
cana-4522	67	14	,	,	PUNCT
cana-4522	67	15	bmi	bmi	NOUN
cana-4522	67	16	,	,	PUNCT
cana-4522	67	17	and	and	CCONJ
cana-4522	67	18	diabetes	diabete	VERB
cana-4522	67	19	pedigree	pedigree	ADJ
cana-4522	67	20	function	function	NOUN
cana-4522	67	21	are	be	AUX
cana-4522	67	22	cancelled	cancel	VERB
cana-4522	67	23	by	by	ADP
cana-4522	67	24	checking	check	VERB
cana-4522	67	25	its	its	PRON
cana-4522	67	26	values	value	NOUN
cana-4522	67	27	in	in	ADP
cana-4522	67	28	the	the	DET
cana-4522	67	29	correlation	correlation	NOUN
cana-4522	67	30	matrix	matrix	NOUN
cana-4522	67	31	table	table	NOUN
cana-4522	67	32	.	.	PUNCT
cana-4522	68	1	following	follow	VERB
cana-4522	68	2	figure	figure	NOUN
cana-4522	68	3	2(a	2(a	NUM
cana-4522	68	4	)	)	PUNCT
cana-4522	68	5	and	and	CCONJ
cana-4522	68	6	figure	figure	VERB
cana-4522	68	7	2(b	2(b	NUM
cana-4522	68	8	)	)	PUNCT
cana-4522	68	9	shows	show	VERB
cana-4522	68	10	the	the	DET
cana-4522	68	11	correlation	correlation	NOUN
cana-4522	68	12	matrix	matrix	NOUN
cana-4522	68	13	before	before	ADP
cana-4522	68	14	and	and	CCONJ
cana-4522	68	15	after	after	ADP
cana-4522	68	16	feature	feature	NOUN
cana-4522	68	17	selection	selection	NOUN
cana-4522	68	18	.	.	PUNCT
cana-4522	69	1	in	in	ADP
cana-4522	69	2	weka	weka	PROPN
cana-4522	69	3	software	software	PROPN
cana-4522	69	4	,	,	PUNCT
cana-4522	69	5	correlation	correlation	NOUN
cana-4522	69	6	filter	filter	NOUN
cana-4522	69	7	was	be	AUX
cana-4522	69	8	applied	apply	VERB
cana-4522	69	9	to	to	PART
cana-4522	69	10	determine	determine	VERB
cana-4522	69	11	coefficient	coefficient	NOUN
cana-4522	69	12	correlation	correlation	NOUN
cana-4522	69	13	between	between	ADP
cana-4522	69	14	all	all	DET
cana-4522	69	15	the	the	DET
cana-4522	69	16	attributes	attribute	NOUN
cana-4522	69	17	,	,	PUNCT
cana-4522	69	18	with	with	ADP
cana-4522	69	19	the	the	DET
cana-4522	69	20	results	result	NOUN
cana-4522	69	21	displayed	display	VERB
cana-4522	69	22	in	in	ADP
cana-4522	69	23	table	table	NOUN
cana-4522	69	24	2	2	NUM
cana-4522	69	25	.	.	PUNCT
cana-4522	69	26	a	a	DET
cana-4522	69	27	cut	cut	VERB
cana-4522	69	28	-	-	PUNCT
cana-4522	69	29	off	off	ADP
cana-4522	69	30	value	value	NOUN
cana-4522	69	31	of	of	ADP
cana-4522	69	32	2	2	NUM
cana-4522	69	33	was	be	AUX
cana-4522	69	34	used	use	VERB
cana-4522	69	35	to	to	PART
cana-4522	69	36	identify	identify	VERB
cana-4522	69	37	relevant	relevant	ADJ
cana-4522	69	38	features	feature	NOUN
cana-4522	69	39	,	,	PUNCT
cana-4522	69	40	leading	lead	VERB
cana-4522	69	41	to	to	ADP
cana-4522	69	42	exclusion	exclusion	NOUN
cana-4522	69	43	of	of	ADP
cana-4522	69	44	three	three	NUM
cana-4522	69	45	features	feature	NOUN
cana-4522	69	46	from	from	ADP
cana-4522	69	47	the	the	DET
cana-4522	69	48	dataset	dataset	NOUN
cana-4522	69	49	:	:	PUNCT
cana-4522	69	50	skin	skin	NOUN
cana-4522	69	51	thickness	thickness	NOUN
cana-4522	69	52	,	,	PUNCT
cana-4522	69	53	bp	bp	PROPN
cana-4522	69	54	and	and	CCONJ
cana-4522	69	55	diabetes	diabete	VERB
cana-4522	69	56	pedigree	pedigree	ADJ
cana-4522	69	57	function	function	NOUN
cana-4522	69	58	.	.	PUNCT
cana-4522	70	1	the	the	DET
cana-4522	70	2	final	final	ADJ
cana-4522	70	3	model	model	NOUN
cana-4522	70	4	has	have	AUX
cana-4522	70	5	selected	select	VERB
cana-4522	70	6	five	five	NUM
cana-4522	70	7	highly	highly	ADV
cana-4522	70	8	correlated	correlate	VERB
cana-4522	70	9	attributes	attribute	NOUN
cana-4522	70	10	such	such	ADJ
cana-4522	70	11	as	as	ADP
cana-4522	70	12	glucose	glucose	NOUN
cana-4522	70	13	,	,	PUNCT
cana-4522	70	14	bmi	bmi	NOUN
cana-4522	70	15	,	,	PUNCT
cana-4522	70	16	insulin	insulin	NOUN
cana-4522	70	17	,	,	PUNCT
cana-4522	70	18	pregnancies	pregnancy	NOUN
cana-4522	70	19	,	,	PUNCT
cana-4522	70	20	and	and	CCONJ
cana-4522	70	21	age	age	NOUN
cana-4522	70	22	as	as	ADP
cana-4522	70	23	the	the	DET
cana-4522	70	24	most	most	ADV
cana-4522	70	25	related	related	ADJ
cana-4522	70	26	features	feature	NOUN
cana-4522	70	27	for	for	ADP
cana-4522	70	28	diabetes	diabetes	NOUN
cana-4522	70	29	prediction	prediction	NOUN
cana-4522	70	30	.	.	PUNCT
cana-4522	71	1	figure	figure	NOUN
cana-4522	71	2	2(a	2(a	NUM
cana-4522	71	3	)	)	PUNCT
cana-4522	71	4	.	.	PUNCT
cana-4522	72	1	before	before	ADP
cana-4522	72	2	feature	feature	NOUN
cana-4522	72	3	selection	selection	NOUN
cana-4522	72	4	correlation	correlation	NOUN
cana-4522	72	5	between	between	ADP
cana-4522	72	6	attribute	attribute	NOUN
cana-4522	72	7	.	.	PUNCT
cana-4522	73	1	figure	figure	NOUN
cana-4522	73	2	2	2	NUM
cana-4522	73	3	(	(	PUNCT
cana-4522	73	4	b	b	NOUN
cana-4522	73	5	)	)	PUNCT
cana-4522	73	6	.	.	PUNCT
cana-4522	74	1	after	after	ADP
cana-4522	74	2	feature	feature	NOUN
cana-4522	74	3	selection	selection	NOUN
cana-4522	74	4	correlation	correlation	NOUN
cana-4522	74	5	between	between	ADP
cana-4522	74	6	attributes	attribute	NOUN
cana-4522	74	7	.	.	PUNCT
cana-4522	75	1	communications	communication	NOUN
cana-4522	75	2	on	on	ADP
cana-4522	75	3	applied	apply	VERB
cana-4522	75	4	nonlinear	nonlinear	ADJ
cana-4522	75	5	analysis	analysis	NOUN
cana-4522	75	6	issn	issn	NOUN
cana-4522	75	7	:	:	PUNCT
cana-4522	75	8	1074	1074	NUM
cana-4522	75	9	-	-	PUNCT
cana-4522	75	10	133x	133x	NUM
cana-4522	75	11	vol	vol	NOUN
cana-4522	75	12	32	32	NUM
cana-4522	75	13	no	no	NOUN
cana-4522	75	14	.	.	PUNCT
cana-4522	76	1	9s	9s	NUM
cana-4522	76	2	(	(	PUNCT
cana-4522	76	3	2025	2025	NUM
cana-4522	76	4	)	)	PUNCT
cana-4522	76	5	2352	2352	NUM
cana-4522	77	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-4522	77	2	table	table	NOUN
cana-4522	77	3	2	2	NUM
cana-4522	77	4	.	.	PUNCT
cana-4522	78	1	the	the	DET
cana-4522	78	2	relation	relation	NOUN
cana-4522	78	3	between	between	ADP
cana-4522	78	4	input	input	NOUN
cana-4522	78	5	and	and	CCONJ
cana-4522	78	6	output	output	NOUN
cana-4522	78	7	features	feature	NOUN
cana-4522	78	8	using	use	VERB
cana-4522	78	9	weka	weka	PROPN
cana-4522	78	10	tool	tool	PROPN
cana-4522	78	11	.	.	PUNCT
cana-4522	79	1	attributes	attribute	VERB
cana-4522	79	2	correlation	correlation	NOUN
cana-4522	79	3	coefficient	coefficient	NOUN
cana-4522	79	4	glucose	glucose	NOUN
cana-4522	79	5	0.48	0.48	NUM
cana-4522	79	6	bmi	bmi	NOUN
cana-4522	79	7	0.32	0.32	NUM
cana-4522	79	8	insulin	insulin	NOUN
cana-4522	79	9	0.26	0.26	NUM
cana-4522	79	10	pregnancies	pregnancy	NOUN
cana-4522	79	11	0.23	0.23	NUM
cana-4522	79	12	age	age	NOUN
cana-4522	79	13	0.22	0.22	NUM
cana-4522	79	14	skin	skin	NOUN
cana-4522	79	15	thickness	thickness	NOUN
cana-4522	79	16	0.19	0.19	NUM
cana-4522	79	17	blood	blood	NOUN
cana-4522	79	18	pressure	pressure	NOUN
cana-4522	79	19	0.18	0.18	NUM
cana-4522	79	20	diabetes	diabetes	NOUN
cana-4522	79	21	pedigree	pedigree	ADJ
cana-4522	79	22	function	function	NOUN
cana-4522	79	23	0.18	0.18	NUM
cana-4522	79	24	from	from	ADP
cana-4522	79	25	above	above	ADP
cana-4522	79	26	table-2	table-2	NUM
cana-4522	79	27	,	,	PUNCT
cana-4522	79	28	it	it	PRON
cana-4522	79	29	has	have	AUX
cana-4522	79	30	been	be	AUX
cana-4522	79	31	observed	observe	VERB
cana-4522	79	32	that	that	SCONJ
cana-4522	79	33	these	these	DET
cana-4522	79	34	three	three	NUM
cana-4522	79	35	features	feature	NOUN
cana-4522	79	36	are	be	AUX
cana-4522	79	37	not	not	PART
cana-4522	79	38	so	so	ADV
cana-4522	79	39	much	much	ADV
cana-4522	79	40	correlated	correlate	VERB
cana-4522	79	41	to	to	AUX
cana-4522	79	42	diabetes	diabetes	NOUN
cana-4522	79	43	prediction	prediction	NOUN
cana-4522	79	44	,	,	PUNCT
cana-4522	79	45	and	and	CCONJ
cana-4522	79	46	hence	hence	ADV
cana-4522	79	47	not	not	PART
cana-4522	79	48	significant	significant	ADJ
cana-4522	79	49	in	in	ADP
cana-4522	79	50	the	the	DET
cana-4522	79	51	diabetes	diabetes	NOUN
cana-4522	79	52	prediction	prediction	NOUN
cana-4522	79	53	.	.	PUNCT
cana-4522	80	1	therefore	therefore	ADV
cana-4522	80	2	,	,	PUNCT
cana-4522	80	3	only	only	ADV
cana-4522	80	4	significant	significant	ADJ
cana-4522	80	5	correlated	correlate	VERB
cana-4522	80	6	features	feature	NOUN
cana-4522	80	7	are	be	AUX
cana-4522	80	8	selected	select	VERB
cana-4522	80	9	and	and	CCONJ
cana-4522	80	10	used	use	VERB
cana-4522	80	11	in	in	ADP
cana-4522	80	12	the	the	DET
cana-4522	80	13	diabetes	diabetes	NOUN
cana-4522	80	14	prediction	prediction	NOUN
cana-4522	80	15	.	.	PUNCT
cana-4522	81	1	now	now	ADV
cana-4522	81	2	,	,	PUNCT
cana-4522	81	3	dataset	dataset	PROPN
cana-4522	81	4	has	have	VERB
cana-4522	81	5	five	five	NUM
cana-4522	81	6	independent	independent	ADJ
cana-4522	81	7	features	feature	NOUN
cana-4522	81	8	like	like	ADP
cana-4522	81	9	pregnancies	pregnancy	NOUN
cana-4522	81	10	,	,	PUNCT
cana-4522	81	11	glucose	glucose	NOUN
cana-4522	81	12	,	,	PUNCT
cana-4522	81	13	blood	blood	NOUN
cana-4522	81	14	pressure	pressure	NOUN
cana-4522	81	15	,	,	PUNCT
cana-4522	81	16	insulin	insulin	NOUN
cana-4522	81	17	,	,	PUNCT
cana-4522	81	18	age	age	NOUN
cana-4522	81	19	and	and	CCONJ
cana-4522	81	20	one	one	NUM
cana-4522	81	21	dependent	dependent	ADJ
cana-4522	81	22	feature	feature	NOUN
cana-4522	81	23	i.e.	i.e.	X
cana-4522	81	24	outcome	outcome	NOUN
cana-4522	81	25	.	.	PUNCT
cana-4522	82	1	the	the	DET
cana-4522	82	2	‘	'	PUNCT
cana-4522	82	3	glucose	glucose	NOUN
cana-4522	82	4	’	'	PUNCT
cana-4522	82	5	and	and	CCONJ
cana-4522	82	6	‘	'	PUNCT
cana-4522	82	7	outcome	outcome	NOUN
cana-4522	82	8	’	'	PUNCT
cana-4522	82	9	attributes	attribute	NOUN
cana-4522	82	10	are	be	AUX
cana-4522	82	11	having	have	VERB
cana-4522	82	12	0.49	0.49	NUM
cana-4522	82	13	correlation	correlation	NOUN
cana-4522	82	14	coefficient	coefficient	NOUN
cana-4522	82	15	value	value	NOUN
cana-4522	82	16	and	and	CCONJ
cana-4522	82	17	hence	hence	ADV
cana-4522	82	18	these	these	PRON
cana-4522	82	19	are	be	AUX
cana-4522	82	20	highly	highly	ADV
cana-4522	82	21	correlated	correlate	VERB
cana-4522	82	22	.	.	PUNCT
cana-4522	83	1	the	the	DET
cana-4522	83	2	following	follow	VERB
cana-4522	83	3	table	table	NOUN
cana-4522	83	4	3	3	NUM
cana-4522	83	5	shows	show	NOUN
cana-4522	83	6	selected	select	VERB
cana-4522	83	7	significant	significant	ADJ
cana-4522	83	8	features	feature	NOUN
cana-4522	83	9	for	for	ADP
cana-4522	83	10	the	the	DET
cana-4522	83	11	proposed	propose	VERB
cana-4522	83	12	approach	approach	NOUN
cana-4522	83	13	.	.	PUNCT
cana-4522	84	1	table	table	NOUN
cana-4522	84	2	3	3	NUM
cana-4522	84	3	.	.	PUNCT
cana-4522	85	1	pima	pima	PROPN
cana-4522	85	2	indian	indian	PROPN
cana-4522	85	3	diabetes	diabete	NOUN
cana-4522	85	4	dataset	dataset	VERB
cana-4522	85	5	after	after	ADP
cana-4522	85	6	significant	significant	ADJ
cana-4522	85	7	feature	feature	NOUN
cana-4522	85	8	selection	selection	NOUN
cana-4522	85	9	s.	s.	PROPN
cana-4522	85	10	n.	n.	PROPN
cana-4522	85	11	feature	feature	NOUN
cana-4522	85	12	/	/	SYM
cana-4522	85	13	attribute	attribute	NOUN
cana-4522	85	14	data	datum	NOUN
cana-4522	85	15	type	type	NOUN
cana-4522	85	16	description	description	NOUN
cana-4522	85	17	1	1	NUM
cana-4522	85	18	pregnancies	pregnancy	NOUN
cana-4522	85	19	numeric	numeric	ADJ
cana-4522	85	20	number	number	NOUN
cana-4522	85	21	of	of	ADP
cana-4522	85	22	times	time	NOUN
cana-4522	85	23	pregnancies	pregnancy	NOUN
cana-4522	85	24	2	2	NUM
cana-4522	85	25	glucose	glucose	NOUN
cana-4522	85	26	numeric	numeric	ADJ
cana-4522	85	27	glucose	glucose	NOUN
cana-4522	85	28	level	level	NOUN
cana-4522	85	29	in	in	ADP
cana-4522	85	30	blood	blood	NOUN
cana-4522	85	31	3	3	NUM
cana-4522	85	32	blood	blood	NOUN
cana-4522	85	33	pressure	pressure	NOUN
cana-4522	85	34	numeric	numeric	ADP
cana-4522	85	35	diastolic	diastolic	ADJ
cana-4522	85	36	blood	blood	NOUN
cana-4522	85	37	pressure	pressure	NOUN
cana-4522	85	38	measurement	measurement	NOUN
cana-4522	85	39	4	4	NUM
cana-4522	85	40	insulin	insulin	NOUN
cana-4522	85	41	numeric	numeric	ADJ
cana-4522	85	42	2	2	NUM
cana-4522	85	43	hour	hour	NOUN
cana-4522	85	44	serum	serum	NOUN
cana-4522	85	45	insulin	insulin	NOUN
cana-4522	85	46	level	level	NOUN
cana-4522	85	47	in	in	ADP
cana-4522	85	48	blood	blood	NOUN
cana-4522	85	49	5	5	NUM
cana-4522	85	50	age	age	NOUN
cana-4522	85	51	numeric	numeric	ADJ
cana-4522	85	52	patients	patient	NOUN
cana-4522	85	53	age	age	VERB
cana-4522	85	54	6	6	NUM
cana-4522	85	55	outcome	outcome	NOUN
cana-4522	85	56	numeric	numeric	ADJ
cana-4522	85	57	class	class	NOUN
cana-4522	85	58	0	0	NUM
cana-4522	85	59	-	-	PUNCT
cana-4522	85	60	for	for	ADP
cana-4522	85	61	negative	negative	ADJ
cana-4522	85	62	,	,	PUNCT
cana-4522	85	63	1	1	NUM
cana-4522	85	64	-	-	PUNCT
cana-4522	85	65	for	for	ADP
cana-4522	85	66	positive	positive	ADJ
cana-4522	85	67	after	after	ADP
cana-4522	85	68	applying	apply	VERB
cana-4522	85	69	the	the	DET
cana-4522	85	70	different	different	ADJ
cana-4522	85	71	pre	pre	ADJ
cana-4522	85	72	-	-	ADJ
cana-4522	85	73	processing	processing	ADJ
cana-4522	85	74	techniques	technique	NOUN
cana-4522	85	75	on	on	ADP
cana-4522	85	76	the	the	DET
cana-4522	85	77	pima	pima	PROPN
cana-4522	85	78	diabetes	diabetes	NOUN
cana-4522	85	79	dataset	dataset	VERB
cana-4522	85	80	following	follow	VERB
cana-4522	85	81	observations	observation	NOUN
cana-4522	85	82	are	be	AUX
cana-4522	85	83	illustrated	illustrate	VERB
cana-4522	85	84	using	use	VERB
cana-4522	85	85	histogram	histogram	NOUN
cana-4522	85	86	of	of	ADP
cana-4522	85	87	the	the	DET
cana-4522	85	88	ranges	range	NOUN
cana-4522	85	89	and	and	CCONJ
cana-4522	85	90	distribution	distribution	NOUN
cana-4522	85	91	of	of	ADP
cana-4522	85	92	features	feature	NOUN
cana-4522	85	93	which	which	PRON
cana-4522	85	94	is	be	AUX
cana-4522	85	95	shown	show	VERB
cana-4522	85	96	in	in	ADP
cana-4522	85	97	the	the	DET
cana-4522	85	98	following	follow	VERB
cana-4522	85	99	figure	figure	NOUN
cana-4522	85	100	3	3	NUM
cana-4522	85	101	.	.	PUNCT
cana-4522	85	102	figure	figure	NOUN
cana-4522	85	103	3	3	NUM
cana-4522	85	104	.	.	PUNCT
cana-4522	85	105	histogram	histogram	NOUN
cana-4522	85	106	of	of	ADP
cana-4522	85	107	attribute	attribute	NOUN
cana-4522	85	108	ranges	range	VERB
cana-4522	85	109	and	and	CCONJ
cana-4522	85	110	distribution	distribution	NOUN
cana-4522	85	111	after	after	SCONJ
cana-4522	85	112	pre	pre	ADJ
cana-4522	85	113	-	-	ADJ
cana-4522	85	114	processing	processing	ADJ
cana-4522	85	115	communications	communication	NOUN
cana-4522	85	116	on	on	ADP
cana-4522	85	117	applied	apply	VERB
cana-4522	85	118	nonlinear	nonlinear	ADJ
cana-4522	85	119	analysis	analysis	NOUN
cana-4522	85	120	issn	issn	NOUN
cana-4522	85	121	:	:	PUNCT
cana-4522	85	122	1074	1074	NUM
cana-4522	85	123	-	-	PUNCT
cana-4522	85	124	133x	133x	NUM
cana-4522	85	125	vol	vol	NOUN
cana-4522	85	126	32	32	NUM
cana-4522	85	127	no	no	NOUN
cana-4522	85	128	.	.	PUNCT
cana-4522	86	1	9s	9s	NUM
cana-4522	86	2	(	(	PUNCT
cana-4522	86	3	2025	2025	NUM
cana-4522	86	4	)	)	PUNCT
cana-4522	86	5	2353	2353	NUM
cana-4522	86	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4522	86	7	c.	c.	PROPN
cana-4522	86	8	standard	standard	PROPN
cana-4522	86	9	feature	feature	NOUN
cana-4522	86	10	scaling	scale	VERB
cana-4522	86	11	technique	technique	NOUN
cana-4522	86	12	the	the	DET
cana-4522	86	13	diabetes	diabetes	NOUN
cana-4522	86	14	dataset	dataset	VERB
cana-4522	86	15	used	use	VERB
cana-4522	86	16	for	for	ADP
cana-4522	86	17	training	train	VERB
cana-4522	86	18	the	the	DET
cana-4522	86	19	machine	machine	NOUN
cana-4522	86	20	learning	learning	NOUN
cana-4522	86	21	model	model	NOUN
cana-4522	86	22	generally	generally	ADV
cana-4522	86	23	contains	contain	VERB
cana-4522	86	24	uneven	uneven	ADJ
cana-4522	86	25	range	range	NOUN
cana-4522	86	26	of	of	ADP
cana-4522	86	27	the	the	DET
cana-4522	86	28	values	value	NOUN
cana-4522	86	29	that	that	PRON
cana-4522	86	30	affects	affect	VERB
cana-4522	86	31	the	the	DET
cana-4522	86	32	prediction	prediction	NOUN
cana-4522	86	33	accuracy	accuracy	NOUN
cana-4522	86	34	.	.	PUNCT
cana-4522	87	1	the	the	DET
cana-4522	87	2	machine	machine	NOUN
cana-4522	87	3	learning	learn	VERB
cana-4522	87	4	algorithms	algorithm	NOUN
cana-4522	87	5	work	work	VERB
cana-4522	87	6	on	on	ADP
cana-4522	87	7	numeric	numeric	ADJ
cana-4522	87	8	values	value	NOUN
cana-4522	87	9	and	and	CCONJ
cana-4522	87	10	do	do	AUX
cana-4522	87	11	not	not	PART
cana-4522	87	12	know	know	VERB
cana-4522	87	13	what	what	PRON
cana-4522	87	14	that	that	SCONJ
cana-4522	87	15	numbers	number	NOUN
cana-4522	87	16	represents	represent	VERB
cana-4522	87	17	.	.	PUNCT
cana-4522	88	1	the	the	DET
cana-4522	88	2	feature	feature	NOUN
cana-4522	88	3	scaling	scale	VERB
cana-4522	88	4	techniques	technique	NOUN
cana-4522	88	5	are	be	AUX
cana-4522	88	6	used	use	VERB
cana-4522	88	7	for	for	ADP
cana-4522	88	8	solving	solve	VERB
cana-4522	88	9	this	this	DET
cana-4522	88	10	problem	problem	NOUN
cana-4522	88	11	and	and	CCONJ
cana-4522	88	12	by	by	ADP
cana-4522	88	13	adjusting	adjust	VERB
cana-4522	88	14	the	the	DET
cana-4522	88	15	values	value	NOUN
cana-4522	88	16	in	in	ADP
cana-4522	88	17	same	same	ADJ
cana-4522	88	18	range	range	NOUN
cana-4522	88	19	and	and	CCONJ
cana-4522	88	20	produce	produce	VERB
cana-4522	88	21	best	good	ADJ
cana-4522	88	22	predictions	prediction	NOUN
cana-4522	88	23	results	result	NOUN
cana-4522	88	24	.	.	PUNCT
cana-4522	89	1	therefore	therefore	ADV
cana-4522	89	2	,	,	PUNCT
cana-4522	89	3	in	in	ADP
cana-4522	89	4	this	this	DET
cana-4522	89	5	research	research	NOUN
cana-4522	89	6	work	work	NOUN
cana-4522	89	7	standard	standard	ADJ
cana-4522	89	8	feature	feature	NOUN
cana-4522	89	9	scaling	scale	VERB
cana-4522	89	10	technique	technique	NOUN
cana-4522	89	11	is	be	AUX
cana-4522	89	12	used	use	VERB
cana-4522	89	13	for	for	ADP
cana-4522	89	14	scaling	scale	VERB
cana-4522	89	15	dataset	dataset	NOUN
cana-4522	89	16	that	that	PRON
cana-4522	89	17	boosted	boost	VERB
cana-4522	89	18	the	the	DET
cana-4522	89	19	diabetic	diabetic	ADJ
cana-4522	89	20	prediction	prediction	NOUN
cana-4522	89	21	accuracy	accuracy	NOUN
cana-4522	89	22	.	.	PUNCT
cana-4522	90	1	the	the	DET
cana-4522	90	2	standard	standard	ADJ
cana-4522	90	3	scalar	scalar	NOUN
cana-4522	90	4	is	be	AUX
cana-4522	90	5	also	also	ADV
cana-4522	90	6	known	know	VERB
cana-4522	90	7	as	as	ADP
cana-4522	90	8	z	z	NOUN
cana-4522	90	9	-	-	PUNCT
cana-4522	90	10	score	score	NOUN
cana-4522	90	11	standardization	standardization	NOUN
cana-4522	90	12	.	.	PUNCT
cana-4522	91	1	it	it	PRON
cana-4522	91	2	is	be	AUX
cana-4522	91	3	a	a	DET
cana-4522	91	4	scaling	scaling	NOUN
cana-4522	91	5	method	method	NOUN
cana-4522	91	6	where	where	SCONJ
cana-4522	91	7	the	the	DET
cana-4522	91	8	values	value	NOUN
cana-4522	91	9	are	be	AUX
cana-4522	91	10	centered	center	VERB
cana-4522	91	11	on	on	ADP
cana-4522	91	12	the	the	DET
cana-4522	91	13	mean	mean	NOUN
cana-4522	91	14	with	with	ADP
cana-4522	91	15	a	a	DET
cana-4522	91	16	unit	unit	NOUN
cana-4522	91	17	standard	standard	ADJ
cana-4522	91	18	deviation	deviation	NOUN
cana-4522	91	19	.	.	PUNCT
cana-4522	92	1	this	this	PRON
cana-4522	92	2	implies	imply	VERB
cana-4522	92	3	that	that	SCONJ
cana-4522	92	4	the	the	DET
cana-4522	92	5	mean	mean	NOUN
cana-4522	92	6	of	of	ADP
cana-4522	92	7	the	the	DET
cana-4522	92	8	attribute	attribute	NOUN
cana-4522	92	9	is	be	AUX
cana-4522	92	10	shifted	shift	VERB
cana-4522	92	11	to	to	ADP
cana-4522	92	12	zero	zero	NUM
cana-4522	92	13	and	and	CCONJ
cana-4522	92	14	the	the	DET
cana-4522	92	15	resulting	result	VERB
cana-4522	92	16	spreading	spreading	NOUN
cana-4522	92	17	has	have	VERB
cana-4522	92	18	a	a	DET
cana-4522	92	19	standard	standard	ADJ
cana-4522	92	20	divergence	divergence	NOUN
cana-4522	92	21	of	of	ADP
cana-4522	92	22	one	one	NUM
cana-4522	92	23	.	.	PUNCT
cana-4522	93	1	the	the	DET
cana-4522	93	2	mathematical	mathematical	ADJ
cana-4522	93	3	representation	representation	NOUN
cana-4522	93	4	of	of	ADP
cana-4522	93	5	standard	standard	ADJ
cana-4522	93	6	scalar	scalar	NOUN
cana-4522	93	7	is	be	AUX
cana-4522	93	8	as	as	ADP
cana-4522	93	9	below	below	ADP
cana-4522	93	10	[	[	X
cana-4522	93	11	7	7	NUM
cana-4522	93	12	]	]	PUNCT
cana-4522	93	13	,	,	PUNCT
cana-4522	93	14	[	[	X
cana-4522	93	15	9	9	NUM
cana-4522	93	16	]	]	PUNCT
cana-4522	93	17	.	.	PUNCT
cana-4522	94	1	𝑋𝑛𝑒𝑤	𝑋𝑛𝑒𝑤	PROPN
cana-4522	94	2	=	=	SYM
cana-4522	94	3	𝑋𝑖−𝑋𝑚𝑒𝑎𝑛	𝑋𝑖−𝑋𝑚𝑒𝑎𝑛	PROPN
cana-4522	94	4	𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑	𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑	PROPN
cana-4522	94	5	𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛	𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛	PROPN
cana-4522	94	6	(	(	PUNCT
cana-4522	94	7	1	1	X
cana-4522	94	8	)	)	PUNCT
cana-4522	94	9	d.	d.	NOUN
cana-4522	94	10	classification	classification	NOUN
cana-4522	94	11	algorithms	algorithm	NOUN
cana-4522	95	1	after	after	ADP
cana-4522	95	2	applying	apply	VERB
cana-4522	95	3	the	the	DET
cana-4522	95	4	pre	pre	ADJ
cana-4522	95	5	-	-	ADJ
cana-4522	95	6	processing	processing	ADJ
cana-4522	95	7	techniques	technique	NOUN
cana-4522	95	8	and	and	CCONJ
cana-4522	95	9	selecting	select	VERB
cana-4522	95	10	the	the	DET
cana-4522	95	11	significant	significant	ADJ
cana-4522	95	12	features	feature	NOUN
cana-4522	95	13	,	,	PUNCT
cana-4522	95	14	the	the	DET
cana-4522	95	15	dataset	dataset	NOUN
cana-4522	95	16	has	have	AUX
cana-4522	95	17	been	be	AUX
cana-4522	95	18	prepared	prepare	VERB
cana-4522	95	19	to	to	PART
cana-4522	95	20	train	train	VERB
cana-4522	95	21	and	and	CCONJ
cana-4522	95	22	test	test	NOUN
cana-4522	95	23	using	use	VERB
cana-4522	95	24	machine	machine	NOUN
cana-4522	95	25	learning	learning	NOUN
cana-4522	95	26	algorithms	algorithm	NOUN
cana-4522	95	27	.	.	PUNCT
cana-4522	96	1	in	in	ADP
cana-4522	96	2	the	the	DET
cana-4522	96	3	proposed	propose	VERB
cana-4522	96	4	approach	approach	NOUN
cana-4522	96	5	,	,	PUNCT
cana-4522	96	6	six	six	NUM
cana-4522	96	7	different	different	ADJ
cana-4522	96	8	ml	ml	NOUN
cana-4522	96	9	algorithms	algorithm	NOUN
cana-4522	96	10	are	be	AUX
cana-4522	96	11	applied	apply	VERB
cana-4522	96	12	to	to	ADP
cana-4522	96	13	the	the	DET
cana-4522	96	14	pima	pima	PROPN
cana-4522	96	15	diabetes	diabetes	NOUN
cana-4522	96	16	dataset	dataset	VERB
cana-4522	96	17	for	for	ADP
cana-4522	96	18	classification	classification	NOUN
cana-4522	96	19	of	of	ADP
cana-4522	96	20	dataset	dataset	NOUN
cana-4522	96	21	into	into	ADP
cana-4522	96	22	two	two	NUM
cana-4522	96	23	predefined	predefine	VERB
cana-4522	96	24	classes	class	NOUN
cana-4522	96	25	such	such	ADJ
cana-4522	96	26	as	as	ADP
cana-4522	96	27	diabetic	diabetic	ADJ
cana-4522	96	28	and	and	CCONJ
cana-4522	96	29	non	non	ADJ
cana-4522	96	30	-	-	ADJ
cana-4522	96	31	diabetic	diabetic	ADJ
cana-4522	96	32	.	.	PUNCT
cana-4522	97	1	4	4	X
cana-4522	97	2	.	.	X
cana-4522	97	3	results	result	NOUN
cana-4522	97	4	in	in	ADP
cana-4522	97	5	proposed	propose	VERB
cana-4522	97	6	methodology	methodology	NOUN
cana-4522	97	7	,	,	PUNCT
cana-4522	97	8	experiment	experiment	NOUN
cana-4522	97	9	is	be	AUX
cana-4522	97	10	conducted	conduct	VERB
cana-4522	97	11	using	use	VERB
cana-4522	97	12	different	different	ADJ
cana-4522	97	13	ml	ml	NOUN
cana-4522	97	14	algorithms	algorithm	NOUN
cana-4522	97	15	with	with	ADP
cana-4522	97	16	feature	feature	NOUN
cana-4522	97	17	scaling	scaling	NOUN
cana-4522	97	18	,	,	PUNCT
cana-4522	97	19	features	feature	VERB
cana-4522	97	20	selection	selection	NOUN
cana-4522	97	21	and	and	CCONJ
cana-4522	97	22	hyper	hyper	ADJ
cana-4522	97	23	parameter	parameter	NOUN
cana-4522	97	24	tuning	tuning	NOUN
cana-4522	97	25	method	method	NOUN
cana-4522	97	26	on	on	ADP
cana-4522	97	27	pima	pima	PROPN
cana-4522	97	28	diabetes	diabetes	NOUN
cana-4522	97	29	dataset	dataset	VERB
cana-4522	97	30	for	for	ADP
cana-4522	97	31	classification	classification	NOUN
cana-4522	97	32	of	of	ADP
cana-4522	97	33	the	the	DET
cana-4522	97	34	diabetes	diabetes	NOUN
cana-4522	97	35	.	.	PUNCT
cana-4522	98	1	the	the	DET
cana-4522	98	2	hyper	hyper	ADJ
cana-4522	98	3	parameter	parameter	NOUN
cana-4522	98	4	tuning	tuning	NOUN
cana-4522	98	5	method	method	NOUN
cana-4522	98	6	is	be	AUX
cana-4522	98	7	applied	apply	VERB
cana-4522	98	8	on	on	ADP
cana-4522	98	9	the	the	DET
cana-4522	98	10	classifiers	classifier	NOUN
cana-4522	98	11	by	by	ADP
cana-4522	98	12	selecting	select	VERB
cana-4522	98	13	appropriate	appropriate	ADJ
cana-4522	98	14	parameters	parameter	NOUN
cana-4522	98	15	that	that	PRON
cana-4522	98	16	improves	improve	VERB
cana-4522	98	17	the	the	DET
cana-4522	98	18	diabetes	diabetes	NOUN
cana-4522	98	19	prediction	prediction	NOUN
cana-4522	98	20	accuracy	accuracy	NOUN
cana-4522	98	21	.	.	PUNCT
cana-4522	99	1	in	in	ADP
cana-4522	99	2	the	the	DET
cana-4522	99	3	hyper	hyper	ADJ
cana-4522	99	4	parameter	parameter	NOUN
cana-4522	99	5	tuning	tune	VERB
cana-4522	99	6	for	for	ADP
cana-4522	99	7	k	k	PROPN
cana-4522	99	8	-	-	PUNCT
cana-4522	99	9	nn	nn	PROPN
cana-4522	99	10	algorithm	algorithm	PROPN
cana-4522	99	11	kth	kth	PROPN
cana-4522	99	12	value	value	NOUN
cana-4522	99	13	is	be	AUX
cana-4522	99	14	set	set	VERB
cana-4522	99	15	to	to	ADP
cana-4522	99	16	21	21	NUM
cana-4522	99	17	.	.	PUNCT
cana-4522	100	1	the	the	DET
cana-4522	100	2	experiments	experiment	NOUN
cana-4522	100	3	of	of	ADP
cana-4522	100	4	the	the	DET
cana-4522	100	5	proposed	propose	VERB
cana-4522	100	6	approach	approach	NOUN
cana-4522	100	7	are	be	AUX
cana-4522	100	8	conducted	conduct	VERB
cana-4522	100	9	in	in	ADP
cana-4522	100	10	the	the	DET
cana-4522	100	11	python	python	NOUN
cana-4522	100	12	's	's	PART
cana-4522	100	13	jupyter	jupyter	ADJ
cana-4522	100	14	notebook	notebook	NOUN
cana-4522	100	15	environment	environment	NOUN
cana-4522	100	16	.	.	PUNCT
cana-4522	101	1	the	the	DET
cana-4522	101	2	diabetes	diabetes	NOUN
cana-4522	101	3	prediction	prediction	NOUN
cana-4522	101	4	results	result	NOUN
cana-4522	101	5	using	use	VERB
cana-4522	101	6	proposed	propose	VERB
cana-4522	101	7	approach	approach	NOUN
cana-4522	101	8	on	on	ADP
cana-4522	101	9	the	the	DET
cana-4522	101	10	pima	pima	PROPN
cana-4522	101	11	diabetes	diabetes	NOUN
cana-4522	101	12	set	set	VERB
cana-4522	101	13	of	of	ADP
cana-4522	101	14	data	datum	NOUN
cana-4522	101	15	is	be	AUX
cana-4522	101	16	given	give	VERB
cana-4522	101	17	in	in	ADP
cana-4522	101	18	the	the	DET
cana-4522	101	19	following	follow	VERB
cana-4522	101	20	table-4	table-4	NOUN
cana-4522	101	21	.	.	PUNCT
cana-4522	101	22	table	table	NOUN
cana-4522	101	23	4	4	NUM
cana-4522	101	24	:	:	PUNCT
cana-4522	101	25	prediction	prediction	NOUN
cana-4522	101	26	accuracy	accuracy	NOUN
cana-4522	101	27	before	before	ADV
cana-4522	101	28	and	and	CCONJ
cana-4522	101	29	after	after	ADP
cana-4522	101	30	applying	apply	VERB
cana-4522	101	31	feature	feature	NOUN
cana-4522	101	32	selection	selection	NOUN
cana-4522	101	33	,	,	PUNCT
cana-4522	101	34	feature	feature	VERB
cana-4522	101	35	scaling	scaling	NOUN
cana-4522	101	36	and	and	CCONJ
cana-4522	101	37	hyperparameter	hyperparameter	NOUN
cana-4522	101	38	tuning	tune	VERB
cana-4522	101	39	using	use	VERB
cana-4522	101	40	pima	pima	PROPN
cana-4522	101	41	dataset	dataset	PROPN
cana-4522	101	42	.	.	PUNCT
cana-4522	102	1	ml	ml	NOUN
cana-4522	102	2	technique	technique	NOUN
cana-4522	102	3	prediction	prediction	NOUN
cana-4522	102	4	accuracy	accuracy	NOUN
cana-4522	102	5	before	before	ADP
cana-4522	102	6	feature	feature	NOUN
cana-4522	102	7	selection	selection	NOUN
cana-4522	102	8	,	,	PUNCT
cana-4522	102	9	scaling	scale	VERB
cana-4522	102	10	(	(	PUNCT
cana-4522	102	11	with	with	ADP
cana-4522	102	12	all	all	DET
cana-4522	102	13	8	8	NUM
cana-4522	102	14	features	feature	NOUN
cana-4522	102	15	)	)	PUNCT
cana-4522	102	16	prediction	prediction	NOUN
cana-4522	102	17	accuracy	accuracy	NOUN
cana-4522	102	18	after	after	ADP
cana-4522	102	19	feature	feature	NOUN
cana-4522	102	20	selection	selection	NOUN
cana-4522	102	21	,	,	PUNCT
cana-4522	102	22	scaling	scale	VERB
cana-4522	102	23	with	with	ADP
cana-4522	102	24	hyper	hyper	ADJ
cana-4522	102	25	parameter	parameter	NOUN
cana-4522	102	26	tuning	tuning	NOUN
cana-4522	102	27	(	(	PUNCT
cana-4522	102	28	selected	select	VERB
cana-4522	102	29	5	5	NUM
cana-4522	102	30	features	feature	NOUN
cana-4522	102	31	)	)	PUNCT
cana-4522	102	32	logistic	logistic	ADJ
cana-4522	102	33	regression	regression	NOUN
cana-4522	102	34	(	(	PUNCT
cana-4522	102	35	lr	lr	NOUN
cana-4522	102	36	)	)	PUNCT
cana-4522	102	37	75.97	75.97	NUM
cana-4522	102	38	%	%	NOUN
cana-4522	102	39	79.87	79.87	NUM
cana-4522	102	40	%	%	NOUN
cana-4522	103	1	k	k	ADV
cana-4522	103	2	-	-	PUNCT
cana-4522	103	3	nearest	near	ADJ
cana-4522	103	4	neighbor	neighbor	NOUN
cana-4522	103	5	(	(	PUNCT
cana-4522	103	6	knn	knn	PROPN
cana-4522	103	7	)	)	PUNCT
cana-4522	103	8	72.72	72.72	NUM
cana-4522	103	9	%	%	NOUN
cana-4522	103	10	81.82	81.82	NUM
cana-4522	103	11	%	%	NOUN
cana-4522	103	12	naïve	naïve	ADJ
cana-4522	103	13	bayes	bayes	NOUN
cana-4522	103	14	(	(	PUNCT
cana-4522	103	15	nb	nb	NOUN
cana-4522	103	16	)	)	PUNCT
cana-4522	103	17	77.27	77.27	NUM
cana-4522	103	18	%	%	NOUN
cana-4522	103	19	81.82	81.82	NUM
cana-4522	103	20	%	%	NOUN
cana-4522	103	21	support	support	NOUN
cana-4522	103	22	vector	vector	NOUN
cana-4522	103	23	machine	machine	NOUN
cana-4522	103	24	(	(	PUNCT
cana-4522	103	25	svm	svm	PROPN
cana-4522	103	26	)	)	PUNCT
cana-4522	103	27	77.27	77.27	NUM
cana-4522	103	28	%	%	NOUN
cana-4522	103	29	79.22	79.22	NUM
cana-4522	103	30	%	%	NOUN
cana-4522	103	31	decision	decision	NOUN
cana-4522	103	32	tree	tree	NOUN
cana-4522	103	33	(	(	PUNCT
cana-4522	103	34	dt	dt	PROPN
cana-4522	103	35	)	)	PUNCT
cana-4522	103	36	69.48	69.48	NUM
cana-4522	103	37	%	%	NOUN
cana-4522	103	38	75.98	75.98	NUM
cana-4522	103	39	%	%	NOUN
cana-4522	103	40	random	random	ADJ
cana-4522	103	41	forest	forest	NOUN
cana-4522	103	42	(	(	PUNCT
cana-4522	103	43	rf	rf	NOUN
cana-4522	103	44	)	)	PUNCT
cana-4522	103	45	75.97	75.97	NUM
cana-4522	103	46	%	%	NOUN
cana-4522	103	47	77.27	77.27	NUM
cana-4522	103	48	%	%	NOUN
cana-4522	103	49	from	from	ADP
cana-4522	103	50	the	the	DET
cana-4522	103	51	above	above	ADJ
cana-4522	103	52	table	table	NOUN
cana-4522	103	53	is	be	AUX
cana-4522	103	54	has	have	AUX
cana-4522	103	55	been	be	AUX
cana-4522	103	56	observed	observe	VERB
cana-4522	103	57	that	that	SCONJ
cana-4522	103	58	the	the	DET
cana-4522	103	59	prediction	prediction	NOUN
cana-4522	103	60	results	result	VERB
cana-4522	103	61	of	of	ADP
cana-4522	103	62	after	after	ADP
cana-4522	103	63	applying	apply	VERB
cana-4522	103	64	features	feature	NOUN
cana-4522	103	65	communications	communication	NOUN
cana-4522	103	66	on	on	ADP
cana-4522	103	67	applied	apply	VERB
cana-4522	103	68	nonlinear	nonlinear	ADJ
cana-4522	103	69	analysis	analysis	NOUN
cana-4522	103	70	issn	issn	NOUN
cana-4522	103	71	:	:	PUNCT
cana-4522	103	72	1074	1074	NUM
cana-4522	103	73	-	-	PUNCT
cana-4522	103	74	133x	133x	NUM
cana-4522	103	75	vol	vol	NOUN
cana-4522	103	76	32	32	NUM
cana-4522	104	1	no	no	NOUN
cana-4522	104	2	.	.	PUNCT
cana-4522	105	1	9s	9s	NUM
cana-4522	105	2	(	(	PUNCT
cana-4522	105	3	2025	2025	NUM
cana-4522	105	4	)	)	PUNCT
cana-4522	105	5	2354	2354	NUM
cana-4522	105	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4522	105	7	selection	selection	NOUN
cana-4522	105	8	,	,	PUNCT
cana-4522	105	9	feature	feature	VERB
cana-4522	105	10	scaling	scaling	NOUN
cana-4522	105	11	and	and	CCONJ
cana-4522	105	12	hyper	hyper	ADJ
cana-4522	105	13	parameter	parameter	NOUN
cana-4522	105	14	tuning	tuning	NOUN
cana-4522	105	15	method	method	NOUN
cana-4522	105	16	are	be	AUX
cana-4522	105	17	better	well	ADJ
cana-4522	105	18	than	than	ADP
cana-4522	105	19	without	without	ADP
cana-4522	105	20	applying	apply	VERB
cana-4522	105	21	preprocessing	preprocessing	NOUN
cana-4522	105	22	techniques	technique	NOUN
cana-4522	105	23	.	.	PUNCT
cana-4522	106	1	in	in	ADP
cana-4522	106	2	case	case	NOUN
cana-4522	106	3	of	of	ADP
cana-4522	106	4	logistic	logistic	ADJ
cana-4522	106	5	regression	regression	NOUN
cana-4522	106	6	classifier	classifier	NOUN
cana-4522	106	7	prediction	prediction	NOUN
cana-4522	106	8	accuracy	accuracy	NOUN
cana-4522	106	9	is	be	AUX
cana-4522	106	10	increased	increase	VERB
cana-4522	106	11	from	from	ADP
cana-4522	106	12	75.97	75.97	NUM
cana-4522	106	13	%	%	NOUN
cana-4522	106	14	to	to	ADP
cana-4522	106	15	79.87	79.87	NUM
cana-4522	106	16	%	%	NOUN
cana-4522	106	17	,	,	PUNCT
cana-4522	106	18	knn	knn	PROPN
cana-4522	106	19	accuracy	accuracy	NOUN
cana-4522	106	20	increased	increase	VERB
cana-4522	106	21	from	from	ADP
cana-4522	106	22	72.72	72.72	NUM
cana-4522	106	23	%	%	NOUN
cana-4522	106	24	to	to	ADP
cana-4522	106	25	81.82	81.82	NUM
cana-4522	106	26	%	%	NOUN
cana-4522	106	27	,	,	PUNCT
cana-4522	106	28	naive	naive	ADJ
cana-4522	106	29	bayes	bayes	NOUN
cana-4522	106	30	accuracy	accuracy	NOUN
cana-4522	106	31	is	be	AUX
cana-4522	106	32	increased	increase	VERB
cana-4522	106	33	from	from	ADP
cana-4522	106	34	77.27	77.27	NUM
cana-4522	106	35	%	%	NOUN
cana-4522	106	36	to	to	ADP
cana-4522	106	37	81.82	81.82	NUM
cana-4522	106	38	%	%	NOUN
cana-4522	106	39	that	that	PRON
cana-4522	106	40	shows	show	VERB
cana-4522	106	41	the	the	DET
cana-4522	106	42	significant	significant	ADJ
cana-4522	106	43	transformation	transformation	NOUN
cana-4522	106	44	in	in	ADP
cana-4522	106	45	diabetes	diabetes	NOUN
cana-4522	106	46	prediction	prediction	NOUN
cana-4522	106	47	accuracy	accuracy	NOUN
cana-4522	106	48	.	.	PUNCT
cana-4522	107	1	in	in	ADP
cana-4522	107	2	the	the	DET
cana-4522	107	3	proposed	propose	VERB
cana-4522	107	4	model	model	NOUN
cana-4522	107	5	the	the	DET
cana-4522	107	6	knn	knn	PROPN
cana-4522	107	7	and	and	CCONJ
cana-4522	107	8	nb	nb	PROPN
cana-4522	107	9	classifiers	classifier	NOUN
cana-4522	107	10	have	have	AUX
cana-4522	107	11	shown	show	VERB
cana-4522	107	12	81.82	81.82	NUM
cana-4522	107	13	%	%	NOUN
cana-4522	107	14	as	as	ADP
cana-4522	107	15	the	the	DET
cana-4522	107	16	highest	high	ADJ
cana-4522	107	17	prediction	prediction	NOUN
cana-4522	107	18	accuracy	accuracy	NOUN
cana-4522	107	19	as	as	ADP
cana-4522	107	20	compare	compare	NOUN
cana-4522	107	21	to	to	ADP
cana-4522	107	22	other	other	ADJ
cana-4522	107	23	classifiers	classifier	NOUN
cana-4522	107	24	.	.	PUNCT
cana-4522	108	1	the	the	DET
cana-4522	108	2	following	follow	VERB
cana-4522	108	3	figures	figure	NOUN
cana-4522	108	4	shows	show	VERB
cana-4522	108	5	prediction	prediction	NOUN
cana-4522	108	6	accuracy	accuracy	NOUN
cana-4522	108	7	before	before	ADV
cana-4522	108	8	and	and	CCONJ
cana-4522	108	9	after	after	ADP
cana-4522	108	10	applying	apply	VERB
cana-4522	108	11	the	the	DET
cana-4522	108	12	feature	feature	NOUN
cana-4522	108	13	selection	selection	NOUN
cana-4522	108	14	,	,	PUNCT
cana-4522	108	15	feature	feature	VERB
cana-4522	108	16	scaling	scaling	NOUN
cana-4522	108	17	and	and	CCONJ
cana-4522	108	18	hyper	hyper	ADJ
cana-4522	108	19	parameter	parameter	NOUN
cana-4522	108	20	tuning	tune	VERB
cana-4522	108	21	techniques	technique	NOUN
cana-4522	108	22	on	on	ADP
cana-4522	108	23	the	the	DET
cana-4522	108	24	pima	pima	PROPN
cana-4522	108	25	dataset	dataset	PROPN
cana-4522	108	26	.	.	PUNCT
cana-4522	109	1	figure	figure	NOUN
cana-4522	109	2	4	4	NUM
cana-4522	109	3	.	.	NOUN
cana-4522	110	1	diabetes	diabetes	NOUN
cana-4522	110	2	prediction	prediction	NOUN
cana-4522	110	3	accuracy	accuracy	NOUN
cana-4522	110	4	with	with	ADP
cana-4522	110	5	and	and	CCONJ
cana-4522	110	6	without	without	ADP
cana-4522	110	7	pre	pre	ADJ
cana-4522	110	8	-	-	ADJ
cana-4522	110	9	processing	process	VERB
cana-4522	110	10	the	the	DET
cana-4522	110	11	performance	performance	NOUN
cana-4522	110	12	evaluation	evaluation	NOUN
cana-4522	110	13	and	and	CCONJ
cana-4522	110	14	classification	classification	NOUN
cana-4522	110	15	report	report	NOUN
cana-4522	110	16	with	with	ADP
cana-4522	110	17	confusion	confusion	NOUN
cana-4522	110	18	matrix	matrix	NOUN
cana-4522	110	19	of	of	ADP
cana-4522	110	20	the	the	DET
cana-4522	110	21	proposed	propose	VERB
cana-4522	110	22	machine	machine	NOUN
cana-4522	110	23	learning	learn	VERB
cana-4522	110	24	approach	approach	NOUN
cana-4522	110	25	using	use	VERB
cana-4522	110	26	pima	pima	PROPN
cana-4522	110	27	diabetes	diabetes	NOUN
cana-4522	110	28	dataset	dataset	NOUN
cana-4522	110	29	is	be	AUX
cana-4522	110	30	shown	show	VERB
cana-4522	110	31	in	in	ADP
cana-4522	110	32	the	the	DET
cana-4522	110	33	following	follow	VERB
cana-4522	110	34	table	table	NOUN
cana-4522	110	35	.	.	PUNCT
cana-4522	111	1	table-5	table-5	PROPN
cana-4522	111	2	.	.	PUNCT
cana-4522	111	3	performance	performance	NOUN
cana-4522	111	4	evaluation	evaluation	NOUN
cana-4522	111	5	and	and	CCONJ
cana-4522	111	6	classification	classification	NOUN
cana-4522	111	7	report	report	NOUN
cana-4522	111	8	of	of	ADP
cana-4522	111	9	the	the	DET
cana-4522	111	10	proposed	propose	VERB
cana-4522	111	11	machine	machine	NOUN
cana-4522	111	12	learning	learn	VERB
cana-4522	111	13	approach	approach	NOUN
cana-4522	111	14	ml	ml	ADP
cana-4522	111	15	algorithms	algorithms	PROPN
cana-4522	111	16	classification	classification	NOUN
cana-4522	111	17	report	report	NOUN
cana-4522	111	18	confusion	confusion	NOUN
cana-4522	111	19	matrix	matrix	NOUN
cana-4522	111	20	logistic	logistic	ADJ
cana-4522	111	21	regression	regression	NOUN
cana-4522	111	22	diabetes	diabetes	NOUN
cana-4522	111	23	prediction	prediction	NOUN
cana-4522	111	24	accuracy	accuracy	NOUN
cana-4522	111	25	with	with	ADP
cana-4522	111	26	and	and	CCONJ
cana-4522	111	27	without	without	ADP
cana-4522	111	28	feature	feature	NOUN
cana-4522	111	29	selection	selection	NOUN
cana-4522	111	30	85.00	85.00	NUM
cana-4522	111	31	%	%	NOUN
cana-4522	111	32	80.00	80.00	NUM
cana-4522	111	33	%	%	NOUN
cana-4522	111	34	75.00	75.00	NUM
cana-4522	111	35	%	%	NOUN
cana-4522	111	36	70.00	70.00	NUM
cana-4522	111	37	%	%	NOUN
cana-4522	111	38	65.00	65.00	NUM
cana-4522	111	39	%	%	NOUN
cana-4522	111	40	60.00	60.00	NUM
cana-4522	111	41	%	%	NOUN
cana-4522	111	42	logistic	logistic	ADJ
cana-4522	111	43	regression	regression	NOUN
cana-4522	111	44	(	(	PUNCT
cana-4522	111	45	lr	lr	NOUN
cana-4522	111	46	)	)	PUNCT
cana-4522	111	47	k	k	NOUN
cana-4522	111	48	-	-	PUNCT
cana-4522	111	49	nearest	near	ADJ
cana-4522	111	50	neighbor	neighbor	NOUN
cana-4522	111	51	(	(	PUNCT
cana-4522	111	52	knn	knn	PROPN
cana-4522	111	53	)	)	PUNCT
cana-4522	111	54	naïve	naïve	ADJ
cana-4522	111	55	bayes	bayes	PROPN
cana-4522	111	56	support	support	VERB
cana-4522	111	57	vector	vector	NOUN
cana-4522	111	58	decision	decision	NOUN
cana-4522	111	59	tree	tree	NOUN
cana-4522	111	60	random	random	ADJ
cana-4522	111	61	forest	forest	NOUN
cana-4522	111	62	(	(	PUNCT
cana-4522	111	63	nb	nb	NOUN
cana-4522	111	64	)	)	PUNCT
cana-4522	111	65	machine	machine	NOUN
cana-4522	111	66	svm	svm	NOUN
cana-4522	111	67	(	(	PUNCT
cana-4522	111	68	dt	dt	PROPN
cana-4522	111	69	)	)	PUNCT
cana-4522	111	70	(	(	PUNCT
cana-4522	111	71	rf	rf	NOUN
cana-4522	111	72	)	)	PUNCT
cana-4522	111	73	prediction	prediction	NOUN
cana-4522	111	74	accuracy	accuracy	NOUN
cana-4522	111	75	before	before	ADP
cana-4522	111	76	feature	feature	NOUN
cana-4522	111	77	selection	selection	NOUN
cana-4522	111	78	(	(	PUNCT
cana-4522	111	79	with	with	ADP
cana-4522	111	80	all	all	PRON
cana-4522	111	81	8	8	NUM
cana-4522	111	82	features	feature	NOUN
cana-4522	111	83	)	)	PUNCT
cana-4522	111	84	prediction	prediction	NOUN
cana-4522	111	85	accuracy	accuracy	NOUN
cana-4522	111	86	after	after	ADP
cana-4522	111	87	feature	feature	NOUN
cana-4522	111	88	selection	selection	NOUN
cana-4522	111	89	(	(	PUNCT
cana-4522	111	90	with	with	ADP
cana-4522	111	91	selected	select	VERB
cana-4522	111	92	5	5	NUM
cana-4522	111	93	features	feature	NOUN
cana-4522	111	94	)	)	PUNCT
cana-4522	112	1	p	p	X
cana-4522	112	2	re	re	ADP
cana-4522	112	3	d	d	NOUN
cana-4522	112	4	ic	ic	X
cana-4522	112	5	ti	ti	X
cana-4522	112	6	o	o	PROPN
cana-4522	112	7	n	n	CCONJ
cana-4522	112	8	a	a	DET
cana-4522	112	9	cc	cc	X
cana-4522	112	10	u	u	NOUN
cana-4522	112	11	ra	ra	PROPN
cana-4522	112	12	cy	cy	PROPN
cana-4522	112	13	communications	communication	NOUN
cana-4522	112	14	on	on	ADP
cana-4522	112	15	applied	apply	VERB
cana-4522	112	16	nonlinear	nonlinear	ADJ
cana-4522	112	17	analysis	analysis	NOUN
cana-4522	112	18	issn	issn	NOUN
cana-4522	112	19	:	:	PUNCT
cana-4522	112	20	1074	1074	NUM
cana-4522	112	21	-	-	PUNCT
cana-4522	112	22	133x	133x	NUM
cana-4522	112	23	vol	vol	NOUN
cana-4522	112	24	32	32	NUM
cana-4522	112	25	no	no	NOUN
cana-4522	112	26	.	.	PUNCT
cana-4522	113	1	9s	9s	NUM
cana-4522	113	2	(	(	PUNCT
cana-4522	113	3	2025	2025	NUM
cana-4522	113	4	)	)	PUNCT
cana-4522	113	5	2355	2355	NUM
cana-4522	113	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4522	114	1	k	k	X
cana-4522	114	2	-	-	PUNCT
cana-4522	114	3	nearest	near	ADJ
cana-4522	114	4	neighbor	neighbor	NOUN
cana-4522	114	5	naïve	naïve	ADJ
cana-4522	114	6	bayes	bayes	PROPN
cana-4522	114	7	support	support	VERB
cana-4522	114	8	vector	vector	NOUN
cana-4522	114	9	machine	machine	NOUN
cana-4522	114	10	decision	decision	NOUN
cana-4522	114	11	tree	tree	NOUN
cana-4522	114	12	random	random	ADJ
cana-4522	114	13	forest	forest	NOUN
cana-4522	114	14	the	the	DET
cana-4522	114	15	performance	performance	NOUN
cana-4522	114	16	of	of	ADP
cana-4522	114	17	the	the	DET
cana-4522	114	18	proposed	propose	VERB
cana-4522	114	19	methodology	methodology	NOUN
cana-4522	114	20	is	be	AUX
cana-4522	114	21	compared	compare	VERB
cana-4522	114	22	with	with	ADP
cana-4522	114	23	performances	performance	NOUN
cana-4522	114	24	of	of	ADP
cana-4522	114	25	different	different	ADJ
cana-4522	114	26	techniques	technique	NOUN
cana-4522	114	27	form	form	VERB
cana-4522	114	28	the	the	DET
cana-4522	114	29	literature	literature	NOUN
cana-4522	114	30	.	.	PUNCT
cana-4522	115	1	following	follow	VERB
cana-4522	115	2	table	table	NOUN
cana-4522	115	3	shows	show	VERB
cana-4522	115	4	diabetes	diabetes	NOUN
cana-4522	115	5	prediction	prediction	NOUN
cana-4522	115	6	accuracies	accuracy	NOUN
cana-4522	115	7	achieved	achieve	VERB
cana-4522	115	8	by	by	ADP
cana-4522	115	9	various	various	ADJ
cana-4522	115	10	researchers	researcher	NOUN
cana-4522	115	11	using	use	VERB
cana-4522	115	12	different	different	ADJ
cana-4522	115	13	machine	machine	NOUN
cana-4522	115	14	learning	learn	VERB
cana-4522	115	15	classification	classification	NOUN
cana-4522	115	16	methods	method	NOUN
cana-4522	115	17	and	and	CCONJ
cana-4522	115	18	compared	compare	VERB
cana-4522	115	19	with	with	ADP
cana-4522	115	20	accuracy	accuracy	NOUN
cana-4522	115	21	of	of	ADP
cana-4522	115	22	proposed	propose	VERB
cana-4522	115	23	methodology	methodology	NOUN
cana-4522	115	24	.	.	PUNCT
cana-4522	116	1	table6	table6	NOUN
cana-4522	116	2	.	.	PUNCT
cana-4522	117	1	diabetes	diabetes	NOUN
cana-4522	117	2	prediction	prediction	NOUN
cana-4522	117	3	accuracy	accuracy	NOUN
cana-4522	117	4	comparison	comparison	NOUN
cana-4522	117	5	of	of	ADP
cana-4522	117	6	proposed	propose	VERB
cana-4522	117	7	method	method	NOUN
cana-4522	117	8	with	with	ADP
cana-4522	117	9	existing	exist	VERB
cana-4522	117	10	methods	method	NOUN
cana-4522	117	11	ref	ref	VERB
cana-4522	117	12	.	.	PUNCT
cana-4522	118	1	no	no	DET
cana-4522	118	2	.	.	NOUN
cana-4522	118	3	technique	technique	NOUN
cana-4522	118	4	used	use	VERB
cana-4522	118	5	lr	lr	PROPN
cana-4522	118	6	k	k	PROPN
cana-4522	118	7	–	–	PUNCT
cana-4522	118	8	nn	nn	X
cana-4522	118	9	naive	naive	ADJ
cana-4522	118	10	bayes	bayes	NOUN
cana-4522	118	11	svm	svm	VERB
cana-4522	118	12	decision	decision	NOUN
cana-4522	118	13	tree	tree	NOUN
cana-4522	118	14	random	random	ADJ
cana-4522	118	15	forest	forest	NOUN
cana-4522	118	16	gangani	gangani	ADJ
cana-4522	118	17	dharmarathne	dharmarathne	NOUN
cana-4522	119	1	[	[	X
cana-4522	119	2	9	9	NUM
cana-4522	119	3	]	]	SYM
cana-4522	119	4	dt	dt	PROPN
cana-4522	119	5	,	,	PUNCT
cana-4522	119	6	knn	knn	PROPN
cana-4522	119	7	,	,	PUNCT
cana-4522	119	8	svc	svc	PROPN
cana-4522	119	9	,	,	PUNCT
cana-4522	119	10	xgb	xgb	NOUN
cana-4522	119	11	77.0	77.0	NUM
cana-4522	119	12	%	%	NOUN
cana-4522	119	13	-	-	PUNCT
cana-4522	119	14	77.0	77.0	NUM
cana-4522	119	15	%	%	NOUN
cana-4522	119	16	76.0	76.0	NUM
cana-4522	119	17	%	%	NOUN
cana-4522	119	18	-	-	PUNCT
cana-4522	119	19	communications	communication	NOUN
cana-4522	119	20	on	on	ADP
cana-4522	119	21	applied	apply	VERB
cana-4522	119	22	nonlinear	nonlinear	ADJ
cana-4522	119	23	analysis	analysis	NOUN
cana-4522	119	24	issn	issn	NOUN
cana-4522	119	25	:	:	PUNCT
cana-4522	119	26	1074	1074	NUM
cana-4522	119	27	-	-	PUNCT
cana-4522	119	28	133x	133x	NUM
cana-4522	119	29	vol	vol	NOUN
cana-4522	119	30	32	32	NUM
cana-4522	119	31	no	no	NOUN
cana-4522	119	32	.	.	PUNCT
cana-4522	120	1	9s	9s	NUM
cana-4522	120	2	(	(	PUNCT
cana-4522	120	3	2025	2025	NUM
cana-4522	120	4	)	)	PUNCT
cana-4522	120	5	2356	2356	NUM
cana-4522	120	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4522	121	1	muhamad	muhamad	PROPN
cana-4522	121	2	exell	exell	PROPN
cana-4522	121	3	febriana	febriana	PROPN
cana-4522	122	1	[	[	X
cana-4522	122	2	10	10	NUM
cana-4522	122	3	]	]	X
cana-4522	122	4	knn	knn	PROPN
cana-4522	122	5	,	,	PUNCT
cana-4522	122	6	naive	naive	ADJ
cana-4522	122	7	bayes	baye	NOUN
cana-4522	122	8	73.33	73.33	NUM
cana-4522	122	9	%	%	NOUN
cana-4522	122	10	76.07	76.07	NUM
cana-4522	122	11	%	%	NOUN
cana-4522	122	12	-	-	PUNCT
cana-4522	122	13	-	-	PUNCT
cana-4522	122	14	-	-	PUNCT
cana-4522	122	15	hakim	hakim	PROPN
cana-4522	122	16	el	el	PROPN
cana-4522	122	17	massari	massari	PROPN
cana-4522	123	1	[	[	X
cana-4522	123	2	11	11	NUM
cana-4522	123	3	]	]	SYM
cana-4522	123	4	svm	svm	PROPN
cana-4522	123	5	,	,	PUNCT
cana-4522	123	6	knn	knn	PROPN
cana-4522	123	7	,	,	PUNCT
cana-4522	123	8	a	a	DET
cana-4522	123	9	nn	nn	PROPN
cana-4522	123	10	,	,	PUNCT
cana-4522	123	11	nb	nb	PROPN
cana-4522	123	12	,	,	PUNCT
cana-4522	123	13	lr	lr	PROPN
cana-4522	123	14	,	,	PUNCT
cana-4522	123	15	dt	dt	NOUN
cana-4522	123	16	,	,	PUNCT
cana-4522	123	17	ontology	ontology	NOUN
cana-4522	123	18	77.2	77.2	NUM
cana-4522	123	19	%	%	NOUN
cana-4522	123	20	70.2	70.2	NUM
cana-4522	123	21	%	%	NOUN
cana-4522	123	22	79.0	79.0	NUM
cana-4522	123	23	%	%	NOUN
cana-4522	123	24	77.3	77.3	NUM
cana-4522	123	25	%	%	NOUN
cana-4522	123	26	-	-	PUNCT
cana-4522	123	27	73.8	73.8	NUM
cana-4522	123	28	%	%	NOUN
cana-4522	123	29	jobeda	jobeda	PROPN
cana-4522	123	30	jamal	jamal	PROPN
cana-4522	124	1	[	[	X
cana-4522	124	2	12	12	NUM
cana-4522	124	3	]	]	X
cana-4522	124	4	dt	dt	X
cana-4522	124	5	,	,	PUNCT
cana-4522	124	6	rf	rf	PROPN
cana-4522	124	7	,	,	PUNCT
cana-4522	124	8	nb	nb	INTJ
cana-4522	124	9	,	,	PUNCT
cana-4522	124	10	l	l	NOUN
cana-4522	124	11	r	r	PROPN
cana-4522	124	12	,	,	PUNCT
cana-4522	124	13	knn	knn	PROPN
cana-4522	124	14	,	,	PUNCT
cana-4522	124	15	ab	ab	PROPN
cana-4522	124	16	,	,	PUNCT
cana-4522	124	17	svm	svm	PROPN
cana-4522	124	18	78.86	78.86	NUM
cana-4522	124	19	%	%	NOUN
cana-4522	124	20	79.42	79.42	NUM
cana-4522	124	21	%	%	NOUN
cana-4522	124	22	78.28	78.28	NUM
cana-4522	124	23	%	%	NOUN
cana-4522	124	24	77.71	77.71	NUM
cana-4522	124	25	%	%	NOUN
cana-4522	124	26	73.14	73.14	NUM
cana-4522	124	27	%	%	NOUN
cana-4522	124	28	77.34	77.34	NUM
cana-4522	124	29	%	%	NOUN
cana-4522	124	30	neha	neha	NOUN
cana-4522	124	31	p.	p.	NOUN
cana-4522	124	32	tiggaa	tiggaa	NOUN
cana-4522	125	1	[	[	X
cana-4522	125	2	13	13	NUM
cana-4522	125	3	]	]	SYM
cana-4522	125	4	lr	lr	PROPN
cana-4522	125	5	,	,	PUNCT
cana-4522	125	6	knn	knn	PROPN
cana-4522	125	7	,	,	PUNCT
cana-4522	125	8	svm	svm	PROPN
cana-4522	125	9	,	,	PUNCT
cana-4522	125	10	n	n	PROPN
cana-4522	125	11	b	b	NOUN
cana-4522	125	12	,	,	PUNCT
cana-4522	125	13	rf	rf	VERB
cana-4522	125	14	74.4	74.4	NUM
cana-4522	125	15	%	%	NOUN
cana-4522	125	16	70.8	70.8	NUM
cana-4522	125	17	%	%	NOUN
cana-4522	125	18	68.9	68.9	NUM
cana-4522	125	19	%	%	NOUN
cana-4522	125	20	74.4	74.4	NUM
cana-4522	125	21	%	%	NOUN
cana-4522	125	22	69.7	69.7	NUM
cana-4522	125	23	%	%	NOUN
cana-4522	125	24	75.0	75.0	NUM
cana-4522	125	25	%	%	NOUN
cana-4522	125	26	gaurav	gaurav	PROPN
cana-4522	125	27	tripathi	tripathi	PROPN
cana-4522	126	1	[	[	X
cana-4522	126	2	14	14	NUM
cana-4522	126	3	]	]	X
cana-4522	126	4	lda	lda	PROPN
cana-4522	126	5	,	,	PUNCT
cana-4522	126	6	knn	knn	PROPN
cana-4522	126	7	,	,	PUNCT
cana-4522	126	8	svm	svm	PROPN
cana-4522	126	9	,	,	PUNCT
cana-4522	126	10	rf	rf	VERB
cana-4522	126	11	79.24	79.24	NUM
cana-4522	126	12	%	%	NOUN
cana-4522	126	13	-	-	PUNCT
cana-4522	126	14	80.85	80.85	NUM
cana-4522	126	15	%	%	NOUN
cana-4522	126	16	-	-	PUNCT
cana-4522	126	17	87.66	87.66	NUM
cana-4522	126	18	%	%	NOUN
cana-4522	126	19	km	km	NOUN
cana-4522	126	20	jyoti	jyoti	PROPN
cana-4522	127	1	[	[	X
cana-4522	127	2	17	17	NUM
cana-4522	127	3	]	]	X
cana-4522	127	4	knn	knn	PROPN
cana-4522	127	5	,	,	PUNCT
cana-4522	127	6	lr	lr	INTJ
cana-4522	127	7	,	,	PUNCT
cana-4522	127	8	svm	svm	NOUN
cana-4522	127	9	78.0	78.0	NUM
cana-4522	127	10	%	%	NOUN
cana-4522	127	11	78.0	78.0	NUM
cana-4522	127	12	%	%	NOUN
cana-4522	127	13	-77.0	-77.0	NUM
cana-4522	127	14	%	%	NOUN
cana-4522	127	15	--	--	PUNCT
cana-4522	127	16	priyanka	priyanka	NOUN
cana-4522	127	17	sonar	sonar	NOUN
cana-4522	128	1	[	[	X
cana-4522	128	2	18	18	NUM
cana-4522	128	3	]	]	SYM
cana-4522	128	4	dt	dt	PROPN
cana-4522	128	5	,	,	PUNCT
cana-4522	128	6	ann	ann	PROPN
cana-4522	128	7	,	,	PUNCT
cana-4522	128	8	nb	nb	PROPN
cana-4522	128	9	,	,	PUNCT
cana-4522	128	10	svm	svm	VERB
cana-4522	128	11	-77.0	-77.0	NUM
cana-4522	128	12	%	%	NOUN
cana-4522	128	13	77.3	77.3	NUM
cana-4522	128	14	%	%	NOUN
cana-4522	128	15	85.0	85.0	NUM
cana-4522	128	16	%	%	NOUN
cana-4522	128	17	-	-	PUNCT
cana-4522	128	18	proposed	propose	VERB
cana-4522	128	19	method	method	NOUN
cana-4522	128	20	results	result	VERB
cana-4522	128	21	lr	lr	PROPN
cana-4522	128	22	,	,	PUNCT
cana-4522	128	23	knn	knn	PROPN
cana-4522	128	24	,	,	PUNCT
cana-4522	128	25	nb	nb	PROPN
cana-4522	128	26	,	,	PUNCT
cana-4522	128	27	svm	svm	PROPN
cana-4522	128	28	,	,	PUNCT
cana-4522	128	29	dt	dt	X
cana-4522	128	30	,	,	PUNCT
cana-4522	128	31	rf	rf	VERB
cana-4522	128	32	79.87	79.87	NUM
cana-4522	128	33	%	%	NOUN
cana-4522	128	34	81.82	81.82	NUM
cana-4522	128	35	%	%	NOUN
cana-4522	128	36	81.82	81.82	NUM
cana-4522	128	37	%	%	NOUN
cana-4522	128	38	79.22	79.22	NUM
cana-4522	128	39	%	%	NOUN
cana-4522	128	40	75.98	75.98	NUM
cana-4522	128	41	%	%	NOUN
cana-4522	128	42	77.27	77.27	NUM
cana-4522	128	43	%	%	NOUN
cana-4522	128	44	from	from	ADP
cana-4522	128	45	the	the	DET
cana-4522	128	46	above	above	ADJ
cana-4522	128	47	predication	predication	NOUN
cana-4522	128	48	accuracy	accuracy	NOUN
cana-4522	128	49	table-6	table-6	PUNCT
cana-4522	129	1	it	it	PRON
cana-4522	129	2	was	be	AUX
cana-4522	129	3	clear	clear	ADJ
cana-4522	129	4	that	that	SCONJ
cana-4522	129	5	the	the	DET
cana-4522	129	6	proposed	propose	VERB
cana-4522	129	7	ml	ml	X
cana-4522	129	8	approach	approach	NOUN
cana-4522	129	9	using	use	VERB
cana-4522	129	10	different	different	ADJ
cana-4522	129	11	algorithms	algorithm	NOUN
cana-4522	129	12	performed	perform	VERB
cana-4522	129	13	acceptable	acceptable	ADJ
cana-4522	129	14	prediction	prediction	NOUN
cana-4522	129	15	accuracy	accuracy	NOUN
cana-4522	129	16	in	in	ADP
cana-4522	129	17	comparison	comparison	NOUN
cana-4522	129	18	with	with	ADP
cana-4522	129	19	other	other	ADJ
cana-4522	129	20	methodologies	methodology	NOUN
cana-4522	129	21	form	form	VERB
cana-4522	129	22	the	the	DET
cana-4522	129	23	literature	literature	NOUN
cana-4522	129	24	.	.	PUNCT
cana-4522	130	1	the	the	DET
cana-4522	130	2	better	well	ADJ
cana-4522	130	3	diabetes	diabetes	NOUN
cana-4522	130	4	prediction	prediction	NOUN
cana-4522	130	5	accuracy	accuracy	NOUN
cana-4522	130	6	result	result	NOUN
cana-4522	130	7	obtained	obtain	VERB
cana-4522	130	8	by	by	ADP
cana-4522	130	9	knn	knn	PROPN
cana-4522	130	10	and	and	CCONJ
cana-4522	130	11	naïve	naïve	ADJ
cana-4522	130	12	bayes	bayes	PROPN
cana-4522	130	13	is	be	AUX
cana-4522	130	14	81.82	81.82	NUM
cana-4522	130	15	%	%	NOUN
cana-4522	130	16	as	as	SCONJ
cana-4522	130	17	compare	compare	VERB
cana-4522	130	18	to	to	ADP
cana-4522	130	19	the	the	DET
cana-4522	130	20	other	other	ADJ
cana-4522	130	21	classifiers	classifier	NOUN
cana-4522	130	22	.	.	PUNCT
cana-4522	131	1	5	5	X
cana-4522	131	2	.	.	X
cana-4522	131	3	discussion	discussion	NOUN
cana-4522	131	4	in	in	ADP
cana-4522	131	5	this	this	DET
cana-4522	131	6	research	research	NOUN
cana-4522	131	7	work	work	NOUN
cana-4522	131	8	a	a	DET
cana-4522	131	9	novel	novel	ADJ
cana-4522	131	10	approach	approach	NOUN
cana-4522	131	11	was	be	AUX
cana-4522	131	12	developed	develop	VERB
cana-4522	131	13	using	use	VERB
cana-4522	131	14	machine	machine	NOUN
cana-4522	131	15	learning	learn	VERB
cana-4522	131	16	algorithms	algorithm	NOUN
cana-4522	131	17	with	with	ADP
cana-4522	131	18	features	feature	NOUN
cana-4522	131	19	selection	selection	NOUN
cana-4522	131	20	,	,	PUNCT
cana-4522	131	21	features	feature	VERB
cana-4522	131	22	scaling	scaling	NOUN
cana-4522	131	23	and	and	CCONJ
cana-4522	131	24	hyper	hyper	ADJ
cana-4522	131	25	parameter	parameter	NOUN
cana-4522	131	26	tuning	tuning	NOUN
cana-4522	131	27	technique	technique	NOUN
cana-4522	131	28	for	for	ADP
cana-4522	131	29	diabetes	diabetes	NOUN
cana-4522	131	30	disease	disease	NOUN
cana-4522	131	31	prediction	prediction	NOUN
cana-4522	131	32	.	.	PUNCT
cana-4522	132	1	the	the	DET
cana-4522	132	2	significant	significant	ADJ
cana-4522	132	3	and	and	CCONJ
cana-4522	132	4	robust	robust	ADJ
cana-4522	132	5	features	feature	NOUN
cana-4522	132	6	from	from	ADP
cana-4522	132	7	dataset	dataset	NOUN
cana-4522	132	8	are	be	AUX
cana-4522	132	9	selected	select	VERB
cana-4522	132	10	using	use	VERB
cana-4522	132	11	attribute	attribute	NOUN
cana-4522	132	12	selection	selection	NOUN
cana-4522	132	13	tool	tool	NOUN
cana-4522	132	14	and	and	CCONJ
cana-4522	132	15	correlation	correlation	NOUN
cana-4522	132	16	attribute	attribute	NOUN
cana-4522	132	17	estimation	estimation	NOUN
cana-4522	132	18	method	method	NOUN
cana-4522	132	19	in	in	ADP
cana-4522	132	20	weka	weka	PROPN
cana-4522	132	21	software	software	PROPN
cana-4522	132	22	.	.	PUNCT
cana-4522	133	1	afterwards	afterwards	ADV
cana-4522	133	2	,	,	PUNCT
cana-4522	133	3	the	the	DET
cana-4522	133	4	standard	standard	NOUN
cana-4522	133	5	features	feature	VERB
cana-4522	133	6	scaling	scale	VERB
cana-4522	133	7	technique	technique	NOUN
cana-4522	133	8	is	be	AUX
cana-4522	133	9	used	use	VERB
cana-4522	133	10	for	for	ADP
cana-4522	133	11	the	the	DET
cana-4522	133	12	scaling	scaling	NOUN
cana-4522	133	13	of	of	ADP
cana-4522	133	14	selected	select	VERB
cana-4522	133	15	significant	significant	ADJ
cana-4522	133	16	features	feature	NOUN
cana-4522	133	17	.	.	PUNCT
cana-4522	134	1	then	then	ADV
cana-4522	134	2	,	,	PUNCT
cana-4522	134	3	ml	ml	ADP
cana-4522	134	4	algorithms	algorithm	NOUN
cana-4522	134	5	such	such	ADJ
cana-4522	134	6	as	as	ADP
cana-4522	134	7	lr	lr	PROPN
cana-4522	134	8	,	,	PUNCT
cana-4522	134	9	knn	knn	PROPN
cana-4522	134	10	,	,	PUNCT
cana-4522	134	11	naive	naive	ADJ
cana-4522	134	12	bayes	bayes	NOUN
cana-4522	134	13	,	,	PUNCT
cana-4522	134	14	svm	svm	NOUN
cana-4522	134	15	,	,	PUNCT
cana-4522	134	16	dt	dt	X
cana-4522	134	17	,	,	PUNCT
cana-4522	134	18	and	and	CCONJ
cana-4522	134	19	rf	rf	VERB
cana-4522	134	20	along	along	ADP
cana-4522	134	21	with	with	ADP
cana-4522	134	22	hyper	hyper	ADJ
cana-4522	134	23	parameter	parameter	NOUN
cana-4522	134	24	tuning	tuning	NOUN
cana-4522	134	25	technique	technique	NOUN
cana-4522	134	26	are	be	AUX
cana-4522	134	27	used	use	VERB
cana-4522	134	28	for	for	ADP
cana-4522	134	29	the	the	DET
cana-4522	134	30	classification	classification	NOUN
cana-4522	134	31	purpose	purpose	NOUN
cana-4522	134	32	.	.	PUNCT
cana-4522	135	1	after	after	ADP
cana-4522	135	2	conducting	conduct	VERB
cana-4522	135	3	experiments	experiment	NOUN
cana-4522	135	4	on	on	ADP
cana-4522	135	5	pima	pima	PROPN
cana-4522	135	6	indian	indian	PROPN
cana-4522	135	7	dataset	dataset	VERB
cana-4522	135	8	with	with	ADP
cana-4522	135	9	selected	select	VERB
cana-4522	135	10	significant	significant	ADJ
cana-4522	135	11	features	feature	NOUN
cana-4522	135	12	and	and	CCONJ
cana-4522	135	13	above	above	ADP
cana-4522	135	14	classification	classification	NOUN
cana-4522	135	15	algorithms	algorithm	NOUN
cana-4522	135	16	with	with	ADP
cana-4522	135	17	hyper	hyper	ADJ
cana-4522	135	18	parameter	parameter	NOUN
cana-4522	135	19	tuning	tuning	NOUN
cana-4522	135	20	,	,	PUNCT
cana-4522	135	21	knn	knn	PROPN
cana-4522	135	22	and	and	CCONJ
cana-4522	135	23	nb	nb	PROPN
cana-4522	135	24	classifiers	classifier	NOUN
cana-4522	135	25	have	have	AUX
cana-4522	135	26	shown	show	VERB
cana-4522	135	27	the	the	DET
cana-4522	135	28	highest	high	ADJ
cana-4522	135	29	prediction	prediction	NOUN
cana-4522	135	30	accuracy	accuracy	NOUN
cana-4522	135	31	of	of	ADP
cana-4522	135	32	81.82	81.82	NUM
cana-4522	135	33	%	%	NOUN
cana-4522	135	34	.	.	PUNCT
cana-4522	136	1	then	then	ADV
cana-4522	136	2	,	,	PUNCT
cana-4522	136	3	logistic	logistic	ADJ
cana-4522	136	4	regression	regression	NOUN
cana-4522	136	5	,	,	PUNCT
cana-4522	136	6	support	support	NOUN
cana-4522	136	7	vector	vector	NOUN
cana-4522	136	8	machine	machine	NOUN
cana-4522	136	9	and	and	CCONJ
cana-4522	136	10	random	random	ADJ
cana-4522	136	11	forest	forest	NOUN
cana-4522	136	12	have	have	AUX
cana-4522	136	13	shown	show	VERB
cana-4522	136	14	79.87	79.87	NUM
cana-4522	136	15	%	%	NOUN
cana-4522	136	16	,	,	PUNCT
cana-4522	136	17	79.22	79.22	NUM
cana-4522	136	18	%	%	NOUN
cana-4522	136	19	,	,	PUNCT
cana-4522	136	20	and	and	CCONJ
cana-4522	136	21	77.27	77.27	NUM
cana-4522	136	22	%	%	NOUN
cana-4522	136	23	of	of	ADP
cana-4522	136	24	prediction	prediction	NOUN
cana-4522	136	25	accuracy	accuracy	NOUN
cana-4522	136	26	respectively	respectively	ADV
cana-4522	136	27	.	.	PUNCT
cana-4522	137	1	in	in	ADP
cana-4522	137	2	the	the	DET
cana-4522	137	3	forthcoming	forthcoming	ADJ
cana-4522	137	4	research	research	NOUN
cana-4522	137	5	,	,	PUNCT
cana-4522	137	6	we	we	PRON
cana-4522	137	7	aim	aim	VERB
cana-4522	137	8	to	to	PART
cana-4522	137	9	develop	develop	VERB
cana-4522	137	10	a	a	DET
cana-4522	137	11	robust	robust	ADJ
cana-4522	137	12	methodology	methodology	NOUN
cana-4522	137	13	using	use	VERB
cana-4522	137	14	machine	machine	NOUN
cana-4522	137	15	learning	learn	VERB
cana-4522	137	16	algorithms	algorithm	NOUN
cana-4522	137	17	for	for	ADP
cana-4522	137	18	diabetes	diabetes	NOUN
cana-4522	137	19	prediction	prediction	NOUN
cana-4522	137	20	that	that	PRON
cana-4522	137	21	would	would	AUX
cana-4522	137	22	be	be	AUX
cana-4522	137	23	focusing	focus	VERB
cana-4522	137	24	on	on	ADP
cana-4522	137	25	better	well	ADJ
cana-4522	137	26	prediction	prediction	NOUN
cana-4522	137	27	accuracy	accuracy	NOUN
cana-4522	137	28	using	use	VERB
cana-4522	137	29	different	different	ADJ
cana-4522	137	30	techniques	technique	NOUN
cana-4522	137	31	.	.	PUNCT
cana-4522	138	1	refrences	refrence	VERB
cana-4522	138	2	[	[	X
cana-4522	138	3	1	1	X
cana-4522	138	4	]	]	PUNCT
cana-4522	138	5	world	world	PROPN
cana-4522	138	6	health	health	PROPN
cana-4522	138	7	organization	organization	PROPN
cana-4522	138	8	’s	’s	PART
cana-4522	138	9	health	health	NOUN
cana-4522	138	10	topics	topic	NOUN
cana-4522	138	11	,	,	PUNCT
cana-4522	138	12	https://www.who.int/healthtopics/diabetes#tab=tab_1	https://www.who.int/healthtopics/diabetes#tab=tab_1	X
cana-4522	138	13	,	,	PUNCT
cana-4522	138	14	accessed	access	VERB
cana-4522	138	15	on	on	ADP
cana-4522	138	16	january	january	PROPN
cana-4522	138	17	2024	2024	NUM
cana-4522	138	18	.	.	PUNCT
cana-4522	139	1	[	[	X
cana-4522	139	2	2	2	NUM
cana-4522	139	3	]	]	X
cana-4522	139	4	guidance	guidance	NOUN
cana-4522	139	5	on	on	ADP
cana-4522	139	6	global	global	ADJ
cana-4522	139	7	monitoring	monitoring	NOUN
cana-4522	139	8	for	for	ADP
cana-4522	139	9	diabetes	diabetes	NOUN
cana-4522	139	10	prevention	prevention	NOUN
cana-4522	139	11	and	and	CCONJ
cana-4522	139	12	control	control	NOUN
cana-4522	139	13	:	:	PUNCT
cana-4522	139	14	framework	framework	NOUN
cana-4522	139	15	,	,	PUNCT
cana-4522	139	16	indicators	indicator	NOUN
cana-4522	139	17	and	and	CCONJ
cana-4522	139	18	application	application	NOUN
cana-4522	139	19	,	,	PUNCT
cana-4522	139	20	geneva	geneva	PROPN
cana-4522	139	21	:	:	PUNCT
cana-4522	139	22	world	world	PROPN
cana-4522	139	23	health	health	NOUN
cana-4522	139	24	organization	organization	NOUN
cana-4522	139	25	;	;	PUNCT
cana-4522	139	26	2024	2024	NUM
cana-4522	139	27	,	,	PUNCT
cana-4522	139	28	https://iris.who.int/bitstream/handle/10665/379529/9789240102248-eng.pdf?sequence=1	https://iris.who.int/bitstream/handle/10665/379529/9789240102248-eng.pdf?sequence=1	PUNCT
cana-4522	139	29	[	[	X
cana-4522	139	30	3	3	X
cana-4522	139	31	]	]	X
cana-4522	139	32	international	international	ADJ
cana-4522	139	33	diabetes	diabetes	NOUN
cana-4522	139	34	federation	federation	PROPN
cana-4522	139	35	(	(	PUNCT
cana-4522	139	36	idf	idf	PROPN
cana-4522	139	37	)	)	PUNCT
cana-4522	139	38	,	,	PUNCT
cana-4522	139	39	diabetes	diabetes	NOUN
cana-4522	139	40	atlas	atlas	PROPN
cana-4522	139	41	,	,	PUNCT
cana-4522	139	42	brussels	brussels	PROPN
cana-4522	139	43	,	,	PUNCT
cana-4522	139	44	belgium	belgium	NOUN
cana-4522	139	45	:	:	PUNCT
cana-4522	139	46	2023	2023	NUM
cana-4522	139	47	,	,	PUNCT
cana-4522	139	48	online	online	ADV
cana-4522	139	49	available	available	ADJ
cana-4522	139	50	at	at	ADP
cana-4522	139	51	https://diabetesatlas.org/atlas/diabetes-and-kidney-diseases/	https://diabetesatlas.org/atlas/diabetes-and-kidney-diseases/	PROPN
cana-4522	140	1	[	[	X
cana-4522	140	2	4	4	X
cana-4522	140	3	]	]	PUNCT
cana-4522	140	4	american	american	ADJ
cana-4522	140	5	diabetes	diabetes	PROPN
cana-4522	140	6	association	association	NOUN
cana-4522	140	7	:	:	PUNCT
cana-4522	140	8	diagnosis	diagnosis	NOUN
cana-4522	140	9	and	and	CCONJ
cana-4522	140	10	classification	classification	NOUN
cana-4522	140	11	of	of	ADP
cana-4522	140	12	diabetes	diabetes	NOUN
cana-4522	140	13	mellitus	mellitus	NOUN
cana-4522	140	14	in	in	ADP
cana-4522	140	15	:	:	PUNCT
cana-4522	140	16	diabetes	diabetes	NOUN
cana-4522	140	17	care	care	NOUN
cana-4522	140	18	,	,	PUNCT
cana-4522	140	19	volume	volume	NOUN
cana-4522	140	20	3	3	NUM
cana-4522	140	21	,	,	PUNCT
cana-4522	140	22	supplement	supplement	NOUN
cana-4522	140	23	1	1	NUM
cana-4522	140	24	,	,	PUNCT
cana-4522	140	25	pp	pp	ADJ
cana-4522	140	26	.	.	PUNCT
cana-4522	141	1	s81s90	s81s90	NOUN
cana-4522	141	2	,	,	PUNCT
cana-4522	141	3	january	january	PROPN
cana-4522	141	4	2014	2014	NUM
cana-4522	141	5	.	.	PUNCT
cana-4522	142	1	https://www.who.int/health-topics/diabetes#tab=tab_1	https://www.who.int/health-topics/diabetes#tab=tab_1	X
cana-4522	142	2	https://www.who.int/health-topics/diabetes#tab=tab_1	https://www.who.int/health-topics/diabetes#tab=tab_1	X
cana-4522	142	3	https://diabetesatlas.org/atlas/diabetes-and-kidney-diseases/	https://diabetesatlas.org/atlas/diabetes-and-kidney-diseases/	PROPN
cana-4522	142	4	communications	communication	NOUN
cana-4522	142	5	on	on	ADP
cana-4522	142	6	applied	apply	VERB
cana-4522	142	7	nonlinear	nonlinear	ADJ
cana-4522	142	8	analysis	analysis	NOUN
cana-4522	142	9	issn	issn	NOUN
cana-4522	142	10	:	:	PUNCT
cana-4522	142	11	1074	1074	NUM
cana-4522	142	12	-	-	PUNCT
cana-4522	142	13	133x	133x	NUM
cana-4522	142	14	vol	vol	NOUN
cana-4522	142	15	32	32	NUM
cana-4522	142	16	no	no	NOUN
cana-4522	142	17	.	.	PUNCT
cana-4522	143	1	9s	9s	NUM
cana-4522	143	2	(	(	PUNCT
cana-4522	143	3	2025	2025	NUM
cana-4522	143	4	)	)	PUNCT
cana-4522	143	5	2357	2357	NUM
cana-4522	143	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4522	144	1	[	[	X
cana-4522	144	2	5	5	X
cana-4522	144	3	]	]	PUNCT
cana-4522	144	4	zidian	zidian	PROPN
cana-4522	144	5	xie	xie	PROPN
cana-4522	144	6	,	,	PUNCT
cana-4522	144	7	olga	olga	PROPN
cana-4522	144	8	nikolayeva	nikolayeva	PROPN
cana-4522	144	9	,	,	PUNCT
cana-4522	144	10	jiebo	jiebo	PROPN
cana-4522	144	11	luo	luo	PROPN
cana-4522	144	12	:	:	PUNCT
cana-4522	144	13	building	build	VERB
cana-4522	144	14	risk	risk	NOUN
cana-4522	144	15	prediction	prediction	NOUN
cana-4522	144	16	models	model	NOUN
cana-4522	144	17	for	for	ADP
cana-4522	144	18	type	type	NOUN
cana-4522	144	19	2	2	NUM
cana-4522	144	20	diabetes	diabetes	NOUN
cana-4522	144	21	using	use	VERB
cana-4522	144	22	machine	machine	NOUN
cana-4522	144	23	learning	learn	VERB
cana-4522	144	24	techniques	technique	NOUN
cana-4522	144	25	in	in	ADP
cana-4522	144	26	preventing	prevent	VERB
cana-4522	144	27	chronic	chronic	ADJ
cana-4522	144	28	disease	disease	NOUN
cana-4522	144	29	,	,	PUNCT
cana-4522	144	30	public	public	ADJ
cana-4522	144	31	health	health	NOUN
cana-4522	144	32	research	research	NOUN
cana-4522	144	33	,	,	PUNCT
cana-4522	144	34	practice	practice	NOUN
cana-4522	144	35	,	,	PUNCT
cana-4522	144	36	and	and	CCONJ
cana-4522	144	37	policy	policy	NOUN
cana-4522	144	38	,	,	PUNCT
cana-4522	144	39	vol	vol	NOUN
cana-4522	144	40	.	.	PROPN
cana-4522	144	41	16	16	NUM
cana-4522	144	42	,	,	PUNCT
cana-4522	144	43	e-130	e-130	NOUN
cana-4522	144	44	,	,	PUNCT
cana-4522	144	45	pp.1	pp.1	NOUN
cana-4522	144	46	-	-	PUNCT
cana-4522	144	47	6	6	NUM
cana-4522	144	48	,	,	PUNCT
cana-4522	144	49	september-2019	september-2019	ADJ
cana-4522	144	50	[	[	X
cana-4522	144	51	6	6	NUM
cana-4522	144	52	]	]	PUNCT
cana-4522	144	53	santosh	santosh	PROPN
cana-4522	144	54	p.	p.	PROPN
cana-4522	144	55	shrikhande	shrikhande	PROPN
cana-4522	144	56	,	,	PUNCT
cana-4522	144	57	prashant	prashant	PROPN
cana-4522	144	58	p.	p.	PROPN
cana-4522	144	59	agnihotri	agnihotri	PROPN
cana-4522	144	60	,	,	PUNCT
cana-4522	144	61	“	"	PUNCT
cana-4522	144	62	comparative	comparative	ADJ
cana-4522	144	63	study	study	NOUN
cana-4522	144	64	of	of	ADP
cana-4522	144	65	various	various	ADJ
cana-4522	144	66	data	datum	NOUN
cana-4522	144	67	mining	mining	NOUN
cana-4522	144	68	techniques	technique	NOUN
cana-4522	144	69	for	for	ADP
cana-4522	144	70	early	early	ADJ
cana-4522	144	71	prediction	prediction	NOUN
cana-4522	144	72	of	of	ADP
cana-4522	144	73	diabetes	diabetes	NOUN
cana-4522	144	74	disease	disease	NOUN
cana-4522	144	75	”	"	PUNCT
cana-4522	144	76	,	,	PUNCT
cana-4522	144	77	international	international	ADJ
cana-4522	144	78	journal	journal	NOUN
cana-4522	144	79	of	of	ADP
cana-4522	144	80	scientific	scientific	ADJ
cana-4522	144	81	research	research	NOUN
cana-4522	144	82	in	in	ADP
cana-4522	144	83	computer	computer	NOUN
cana-4522	144	84	science	science	NOUN
cana-4522	144	85	,	,	PUNCT
cana-4522	144	86	engineering	engineering	NOUN
cana-4522	144	87	and	and	CCONJ
cana-4522	144	88	information	information	NOUN
cana-4522	144	89	technology	technology	NOUN
cana-4522	144	90	,	,	PUNCT
cana-4522	144	91	volume	volume	NOUN
cana-4522	144	92	8	8	NUM
cana-4522	144	93	,	,	PUNCT
cana-4522	144	94	issue	issue	NOUN
cana-4522	144	95	1	1	NUM
cana-4522	144	96	,	,	PUNCT
cana-4522	144	97	pp	pp	ADJ
cana-4522	144	98	.	.	PUNCT
cana-4522	144	99	287295	287295	NUM
cana-4522	144	100	,	,	PUNCT
cana-4522	144	101	2022	2022	NUM
cana-4522	144	102	.	.	PUNCT
cana-4522	145	1	[	[	X
cana-4522	145	2	7	7	X
cana-4522	145	3	]	]	X
cana-4522	145	4	ong	ong	PROPN
cana-4522	146	1	yee	yee	PROPN
cana-4522	146	2	hang	hang	PROPN
cana-4522	146	3	,	,	PUNCT
cana-4522	146	4	virgianti	virgianti	PROPN
cana-4522	146	5	wiwied	wiwie	VERB
cana-4522	146	6	,	,	PUNCT
cana-4522	146	7	rosly	rosly	ADV
cana-4522	146	8	rosaida	rosaida	VERB
cana-4522	146	9	,	,	PUNCT
cana-4522	146	10	“	"	PUNCT
cana-4522	146	11	diabetes	diabetes	NOUN
cana-4522	146	12	prediction	prediction	NOUN
cana-4522	146	13	using	use	VERB
cana-4522	146	14	machine	machine	NOUN
cana-4522	146	15	learning	learning	NOUN
cana-4522	146	16	”	"	PUNCT
cana-4522	146	17	,	,	PUNCT
cana-4522	146	18	journal	journal	NOUN
cana-4522	146	19	of	of	ADP
cana-4522	146	20	advanced	advanced	ADJ
cana-4522	146	21	research	research	NOUN
cana-4522	146	22	in	in	ADP
cana-4522	146	23	applied	apply	VERB
cana-4522	146	24	sciences	science	NOUN
cana-4522	146	25	and	and	CCONJ
cana-4522	146	26	engineering	engineering	NOUN
cana-4522	146	27	technology	technology	NOUN
cana-4522	146	28	,	,	PUNCT
cana-4522	146	29	37	37	NUM
cana-4522	146	30	,	,	PUNCT
cana-4522	146	31	issue	issue	NOUN
cana-4522	146	32	1	1	NUM
cana-4522	146	33	,	,	PUNCT
cana-4522	146	34	2024	2024	NUM
cana-4522	146	35	[	[	X
cana-4522	146	36	8	8	NUM
cana-4522	146	37	]	]	X
cana-4522	146	38	mazen	mazen	PROPN
cana-4522	146	39	alzyoud	alzyoud	PROPN
cana-4522	146	40	,	,	PUNCT
cana-4522	146	41	raed	raed	PROPN
cana-4522	146	42	alazaidah	alazaidah	PROPN
cana-4522	146	43	,	,	PUNCT
cana-4522	146	44	mohammad	mohammad	PROPN
cana-4522	146	45	aljaidi	aljaidi	NOUN
cana-4522	146	46	,	,	PUNCT
cana-4522	146	47	ghassan	ghassan	PROPN
cana-4522	146	48	samara	samara	PROPN
cana-4522	146	49	,	,	PUNCT
cana-4522	146	50	mais	mais	X
cana-4522	146	51	haj	haj	PROPN
cana-4522	146	52	qasem	qasem	PROPN
cana-4522	146	53	,	,	PUNCT
cana-4522	146	54	muhammad	muhammad	PROPN
cana-4522	146	55	khalid	khalid	PROPN
cana-4522	146	56	,	,	PUNCT
cana-4522	146	57	and	and	CCONJ
cana-4522	146	58	najah	najah	ADV
cana-4522	146	59	alshanableh	alshanableh	PROPN
cana-4522	146	60	,	,	PUNCT
cana-4522	146	61	“	"	PUNCT
cana-4522	146	62	diagnosing	diagnose	VERB
cana-4522	146	63	diabetes	diabetes	NOUN
cana-4522	146	64	mellitus	mellitus	NOUN
cana-4522	146	65	using	use	VERB
cana-4522	146	66	machine	machine	NOUN
cana-4522	146	67	learning	learn	VERB
cana-4522	146	68	techniques	technique	NOUN
cana-4522	146	69	”	"	PUNCT
cana-4522	146	70	,	,	PUNCT
cana-4522	146	71	international	international	ADJ
cana-4522	146	72	journal	journal	NOUN
cana-4522	146	73	of	of	ADP
cana-4522	146	74	data	datum	NOUN
cana-4522	146	75	and	and	CCONJ
cana-4522	146	76	network	network	NOUN
cana-4522	146	77	science	science	NOUN
cana-4522	146	78	8	8	NUM
cana-4522	146	79	,	,	PUNCT
cana-4522	146	80	pp	pp	ADJ
cana-4522	146	81	.	.	PUNCT
cana-4522	147	1	179–188	179–188	NUM
cana-4522	147	2	,	,	PUNCT
cana-4522	147	3	2024	2024	NUM
cana-4522	147	4	[	[	X
cana-4522	147	5	9	9	NUM
cana-4522	147	6	]	]	X
cana-4522	147	7	gangani	gangani	ADJ
cana-4522	147	8	dharmarathne	dharmarathne	NOUN
cana-4522	147	9	,	,	PUNCT
cana-4522	147	10	thilini	thilini	PROPN
cana-4522	147	11	n.	n.	PROPN
cana-4522	147	12	jayasinghe	jayasinghe	PROPN
cana-4522	147	13	,	,	PUNCT
cana-4522	147	14	madhusha	madhusha	PROPN
cana-4522	147	15	bogahawaththa	bogahawaththa	NOUN
cana-4522	147	16	,	,	PUNCT
cana-4522	147	17	d.p.p	d.p.p	INTJ
cana-4522	147	18	.	.	PUNCT
cana-4522	148	1	meddage	meddage	NOUN
cana-4522	148	2	,	,	PUNCT
cana-4522	148	3	upaka	upaka	NOUN
cana-4522	148	4	rathnayake	rathnayake	NOUN
cana-4522	148	5	,	,	PUNCT
cana-4522	148	6	“	"	PUNCT
cana-4522	148	7	a	a	DET
cana-4522	148	8	novel	novel	ADJ
cana-4522	148	9	machine	machine	NOUN
cana-4522	148	10	learning	learn	VERB
cana-4522	148	11	approach	approach	NOUN
cana-4522	148	12	for	for	ADP
cana-4522	148	13	diagnosing	diagnose	VERB
cana-4522	148	14	diabetes	diabetes	NOUN
cana-4522	148	15	with	with	ADP
cana-4522	148	16	a	a	DET
cana-4522	148	17	self	self	NOUN
cana-4522	148	18	explainable	explainable	ADJ
cana-4522	148	19	interface	interface	NOUN
cana-4522	148	20	”	"	PUNCT
cana-4522	148	21	,	,	PUNCT
cana-4522	148	22	elsevier	elsevier	PROPN
cana-4522	148	23	healthcare	healthcare	PROPN
cana-4522	148	24	analytics	analytic	NOUN
cana-4522	148	25	5	5	NUM
cana-4522	148	26	,	,	PUNCT
cana-4522	148	27	2024	2024	NUM
cana-4522	148	28	[	[	X
cana-4522	148	29	10	10	NUM
cana-4522	148	30	]	]	PUNCT
cana-4522	148	31	muhammad	muhammad	PROPN
cana-4522	148	32	exell	exell	PROPN
cana-4522	148	33	febrian	febrian	PROPN
cana-4522	148	34	,	,	PUNCT
cana-4522	148	35	fransiskus	fransiskus	PROPN
cana-4522	148	36	xaverius	xaverius	PROPN
cana-4522	148	37	ferdinana	ferdinana	PROPN
cana-4522	148	38	,	,	PUNCT
cana-4522	148	39	gustian	gustian	PROPN
cana-4522	148	40	paul	paul	PROPN
cana-4522	148	41	sendani	sendani	PROPN
cana-4522	148	42	,	,	PUNCT
cana-4522	148	43	kristein	kristein	PROPN
cana-4522	148	44	margi	margi	PROPN
cana-4522	148	45	,	,	PUNCT
cana-4522	148	46	suryanigrum	suryanigrum	NOUN
cana-4522	148	47	,	,	PUNCT
cana-4522	148	48	rezki	rezki	VERB
cana-4522	148	49	yunanda	yunanda	NOUN
cana-4522	148	50	,	,	PUNCT
cana-4522	148	51	“	"	PUNCT
cana-4522	148	52	diabetes	diabetes	NOUN
cana-4522	148	53	prediction	prediction	NOUN
cana-4522	148	54	using	use	VERB
cana-4522	148	55	supervised	supervised	ADJ
cana-4522	148	56	machine	machine	NOUN
cana-4522	148	57	learning	learning	NOUN
cana-4522	148	58	”	"	PUNCT
cana-4522	148	59	,	,	PUNCT
cana-4522	148	60	science	science	NOUN
cana-4522	148	61	direct,7th	direct,7th	PROPN
cana-4522	148	62	international	international	ADJ
cana-4522	148	63	conference	conference	NOUN
cana-4522	148	64	on	on	ADP
cana-4522	148	65	computer	computer	NOUN
cana-4522	148	66	science	science	NOUN
cana-4522	148	67	and	and	CCONJ
cana-4522	148	68	computational	computational	ADJ
cana-4522	148	69	intelligence	intelligence	NOUN
cana-4522	148	70	,	,	PUNCT
cana-4522	148	71	procedia	procedia	NOUN
cana-4522	148	72	computer	computer	NOUN
cana-4522	148	73	science	science	NOUN
cana-4522	148	74	216	216	NUM
cana-4522	148	75	,	,	PUNCT
cana-4522	148	76	pp	pp	ADJ
cana-4522	148	77	.	.	PUNCT
cana-4522	149	1	21–30	21–30	NUM
cana-4522	149	2	,	,	PUNCT
cana-4522	149	3	2023	2023	NUM
cana-4522	149	4	[	[	X
cana-4522	149	5	11	11	NUM
cana-4522	149	6	]	]	X
cana-4522	149	7	hakim	hakim	PROPN
cana-4522	149	8	el	el	PROPN
cana-4522	149	9	massari	massari	PROPN
cana-4522	149	10	,	,	PUNCT
cana-4522	149	11	zineb	zineb	PROPN
cana-4522	149	12	sabouri	sabouri	PROPN
cana-4522	149	13	,	,	PUNCT
cana-4522	149	14	sajida	sajida	PROPN
cana-4522	149	15	mhammedi	mhammedi	PROPN
cana-4522	149	16	,	,	PUNCT
cana-4522	149	17	noreddine	noreddine	NOUN
cana-4522	149	18	gherabi	gherabi	NOUN
cana-4522	149	19	,	,	PUNCT
cana-4522	149	20	“	"	PUNCT
cana-4522	149	21	diabetes	diabetes	NOUN
cana-4522	149	22	prediction	prediction	NOUN
cana-4522	149	23	using	use	VERB
cana-4522	149	24	machine	machine	NOUN
cana-4522	149	25	learning	learn	VERB
cana-4522	149	26	algorithms	algorithm	NOUN
cana-4522	149	27	and	and	CCONJ
cana-4522	149	28	ontology	ontology	NOUN
cana-4522	149	29	”	"	PUNCT
cana-4522	149	30	,	,	PUNCT
cana-4522	149	31	journal	journal	NOUN
cana-4522	149	32	of	of	ADP
cana-4522	149	33	ict	ict	PROPN
cana-4522	149	34	standardization	standardization	NOUN
cana-4522	149	35	,	,	PUNCT
cana-4522	149	36	vol	vol	NOUN
cana-4522	149	37	.	.	PROPN
cana-4522	150	1	10	10	NUM
cana-4522	150	2	2	2	NUM
cana-4522	150	3	,	,	PUNCT
cana-4522	150	4	pp	pp	ADJ
cana-4522	150	5	.	.	PUNCT
cana-4522	151	1	319–338	319–338	NUM
cana-4522	151	2	,	,	PUNCT
cana-4522	151	3	2022	2022	NUM
cana-4522	151	4	.	.	PUNCT
cana-4522	152	1	[	[	X
cana-4522	152	2	12	12	NUM
cana-4522	152	3	]	]	PUNCT
cana-4522	152	4	jobeda	jobeda	PROPN
cana-4522	152	5	j.	j.	PROPN
cana-4522	152	6	khanam	khanam	PROPN
cana-4522	152	7	,	,	PUNCT
cana-4522	152	8	simon	simon	PROPN
cana-4522	152	9	y.	y.	PROPN
cana-4522	152	10	foo	foo	PROPN
cana-4522	152	11	,	,	PUNCT
cana-4522	152	12	“	"	PUNCT
cana-4522	152	13	a	a	DET
cana-4522	152	14	comparison	comparison	NOUN
cana-4522	152	15	of	of	ADP
cana-4522	152	16	machine	machine	NOUN
cana-4522	152	17	learning	learn	VERB
cana-4522	152	18	algorithms	algorithm	NOUN
cana-4522	152	19	for	for	ADP
cana-4522	152	20	diabetes	diabetes	NOUN
cana-4522	152	21	prediction	prediction	NOUN
cana-4522	152	22	”	"	PUNCT
cana-4522	152	23	,	,	PUNCT
cana-4522	152	24	ict	ict	PROPN
cana-4522	152	25	express	express	VERB
cana-4522	152	26	vol-7	vol-7	ADP
cana-4522	152	27	,	,	PUNCT
cana-4522	152	28	no	no	INTJ
cana-4522	152	29	.	.	NOUN
cana-4522	152	30	4	4	NUM
cana-4522	152	31	,	,	PUNCT
cana-4522	152	32	pp	pp	ADJ
cana-4522	152	33	.	.	PUNCT
cana-4522	153	1	432	432	NUM
cana-4522	153	2	-	-	SYM
cana-4522	153	3	439	439	NUM
cana-4522	153	4	,	,	PUNCT
cana-4522	153	5	published	publish	VERB
cana-4522	153	6	by	by	ADP
cana-4522	153	7	elsevier	elsevier	PROPN
cana-4522	153	8	,	,	PUNCT
cana-4522	153	9	pp.1	pp.1	PROPN
cana-4522	153	10	-	-	PUNCT
cana-4522	153	11	8	8	NUM
cana-4522	153	12	,	,	PUNCT
cana-4522	153	13	2021	2021	NUM
cana-4522	153	14	.	.	PUNCT
cana-4522	154	1	[	[	X
cana-4522	154	2	13	13	NUM
cana-4522	154	3	]	]	PUNCT
cana-4522	154	4	neha	neha	NOUN
cana-4522	154	5	prerna	prerna	PROPN
cana-4522	154	6	tiggaa	tiggaa	PROPN
cana-4522	154	7	,	,	PUNCT
cana-4522	154	8	shruti	shruti	PROPN
cana-4522	154	9	garg	garg	PROPN
cana-4522	154	10	,	,	PUNCT
cana-4522	154	11	“	"	PUNCT
cana-4522	154	12	prediction	prediction	NOUN
cana-4522	154	13	of	of	ADP
cana-4522	154	14	type	type	NOUN
cana-4522	154	15	2	2	NUM
cana-4522	154	16	diabetes	diabetes	NOUN
cana-4522	154	17	using	use	VERB
cana-4522	154	18	machine	machine	NOUN
cana-4522	154	19	learning	learn	VERB
cana-4522	154	20	classification	classification	NOUN
cana-4522	154	21	methods	method	NOUN
cana-4522	154	22	”	"	PUNCT
cana-4522	154	23	,	,	PUNCT
cana-4522	154	24	international	international	ADJ
cana-4522	154	25	conference	conference	NOUN
cana-4522	154	26	on	on	ADP
cana-4522	154	27	computational	computational	ADJ
cana-4522	154	28	intelligence	intelligence	NOUN
cana-4522	154	29	and	and	CCONJ
cana-4522	154	30	data	data	NOUN
cana-4522	154	31	science	science	NOUN
cana-4522	154	32	(	(	PUNCT
cana-4522	154	33	iccids	iccid	NOUN
cana-4522	154	34	)	)	PUNCT
cana-4522	154	35	,	,	PUNCT
cana-4522	154	36	2019	2019	NUM
cana-4522	154	37	[	[	SYM
cana-4522	154	38	14	14	NUM
cana-4522	154	39	]	]	X
cana-4522	154	40	gaurav	gaurav	PROPN
cana-4522	154	41	tripathi	tripathi	PROPN
cana-4522	154	42	,	,	PUNCT
cana-4522	154	43	,	,	PUNCT
cana-4522	154	44	rakesh	rakesh	PROPN
cana-4522	154	45	kumar	kumar	PROPN
cana-4522	154	46	,	,	PUNCT
cana-4522	154	47	“	"	PUNCT
cana-4522	154	48	early	early	ADJ
cana-4522	154	49	prediction	prediction	NOUN
cana-4522	154	50	of	of	ADP
cana-4522	154	51	diabetes	diabetes	NOUN
cana-4522	154	52	mellitus	mellitus	NOUN
cana-4522	154	53	using	use	VERB
cana-4522	154	54	machine	machine	NOUN
cana-4522	154	55	learning	learning	NOUN
cana-4522	154	56	”	"	PUNCT
cana-4522	154	57	,	,	PUNCT
cana-4522	154	58	8th	8th	ADJ
cana-4522	154	59	international	international	ADJ
cana-4522	154	60	conference	conference	NOUN
cana-4522	154	61	on	on	ADP
cana-4522	154	62	reliability	reliability	NOUN
cana-4522	154	63	,	,	PUNCT
cana-4522	154	64	infocom	infocom	NOUN
cana-4522	154	65	technologies	technology	NOUN
cana-4522	154	66	and	and	CCONJ
cana-4522	154	67	optimization	optimization	NOUN
cana-4522	154	68	(	(	PUNCT
cana-4522	154	69	trends	trend	NOUN
cana-4522	154	70	and	and	CCONJ
cana-4522	154	71	future	future	ADJ
cana-4522	154	72	directions	direction	NOUN
cana-4522	154	73	)	)	PUNCT
cana-4522	154	74	(	(	PUNCT
cana-4522	154	75	icrito	icrito	NOUN
cana-4522	154	76	)	)	PUNCT
cana-4522	154	77	,	,	PUNCT
cana-4522	154	78	amity	amity	NOUN
cana-4522	154	79	university	university	PROPN
cana-4522	154	80	,	,	PUNCT
cana-4522	154	81	noida	noida	PROPN
cana-4522	154	82	,	,	PUNCT
cana-4522	154	83	india	india	PROPN
cana-4522	154	84	,	,	PUNCT
cana-4522	154	85	2020	2020	NUM
cana-4522	154	86	.	.	PUNCT
cana-4522	155	1	[	[	X
cana-4522	155	2	15	15	NUM
cana-4522	155	3	]	]	X
cana-4522	155	4	sivaranjani	sivaranjani	PROPN
cana-4522	155	5	s	s	PROPN
cana-4522	155	6	,	,	PUNCT
cana-4522	155	7	ananya	ananya	PROPN
cana-4522	155	8	s	s	PROPN
cana-4522	155	9	,	,	PUNCT
cana-4522	155	10	aravinth	aravinth	PROPN
cana-4522	155	11	j	j	PROPN
cana-4522	155	12	,	,	PUNCT
cana-4522	155	13	karthika	karthika	PROPN
cana-4522	155	14	r.	r.	PROPN
cana-4522	155	15	,	,	PUNCT
cana-4522	155	16	“	"	PUNCT
cana-4522	155	17	diabetes	diabetes	NOUN
cana-4522	155	18	prediction	prediction	NOUN
cana-4522	155	19	using	use	VERB
cana-4522	155	20	machine	machine	NOUN
cana-4522	155	21	learning	learn	VERB
cana-4522	155	22	algorithms	algorithm	NOUN
cana-4522	155	23	with	with	ADP
cana-4522	155	24	feature	feature	NOUN
cana-4522	155	25	selection	selection	NOUN
cana-4522	155	26	and	and	CCONJ
cana-4522	155	27	dimensionality	dimensionality	NOUN
cana-4522	155	28	reduction	reduction	NOUN
cana-4522	155	29	”	"	PUNCT
cana-4522	155	30	,	,	PUNCT
cana-4522	155	31	7th	7th	ADJ
cana-4522	155	32	international	international	ADJ
cana-4522	155	33	conference	conference	NOUN
cana-4522	155	34	on	on	ADP
cana-4522	155	35	advanced	advanced	ADJ
cana-4522	155	36	computing	computing	NOUN
cana-4522	155	37	and	and	CCONJ
cana-4522	155	38	communication	communication	NOUN
cana-4522	155	39	systems	system	NOUN
cana-4522	155	40	(	(	PUNCT
cana-4522	155	41	icaccs	icaccs	NOUN
cana-4522	155	42	)	)	PUNCT
cana-4522	155	43	,	,	PUNCT
cana-4522	155	44	2021	2021	NUM
cana-4522	155	45	.	.	PUNCT
cana-4522	156	1	[	[	X
cana-4522	156	2	16	16	NUM
cana-4522	156	3	]	]	X
cana-4522	156	4	md	md	PROPN
cana-4522	156	5	.	.	PROPN
cana-4522	156	6	kamrul	kamrul	PROPN
cana-4522	156	7	hasan	hasan	PROPN
cana-4522	156	8	,	,	PUNCT
cana-4522	156	9	md	md	PROPN
cana-4522	156	10	.	.	PROPN
cana-4522	156	11	ashraful	ashraful	PROPN
cana-4522	156	12	alam	alam	PROPN
cana-4522	156	13	,	,	PUNCT
cana-4522	156	14	dola	dola	PROPN
cana-4522	156	15	das	das	PROPN
cana-4522	156	16	,	,	PUNCT
cana-4522	156	17	eklas	eklas	PROPN
cana-4522	156	18	hossain	hossain	PROPN
cana-4522	156	19	,	,	PUNCT
cana-4522	156	20	“	"	PUNCT
cana-4522	156	21	diabetes	diabetes	NOUN
cana-4522	156	22	prediction	prediction	NOUN
cana-4522	156	23	using	use	VERB
cana-4522	156	24	ensembling	ensemble	VERB
cana-4522	156	25	of	of	ADP
cana-4522	156	26	different	different	ADJ
cana-4522	156	27	machine	machine	NOUN
cana-4522	156	28	learning	learn	VERB
cana-4522	156	29	classifiers	classifier	NOUN
cana-4522	156	30	”	"	PUNCT
cana-4522	156	31	,	,	PUNCT
cana-4522	156	32	digital	digital	ADJ
cana-4522	156	33	object	object	NOUN
cana-4522	156	34	identifier	identifier	NOUN
cana-4522	156	35	10.1109/.	10.1109/.	PROPN
cana-4522	156	36	doi	doi	NOUN
cana-4522	156	37	10.1109	10.1109	NUM
cana-4522	156	38	/	/	SYM
cana-4522	156	39	access.2020.2989857	access.2020.2989857	NOUN
cana-4522	156	40	,	,	PUNCT
cana-4522	156	41	ieee	ieee	NOUN
cana-4522	156	42	access	access	NOUN
cana-4522	156	43	,	,	PUNCT
cana-4522	156	44	2020	2020	NUM
cana-4522	156	45	[	[	X
cana-4522	156	46	17	17	NUM
cana-4522	156	47	]	]	SYM
cana-4522	156	48	km	km	PROPN
cana-4522	156	49	jyoti	jyoti	PROPN
cana-4522	156	50	,	,	PUNCT
cana-4522	156	51	“	"	PUNCT
cana-4522	156	52	diabetes	diabetes	NOUN
cana-4522	156	53	prediction	prediction	NOUN
cana-4522	156	54	using	use	VERB
cana-4522	156	55	machine	machine	NOUN
cana-4522	156	56	learning	learning	NOUN
cana-4522	156	57	”	"	PUNCT
cana-4522	156	58	,	,	PUNCT
cana-4522	156	59	international	international	ADJ
cana-4522	156	60	journal	journal	NOUN
cana-4522	156	61	of	of	ADP
cana-4522	156	62	scientific	scientific	ADJ
cana-4522	156	63	research	research	NOUN
cana-4522	156	64	in	in	ADP
cana-4522	156	65	computer	computer	NOUN
cana-4522	156	66	science	science	NOUN
cana-4522	156	67	,	,	PUNCT
cana-4522	156	68	engineering	engineering	NOUN
cana-4522	156	69	and	and	CCONJ
cana-4522	156	70	information	information	NOUN
cana-4522	156	71	technology	technology	NOUN
cana-4522	156	72	64718	64718	NUM
cana-4522	156	73	,	,	PUNCT
cana-4522	156	74	2020	2020	NUM
cana-4522	156	75	.	.	PUNCT
cana-4522	157	1	[	[	X
cana-4522	157	2	18	18	NUM
cana-4522	157	3	]	]	X
cana-4522	157	4	priyanka	priyanka	NOUN
cana-4522	157	5	sonar	sonar	PROPN
cana-4522	157	6	,	,	PUNCT
cana-4522	157	7	prof	prof	PROPN
cana-4522	157	8	.	.	PUNCT
cana-4522	158	1	k.	k.	PROPN
cana-4522	158	2	jaya	jaya	PROPN
cana-4522	158	3	malin	malin	PROPN
cana-4522	158	4	,	,	PUNCT
cana-4522	158	5	“	"	PUNCT
cana-4522	158	6	diabetes	diabetes	NOUN
cana-4522	158	7	prediction	prediction	NOUN
cana-4522	158	8	using	use	VERB
cana-4522	158	9	different	different	ADJ
cana-4522	158	10	machine	machine	NOUN
cana-4522	158	11	learning	learn	VERB
cana-4522	158	12	approach	approach	NOUN
cana-4522	158	13	”	"	PUNCT
cana-4522	158	14	,	,	PUNCT
cana-4522	158	15	proceedings	proceeding	NOUN
cana-4522	158	16	of	of	ADP
cana-4522	158	17	third	third	ADJ
cana-4522	158	18	international	international	ADJ
cana-4522	158	19	conference	conference	NOUN
cana-4522	158	20	on	on	ADP
cana-4522	158	21	computing	computing	NOUN
cana-4522	158	22	methodologies	methodology	NOUN
cana-4522	158	23	and	and	CCONJ
cana-4522	158	24	communications	communication	NOUN
cana-4522	158	25	on	on	ADP
cana-4522	158	26	applied	apply	VERB
cana-4522	158	27	nonlinear	nonlinear	ADJ
cana-4522	158	28	analysis	analysis	NOUN
cana-4522	158	29	issn	issn	NOUN
cana-4522	158	30	:	:	PUNCT
cana-4522	158	31	1074	1074	NUM
cana-4522	158	32	-	-	PUNCT
cana-4522	158	33	133x	133x	NUM
cana-4522	158	34	vol	vol	NOUN
cana-4522	158	35	32	32	NUM
cana-4522	158	36	no	no	NOUN
cana-4522	158	37	.	.	PUNCT
cana-4522	159	1	9s	9s	NUM
cana-4522	159	2	(	(	PUNCT
cana-4522	159	3	2025	2025	NUM
cana-4522	159	4	)	)	PUNCT
cana-4522	159	5	2358	2358	NUM
cana-4522	159	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-4522	159	7	communication	communication	NOUN
cana-4522	159	8	(	(	PUNCT
cana-4522	159	9	iccmc	iccmc	NOUN
cana-4522	159	10	2019	2019	NUM
cana-4522	159	11	)	)	PUNCT
cana-4522	159	12	ieee	ieee	NOUN
cana-4522	159	13	xplore	xplore	PROPN
cana-4522	159	14	part	part	NOUN
cana-4522	159	15	number	number	NOUN
cana-4522	159	16	:	:	PUNCT
cana-4522	160	1	cfp19k25	cfp19k25	NOUN
cana-4522	160	2	-	-	NOUN
cana-4522	160	3	art	art	NOUN
cana-4522	160	4	;	;	PUNCT
cana-4522	160	5	isbn	isbn	ADJ
cana-4522	160	6	:	:	PUNCT
cana-4522	160	7	978	978	NUM
cana-4522	160	8	-	-	SYM
cana-4522	160	9	15386	15386	NUM
cana-4522	160	10	-	-	PUNCT
cana-4522	160	11	7808	7808	NUM
cana-4522	160	12	-	-	SYM
cana-4522	160	13	4	4	NUM
cana-4522	160	14	.	.	PUNCT
cana-4522	161	1	[	[	X
cana-4522	161	2	19	19	NUM
cana-4522	161	3	]	]	X
cana-4522	161	4	aishwarya	aishwarya	PROPN
cana-4522	161	5	mujumdar	mujumdar	PROPN
cana-4522	161	6	,	,	PUNCT
cana-4522	161	7	dr	dr	PROPN
cana-4522	161	8	.	.	PROPN
cana-4522	161	9	vaidehi	vaidehi	PROPN
cana-4522	161	10	v	v	PROPN
cana-4522	161	11	,	,	PUNCT
cana-4522	161	12	“	"	PUNCT
cana-4522	161	13	diabetes	diabetes	NOUN
cana-4522	161	14	prediction	prediction	NOUN
cana-4522	161	15	using	use	VERB
cana-4522	161	16	machine	machine	NOUN
cana-4522	161	17	learning	learn	VERB
cana-4522	161	18	algorithms	algorithm	NOUN
cana-4522	161	19	”	"	PUNCT
cana-4522	161	20	,	,	PUNCT
cana-4522	161	21	science	science	NOUN
cana-4522	161	22	direct	direct	ADJ
cana-4522	161	23	,	,	PUNCT
cana-4522	161	24	international	international	ADJ
cana-4522	161	25	conference	conference	NOUN
cana-4522	161	26	on	on	ADP
cana-4522	161	27	recent	recent	ADJ
cana-4522	161	28	trends	trend	NOUN
cana-4522	161	29	in	in	ADP
cana-4522	161	30	advance	advance	NOUN
cana-4522	161	31	computing	computing	NOUN
cana-4522	161	32	,	,	PUNCT
cana-4522	161	33	2019	2019	NUM
cana-4522	161	34	.	.	PUNCT
cana-4522	162	1	[	[	X
cana-4522	162	2	20	20	NUM
cana-4522	162	3	]	]	X
cana-4522	162	4	mariwan	mariwan	PROPN
cana-4522	162	5	ahmed	ahmed	PROPN
cana-4522	162	6	hama	hama	PROPN
cana-4522	162	7	saeed	saeed	PROPN
cana-4522	162	8	,	,	PUNCT
cana-4522	162	9	“	"	PUNCT
cana-4522	162	10	diabetes	diabetes	NOUN
cana-4522	162	11	type	type	NOUN
cana-4522	162	12	2	2	NUM
cana-4522	162	13	classification	classification	NOUN
cana-4522	162	14	using	use	VERB
cana-4522	162	15	machine	machine	NOUN
cana-4522	162	16	learning	learn	VERB
cana-4522	162	17	algorithms	algorithm	NOUN
cana-4522	162	18	with	with	ADP
cana-4522	162	19	up	up	ADP
cana-4522	162	20	-	-	PUNCT
cana-4522	162	21	sam	sam	NOUN
cana-4522	162	22	technique	technique	NOUN
cana-4522	162	23	”	"	PUNCT
cana-4522	162	24	,	,	PUNCT
cana-4522	162	25	springer	springer	NOUN
cana-4522	162	26	open	open	ADJ
cana-4522	162	27	access	access	NOUN
cana-4522	162	28	,	,	PUNCT
cana-4522	162	29	journal	journal	NOUN
cana-4522	162	30	of	of	ADP
cana-4522	162	31	electrical	electrical	ADJ
cana-4522	162	32	systems	system	NOUN
cana-4522	162	33	and	and	CCONJ
cana-4522	162	34	inf	inf	NOUN
cana-4522	162	35	.	.	PUNCT
cana-4522	162	36	technology	technology	PROPN
cana-4522	162	37	(	(	PUNCT
cana-4522	162	38	2023	2023	NUM
cana-4522	162	39	)	)	PUNCT
cana-4522	162	40	.	.	PUNCT
cana-4522	163	1	[	[	X
cana-4522	163	2	21	21	NUM
cana-4522	163	3	]	]	X
cana-4522	163	4	victor	victor	PROPN
cana-4522	163	5	chang	chang	PROPN
cana-4522	163	6	,	,	PUNCT
cana-4522	163	7	jozeene	jozeene	PROPN
cana-4522	163	8	bailey	bailey	PROPN
cana-4522	163	9	,	,	PUNCT
cana-4522	163	10	qianwen	qianwen	PROPN
cana-4522	163	11	ariel	ariel	PROPN
cana-4522	163	12	xu	xu	PROPN
cana-4522	163	13	,	,	PUNCT
cana-4522	163	14	zhili	zhili	PROPN
cana-4522	163	15	sun	sun	PROPN
cana-4522	163	16	,	,	PUNCT
cana-4522	163	17	”	"	PUNCT
cana-4522	163	18	pima	pima	PROPN
cana-4522	163	19	indians	indians	PROPN
cana-4522	163	20	diabetes	diabete	VERB
cana-4522	163	21	mellitus	mellitus	NOUN
cana-4522	163	22	classification	classification	NOUN
cana-4522	163	23	based	base	VERB
cana-4522	163	24	on	on	ADP
cana-4522	163	25	machine	machine	NOUN
cana-4522	163	26	learning	learning	NOUN
cana-4522	163	27	(	(	PUNCT
cana-4522	163	28	ml	ml	NOUN
cana-4522	163	29	)	)	PUNCT
cana-4522	163	30	algorithms	algorithm	NOUN
cana-4522	163	31	”	"	PUNCT
cana-4522	163	32	,	,	PUNCT
cana-4522	163	33	neural	neural	ADJ
cana-4522	163	34	computing	computing	NOUN
cana-4522	163	35	and	and	CCONJ
cana-4522	163	36	applications	application	NOUN
cana-4522	163	37	(	(	PUNCT
cana-4522	163	38	2023)35:16157	2023)35:16157	PROPN
cana-4522	163	39	-	-	SYM
cana-4522	163	40	16173	16173	NUM
cana-4522	163	41	.	.	PUNCT
cana-4522	164	1	[	[	X
cana-4522	164	2	22	22	NUM
cana-4522	164	3	]	]	X
cana-4522	164	4	pima	pima	PROPN
cana-4522	164	5	indian	indian	PROPN
cana-4522	164	6	diabetes	diabetes	NOUN
cana-4522	164	7	database	database	NOUN
cana-4522	164	8	-	-	PUNCT
cana-4522	164	9	predict	predict	VERB
cana-4522	164	10	the	the	DET
cana-4522	164	11	onset	onset	NOUN
cana-4522	164	12	of	of	ADP
cana-4522	164	13	diabetes	diabetes	NOUN
cana-4522	164	14	based	base	VERB
cana-4522	164	15	on	on	ADP
cana-4522	164	16	diagnostic	diagnostic	ADJ
cana-4522	164	17	https://www.kaggle.com/uciml/pima-indians-diabetes-database	https://www.kaggle.com/uciml/pima-indians-diabetes-database	NOUN
cana-4522	164	18	.	.	PUNCT
cana-4522	165	1	[	[	X
cana-4522	165	2	23	23	NUM
cana-4522	165	3	]	]	X
cana-4522	165	4	henock	henock	NOUN
cana-4522	165	5	m.	m.	NOUN
cana-4522	165	6	deberneh	deberneh	NOUN
cana-4522	165	7	and	and	CCONJ
cana-4522	165	8	intaek	intaek	PROPN
cana-4522	165	9	kim	kim	PROPN
cana-4522	165	10	,	,	PUNCT
cana-4522	165	11	“	"	PUNCT
cana-4522	165	12	pridiction	pridiction	NOUN
cana-4522	165	13	of	of	ADP
cana-4522	165	14	type	type	NOUN
cana-4522	165	15	2	2	NUM
cana-4522	165	16	diabetes	diabetes	NOUN
cana-4522	165	17	based	base	VERB
cana-4522	165	18	on	on	ADP
cana-4522	165	19	machine	machine	NOUN
cana-4522	165	20	learning	learning	NOUN
cana-4522	165	21	algorithm	algorithm	NOUN
cana-4522	165	22	”	"	PUNCT
cana-4522	165	23	,	,	PUNCT
cana-4522	165	24	international	international	ADJ
cana-4522	165	25	journal	journal	NOUN
cana-4522	165	26	of	of	ADP
cana-4522	165	27	environmental	environmental	ADJ
cana-4522	165	28	research	research	NOUN
cana-4522	165	29	and	and	CCONJ
cana-4522	165	30	public	public	ADJ
cana-4522	165	31	health	health	NOUN
cana-4522	165	32	2021	2021	NUM
cana-4522	165	33	.	.	PUNCT
cana-4522	166	1	[	[	X
cana-4522	166	2	24	24	NUM
cana-4522	166	3	]	]	X
cana-4522	166	4	md	md	PROPN
cana-4522	166	5	.	.	PROPN
cana-4522	166	6	maniruzzaman	maniruzzaman	PROPN
cana-4522	166	7	,	,	PUNCT
cana-4522	166	8	md	md	PROPN
cana-4522	166	9	.	.	PROPN
cana-4522	166	10	jahanur	jahanur	PROPN
cana-4522	166	11	rahman	rahman	PROPN
cana-4522	166	12	,	,	PUNCT
cana-4522	166	13	benojir	benojir	PROPN
cana-4522	166	14	ahammed	ahamme	VERB
cana-4522	166	15	,	,	PUNCT
cana-4522	166	16	md	md	PROPN
cana-4522	166	17	.	.	PROPN
cana-4522	167	1	menhazul	menhazul	PROPN
cana-4522	167	2	abedin	abedin	PROPN
cana-4522	167	3	,	,	PUNCT
cana-4522	167	4	“	"	PUNCT
cana-4522	167	5	classification	classification	NOUN
cana-4522	167	6	and	and	CCONJ
cana-4522	167	7	prediction	prediction	NOUN
cana-4522	167	8	of	of	ADP
cana-4522	167	9	diabetes	diabetes	NOUN
cana-4522	167	10	disease	disease	NOUN
cana-4522	167	11	using	use	VERB
cana-4522	167	12	machine	machine	NOUN
cana-4522	167	13	learning	learning	NOUN
cana-4522	167	14	paradigm	paradigm	NOUN
cana-4522	167	15	”	"	PUNCT
cana-4522	167	16	,	,	PUNCT
cana-4522	167	17	health	health	NOUN
cana-4522	167	18	information	information	NOUN
cana-4522	167	19	science	science	NOUN
cana-4522	167	20	and	and	CCONJ
cana-4522	167	21	systems	system	NOUN
cana-4522	167	22	(	(	PUNCT
cana-4522	167	23	springer	springer	NOUN
cana-4522	167	24	nature	nature	NOUN
cana-4522	167	25	)	)	PUNCT
cana-4522	167	26	,	,	PUNCT
cana-4522	167	27	pp	pp	ADP
cana-4522	167	28	.	.	PUNCT
cana-4522	168	1	1	1	NUM
cana-4522	168	2	-	-	SYM
cana-4522	168	3	14	14	NUM
cana-4522	168	4	,	,	PUNCT
cana-4522	168	5	2020	2020	NUM
cana-4522	168	6	.	.	PUNCT
cana-4522	169	1	[	[	X
cana-4522	169	2	25	25	NUM
cana-4522	169	3	]	]	PUNCT
cana-4522	169	4	amani	amani	VERB
cana-4522	169	5	yahyaoui	yahyaoui	PROPN
cana-4522	169	6	,	,	PUNCT
cana-4522	169	7	akhtar	akhtar	PROPN
cana-4522	169	8	jamil	jamil	PROPN
cana-4522	169	9	,	,	PUNCT
cana-4522	169	10	jawad	jawad	PROPN
cana-4522	169	11	rasheed	rasheed	PROPN
cana-4522	169	12	,	,	PUNCT
cana-4522	169	13	mirsat	mirsat	NOUN
cana-4522	169	14	yesiltpr	yesiltpr	NOUN
cana-4522	169	15	,	,	PUNCT
cana-4522	169	16	“	"	PUNCT
cana-4522	169	17	a	a	DET
cana-4522	169	18	decision	decision	NOUN
cana-4522	169	19	support	support	NOUN
cana-4522	169	20	system	system	NOUN
cana-4522	169	21	for	for	ADP
cana-4522	169	22	diabetes	diabetes	NOUN
cana-4522	169	23	prediction	prediction	NOUN
cana-4522	169	24	using	use	VERB
cana-4522	169	25	machine	machine	NOUN
cana-4522	169	26	learning	learning	NOUN
cana-4522	169	27	and	and	CCONJ
cana-4522	169	28	deep	deep	ADJ
cana-4522	169	29	learning	learning	NOUN
cana-4522	169	30	techniques	technique	NOUN
cana-4522	169	31	”	"	PUNCT
cana-4522	169	32	2019	2019	NUM
cana-4522	169	33	ieee	ieee	NOUN
cana-4522	169	34	.	.	PUNCT
cana-4522	170	1	[	[	X
cana-4522	170	2	26	26	NUM
cana-4522	170	3	]	]	PUNCT
cana-4522	170	4	muhammad	muhammad	PROPN
cana-4522	170	5	azeem	azeem	PROPN
cana-4522	170	6	sarwar	sarwar	PROPN
cana-4522	170	7	,	,	PUNCT
cana-4522	170	8	nasir	nasir	PROPN
cana-4522	170	9	kamal	kamal	PROPN
cana-4522	170	10	,	,	PUNCT
cana-4522	170	11	wajeeha	wajeeha	NOUN
cana-4522	170	12	hamid	hamid	PROPN
cana-4522	170	13	,	,	PUNCT
cana-4522	170	14	munam	munam	PROPN
cana-4522	170	15	ali	ali	PROPN
cana-4522	170	16	shah	shah	PROPN
cana-4522	170	17	,	,	PUNCT
cana-4522	170	18	“	"	PUNCT
cana-4522	170	19	prediction	prediction	NOUN
cana-4522	170	20	of	of	ADP
cana-4522	170	21	diabetes	diabetes	NOUN
cana-4522	170	22	using	use	VERB
cana-4522	170	23	machine	machine	NOUN
cana-4522	170	24	learning	learn	VERB
cana-4522	170	25	algorithms	algorithm	NOUN
cana-4522	170	26	in	in	ADP
cana-4522	170	27	healthcare	healthcare	PROPN
cana-4522	170	28	”	"	PUNCT
cana-4522	170	29	,	,	PUNCT
cana-4522	170	30	proceedings	proceeding	NOUN
cana-4522	170	31	of	of	ADP
cana-4522	170	32	the	the	DET
cana-4522	170	33	24th	24th	ADJ
cana-4522	170	34	international	international	ADJ
cana-4522	170	35	conference	conference	NOUN
cana-4522	170	36	on	on	ADP
cana-4522	170	37	automation	automation	NOUN
cana-4522	170	38	&	&	CCONJ
cana-4522	170	39	computing	computing	PROPN
cana-4522	170	40	,	,	PUNCT
cana-4522	170	41	newcastle	newcastle	PROPN
cana-4522	170	42	university	university	PROPN
cana-4522	170	43	,	,	PUNCT
cana-4522	170	44	newcastle	newcastle	PROPN
cana-4522	170	45	upon	upon	SCONJ
cana-4522	170	46	tyne	tyne	PROPN
cana-4522	170	47	,	,	PUNCT
cana-4522	170	48	uk	uk	PROPN
cana-4522	170	49	,	,	PUNCT
cana-4522	170	50	6	6	NUM
cana-4522	170	51	-	-	SYM
cana-4522	170	52	7	7	NUM
cana-4522	170	53	september	september	PROPN
cana-4522	170	54	2018	2018	NUM
cana-4522	170	55	.	.	PUNCT
cana-4522	171	1	http://www.kaggle.com/uciml/pima-indians-diabetes-database	http://www.kaggle.com/uciml/pima-indians-diabetes-database	X