id	sid	tid	token	lemma	pos
cana-3690	1	1	communications	communication	NOUN
cana-3690	1	2	on	on	ADP
cana-3690	1	3	applied	apply	VERB
cana-3690	1	4	nonlinear	nonlinear	ADJ
cana-3690	1	5	analysis	analysis	NOUN
cana-3690	1	6	issn	issn	NOUN
cana-3690	1	7	:	:	PUNCT
cana-3690	1	8	1074	1074	NUM
cana-3690	1	9	-	-	PUNCT
cana-3690	1	10	133x	133x	NUM
cana-3690	1	11	vol	vol	NOUN
cana-3690	1	12	32	32	NUM
cana-3690	1	13	no	no	NOUN
cana-3690	1	14	.	.	PUNCT
cana-3690	2	1	8s	8s	PROPN
cana-3690	2	2	(	(	PUNCT
cana-3690	2	3	2025	2025	NUM
cana-3690	2	4	)	)	PUNCT
cana-3690	2	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-3690	2	6	456	456	NUM
cana-3690	2	7	integrating	integrate	VERB
cana-3690	2	8	machine	machine	NOUN
cana-3690	2	9	learning	learning	NOUN
cana-3690	2	10	approaches	approach	NOUN
cana-3690	2	11	for	for	ADP
cana-3690	2	12	predictive	predictive	ADJ
cana-3690	2	13	analysis	analysis	NOUN
cana-3690	2	14	of	of	ADP
cana-3690	2	15	heart	heart	NOUN
cana-3690	2	16	disease	disease	NOUN
cana-3690	2	17	risk	risk	NOUN
cana-3690	2	18	factors	factor	NOUN
cana-3690	2	19	r.	r.	PROPN
cana-3690	2	20	raja1	raja1	PROPN
cana-3690	2	21	,	,	PUNCT
cana-3690	2	22	dr	dr	PROPN
cana-3690	2	23	.	.	PROPN
cana-3690	2	24	p.	p.	PROPN
cana-3690	3	1	rajesh2	rajesh2	PROPN
cana-3690	4	1	*	*	PUNCT
cana-3690	4	2	1assistant	1assistant	NUM
cana-3690	4	3	professor	professor	NOUN
cana-3690	4	4	,	,	PUNCT
cana-3690	4	5	department	department	NOUN
cana-3690	4	6	of	of	ADP
cana-3690	4	7	computer	computer	NOUN
cana-3690	4	8	science	science	NOUN
cana-3690	4	9	,	,	PUNCT
cana-3690	4	10	thiru	thiru	PROPN
cana-3690	4	11	kolanjiappar	kolanjiappar	PROPN
cana-3690	4	12	government	government	PROPN
cana-3690	4	13	arts	arts	PROPN
cana-3690	4	14	college	college	PROPN
cana-3690	4	15	,	,	PUNCT
cana-3690	4	16	viruthachalam	viruthachalam	PROPN
cana-3690	4	17	.	.	PUNCT
cana-3690	5	1	(	(	PUNCT
cana-3690	5	2	deputed	depute	VERB
cana-3690	5	3	from	from	ADP
cana-3690	5	4	annamalai	annamalai	PROPN
cana-3690	5	5	university	university	PROPN
cana-3690	5	6	,	,	PUNCT
cana-3690	5	7	annamalainagar-608	annamalainagar-608	NOUN
cana-3690	5	8	002	002	NUM
cana-3690	5	9	)	)	PUNCT
cana-3690	5	10	tamil	tamil	PROPN
cana-3690	5	11	nadu	nadu	PROPN
cana-3690	5	12	,	,	PUNCT
cana-3690	5	13	india	india	PROPN
cana-3690	5	14	.	.	PUNCT
cana-3690	5	15	2*assistant	2*assistant	PROPN
cana-3690	5	16	professor	professor	NOUN
cana-3690	5	17	,	,	PUNCT
cana-3690	5	18	pg	pg	PROPN
cana-3690	5	19	department	department	PROPN
cana-3690	5	20	of	of	ADP
cana-3690	5	21	computer	computer	NOUN
cana-3690	5	22	science	science	NOUN
cana-3690	5	23	,	,	PUNCT
cana-3690	5	24	government	government	NOUN
cana-3690	5	25	arts	arts	PROPN
cana-3690	5	26	college	college	PROPN
cana-3690	5	27	,	,	PUNCT
cana-3690	5	28	chidambaram	chidambaram	PROPN
cana-3690	5	29	–	–	PUNCT
cana-3690	5	30	608	608	NUM
cana-3690	5	31	102	102	NUM
cana-3690	5	32	,	,	PUNCT
cana-3690	5	33	(	(	PUNCT
cana-3690	5	34	deputed	depute	VERB
cana-3690	5	35	from	from	ADP
cana-3690	5	36	annamalai	annamalai	PROPN
cana-3690	5	37	university	university	PROPN
cana-3690	5	38	,	,	PUNCT
cana-3690	5	39	annamalainagar-608	annamalainagar-608	NOUN
cana-3690	5	40	002	002	NUM
cana-3690	5	41	)	)	PUNCT
cana-3690	5	42	tamil	tamil	PROPN
cana-3690	5	43	nadu	nadu	PROPN
cana-3690	5	44	,	,	PUNCT
cana-3690	5	45	india	india	PROPN
cana-3690	5	46	.	.	PUNCT
cana-3690	5	47	email	email	NOUN
cana-3690	5	48	:	:	PUNCT
cana-3690	5	49	1rajamanira2000@gmail.com	1rajamanira2000@gmail.com	NUM
cana-3690	5	50	corresponding	correspond	VERB
cana-3690	5	51	email	email	NOUN
cana-3690	5	52	:	:	PUNCT
cana-3690	5	53	2*rajeshdatamining@gmail.com	2*rajeshdatamining@gmail.com	NUM
cana-3690	5	54	article	article	NOUN
cana-3690	5	55	history	history	NOUN
cana-3690	5	56	:	:	PUNCT
cana-3690	5	57	received	receive	VERB
cana-3690	5	58	:	:	PUNCT
cana-3690	5	59	30	30	NUM
cana-3690	5	60	-	-	SYM
cana-3690	5	61	10	10	NUM
cana-3690	5	62	-	-	PUNCT
cana-3690	5	63	2024	2024	NUM
cana-3690	5	64	revised:06	revised:06	NOUN
cana-3690	5	65	-	-	PUNCT
cana-3690	5	66	12	12	NUM
cana-3690	5	67	-	-	PUNCT
cana-3690	5	68	2024	2024	NUM
cana-3690	5	69	accepted:29	accepted:29	ADP
cana-3690	5	70	-	-	PUNCT
cana-3690	5	71	12	12	NUM
cana-3690	5	72	-	-	PUNCT
cana-3690	5	73	2024	2024	NUM
cana-3690	5	74	abstract	abstract	NOUN
cana-3690	5	75	:	:	PUNCT
cana-3690	5	76	as	as	ADP
cana-3690	5	77	one	one	NUM
cana-3690	5	78	of	of	ADP
cana-3690	5	79	the	the	DET
cana-3690	5	80	leading	lead	VERB
cana-3690	5	81	causes	cause	NOUN
cana-3690	5	82	of	of	ADP
cana-3690	5	83	global	global	ADJ
cana-3690	5	84	mortality	mortality	NOUN
cana-3690	5	85	,	,	PUNCT
cana-3690	5	86	heart	heart	NOUN
cana-3690	5	87	disease	disease	NOUN
cana-3690	5	88	underscores	underscore	VERB
cana-3690	5	89	the	the	DET
cana-3690	5	90	critical	critical	ADJ
cana-3690	5	91	need	need	NOUN
cana-3690	5	92	for	for	ADP
cana-3690	5	93	precise	precise	ADJ
cana-3690	5	94	and	and	CCONJ
cana-3690	5	95	effective	effective	ADJ
cana-3690	5	96	predictive	predictive	ADJ
cana-3690	5	97	models	model	NOUN
cana-3690	5	98	that	that	PRON
cana-3690	5	99	can	can	AUX
cana-3690	5	100	identify	identify	VERB
cana-3690	5	101	individuals	individual	NOUN
cana-3690	5	102	at	at	ADP
cana-3690	5	103	heightened	heightened	ADJ
cana-3690	5	104	risk	risk	NOUN
cana-3690	5	105	.	.	PUNCT
cana-3690	6	1	this	this	DET
cana-3690	6	2	research	research	NOUN
cana-3690	6	3	focuses	focus	VERB
cana-3690	6	4	on	on	ADP
cana-3690	6	5	predictive	predictive	ADJ
cana-3690	6	6	analysis	analysis	NOUN
cana-3690	6	7	of	of	ADP
cana-3690	6	8	heart	heart	NOUN
cana-3690	6	9	disease	disease	NOUN
cana-3690	6	10	,	,	PUNCT
cana-3690	6	11	employing	employ	VERB
cana-3690	6	12	machine	machine	NOUN
cana-3690	6	13	learning	learn	VERB
cana-3690	6	14	techniques	technique	NOUN
cana-3690	6	15	that	that	PRON
cana-3690	6	16	utilize	utilize	VERB
cana-3690	6	17	a	a	DET
cana-3690	6	18	comprehensive	comprehensive	ADJ
cana-3690	6	19	set	set	NOUN
cana-3690	6	20	of	of	ADP
cana-3690	6	21	clinical	clinical	ADJ
cana-3690	6	22	parameters	parameter	NOUN
cana-3690	6	23	,	,	PUNCT
cana-3690	6	24	including	include	VERB
cana-3690	6	25	age	age	NOUN
cana-3690	6	26	,	,	PUNCT
cana-3690	6	27	sex	sex	NOUN
cana-3690	6	28	,	,	PUNCT
cana-3690	6	29	chest	chest	NOUN
cana-3690	6	30	pain	pain	NOUN
cana-3690	6	31	type	type	NOUN
cana-3690	6	32	(	(	PUNCT
cana-3690	6	33	cp	cp	NOUN
cana-3690	6	34	)	)	PUNCT
cana-3690	6	35	,	,	PUNCT
cana-3690	6	36	resting	rest	VERB
cana-3690	6	37	blood	blood	NOUN
cana-3690	6	38	pressure	pressure	NOUN
cana-3690	6	39	(	(	PUNCT
cana-3690	6	40	trestbps	trestbps	NOUN
cana-3690	6	41	)	)	PUNCT
cana-3690	6	42	,	,	PUNCT
cana-3690	6	43	serum	serum	NOUN
cana-3690	6	44	cholesterol	cholesterol	NOUN
cana-3690	6	45	levels	level	NOUN
cana-3690	6	46	(	(	PUNCT
cana-3690	6	47	chol	chol	NOUN
cana-3690	6	48	)	)	PUNCT
cana-3690	6	49	,	,	PUNCT
cana-3690	6	50	fasting	fast	VERB
cana-3690	6	51	blood	blood	NOUN
cana-3690	6	52	sugar	sugar	NOUN
cana-3690	6	53	(	(	PUNCT
cana-3690	6	54	fbs	fbs	PROPN
cana-3690	6	55	)	)	PUNCT
cana-3690	6	56	,	,	PUNCT
cana-3690	6	57	resting	rest	VERB
cana-3690	6	58	electrocardiographic	electrocardiographic	ADJ
cana-3690	6	59	findings	finding	NOUN
cana-3690	6	60	(	(	PUNCT
cana-3690	6	61	restecg	restecg	NOUN
cana-3690	6	62	)	)	PUNCT
cana-3690	6	63	,	,	PUNCT
cana-3690	6	64	maximum	maximum	ADJ
cana-3690	6	65	heart	heart	NOUN
cana-3690	6	66	rate	rate	NOUN
cana-3690	6	67	achieved	achieve	VERB
cana-3690	6	68	(	(	PUNCT
cana-3690	6	69	thalach	thalach	ADV
cana-3690	6	70	)	)	PUNCT
cana-3690	6	71	,	,	PUNCT
cana-3690	6	72	exercise	exercise	NOUN
cana-3690	6	73	-	-	PUNCT
cana-3690	6	74	induced	induce	VERB
cana-3690	6	75	angina	angina	NOUN
cana-3690	6	76	(	(	PUNCT
cana-3690	6	77	exang	exang	PROPN
cana-3690	6	78	)	)	PUNCT
cana-3690	6	79	,	,	PUNCT
cana-3690	6	80	st	st	PROPN
cana-3690	6	81	depression	depression	NOUN
cana-3690	6	82	(	(	PUNCT
cana-3690	6	83	oldpeak	oldpeak	NOUN
cana-3690	6	84	)	)	PUNCT
cana-3690	6	85	,	,	PUNCT
cana-3690	6	86	slope	slope	NOUN
cana-3690	6	87	of	of	ADP
cana-3690	6	88	the	the	DET
cana-3690	6	89	st	st	PROPN
cana-3690	6	90	segment	segment	PROPN
cana-3690	6	91	(	(	PUNCT
cana-3690	6	92	slope	slope	NOUN
cana-3690	6	93	)	)	PUNCT
cana-3690	6	94	,	,	PUNCT
cana-3690	6	95	number	number	NOUN
cana-3690	6	96	of	of	ADP
cana-3690	6	97	major	major	ADJ
cana-3690	6	98	coronary	coronary	ADJ
cana-3690	6	99	vessels	vessel	NOUN
cana-3690	6	100	(	(	PUNCT
cana-3690	6	101	ca	ca	NOUN
cana-3690	6	102	)	)	PUNCT
cana-3690	6	103	,	,	PUNCT
cana-3690	6	104	thalassemia	thalassemia	NOUN
cana-3690	6	105	(	(	PUNCT
cana-3690	6	106	thal	thal	NOUN
cana-3690	6	107	)	)	PUNCT
cana-3690	6	108	,	,	PUNCT
cana-3690	6	109	and	and	CCONJ
cana-3690	6	110	target	target	VERB
cana-3690	6	111	classification	classification	NOUN
cana-3690	6	112	.	.	PUNCT
cana-3690	7	1	several	several	ADJ
cana-3690	7	2	machine	machine	NOUN
cana-3690	7	3	learning	learn	VERB
cana-3690	7	4	algorithms	algorithm	NOUN
cana-3690	7	5	are	be	AUX
cana-3690	7	6	implemented	implement	VERB
cana-3690	7	7	and	and	CCONJ
cana-3690	7	8	rigorously	rigorously	ADV
cana-3690	7	9	evaluated	evaluate	VERB
cana-3690	7	10	—	—	PUNCT
cana-3690	7	11	namely	namely	ADV
cana-3690	7	12	random	random	ADJ
cana-3690	7	13	forest	forest	NOUN
cana-3690	7	14	,	,	PUNCT
cana-3690	7	15	random	random	ADJ
cana-3690	7	16	tree	tree	NOUN
cana-3690	7	17	,	,	PUNCT
cana-3690	7	18	smoreg	smoreg	NOUN
cana-3690	7	19	,	,	PUNCT
cana-3690	7	20	multilayer	multilayer	PROPN
cana-3690	7	21	perceptron	perceptron	PROPN
cana-3690	7	22	,	,	PUNCT
cana-3690	7	23	linear	linear	ADJ
cana-3690	7	24	regression	regression	NOUN
cana-3690	7	25	,	,	PUNCT
cana-3690	7	26	and	and	CCONJ
cana-3690	7	27	rep	rep	PROPN
cana-3690	7	28	tree	tree	NOUN
cana-3690	7	29	.	.	PUNCT
cana-3690	8	1	to	to	PART
cana-3690	8	2	measure	measure	VERB
cana-3690	8	3	the	the	DET
cana-3690	8	4	predictive	predictive	ADJ
cana-3690	8	5	performance	performance	NOUN
cana-3690	8	6	of	of	ADP
cana-3690	8	7	these	these	DET
cana-3690	8	8	models	model	NOUN
cana-3690	8	9	,	,	PUNCT
cana-3690	8	10	key	key	ADJ
cana-3690	8	11	metrics	metric	NOUN
cana-3690	8	12	such	such	ADJ
cana-3690	8	13	as	as	ADP
cana-3690	8	14	the	the	DET
cana-3690	8	15	correlation	correlation	NOUN
cana-3690	8	16	coefficient	coefficient	NOUN
cana-3690	8	17	,	,	PUNCT
cana-3690	8	18	mean	mean	VERB
cana-3690	8	19	absolute	absolute	ADJ
cana-3690	8	20	error	error	NOUN
cana-3690	8	21	(	(	PUNCT
cana-3690	8	22	mae	mae	PROPN
cana-3690	8	23	)	)	PUNCT
cana-3690	8	24	,	,	PUNCT
cana-3690	8	25	root	root	NOUN
cana-3690	8	26	mean	mean	VERB
cana-3690	8	27	squared	square	VERB
cana-3690	8	28	error	error	NOUN
cana-3690	8	29	(	(	PUNCT
cana-3690	8	30	rmse	rmse	NOUN
cana-3690	8	31	)	)	PUNCT
cana-3690	8	32	,	,	PUNCT
cana-3690	8	33	relative	relative	ADJ
cana-3690	8	34	absolute	absolute	ADJ
cana-3690	8	35	error	error	NOUN
cana-3690	8	36	(	(	PUNCT
cana-3690	8	37	rae	rae	NOUN
cana-3690	8	38	)	)	PUNCT
cana-3690	8	39	,	,	PUNCT
cana-3690	8	40	and	and	CCONJ
cana-3690	8	41	root	root	VERB
cana-3690	8	42	relative	relative	ADJ
cana-3690	8	43	squared	square	VERB
cana-3690	8	44	error	error	NOUN
cana-3690	8	45	(	(	PUNCT
cana-3690	8	46	rrse	rrse	NOUN
cana-3690	8	47	)	)	PUNCT
cana-3690	8	48	are	be	AUX
cana-3690	8	49	utilized	utilize	VERB
cana-3690	8	50	.	.	PUNCT
cana-3690	9	1	the	the	DET
cana-3690	9	2	findings	finding	NOUN
cana-3690	9	3	underscore	underscore	VERB
cana-3690	9	4	the	the	DET
cana-3690	9	5	effectiveness	effectiveness	NOUN
cana-3690	9	6	of	of	ADP
cana-3690	9	7	machine	machine	NOUN
cana-3690	9	8	learning	learning	NOUN
cana-3690	9	9	methodologies	methodology	NOUN
cana-3690	9	10	in	in	ADP
cana-3690	9	11	predicting	predict	VERB
cana-3690	9	12	heart	heart	NOUN
cana-3690	9	13	disease	disease	NOUN
cana-3690	9	14	,	,	PUNCT
cana-3690	9	15	emphasizing	emphasize	VERB
cana-3690	9	16	the	the	DET
cana-3690	9	17	critical	critical	ADJ
cana-3690	9	18	role	role	NOUN
cana-3690	9	19	of	of	ADP
cana-3690	9	20	algorithm	algorithm	NOUN
cana-3690	9	21	selection	selection	NOUN
cana-3690	9	22	and	and	CCONJ
cana-3690	9	23	parameter	parameter	NOUN
cana-3690	9	24	optimization	optimization	NOUN
cana-3690	9	25	in	in	ADP
cana-3690	9	26	enhancing	enhance	VERB
cana-3690	9	27	predictive	predictive	ADJ
cana-3690	9	28	performance	performance	NOUN
cana-3690	9	29	and	and	CCONJ
cana-3690	9	30	accuracy	accuracy	NOUN
cana-3690	9	31	.	.	PUNCT
cana-3690	10	1	keywords	keyword	NOUN
cana-3690	10	2	:	:	PUNCT
cana-3690	10	3	heart	heart	NOUN
cana-3690	10	4	disease	disease	NOUN
cana-3690	10	5	prediction	prediction	NOUN
cana-3690	10	6	,	,	PUNCT
cana-3690	10	7	machine	machine	NOUN
cana-3690	10	8	learning	learning	NOUN
cana-3690	10	9	,	,	PUNCT
cana-3690	10	10	random	random	ADJ
cana-3690	10	11	forest	forest	NOUN
cana-3690	10	12	,	,	PUNCT
cana-3690	10	13	multilayer	multilayer	PROPN
cana-3690	10	14	perceptron	perceptron	PROPN
cana-3690	10	15	,	,	PUNCT
cana-3690	10	16	linear	linear	PROPN
cana-3690	10	17	regression	regression	NOUN
cana-3690	10	18	,	,	PUNCT
cana-3690	10	19	smoreg	smoreg	PROPN
cana-3690	10	20	,	,	PUNCT
cana-3690	10	21	rep	rep	NOUN
cana-3690	10	22	tree	tree	NOUN
cana-3690	10	23	,	,	PUNCT
cana-3690	10	24	clinical	clinical	ADJ
cana-3690	10	25	parameters	parameter	NOUN
cana-3690	10	26	,	,	PUNCT
cana-3690	10	27	predictive	predictive	ADJ
cana-3690	10	28	modeling	modeling	NOUN
cana-3690	10	29	,	,	PUNCT
cana-3690	10	30	performance	performance	NOUN
cana-3690	10	31	metrics	metric	NOUN
cana-3690	10	32	,	,	PUNCT
cana-3690	10	33	correlation	correlation	NOUN
cana-3690	10	34	coefficient	coefficient	NOUN
cana-3690	10	35	,	,	PUNCT
cana-3690	10	36	mae	mae	PROPN
cana-3690	10	37	,	,	PUNCT
cana-3690	10	38	rmse	rmse	PROPN
cana-3690	10	39	,	,	PUNCT
cana-3690	10	40	rae	rae	PROPN
cana-3690	10	41	,	,	PUNCT
cana-3690	10	42	rrse	rrse	NOUN
cana-3690	10	43	.	.	PUNCT
cana-3690	11	1	1	1	X
cana-3690	11	2	.	.	X
cana-3690	11	3	introduction	introduction	NOUN
cana-3690	11	4	and	and	CCONJ
cana-3690	11	5	literature	literature	NOUN
cana-3690	11	6	review	review	VERB
cana-3690	11	7	cardiovascular	cardiovascular	ADJ
cana-3690	11	8	diseases	disease	NOUN
cana-3690	11	9	,	,	PUNCT
cana-3690	11	10	particularly	particularly	ADV
cana-3690	11	11	heart	heart	NOUN
cana-3690	11	12	disease	disease	NOUN
cana-3690	11	13	,	,	PUNCT
cana-3690	11	14	have	have	AUX
cana-3690	11	15	become	become	VERB
cana-3690	11	16	a	a	DET
cana-3690	11	17	critical	critical	ADJ
cana-3690	11	18	global	global	ADJ
cana-3690	11	19	health	health	NOUN
cana-3690	11	20	concern	concern	NOUN
cana-3690	11	21	,	,	PUNCT
cana-3690	11	22	accounting	account	VERB
cana-3690	11	23	for	for	ADP
cana-3690	11	24	a	a	DET
cana-3690	11	25	significant	significant	ADJ
cana-3690	11	26	proportion	proportion	NOUN
cana-3690	11	27	of	of	ADP
cana-3690	11	28	deaths	death	NOUN
cana-3690	11	29	worldwide	worldwide	ADV
cana-3690	11	30	.	.	PUNCT
cana-3690	12	1	this	this	DET
cana-3690	12	2	alarming	alarming	ADJ
cana-3690	12	3	trend	trend	NOUN
cana-3690	12	4	,	,	PUNCT
cana-3690	12	5	driven	drive	VERB
cana-3690	12	6	by	by	ADP
cana-3690	12	7	factors	factor	NOUN
cana-3690	12	8	such	such	ADJ
cana-3690	12	9	as	as	ADP
cana-3690	12	10	sedentary	sedentary	ADJ
cana-3690	12	11	lifestyles	lifestyle	NOUN
cana-3690	12	12	,	,	PUNCT
cana-3690	12	13	an	an	DET
cana-3690	12	14	aging	age	VERB
cana-3690	12	15	population	population	NOUN
cana-3690	12	16	,	,	PUNCT
cana-3690	12	17	and	and	CCONJ
cana-3690	12	18	hereditary	hereditary	ADJ
cana-3690	12	19	predispositions	predisposition	NOUN
cana-3690	12	20	,	,	PUNCT
cana-3690	12	21	underscores	underscore	VERB
cana-3690	12	22	the	the	DET
cana-3690	12	23	urgency	urgency	NOUN
cana-3690	12	24	of	of	ADP
cana-3690	12	25	early	early	ADJ
cana-3690	12	26	detection	detection	NOUN
cana-3690	12	27	to	to	PART
cana-3690	12	28	mitigate	mitigate	VERB
cana-3690	12	29	risks	risk	NOUN
cana-3690	12	30	and	and	CCONJ
cana-3690	12	31	enhance	enhance	VERB
cana-3690	12	32	treatment	treatment	NOUN
cana-3690	12	33	outcomes	outcome	NOUN
cana-3690	12	34	.	.	PUNCT
cana-3690	13	1	advances	advance	NOUN
cana-3690	13	2	in	in	ADP
cana-3690	13	3	data	datum	NOUN
cana-3690	13	4	science	science	NOUN
cana-3690	13	5	and	and	CCONJ
cana-3690	13	6	machine	machine	NOUN
cana-3690	13	7	learning	learning	NOUN
cana-3690	13	8	have	have	AUX
cana-3690	13	9	opened	open	VERB
cana-3690	13	10	new	new	ADJ
cana-3690	13	11	avenues	avenue	NOUN
cana-3690	13	12	for	for	ADP
cana-3690	13	13	addressing	address	VERB
cana-3690	13	14	this	this	DET
cana-3690	13	15	challenge	challenge	NOUN
cana-3690	13	16	,	,	PUNCT
cana-3690	13	17	offering	offer	VERB
cana-3690	13	18	tools	tool	NOUN
cana-3690	13	19	that	that	PRON
cana-3690	13	20	can	can	AUX
cana-3690	13	21	analyze	analyze	VERB
cana-3690	13	22	complex	complex	ADJ
cana-3690	13	23	datasets	dataset	NOUN
cana-3690	13	24	to	to	PART
cana-3690	13	25	produce	produce	VERB
cana-3690	13	26	accurate	accurate	ADJ
cana-3690	13	27	and	and	CCONJ
cana-3690	13	28	timely	timely	ADJ
cana-3690	13	29	predictions	prediction	NOUN
cana-3690	13	30	of	of	ADP
cana-3690	13	31	heart	heart	NOUN
cana-3690	13	32	disease	disease	NOUN
cana-3690	13	33	risk	risk	NOUN
cana-3690	13	34	.	.	PUNCT
cana-3690	14	1	mailto:rajeshdatamining@gmail.com	mailto:rajeshdatamining@gmail.com	X
cana-3690	14	2	communications	communication	NOUN
cana-3690	14	3	on	on	ADP
cana-3690	14	4	applied	apply	VERB
cana-3690	14	5	nonlinear	nonlinear	ADJ
cana-3690	14	6	analysis	analysis	NOUN
cana-3690	14	7	issn	issn	NOUN
cana-3690	14	8	:	:	PUNCT
cana-3690	14	9	1074	1074	NUM
cana-3690	14	10	-	-	PUNCT
cana-3690	14	11	133x	133x	NUM
cana-3690	14	12	vol	vol	NOUN
cana-3690	14	13	32	32	NUM
cana-3690	14	14	no	no	NOUN
cana-3690	14	15	.	.	PUNCT
cana-3690	15	1	8s	8s	PROPN
cana-3690	15	2	(	(	PUNCT
cana-3690	15	3	2025	2025	NUM
cana-3690	15	4	)	)	PUNCT
cana-3690	15	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-3690	15	6	457	457	NUM
cana-3690	15	7	developing	develop	VERB
cana-3690	15	8	robust	robust	ADJ
cana-3690	15	9	predictive	predictive	ADJ
cana-3690	15	10	models	model	NOUN
cana-3690	15	11	necessitates	necessitate	VERB
cana-3690	15	12	the	the	DET
cana-3690	15	13	integration	integration	NOUN
cana-3690	15	14	of	of	ADP
cana-3690	15	15	diverse	diverse	ADJ
cana-3690	15	16	clinical	clinical	ADJ
cana-3690	15	17	parameters	parameter	NOUN
cana-3690	15	18	that	that	PRON
cana-3690	15	19	provide	provide	VERB
cana-3690	15	20	a	a	DET
cana-3690	15	21	comprehensive	comprehensive	ADJ
cana-3690	15	22	view	view	NOUN
cana-3690	15	23	of	of	ADP
cana-3690	15	24	an	an	DET
cana-3690	15	25	individual	individual	NOUN
cana-3690	15	26	's	's	PART
cana-3690	15	27	cardiovascular	cardiovascular	ADJ
cana-3690	15	28	condition	condition	NOUN
cana-3690	15	29	.	.	PUNCT
cana-3690	16	1	features	feature	NOUN
cana-3690	16	2	such	such	ADJ
cana-3690	16	3	as	as	ADP
cana-3690	16	4	age	age	NOUN
cana-3690	16	5	,	,	PUNCT
cana-3690	16	6	sex	sex	NOUN
cana-3690	16	7	,	,	PUNCT
cana-3690	16	8	chest	chest	NOUN
cana-3690	16	9	pain	pain	NOUN
cana-3690	16	10	type	type	NOUN
cana-3690	16	11	,	,	PUNCT
cana-3690	16	12	resting	rest	VERB
cana-3690	16	13	blood	blood	NOUN
cana-3690	16	14	pressure	pressure	NOUN
cana-3690	16	15	,	,	PUNCT
cana-3690	16	16	serum	serum	NOUN
cana-3690	16	17	cholesterol	cholesterol	NOUN
cana-3690	16	18	levels	level	NOUN
cana-3690	16	19	,	,	PUNCT
cana-3690	16	20	fasting	fast	VERB
cana-3690	16	21	blood	blood	NOUN
cana-3690	16	22	sugar	sugar	NOUN
cana-3690	16	23	,	,	PUNCT
cana-3690	16	24	and	and	CCONJ
cana-3690	16	25	electrocardiographic	electrocardiographic	ADJ
cana-3690	16	26	results	result	NOUN
cana-3690	16	27	play	play	VERB
cana-3690	16	28	pivotal	pivotal	ADJ
cana-3690	16	29	roles	role	NOUN
cana-3690	16	30	in	in	ADP
cana-3690	16	31	identifying	identify	VERB
cana-3690	16	32	heart	heart	NOUN
cana-3690	16	33	disease	disease	NOUN
cana-3690	16	34	risk	risk	NOUN
cana-3690	16	35	.	.	PUNCT
cana-3690	17	1	moreover	moreover	ADV
cana-3690	17	2	,	,	PUNCT
cana-3690	17	3	additional	additional	ADJ
cana-3690	17	4	variables	variable	NOUN
cana-3690	17	5	,	,	PUNCT
cana-3690	17	6	including	include	VERB
cana-3690	17	7	exercise	exercise	NOUN
cana-3690	17	8	-	-	PUNCT
cana-3690	17	9	induced	induce	VERB
cana-3690	17	10	angina	angina	NOUN
cana-3690	17	11	,	,	PUNCT
cana-3690	17	12	maximum	maximum	ADJ
cana-3690	17	13	heart	heart	NOUN
cana-3690	17	14	rate	rate	NOUN
cana-3690	17	15	achieved	achieve	VERB
cana-3690	17	16	,	,	PUNCT
cana-3690	17	17	st	st	PROPN
cana-3690	17	18	segment	segment	NOUN
cana-3690	17	19	depression	depression	NOUN
cana-3690	17	20	,	,	PUNCT
cana-3690	17	21	slope	slope	NOUN
cana-3690	17	22	of	of	ADP
cana-3690	17	23	the	the	DET
cana-3690	17	24	st	st	PROPN
cana-3690	17	25	segment	segment	NOUN
cana-3690	17	26	,	,	PUNCT
cana-3690	17	27	number	number	NOUN
cana-3690	17	28	of	of	ADP
cana-3690	17	29	major	major	ADJ
cana-3690	17	30	vessels	vessel	NOUN
cana-3690	17	31	,	,	PUNCT
cana-3690	17	32	and	and	CCONJ
cana-3690	17	33	thalassemia	thalassemia	NOUN
cana-3690	17	34	,	,	PUNCT
cana-3690	17	35	further	far	ADV
cana-3690	17	36	contribute	contribute	VERB
cana-3690	17	37	to	to	ADP
cana-3690	17	38	a	a	DET
cana-3690	17	39	nuanced	nuanced	ADJ
cana-3690	17	40	understanding	understanding	NOUN
cana-3690	17	41	of	of	ADP
cana-3690	17	42	the	the	DET
cana-3690	17	43	multifaceted	multifaceted	ADJ
cana-3690	17	44	nature	nature	NOUN
cana-3690	17	45	of	of	ADP
cana-3690	17	46	the	the	DET
cana-3690	17	47	disease	disease	NOUN
cana-3690	17	48	.	.	PUNCT
cana-3690	18	1	machine	machine	NOUN
cana-3690	18	2	learning	learn	VERB
cana-3690	18	3	techniques	technique	NOUN
cana-3690	18	4	,	,	PUNCT
cana-3690	18	5	renowned	renowne	VERB
cana-3690	18	6	for	for	ADP
cana-3690	18	7	their	their	PRON
cana-3690	18	8	ability	ability	NOUN
cana-3690	18	9	to	to	PART
cana-3690	18	10	detect	detect	VERB
cana-3690	18	11	intricate	intricate	ADJ
cana-3690	18	12	patterns	pattern	NOUN
cana-3690	18	13	and	and	CCONJ
cana-3690	18	14	relationships	relationship	NOUN
cana-3690	18	15	within	within	ADP
cana-3690	18	16	complex	complex	ADJ
cana-3690	18	17	datasets	dataset	NOUN
cana-3690	18	18	,	,	PUNCT
cana-3690	18	19	are	be	AUX
cana-3690	18	20	particularly	particularly	ADV
cana-3690	18	21	wellsuited	wellsuite	VERB
cana-3690	18	22	for	for	ADP
cana-3690	18	23	leveraging	leverage	VERB
cana-3690	18	24	such	such	ADJ
cana-3690	18	25	multidimensional	multidimensional	ADJ
cana-3690	18	26	data	datum	NOUN
cana-3690	18	27	.	.	PUNCT
cana-3690	19	1	this	this	DET
cana-3690	19	2	research	research	NOUN
cana-3690	19	3	aims	aim	VERB
cana-3690	19	4	to	to	PART
cana-3690	19	5	evaluate	evaluate	VERB
cana-3690	19	6	the	the	DET
cana-3690	19	7	effectiveness	effectiveness	NOUN
cana-3690	19	8	of	of	ADP
cana-3690	19	9	several	several	ADJ
cana-3690	19	10	machine	machine	NOUN
cana-3690	19	11	learning	learn	VERB
cana-3690	19	12	algorithms	algorithm	NOUN
cana-3690	19	13	in	in	ADP
cana-3690	19	14	predicting	predict	VERB
cana-3690	19	15	heart	heart	NOUN
cana-3690	19	16	disease	disease	NOUN
cana-3690	19	17	risk	risk	NOUN
cana-3690	19	18	.	.	PUNCT
cana-3690	20	1	specifically	specifically	ADV
cana-3690	20	2	,	,	PUNCT
cana-3690	20	3	models	model	NOUN
cana-3690	20	4	such	such	ADJ
cana-3690	20	5	as	as	ADP
cana-3690	20	6	random	random	ADJ
cana-3690	20	7	forest	forest	NOUN
cana-3690	20	8	,	,	PUNCT
cana-3690	20	9	random	random	ADJ
cana-3690	20	10	tree	tree	NOUN
cana-3690	20	11	,	,	PUNCT
cana-3690	20	12	smoreg	smoreg	NOUN
cana-3690	20	13	,	,	PUNCT
cana-3690	20	14	multilayer	multilayer	PROPN
cana-3690	20	15	perceptron	perceptron	PROPN
cana-3690	20	16	,	,	PUNCT
cana-3690	20	17	linear	linear	ADJ
cana-3690	20	18	regression	regression	NOUN
cana-3690	20	19	,	,	PUNCT
cana-3690	20	20	and	and	CCONJ
cana-3690	20	21	rep	rep	NOUN
cana-3690	20	22	tree	tree	NOUN
cana-3690	20	23	are	be	AUX
cana-3690	20	24	implemented	implement	VERB
cana-3690	20	25	and	and	CCONJ
cana-3690	20	26	assessed	assess	VERB
cana-3690	20	27	for	for	ADP
cana-3690	20	28	their	their	PRON
cana-3690	20	29	predictive	predictive	ADJ
cana-3690	20	30	performance	performance	NOUN
cana-3690	20	31	.	.	PUNCT
cana-3690	21	1	the	the	DET
cana-3690	21	2	evaluation	evaluation	NOUN
cana-3690	21	3	is	be	AUX
cana-3690	21	4	conducted	conduct	VERB
cana-3690	21	5	using	use	VERB
cana-3690	21	6	a	a	DET
cana-3690	21	7	range	range	NOUN
cana-3690	21	8	of	of	ADP
cana-3690	21	9	performance	performance	NOUN
cana-3690	21	10	metrics	metric	NOUN
cana-3690	21	11	,	,	PUNCT
cana-3690	21	12	including	include	VERB
cana-3690	21	13	the	the	DET
cana-3690	21	14	correlation	correlation	NOUN
cana-3690	21	15	coefficient	coefficient	NOUN
cana-3690	21	16	,	,	PUNCT
cana-3690	21	17	mean	mean	VERB
cana-3690	21	18	absolute	absolute	ADJ
cana-3690	21	19	error	error	NOUN
cana-3690	21	20	(	(	PUNCT
cana-3690	21	21	mae	mae	PROPN
cana-3690	21	22	)	)	PUNCT
cana-3690	21	23	,	,	PUNCT
cana-3690	21	24	root	root	NOUN
cana-3690	21	25	mean	mean	VERB
cana-3690	21	26	squared	square	VERB
cana-3690	21	27	error	error	NOUN
cana-3690	21	28	(	(	PUNCT
cana-3690	21	29	rmse	rmse	NOUN
cana-3690	21	30	)	)	PUNCT
cana-3690	21	31	,	,	PUNCT
cana-3690	21	32	relative	relative	ADJ
cana-3690	21	33	absolute	absolute	ADJ
cana-3690	21	34	error	error	NOUN
cana-3690	21	35	(	(	PUNCT
cana-3690	21	36	rae	rae	NOUN
cana-3690	21	37	)	)	PUNCT
cana-3690	21	38	,	,	PUNCT
cana-3690	21	39	and	and	CCONJ
cana-3690	21	40	root	root	VERB
cana-3690	21	41	relative	relative	ADJ
cana-3690	21	42	squared	square	VERB
cana-3690	21	43	error	error	NOUN
cana-3690	21	44	(	(	PUNCT
cana-3690	21	45	rrse	rrse	NOUN
cana-3690	21	46	)	)	PUNCT
cana-3690	21	47	,	,	PUNCT
cana-3690	21	48	to	to	PART
cana-3690	21	49	comprehensively	comprehensively	ADV
cana-3690	21	50	compare	compare	VERB
cana-3690	21	51	the	the	DET
cana-3690	21	52	capabilities	capability	NOUN
cana-3690	21	53	of	of	ADP
cana-3690	21	54	each	each	DET
cana-3690	21	55	approach	approach	NOUN
cana-3690	21	56	.	.	PUNCT
cana-3690	22	1	through	through	ADP
cana-3690	22	2	an	an	DET
cana-3690	22	3	in	in	ADP
cana-3690	22	4	-	-	PUNCT
cana-3690	22	5	depth	depth	NOUN
cana-3690	22	6	exploration	exploration	NOUN
cana-3690	22	7	of	of	ADP
cana-3690	22	8	the	the	DET
cana-3690	22	9	strengths	strength	NOUN
cana-3690	22	10	and	and	CCONJ
cana-3690	22	11	limitations	limitation	NOUN
cana-3690	22	12	of	of	ADP
cana-3690	22	13	these	these	DET
cana-3690	22	14	algorithms	algorithm	NOUN
cana-3690	22	15	,	,	PUNCT
cana-3690	22	16	the	the	DET
cana-3690	22	17	study	study	NOUN
cana-3690	22	18	seeks	seek	VERB
cana-3690	22	19	to	to	PART
cana-3690	22	20	identify	identify	VERB
cana-3690	22	21	optimal	optimal	ADJ
cana-3690	22	22	methods	method	NOUN
cana-3690	22	23	for	for	ADP
cana-3690	22	24	heart	heart	NOUN
cana-3690	22	25	disease	disease	NOUN
cana-3690	22	26	prediction	prediction	NOUN
cana-3690	22	27	and	and	CCONJ
cana-3690	22	28	emphasizes	emphasize	VERB
cana-3690	22	29	the	the	DET
cana-3690	22	30	critical	critical	ADJ
cana-3690	22	31	role	role	NOUN
cana-3690	22	32	of	of	ADP
cana-3690	22	33	parameter	parameter	NOUN
cana-3690	22	34	optimization	optimization	NOUN
cana-3690	22	35	in	in	ADP
cana-3690	22	36	achieving	achieve	VERB
cana-3690	22	37	superior	superior	ADJ
cana-3690	22	38	predictive	predictive	ADJ
cana-3690	22	39	accuracy	accuracy	NOUN
cana-3690	22	40	.	.	PUNCT
cana-3690	23	1	the	the	DET
cana-3690	23	2	insights	insight	NOUN
cana-3690	23	3	gained	gain	VERB
cana-3690	23	4	from	from	ADP
cana-3690	23	5	this	this	DET
cana-3690	23	6	research	research	NOUN
cana-3690	23	7	aim	aim	VERB
cana-3690	23	8	to	to	PART
cana-3690	23	9	contribute	contribute	VERB
cana-3690	23	10	to	to	ADP
cana-3690	23	11	the	the	DET
cana-3690	23	12	development	development	NOUN
cana-3690	23	13	of	of	ADP
cana-3690	23	14	advanced	advanced	ADJ
cana-3690	23	15	diagnostic	diagnostic	ADJ
cana-3690	23	16	tools	tool	NOUN
cana-3690	23	17	that	that	PRON
cana-3690	23	18	can	can	AUX
cana-3690	23	19	enhance	enhance	VERB
cana-3690	23	20	healthcare	healthcare	NOUN
cana-3690	23	21	outcomes	outcome	NOUN
cana-3690	23	22	and	and	CCONJ
cana-3690	23	23	reduce	reduce	VERB
cana-3690	23	24	the	the	DET
cana-3690	23	25	global	global	ADJ
cana-3690	23	26	burden	burden	NOUN
cana-3690	23	27	of	of	ADP
cana-3690	23	28	heart	heart	NOUN
cana-3690	23	29	disease	disease	NOUN
cana-3690	23	30	.	.	PUNCT
cana-3690	24	1	smith	smith	PROPN
cana-3690	24	2	et	et	PROPN
cana-3690	24	3	al	al	PROPN
cana-3690	24	4	.	.	PROPN
cana-3690	25	1	(	(	PUNCT
cana-3690	25	2	2019	2019	NUM
cana-3690	25	3	)	)	PUNCT
cana-3690	25	4	conducted	conduct	VERB
cana-3690	25	5	an	an	DET
cana-3690	25	6	in	in	ADP
cana-3690	25	7	-	-	PUNCT
cana-3690	25	8	depth	depth	NOUN
cana-3690	25	9	analysis	analysis	NOUN
cana-3690	25	10	of	of	ADP
cana-3690	25	11	the	the	DET
cana-3690	25	12	predictive	predictive	ADJ
cana-3690	25	13	potential	potential	NOUN
cana-3690	25	14	of	of	ADP
cana-3690	25	15	various	various	ADJ
cana-3690	25	16	machine	machine	NOUN
cana-3690	25	17	learning	learning	NOUN
cana-3690	25	18	models	model	NOUN
cana-3690	25	19	for	for	ADP
cana-3690	25	20	classifying	classify	VERB
cana-3690	25	21	heart	heart	NOUN
cana-3690	25	22	disease	disease	NOUN
cana-3690	25	23	by	by	ADP
cana-3690	25	24	employing	employ	VERB
cana-3690	25	25	the	the	DET
cana-3690	25	26	cleveland	cleveland	PROPN
cana-3690	25	27	heart	heart	NOUN
cana-3690	25	28	disease	disease	NOUN
cana-3690	25	29	dataset	dataset	VERB
cana-3690	25	30	.	.	PUNCT
cana-3690	26	1	they	they	PRON
cana-3690	26	2	utilized	utilize	VERB
cana-3690	26	3	techniques	technique	NOUN
cana-3690	26	4	such	such	ADJ
cana-3690	26	5	as	as	ADP
cana-3690	26	6	random	random	ADJ
cana-3690	26	7	forest	forest	NOUN
cana-3690	26	8	,	,	PUNCT
cana-3690	26	9	logistic	logistic	ADJ
cana-3690	26	10	regression	regression	NOUN
cana-3690	26	11	,	,	PUNCT
cana-3690	26	12	and	and	CCONJ
cana-3690	26	13	support	support	VERB
cana-3690	26	14	vector	vector	NOUN
cana-3690	26	15	machines	machine	NOUN
cana-3690	26	16	,	,	PUNCT
cana-3690	26	17	assessing	assess	VERB
cana-3690	26	18	their	their	PRON
cana-3690	26	19	effectiveness	effectiveness	NOUN
cana-3690	26	20	using	use	VERB
cana-3690	26	21	accuracy	accuracy	NOUN
cana-3690	26	22	,	,	PUNCT
cana-3690	26	23	precision	precision	NOUN
cana-3690	26	24	,	,	PUNCT
cana-3690	26	25	recall	recall	NOUN
cana-3690	26	26	,	,	PUNCT
cana-3690	26	27	and	and	CCONJ
cana-3690	26	28	f1	f1	NOUN
cana-3690	26	29	-	-	PUNCT
cana-3690	26	30	score	score	NOUN
cana-3690	26	31	.	.	PUNCT
cana-3690	27	1	their	their	PRON
cana-3690	27	2	findings	finding	NOUN
cana-3690	27	3	underscored	underscore	VERB
cana-3690	27	4	that	that	SCONJ
cana-3690	27	5	random	random	ADJ
cana-3690	27	6	forest	forest	NOUN
cana-3690	27	7	excelled	excel	VERB
cana-3690	27	8	at	at	ADP
cana-3690	27	9	capturing	capture	VERB
cana-3690	27	10	intricate	intricate	ADJ
cana-3690	27	11	feature	feature	NOUN
cana-3690	27	12	interactions	interaction	NOUN
cana-3690	27	13	,	,	PUNCT
cana-3690	27	14	especially	especially	ADV
cana-3690	27	15	when	when	SCONJ
cana-3690	27	16	parameters	parameter	NOUN
cana-3690	27	17	like	like	ADP
cana-3690	27	18	age	age	NOUN
cana-3690	27	19	,	,	PUNCT
cana-3690	27	20	chest	chest	NOUN
cana-3690	27	21	pain	pain	NOUN
cana-3690	27	22	type	type	NOUN
cana-3690	27	23	,	,	PUNCT
cana-3690	27	24	and	and	CCONJ
cana-3690	27	25	cholesterol	cholesterol	NOUN
cana-3690	27	26	levels	level	NOUN
cana-3690	27	27	were	be	AUX
cana-3690	27	28	included	include	VERB
cana-3690	27	29	.	.	PUNCT
cana-3690	28	1	the	the	DET
cana-3690	28	2	researchers	researcher	NOUN
cana-3690	28	3	concluded	conclude	VERB
cana-3690	28	4	that	that	SCONJ
cana-3690	28	5	ensemble	ensemble	ADJ
cana-3690	28	6	methods	method	NOUN
cana-3690	28	7	offered	offer	VERB
cana-3690	28	8	superior	superior	ADJ
cana-3690	28	9	performance	performance	NOUN
cana-3690	28	10	compared	compare	VERB
cana-3690	28	11	to	to	ADP
cana-3690	28	12	single	single	ADJ
cana-3690	28	13	-	-	PUNCT
cana-3690	28	14	model	model	NOUN
cana-3690	28	15	approaches	approach	NOUN
cana-3690	28	16	,	,	PUNCT
cana-3690	28	17	but	but	CCONJ
cana-3690	28	18	they	they	PRON
cana-3690	28	19	emphasized	emphasize	VERB
cana-3690	28	20	that	that	SCONJ
cana-3690	28	21	broader	broad	ADJ
cana-3690	28	22	validation	validation	NOUN
cana-3690	28	23	across	across	ADP
cana-3690	28	24	diverse	diverse	ADJ
cana-3690	28	25	datasets	dataset	NOUN
cana-3690	28	26	is	be	AUX
cana-3690	28	27	essential	essential	ADJ
cana-3690	28	28	to	to	PART
cana-3690	28	29	establish	establish	VERB
cana-3690	28	30	the	the	DET
cana-3690	28	31	generalizability	generalizability	NOUN
cana-3690	28	32	of	of	ADP
cana-3690	28	33	their	their	PRON
cana-3690	28	34	results	result	NOUN
cana-3690	28	35	.	.	PUNCT
cana-3690	29	1	johnson	johnson	PROPN
cana-3690	29	2	and	and	CCONJ
cana-3690	29	3	lee	lee	PROPN
cana-3690	29	4	(	(	PUNCT
cana-3690	29	5	2020	2020	NUM
cana-3690	29	6	)	)	PUNCT
cana-3690	29	7	explored	explore	VERB
cana-3690	29	8	the	the	DET
cana-3690	29	9	predictive	predictive	ADJ
cana-3690	29	10	efficacy	efficacy	NOUN
cana-3690	29	11	of	of	ADP
cana-3690	29	12	clinical	clinical	ADJ
cana-3690	29	13	parameters	parameter	NOUN
cana-3690	29	14	in	in	ADP
cana-3690	29	15	heart	heart	NOUN
cana-3690	29	16	disease	disease	NOUN
cana-3690	29	17	risk	risk	NOUN
cana-3690	29	18	assessment	assessment	NOUN
cana-3690	29	19	through	through	ADP
cana-3690	29	20	the	the	DET
cana-3690	29	21	application	application	NOUN
cana-3690	29	22	of	of	ADP
cana-3690	29	23	neural	neural	ADJ
cana-3690	29	24	networks	network	NOUN
cana-3690	29	25	and	and	CCONJ
cana-3690	29	26	decision	decision	NOUN
cana-3690	29	27	tree	tree	NOUN
cana-3690	29	28	models	model	NOUN
cana-3690	29	29	.	.	PUNCT
cana-3690	30	1	they	they	PRON
cana-3690	30	2	employed	employ	VERB
cana-3690	30	3	a	a	DET
cana-3690	30	4	multilayer	multilayer	ADJ
cana-3690	30	5	perceptron	perceptron	NOUN
cana-3690	30	6	(	(	PUNCT
cana-3690	30	7	mlp	mlp	PROPN
cana-3690	30	8	)	)	PUNCT
cana-3690	30	9	with	with	ADP
cana-3690	30	10	fine	fine	ADV
cana-3690	30	11	-	-	PUNCT
cana-3690	30	12	tuned	tune	VERB
cana-3690	30	13	hyperparameters	hyperparameter	NOUN
cana-3690	30	14	,	,	PUNCT
cana-3690	30	15	which	which	PRON
cana-3690	30	16	demonstrated	demonstrate	VERB
cana-3690	30	17	notable	notable	ADJ
cana-3690	30	18	accuracy	accuracy	NOUN
cana-3690	30	19	in	in	ADP
cana-3690	30	20	prediction	prediction	NOUN
cana-3690	30	21	.	.	PUNCT
cana-3690	31	1	their	their	PRON
cana-3690	31	2	study	study	NOUN
cana-3690	31	3	revealed	reveal	VERB
cana-3690	31	4	the	the	DET
cana-3690	31	5	pivotal	pivotal	ADJ
cana-3690	31	6	roles	role	NOUN
cana-3690	31	7	of	of	ADP
cana-3690	31	8	thalach	thalach	NOUN
cana-3690	31	9	(	(	PUNCT
cana-3690	31	10	maximum	maximum	ADJ
cana-3690	31	11	heart	heart	NOUN
cana-3690	31	12	rate	rate	NOUN
cana-3690	31	13	achieved	achieve	VERB
cana-3690	31	14	)	)	PUNCT
cana-3690	31	15	and	and	CCONJ
cana-3690	31	16	oldpeak	oldpeak	PROPN
cana-3690	31	17	(	(	PUNCT
cana-3690	31	18	st	st	NOUN
cana-3690	31	19	depression	depression	NOUN
cana-3690	31	20	)	)	PUNCT
cana-3690	31	21	in	in	ADP
cana-3690	31	22	identifying	identify	VERB
cana-3690	31	23	disease	disease	NOUN
cana-3690	31	24	risk	risk	NOUN
cana-3690	31	25	levels	level	NOUN
cana-3690	31	26	.	.	PUNCT
cana-3690	32	1	while	while	SCONJ
cana-3690	32	2	decision	decision	NOUN
cana-3690	32	3	trees	tree	NOUN
cana-3690	32	4	offered	offer	VERB
cana-3690	32	5	greater	great	ADJ
cana-3690	32	6	interpretability	interpretability	NOUN
cana-3690	32	7	,	,	PUNCT
cana-3690	32	8	their	their	PRON
cana-3690	32	9	performance	performance	NOUN
cana-3690	32	10	metrics	metric	NOUN
cana-3690	32	11	lagged	lag	VERB
cana-3690	32	12	slightly	slightly	ADV
cana-3690	32	13	behind	behind	ADP
cana-3690	32	14	those	those	PRON
cana-3690	32	15	of	of	ADP
cana-3690	32	16	the	the	DET
cana-3690	32	17	mlp	mlp	NOUN
cana-3690	32	18	.	.	PUNCT
cana-3690	33	1	johnson	johnson	PROPN
cana-3690	33	2	and	and	CCONJ
cana-3690	33	3	lee	lee	PROPN
cana-3690	33	4	highlighted	highlight	VERB
cana-3690	33	5	the	the	DET
cana-3690	33	6	ability	ability	NOUN
cana-3690	33	7	of	of	ADP
cana-3690	33	8	neural	neural	ADJ
cana-3690	33	9	networks	network	NOUN
cana-3690	33	10	to	to	AUX
cana-3690	33	11	model	model	VERB
cana-3690	33	12	complex	complex	ADJ
cana-3690	33	13	nonlinear	nonlinear	ADJ
cana-3690	33	14	interactions	interaction	NOUN
cana-3690	33	15	effectively	effectively	ADV
cana-3690	33	16	,	,	PUNCT
cana-3690	33	17	although	although	SCONJ
cana-3690	33	18	their	their	PRON
cana-3690	33	19	computational	computational	ADJ
cana-3690	33	20	demands	demand	NOUN
cana-3690	33	21	may	may	AUX
cana-3690	33	22	limit	limit	VERB
cana-3690	33	23	their	their	PRON
cana-3690	33	24	practical	practical	ADJ
cana-3690	33	25	use	use	NOUN
cana-3690	33	26	in	in	ADP
cana-3690	33	27	resource	resource	NOUN
cana-3690	33	28	-	-	PUNCT
cana-3690	33	29	limited	limit	VERB
cana-3690	33	30	environments	environment	NOUN
cana-3690	33	31	.	.	PUNCT
cana-3690	34	1	patel	patel	PROPN
cana-3690	34	2	et	et	PROPN
cana-3690	34	3	al	al	PROPN
cana-3690	34	4	.	.	PROPN
cana-3690	35	1	(	(	PUNCT
cana-3690	35	2	2018	2018	NUM
cana-3690	35	3	)	)	PUNCT
cana-3690	35	4	investigated	investigate	VERB
cana-3690	35	5	the	the	DET
cana-3690	35	6	role	role	NOUN
cana-3690	35	7	of	of	ADP
cana-3690	35	8	random	random	ADJ
cana-3690	35	9	forest	forest	NOUN
cana-3690	35	10	and	and	CCONJ
cana-3690	35	11	linear	linear	ADJ
cana-3690	35	12	regression	regression	NOUN
cana-3690	35	13	in	in	ADP
cana-3690	35	14	predicting	predict	VERB
cana-3690	35	15	heart	heart	NOUN
cana-3690	35	16	disease	disease	NOUN
cana-3690	35	17	,	,	PUNCT
cana-3690	35	18	focusing	focus	VERB
cana-3690	35	19	on	on	ADP
cana-3690	35	20	the	the	DET
cana-3690	35	21	influence	influence	NOUN
cana-3690	35	22	of	of	ADP
cana-3690	35	23	features	feature	NOUN
cana-3690	35	24	such	such	ADJ
cana-3690	35	25	as	as	ADP
cana-3690	35	26	age	age	NOUN
cana-3690	35	27	,	,	PUNCT
cana-3690	35	28	sex	sex	NOUN
cana-3690	35	29	,	,	PUNCT
cana-3690	35	30	and	and	CCONJ
cana-3690	35	31	resting	rest	VERB
cana-3690	35	32	blood	blood	NOUN
cana-3690	35	33	pressure	pressure	NOUN
cana-3690	35	34	.	.	PUNCT
cana-3690	36	1	they	they	PRON
cana-3690	36	2	analyzed	analyze	VERB
cana-3690	36	3	errors	error	NOUN
cana-3690	36	4	using	use	VERB
cana-3690	36	5	metrics	metric	NOUN
cana-3690	36	6	like	like	ADP
cana-3690	36	7	mae	mae	PROPN
cana-3690	36	8	,	,	PUNCT
cana-3690	36	9	rmse	rmse	NOUN
cana-3690	36	10	,	,	PUNCT
cana-3690	36	11	and	and	CCONJ
cana-3690	36	12	rae	rae	PROPN
cana-3690	36	13	,	,	PUNCT
cana-3690	36	14	finding	find	VERB
cana-3690	36	15	that	that	SCONJ
cana-3690	36	16	random	random	ADJ
cana-3690	36	17	forest	forest	NOUN
cana-3690	36	18	consistently	consistently	ADV
cana-3690	36	19	communications	communication	NOUN
cana-3690	36	20	on	on	ADP
cana-3690	36	21	applied	apply	VERB
cana-3690	36	22	nonlinear	nonlinear	ADJ
cana-3690	36	23	analysis	analysis	NOUN
cana-3690	36	24	issn	issn	NOUN
cana-3690	36	25	:	:	PUNCT
cana-3690	36	26	1074	1074	NUM
cana-3690	36	27	-	-	PUNCT
cana-3690	36	28	133x	133x	NUM
cana-3690	36	29	vol	vol	NOUN
cana-3690	36	30	32	32	NUM
cana-3690	36	31	no	no	NOUN
cana-3690	36	32	.	.	PUNCT
cana-3690	37	1	8s	8s	PROPN
cana-3690	37	2	(	(	PUNCT
cana-3690	37	3	2025	2025	NUM
cana-3690	37	4	)	)	PUNCT
cana-3690	37	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-3690	37	6	458	458	NUM
cana-3690	37	7	delivered	deliver	VERB
cana-3690	37	8	superior	superior	ADJ
cana-3690	37	9	results	result	NOUN
cana-3690	37	10	across	across	ADP
cana-3690	37	11	all	all	DET
cana-3690	37	12	metrics	metric	NOUN
cana-3690	37	13	.	.	PUNCT
cana-3690	38	1	this	this	DET
cana-3690	38	2	model	model	NOUN
cana-3690	38	3	proved	prove	VERB
cana-3690	38	4	particularly	particularly	ADV
cana-3690	38	5	effective	effective	ADJ
cana-3690	38	6	in	in	ADP
cana-3690	38	7	handling	handle	VERB
cana-3690	38	8	imbalanced	imbalanced	ADJ
cana-3690	38	9	datasets	dataset	NOUN
cana-3690	38	10	.	.	PUNCT
cana-3690	39	1	additionally	additionally	ADV
cana-3690	39	2	,	,	PUNCT
cana-3690	39	3	their	their	PRON
cana-3690	39	4	feature	feature	NOUN
cana-3690	39	5	importance	importance	NOUN
cana-3690	39	6	analysis	analysis	NOUN
cana-3690	39	7	identified	identify	VERB
cana-3690	39	8	resting	rest	VERB
cana-3690	39	9	blood	blood	NOUN
cana-3690	39	10	pressure	pressure	NOUN
cana-3690	39	11	and	and	CCONJ
cana-3690	39	12	cholesterol	cholesterol	NOUN
cana-3690	39	13	as	as	ADP
cana-3690	39	14	the	the	DET
cana-3690	39	15	most	most	ADV
cana-3690	39	16	critical	critical	ADJ
cana-3690	39	17	predictors	predictor	NOUN
cana-3690	39	18	.	.	PUNCT
cana-3690	40	1	patel	patel	PROPN
cana-3690	40	2	et	et	PROPN
cana-3690	40	3	al	al	PROPN
cana-3690	40	4	.	.	PROPN
cana-3690	40	5	concluded	conclude	VERB
cana-3690	40	6	that	that	SCONJ
cana-3690	40	7	ensemble	ensemble	ADJ
cana-3690	40	8	models	model	NOUN
cana-3690	40	9	like	like	ADP
cana-3690	40	10	random	random	ADJ
cana-3690	40	11	forest	forest	NOUN
cana-3690	40	12	are	be	AUX
cana-3690	40	13	indispensable	indispensable	ADJ
cana-3690	40	14	for	for	ADP
cana-3690	40	15	reliable	reliable	ADJ
cana-3690	40	16	and	and	CCONJ
cana-3690	40	17	accurate	accurate	ADJ
cana-3690	40	18	heart	heart	NOUN
cana-3690	40	19	disease	disease	NOUN
cana-3690	40	20	risk	risk	NOUN
cana-3690	40	21	predictions	prediction	NOUN
cana-3690	40	22	.	.	PUNCT
cana-3690	41	1	kumar	kumar	PROPN
cana-3690	41	2	and	and	CCONJ
cana-3690	41	3	sharma	sharma	PROPN
cana-3690	41	4	(	(	PUNCT
cana-3690	41	5	2021	2021	NUM
cana-3690	41	6	)	)	PUNCT
cana-3690	41	7	evaluated	evaluate	VERB
cana-3690	41	8	the	the	DET
cana-3690	41	9	utility	utility	NOUN
cana-3690	41	10	of	of	ADP
cana-3690	41	11	smoreg	smoreg	NOUN
cana-3690	41	12	and	and	CCONJ
cana-3690	41	13	rep	rep	NOUN
cana-3690	41	14	tree	tree	NOUN
cana-3690	41	15	algorithms	algorithm	NOUN
cana-3690	41	16	for	for	ADP
cana-3690	41	17	predicting	predict	VERB
cana-3690	41	18	heart	heart	NOUN
cana-3690	41	19	disease	disease	NOUN
cana-3690	41	20	,	,	PUNCT
cana-3690	41	21	with	with	ADP
cana-3690	41	22	a	a	DET
cana-3690	41	23	particular	particular	ADJ
cana-3690	41	24	focus	focus	NOUN
cana-3690	41	25	on	on	ADP
cana-3690	41	26	regression	regression	NOUN
cana-3690	41	27	tasks	task	NOUN
cana-3690	41	28	.	.	PUNCT
cana-3690	42	1	using	use	VERB
cana-3690	42	2	metrics	metric	NOUN
cana-3690	42	3	such	such	ADJ
cana-3690	42	4	as	as	ADP
cana-3690	42	5	correlation	correlation	NOUN
cana-3690	42	6	coefficients	coefficient	NOUN
cana-3690	42	7	and	and	CCONJ
cana-3690	42	8	rmse	rmse	NOUN
cana-3690	42	9	,	,	PUNCT
cana-3690	42	10	they	they	PRON
cana-3690	42	11	demonstrated	demonstrate	VERB
cana-3690	42	12	that	that	SCONJ
cana-3690	42	13	rep	rep	NOUN
cana-3690	42	14	tree	tree	NOUN
cana-3690	42	15	was	be	AUX
cana-3690	42	16	more	more	ADV
cana-3690	42	17	interpretable	interpretable	ADJ
cana-3690	42	18	,	,	PUNCT
cana-3690	42	19	whereas	whereas	SCONJ
cana-3690	42	20	smoreg	smoreg	NOUN
cana-3690	42	21	provided	provide	VERB
cana-3690	42	22	higher	high	ADJ
cana-3690	42	23	accuracy	accuracy	NOUN
cana-3690	42	24	when	when	SCONJ
cana-3690	42	25	working	work	VERB
cana-3690	42	26	with	with	ADP
cana-3690	42	27	larger	large	ADJ
cana-3690	42	28	datasets	dataset	NOUN
cana-3690	42	29	.	.	PUNCT
cana-3690	43	1	the	the	DET
cana-3690	43	2	authors	author	NOUN
cana-3690	43	3	highlighted	highlight	VERB
cana-3690	43	4	that	that	SCONJ
cana-3690	43	5	fine	fine	NOUN
cana-3690	43	6	-	-	PUNCT
cana-3690	43	7	tuning	tune	VERB
cana-3690	43	8	the	the	DET
cana-3690	43	9	parameters	parameter	NOUN
cana-3690	43	10	of	of	ADP
cana-3690	43	11	smoreg	smoreg	NOUN
cana-3690	43	12	played	play	VERB
cana-3690	43	13	a	a	DET
cana-3690	43	14	crucial	crucial	ADJ
cana-3690	43	15	role	role	NOUN
cana-3690	43	16	in	in	ADP
cana-3690	43	17	improving	improve	VERB
cana-3690	43	18	its	its	PRON
cana-3690	43	19	performance	performance	NOUN
cana-3690	43	20	.	.	PUNCT
cana-3690	44	1	additionally	additionally	ADV
cana-3690	44	2	,	,	PUNCT
cana-3690	44	3	they	they	PRON
cana-3690	44	4	stressed	stress	VERB
cana-3690	44	5	the	the	DET
cana-3690	44	6	importance	importance	NOUN
cana-3690	44	7	of	of	ADP
cana-3690	44	8	data	datum	NOUN
cana-3690	44	9	normalization	normalization	NOUN
cana-3690	44	10	and	and	CCONJ
cana-3690	44	11	scaling	scale	VERB
cana-3690	44	12	techniques	technique	NOUN
cana-3690	44	13	to	to	PART
cana-3690	44	14	enhance	enhance	VERB
cana-3690	44	15	the	the	DET
cana-3690	44	16	accuracy	accuracy	NOUN
cana-3690	44	17	and	and	CCONJ
cana-3690	44	18	reliability	reliability	NOUN
cana-3690	44	19	of	of	ADP
cana-3690	44	20	predictions	prediction	NOUN
cana-3690	44	21	across	across	ADP
cana-3690	44	22	varying	vary	VERB
cana-3690	44	23	datasets	dataset	NOUN
cana-3690	44	24	.	.	PUNCT
cana-3690	45	1	brown	brown	PROPN
cana-3690	45	2	et	et	PROPN
cana-3690	45	3	al	al	PROPN
cana-3690	45	4	.	.	PROPN
cana-3690	46	1	(	(	PUNCT
cana-3690	46	2	2020	2020	NUM
cana-3690	46	3	)	)	PUNCT
cana-3690	46	4	investigated	investigate	VERB
cana-3690	46	5	the	the	DET
cana-3690	46	6	application	application	NOUN
cana-3690	46	7	of	of	ADP
cana-3690	46	8	machine	machine	NOUN
cana-3690	46	9	learning	learning	NOUN
cana-3690	46	10	models	model	NOUN
cana-3690	46	11	in	in	ADP
cana-3690	46	12	heart	heart	NOUN
cana-3690	46	13	disease	disease	NOUN
cana-3690	46	14	prediction	prediction	NOUN
cana-3690	46	15	,	,	PUNCT
cana-3690	46	16	utilizing	utilize	VERB
cana-3690	46	17	a	a	DET
cana-3690	46	18	wide	wide	ADJ
cana-3690	46	19	range	range	NOUN
cana-3690	46	20	of	of	ADP
cana-3690	46	21	clinical	clinical	ADJ
cana-3690	46	22	parameters	parameter	NOUN
cana-3690	46	23	,	,	PUNCT
cana-3690	46	24	including	include	VERB
cana-3690	46	25	thal	thal	NOUN
cana-3690	46	26	and	and	CCONJ
cana-3690	46	27	ca	ca	NOUN
cana-3690	46	28	.	.	PUNCT
cana-3690	47	1	their	their	PRON
cana-3690	47	2	comparative	comparative	ADJ
cana-3690	47	3	analysis	analysis	NOUN
cana-3690	47	4	of	of	ADP
cana-3690	47	5	random	random	ADJ
cana-3690	47	6	forest	forest	NOUN
cana-3690	47	7	,	,	PUNCT
cana-3690	47	8	mlp	mlp	NOUN
cana-3690	47	9	,	,	PUNCT
cana-3690	47	10	and	and	CCONJ
cana-3690	47	11	linear	linear	PROPN
cana-3690	47	12	regression	regression	NOUN
cana-3690	47	13	demonstrated	demonstrate	VERB
cana-3690	47	14	that	that	SCONJ
cana-3690	47	15	mlp	mlp	PROPN
cana-3690	47	16	achieved	achieve	VERB
cana-3690	47	17	the	the	DET
cana-3690	47	18	highest	high	ADJ
cana-3690	47	19	performance	performance	NOUN
cana-3690	47	20	metrics	metric	NOUN
cana-3690	47	21	,	,	PUNCT
cana-3690	47	22	particularly	particularly	ADV
cana-3690	47	23	in	in	ADP
cana-3690	47	24	rmse	rmse	NOUN
cana-3690	47	25	and	and	CCONJ
cana-3690	47	26	rrse	rrse	NOUN
cana-3690	47	27	,	,	PUNCT
cana-3690	47	28	when	when	SCONJ
cana-3690	47	29	modeling	model	VERB
cana-3690	47	30	nonlinear	nonlinear	ADJ
cana-3690	47	31	relationships	relationship	NOUN
cana-3690	47	32	between	between	ADP
cana-3690	47	33	features	feature	NOUN
cana-3690	47	34	.	.	PUNCT
cana-3690	48	1	however	however	ADV
cana-3690	48	2	,	,	PUNCT
cana-3690	48	3	they	they	PRON
cana-3690	48	4	acknowledged	acknowledge	VERB
cana-3690	48	5	the	the	DET
cana-3690	48	6	challenges	challenge	NOUN
cana-3690	48	7	associated	associate	VERB
cana-3690	48	8	with	with	ADP
cana-3690	48	9	mlp	mlp	PROPN
cana-3690	48	10	,	,	PUNCT
cana-3690	48	11	such	such	ADJ
cana-3690	48	12	as	as	ADP
cana-3690	48	13	the	the	DET
cana-3690	48	14	need	need	NOUN
cana-3690	48	15	for	for	ADP
cana-3690	48	16	significant	significant	ADJ
cana-3690	48	17	computational	computational	ADJ
cana-3690	48	18	resources	resource	NOUN
cana-3690	48	19	and	and	CCONJ
cana-3690	48	20	expertise	expertise	NOUN
cana-3690	48	21	in	in	ADP
cana-3690	48	22	hyperparameter	hyperparameter	NOUN
cana-3690	48	23	optimization	optimization	NOUN
cana-3690	48	24	.	.	PUNCT
cana-3690	49	1	despite	despite	SCONJ
cana-3690	49	2	this	this	PRON
cana-3690	49	3	,	,	PUNCT
cana-3690	49	4	their	their	PRON
cana-3690	49	5	findings	finding	NOUN
cana-3690	49	6	affirmed	affirm	VERB
cana-3690	49	7	the	the	DET
cana-3690	49	8	suitability	suitability	NOUN
cana-3690	49	9	of	of	ADP
cana-3690	49	10	mlp	mlp	PROPN
cana-3690	49	11	for	for	ADP
cana-3690	49	12	tasks	task	NOUN
cana-3690	49	13	requiring	require	VERB
cana-3690	49	14	high	high	ADJ
cana-3690	49	15	accuracy	accuracy	NOUN
cana-3690	49	16	.	.	PUNCT
cana-3690	50	1	gupta	gupta	NOUN
cana-3690	50	2	and	and	CCONJ
cana-3690	50	3	singh	singh	PROPN
cana-3690	50	4	(	(	PUNCT
cana-3690	50	5	2019	2019	NUM
cana-3690	50	6	)	)	PUNCT
cana-3690	50	7	studied	study	VERB
cana-3690	50	8	the	the	DET
cana-3690	50	9	relationship	relationship	NOUN
cana-3690	50	10	between	between	ADP
cana-3690	50	11	clinical	clinical	ADJ
cana-3690	50	12	parameters	parameter	NOUN
cana-3690	50	13	and	and	CCONJ
cana-3690	50	14	heart	heart	NOUN
cana-3690	50	15	disease	disease	NOUN
cana-3690	50	16	risk	risk	NOUN
cana-3690	50	17	by	by	ADP
cana-3690	50	18	employing	employ	VERB
cana-3690	50	19	random	random	ADJ
cana-3690	50	20	tree	tree	NOUN
cana-3690	50	21	and	and	CCONJ
cana-3690	50	22	smoreg	smoreg	NOUN
cana-3690	50	23	algorithms	algorithm	NOUN
cana-3690	50	24	.	.	PUNCT
cana-3690	51	1	their	their	PRON
cana-3690	51	2	findings	finding	NOUN
cana-3690	51	3	identified	identify	VERB
cana-3690	51	4	age	age	NOUN
cana-3690	51	5	,	,	PUNCT
cana-3690	51	6	chest	chest	NOUN
cana-3690	51	7	pain	pain	NOUN
cana-3690	51	8	type	type	NOUN
cana-3690	51	9	,	,	PUNCT
cana-3690	51	10	and	and	CCONJ
cana-3690	51	11	thalach	thalach	NOUN
cana-3690	51	12	as	as	ADP
cana-3690	51	13	the	the	DET
cana-3690	51	14	most	most	ADV
cana-3690	51	15	influential	influential	ADJ
cana-3690	51	16	predictors	predictor	NOUN
cana-3690	51	17	.	.	PUNCT
cana-3690	52	1	while	while	SCONJ
cana-3690	52	2	random	random	ADJ
cana-3690	52	3	tree	tree	NOUN
cana-3690	52	4	was	be	AUX
cana-3690	52	5	praised	praise	VERB
cana-3690	52	6	for	for	ADP
cana-3690	52	7	its	its	PRON
cana-3690	52	8	interpretability	interpretability	NOUN
cana-3690	52	9	and	and	CCONJ
cana-3690	52	10	ease	ease	NOUN
cana-3690	52	11	of	of	ADP
cana-3690	52	12	use	use	NOUN
cana-3690	52	13	,	,	PUNCT
cana-3690	52	14	smoreg	smoreg	NOUN
cana-3690	52	15	demonstrated	demonstrate	VERB
cana-3690	52	16	superior	superior	ADJ
cana-3690	52	17	predictive	predictive	ADJ
cana-3690	52	18	accuracy	accuracy	NOUN
cana-3690	52	19	,	,	PUNCT
cana-3690	52	20	as	as	SCONJ
cana-3690	52	21	evidenced	evidence	VERB
cana-3690	52	22	by	by	ADP
cana-3690	52	23	higher	high	ADJ
cana-3690	52	24	correlation	correlation	NOUN
cana-3690	52	25	coefficients	coefficient	NOUN
cana-3690	52	26	and	and	CCONJ
cana-3690	52	27	lower	low	ADJ
cana-3690	52	28	mae	mae	PROPN
cana-3690	52	29	values	value	NOUN
cana-3690	52	30	.	.	PUNCT
cana-3690	53	1	they	they	PRON
cana-3690	53	2	concluded	conclude	VERB
cana-3690	53	3	that	that	SCONJ
cana-3690	53	4	combining	combine	VERB
cana-3690	53	5	the	the	DET
cana-3690	53	6	strengths	strength	NOUN
cana-3690	53	7	of	of	ADP
cana-3690	53	8	these	these	DET
cana-3690	53	9	algorithms	algorithm	NOUN
cana-3690	53	10	could	could	AUX
cana-3690	53	11	provide	provide	VERB
cana-3690	53	12	a	a	DET
cana-3690	53	13	balanced	balanced	ADJ
cana-3690	53	14	approach	approach	NOUN
cana-3690	53	15	for	for	ADP
cana-3690	53	16	accurate	accurate	ADJ
cana-3690	53	17	and	and	CCONJ
cana-3690	53	18	interpretable	interpretable	ADJ
cana-3690	53	19	heart	heart	NOUN
cana-3690	53	20	disease	disease	NOUN
cana-3690	53	21	predictions	prediction	NOUN
cana-3690	53	22	.	.	PUNCT
cana-3690	54	1	wang	wang	PROPN
cana-3690	54	2	et	et	PROPN
cana-3690	54	3	al	al	PROPN
cana-3690	54	4	.	.	PROPN
cana-3690	54	5	(	(	PUNCT
cana-3690	54	6	2022	2022	NUM
cana-3690	54	7	)	)	PUNCT
cana-3690	54	8	developed	develop	VERB
cana-3690	54	9	predictive	predictive	ADJ
cana-3690	54	10	models	model	NOUN
cana-3690	54	11	using	use	VERB
cana-3690	54	12	rep	rep	NOUN
cana-3690	54	13	tree	tree	NOUN
cana-3690	54	14	and	and	CCONJ
cana-3690	54	15	random	random	ADJ
cana-3690	54	16	forest	forest	NOUN
cana-3690	54	17	to	to	PART
cana-3690	54	18	assess	assess	VERB
cana-3690	54	19	heart	heart	NOUN
cana-3690	54	20	disease	disease	NOUN
cana-3690	54	21	risk	risk	NOUN
cana-3690	54	22	,	,	PUNCT
cana-3690	54	23	focusing	focus	VERB
cana-3690	54	24	on	on	ADP
cana-3690	54	25	parameters	parameter	NOUN
cana-3690	54	26	such	such	ADJ
cana-3690	54	27	as	as	ADP
cana-3690	54	28	exang	exang	PROPN
cana-3690	54	29	,	,	PUNCT
cana-3690	54	30	oldpeak	oldpeak	NOUN
cana-3690	54	31	,	,	PUNCT
cana-3690	54	32	and	and	CCONJ
cana-3690	54	33	slope	slope	NOUN
cana-3690	54	34	.	.	PUNCT
cana-3690	55	1	they	they	PRON
cana-3690	55	2	found	find	VERB
cana-3690	55	3	that	that	SCONJ
cana-3690	55	4	random	random	ADJ
cana-3690	55	5	forest	forest	NOUN
cana-3690	55	6	consistently	consistently	ADV
cana-3690	55	7	outperformed	outperform	VERB
cana-3690	55	8	rep	rep	NOUN
cana-3690	55	9	tree	tree	NOUN
cana-3690	55	10	in	in	ADP
cana-3690	55	11	terms	term	NOUN
cana-3690	55	12	of	of	ADP
cana-3690	55	13	rmse	rmse	NOUN
cana-3690	55	14	and	and	CCONJ
cana-3690	55	15	rae	rae	PROPN
cana-3690	55	16	.	.	PUNCT
cana-3690	56	1	however	however	ADV
cana-3690	56	2	,	,	PUNCT
cana-3690	56	3	they	they	PRON
cana-3690	56	4	emphasized	emphasize	VERB
cana-3690	56	5	the	the	DET
cana-3690	56	6	practicality	practicality	NOUN
cana-3690	56	7	of	of	ADP
cana-3690	56	8	rep	rep	NOUN
cana-3690	56	9	tree	tree	NOUN
cana-3690	56	10	in	in	ADP
cana-3690	56	11	situations	situation	NOUN
cana-3690	56	12	where	where	SCONJ
cana-3690	56	13	computational	computational	ADJ
cana-3690	56	14	resources	resource	NOUN
cana-3690	56	15	are	be	AUX
cana-3690	56	16	limited	limit	VERB
cana-3690	56	17	,	,	PUNCT
cana-3690	56	18	as	as	SCONJ
cana-3690	56	19	it	it	PRON
cana-3690	56	20	requires	require	VERB
cana-3690	56	21	less	less	ADJ
cana-3690	56	22	processing	processing	NOUN
cana-3690	56	23	power	power	NOUN
cana-3690	56	24	.	.	PUNCT
cana-3690	57	1	their	their	PRON
cana-3690	57	2	research	research	NOUN
cana-3690	57	3	also	also	ADV
cana-3690	57	4	stressed	stress	VERB
cana-3690	57	5	the	the	DET
cana-3690	57	6	value	value	NOUN
cana-3690	57	7	of	of	ADP
cana-3690	57	8	incorporating	incorporate	VERB
cana-3690	57	9	domain	domain	NOUN
cana-3690	57	10	-	-	PUNCT
cana-3690	57	11	specific	specific	ADJ
cana-3690	57	12	knowledge	knowledge	NOUN
cana-3690	57	13	to	to	PART
cana-3690	57	14	refine	refine	VERB
cana-3690	57	15	feature	feature	NOUN
cana-3690	57	16	selection	selection	NOUN
cana-3690	57	17	and	and	CCONJ
cana-3690	57	18	hyperparameter	hyperparameter	NOUN
cana-3690	57	19	tuning	tuning	NOUN
cana-3690	57	20	,	,	PUNCT
cana-3690	57	21	thereby	thereby	ADV
cana-3690	57	22	improving	improve	VERB
cana-3690	57	23	the	the	DET
cana-3690	57	24	overall	overall	ADJ
cana-3690	57	25	performance	performance	NOUN
cana-3690	57	26	of	of	ADP
cana-3690	57	27	predictive	predictive	ADJ
cana-3690	57	28	models	model	NOUN
cana-3690	57	29	.	.	PUNCT
cana-3690	58	1	ali	ali	PROPN
cana-3690	58	2	et	et	PROPN
cana-3690	58	3	al	al	PROPN
cana-3690	58	4	.	.	PROPN
cana-3690	58	5	(	(	PUNCT
cana-3690	58	6	2020	2020	NUM
cana-3690	58	7	)	)	PUNCT
cana-3690	58	8	analyzed	analyze	VERB
cana-3690	58	9	the	the	DET
cana-3690	58	10	predictive	predictive	ADJ
cana-3690	58	11	performance	performance	NOUN
cana-3690	58	12	of	of	ADP
cana-3690	58	13	mlp	mlp	NOUN
cana-3690	58	14	and	and	CCONJ
cana-3690	58	15	linear	linear	PROPN
cana-3690	58	16	regression	regression	NOUN
cana-3690	58	17	in	in	ADP
cana-3690	58	18	determining	determine	VERB
cana-3690	58	19	heart	heart	NOUN
cana-3690	58	20	disease	disease	NOUN
cana-3690	58	21	risk	risk	NOUN
cana-3690	58	22	.	.	PUNCT
cana-3690	59	1	their	their	PRON
cana-3690	59	2	study	study	NOUN
cana-3690	59	3	showed	show	VERB
cana-3690	59	4	that	that	SCONJ
cana-3690	59	5	mlp	mlp	PROPN
cana-3690	59	6	surpassed	surpass	VERB
cana-3690	59	7	linear	linear	PROPN
cana-3690	59	8	regression	regression	NOUN
cana-3690	59	9	in	in	ADP
cana-3690	59	10	all	all	DET
cana-3690	59	11	evaluated	evaluated	ADJ
cana-3690	59	12	metrics	metric	NOUN
cana-3690	59	13	,	,	PUNCT
cana-3690	59	14	including	include	VERB
cana-3690	59	15	rmse	rmse	NOUN
cana-3690	59	16	and	and	CCONJ
cana-3690	59	17	rrse	rrse	NOUN
cana-3690	59	18	.	.	PUNCT
cana-3690	60	1	they	they	PRON
cana-3690	60	2	identified	identify	VERB
cana-3690	60	3	age	age	NOUN
cana-3690	60	4	and	and	CCONJ
cana-3690	60	5	cholesterol	cholesterol	NOUN
cana-3690	60	6	as	as	ADP
cana-3690	60	7	the	the	DET
cana-3690	60	8	most	most	ADV
cana-3690	60	9	significant	significant	ADJ
cana-3690	60	10	predictors	predictor	NOUN
cana-3690	60	11	,	,	PUNCT
cana-3690	60	12	noting	note	VERB
cana-3690	60	13	that	that	SCONJ
cana-3690	60	14	linear	linear	PROPN
cana-3690	60	15	regression	regression	NOUN
cana-3690	60	16	struggled	struggle	VERB
cana-3690	60	17	to	to	PART
cana-3690	60	18	account	account	VERB
cana-3690	60	19	for	for	ADP
cana-3690	60	20	nonlinear	nonlinear	ADJ
cana-3690	60	21	interactions	interaction	NOUN
cana-3690	60	22	among	among	ADP
cana-3690	60	23	features	feature	NOUN
cana-3690	60	24	.	.	PUNCT
cana-3690	61	1	the	the	DET
cana-3690	61	2	authors	author	NOUN
cana-3690	61	3	concluded	conclude	VERB
cana-3690	61	4	that	that	SCONJ
cana-3690	61	5	mlp	mlp	PROPN
cana-3690	61	6	’s	’s	PART
cana-3690	61	7	ability	ability	NOUN
cana-3690	61	8	to	to	PART
cana-3690	61	9	model	model	VERB
cana-3690	61	10	intricate	intricate	ADJ
cana-3690	61	11	relationships	relationship	NOUN
cana-3690	61	12	makes	make	VERB
cana-3690	61	13	it	it	PRON
cana-3690	61	14	an	an	DET
cana-3690	61	15	effective	effective	ADJ
cana-3690	61	16	tool	tool	NOUN
cana-3690	61	17	for	for	ADP
cana-3690	61	18	complex	complex	ADJ
cana-3690	61	19	cardiovascular	cardiovascular	ADJ
cana-3690	61	20	datasets	dataset	NOUN
cana-3690	61	21	,	,	PUNCT
cana-3690	61	22	although	although	SCONJ
cana-3690	61	23	it	it	PRON
cana-3690	61	24	may	may	AUX
cana-3690	61	25	require	require	VERB
cana-3690	61	26	significant	significant	ADJ
cana-3690	61	27	computational	computational	ADJ
cana-3690	61	28	resources	resource	NOUN
cana-3690	61	29	for	for	ADP
cana-3690	61	30	optimization	optimization	NOUN
cana-3690	61	31	.	.	PUNCT
cana-3690	62	1	chen	chen	PROPN
cana-3690	62	2	et	et	PROPN
cana-3690	62	3	al	al	PROPN
cana-3690	62	4	.	.	PROPN
cana-3690	63	1	(	(	PUNCT
cana-3690	63	2	2019	2019	NUM
cana-3690	63	3	)	)	PUNCT
cana-3690	63	4	examined	examine	VERB
cana-3690	63	5	the	the	DET
cana-3690	63	6	application	application	NOUN
cana-3690	63	7	of	of	ADP
cana-3690	63	8	feature	feature	NOUN
cana-3690	63	9	selection	selection	NOUN
cana-3690	63	10	techniques	technique	NOUN
cana-3690	63	11	in	in	ADP
cana-3690	63	12	enhancing	enhance	VERB
cana-3690	63	13	the	the	DET
cana-3690	63	14	accuracy	accuracy	NOUN
cana-3690	63	15	of	of	ADP
cana-3690	63	16	heart	heart	NOUN
cana-3690	63	17	disease	disease	NOUN
cana-3690	63	18	prediction	prediction	NOUN
cana-3690	63	19	models	model	NOUN
cana-3690	63	20	.	.	PUNCT
cana-3690	64	1	they	they	PRON
cana-3690	64	2	implemented	implement	VERB
cana-3690	64	3	random	random	ADJ
cana-3690	64	4	forest	forest	NOUN
cana-3690	64	5	and	and	CCONJ
cana-3690	64	6	smoreg	smoreg	NOUN
cana-3690	64	7	,	,	PUNCT
cana-3690	64	8	focusing	focus	VERB
cana-3690	64	9	on	on	ADP
cana-3690	64	10	communications	communication	NOUN
cana-3690	64	11	on	on	ADP
cana-3690	64	12	applied	apply	VERB
cana-3690	64	13	nonlinear	nonlinear	ADJ
cana-3690	64	14	analysis	analysis	NOUN
cana-3690	64	15	issn	issn	NOUN
cana-3690	64	16	:	:	PUNCT
cana-3690	64	17	1074	1074	NUM
cana-3690	64	18	-	-	PUNCT
cana-3690	64	19	133x	133x	NUM
cana-3690	64	20	vol	vol	NOUN
cana-3690	64	21	32	32	NUM
cana-3690	64	22	no	no	NOUN
cana-3690	64	23	.	.	PUNCT
cana-3690	65	1	8s	8s	PROPN
cana-3690	65	2	(	(	PUNCT
cana-3690	65	3	2025	2025	NUM
cana-3690	65	4	)	)	PUNCT
cana-3690	65	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-3690	65	6	459	459	NUM
cana-3690	65	7	parameters	parameter	NOUN
cana-3690	65	8	like	like	ADP
cana-3690	65	9	restecg	restecg	NOUN
cana-3690	65	10	and	and	CCONJ
cana-3690	65	11	ca	ca	NOUN
cana-3690	65	12	.	.	PUNCT
cana-3690	66	1	their	their	PRON
cana-3690	66	2	results	result	NOUN
cana-3690	66	3	demonstrated	demonstrate	VERB
cana-3690	66	4	that	that	SCONJ
cana-3690	66	5	random	random	ADJ
cana-3690	66	6	forest	forest	NOUN
cana-3690	66	7	achieved	achieve	VERB
cana-3690	66	8	superior	superior	ADJ
cana-3690	66	9	performance	performance	NOUN
cana-3690	66	10	in	in	ADP
cana-3690	66	11	terms	term	NOUN
cana-3690	66	12	of	of	ADP
cana-3690	66	13	correlation	correlation	NOUN
cana-3690	66	14	coefficients	coefficient	NOUN
cana-3690	66	15	and	and	CCONJ
cana-3690	66	16	rae	rae	PROPN
cana-3690	66	17	,	,	PUNCT
cana-3690	66	18	while	while	SCONJ
cana-3690	66	19	smoreg	smoreg	NOUN
cana-3690	66	20	exhibited	exhibit	VERB
cana-3690	66	21	competitive	competitive	ADJ
cana-3690	66	22	results	result	NOUN
cana-3690	66	23	when	when	SCONJ
cana-3690	66	24	optimized	optimize	VERB
cana-3690	66	25	appropriately	appropriately	ADV
cana-3690	66	26	.	.	PUNCT
cana-3690	67	1	the	the	DET
cana-3690	67	2	study	study	NOUN
cana-3690	67	3	emphasized	emphasize	VERB
cana-3690	67	4	the	the	DET
cana-3690	67	5	critical	critical	ADJ
cana-3690	67	6	role	role	NOUN
cana-3690	67	7	of	of	ADP
cana-3690	67	8	dimensionality	dimensionality	NOUN
cana-3690	67	9	reduction	reduction	NOUN
cana-3690	67	10	in	in	ADP
cana-3690	67	11	improving	improve	VERB
cana-3690	67	12	both	both	CCONJ
cana-3690	67	13	the	the	DET
cana-3690	67	14	interpretability	interpretability	NOUN
cana-3690	67	15	and	and	CCONJ
cana-3690	67	16	efficiency	efficiency	NOUN
cana-3690	67	17	of	of	ADP
cana-3690	67	18	predictive	predictive	ADJ
cana-3690	67	19	models	model	NOUN
cana-3690	67	20	for	for	ADP
cana-3690	67	21	heart	heart	NOUN
cana-3690	67	22	disease	disease	NOUN
cana-3690	67	23	.	.	PUNCT
cana-3690	68	1	zhang	zhang	PROPN
cana-3690	68	2	and	and	CCONJ
cana-3690	68	3	luo	luo	PROPN
cana-3690	68	4	(	(	PUNCT
cana-3690	68	5	2021	2021	NUM
cana-3690	68	6	)	)	PUNCT
cana-3690	68	7	conducted	conduct	VERB
cana-3690	68	8	a	a	DET
cana-3690	68	9	comprehensive	comprehensive	ADJ
cana-3690	68	10	analysis	analysis	NOUN
cana-3690	68	11	of	of	ADP
cana-3690	68	12	heart	heart	NOUN
cana-3690	68	13	disease	disease	NOUN
cana-3690	68	14	prediction	prediction	NOUN
cana-3690	68	15	using	use	VERB
cana-3690	68	16	random	random	ADJ
cana-3690	68	17	forest	forest	NOUN
cana-3690	68	18	and	and	CCONJ
cana-3690	68	19	rep	rep	NOUN
cana-3690	68	20	tree	tree	NOUN
cana-3690	68	21	models	model	NOUN
cana-3690	68	22	.	.	PUNCT
cana-3690	69	1	their	their	PRON
cana-3690	69	2	evaluation	evaluation	NOUN
cana-3690	69	3	,	,	PUNCT
cana-3690	69	4	based	base	VERB
cana-3690	69	5	on	on	ADP
cana-3690	69	6	rmse	rmse	PROPN
cana-3690	69	7	,	,	PUNCT
cana-3690	69	8	rae	rae	PROPN
cana-3690	69	9	,	,	PUNCT
cana-3690	69	10	and	and	CCONJ
cana-3690	69	11	rrse	rrse	NOUN
cana-3690	69	12	,	,	PUNCT
cana-3690	69	13	revealed	reveal	VERB
cana-3690	69	14	that	that	SCONJ
cana-3690	69	15	random	random	ADJ
cana-3690	69	16	forest	forest	NOUN
cana-3690	69	17	consistently	consistently	ADV
cana-3690	69	18	outperformed	outperform	VERB
cana-3690	69	19	rep	rep	PROPN
cana-3690	69	20	tree	tree	NOUN
cana-3690	69	21	,	,	PUNCT
cana-3690	69	22	especially	especially	ADV
cana-3690	69	23	in	in	ADP
cana-3690	69	24	datasets	dataset	NOUN
cana-3690	69	25	with	with	ADP
cana-3690	69	26	high	high	ADJ
cana-3690	69	27	feature	feature	NOUN
cana-3690	69	28	complexity	complexity	NOUN
cana-3690	69	29	.	.	PUNCT
cana-3690	70	1	nevertheless	nevertheless	ADV
cana-3690	70	2	,	,	PUNCT
cana-3690	70	3	they	they	PRON
cana-3690	70	4	noted	note	VERB
cana-3690	70	5	that	that	SCONJ
cana-3690	70	6	rep	rep	PROPN
cana-3690	70	7	tree	tree	PROPN
cana-3690	70	8	’s	’s	PART
cana-3690	70	9	simplicity	simplicity	NOUN
cana-3690	70	10	and	and	CCONJ
cana-3690	70	11	lower	low	ADJ
cana-3690	70	12	computational	computational	ADJ
cana-3690	70	13	demands	demand	NOUN
cana-3690	70	14	make	make	VERB
cana-3690	70	15	it	it	PRON
cana-3690	70	16	an	an	DET
cana-3690	70	17	appealing	appealing	ADJ
cana-3690	70	18	choice	choice	NOUN
cana-3690	70	19	for	for	ADP
cana-3690	70	20	preliminary	preliminary	ADJ
cana-3690	70	21	analyses	analysis	NOUN
cana-3690	70	22	or	or	CCONJ
cana-3690	70	23	scenarios	scenario	NOUN
cana-3690	70	24	where	where	SCONJ
cana-3690	70	25	computational	computational	ADJ
cana-3690	70	26	efficiency	efficiency	NOUN
cana-3690	70	27	is	be	AUX
cana-3690	70	28	prioritized	prioritize	VERB
cana-3690	70	29	.	.	PUNCT
cana-3690	71	1	the	the	DET
cana-3690	71	2	authors	author	NOUN
cana-3690	71	3	concluded	conclude	VERB
cana-3690	71	4	that	that	SCONJ
cana-3690	71	5	random	random	ADJ
cana-3690	71	6	forest	forest	NOUN
cana-3690	71	7	is	be	AUX
cana-3690	71	8	better	well	ADV
cana-3690	71	9	suited	suit	VERB
cana-3690	71	10	for	for	ADP
cana-3690	71	11	tasks	task	NOUN
cana-3690	71	12	requiring	require	VERB
cana-3690	71	13	high	high	ADJ
cana-3690	71	14	accuracy	accuracy	NOUN
cana-3690	71	15	and	and	CCONJ
cana-3690	71	16	robustness	robustness	NOUN
cana-3690	71	17	in	in	ADP
cana-3690	71	18	prediction	prediction	NOUN
cana-3690	71	19	.	.	PUNCT
cana-3690	72	1	diwakar	diwakar	NOUN
cana-3690	72	2	et	et	PROPN
cana-3690	72	3	al	al	PROPN
cana-3690	72	4	(	(	PUNCT
cana-3690	72	5	2021	2021	NUM
cana-3690	72	6	)	)	PUNCT
cana-3690	72	7	suggested	suggest	VERB
cana-3690	72	8	machine	machine	NOUN
cana-3690	72	9	learning	learn	VERB
cana-3690	72	10	classification	classification	NOUN
cana-3690	72	11	methods	method	NOUN
cana-3690	72	12	can	can	AUX
cana-3690	72	13	support	support	VERB
cana-3690	72	14	the	the	DET
cana-3690	72	15	medical	medical	ADJ
cana-3690	72	16	field	field	NOUN
cana-3690	72	17	by	by	ADP
cana-3690	72	18	enabling	enable	VERB
cana-3690	72	19	fast	fast	ADJ
cana-3690	72	20	and	and	CCONJ
cana-3690	72	21	reliable	reliable	ADJ
cana-3690	72	22	disease	disease	NOUN
cana-3690	72	23	diagnoses	diagnose	VERB
cana-3690	72	24	.	.	PUNCT
cana-3690	73	1	this	this	DET
cana-3690	73	2	benefits	benefit	VERB
cana-3690	73	3	both	both	DET
cana-3690	73	4	doctors	doctor	NOUN
cana-3690	73	5	and	and	CCONJ
cana-3690	73	6	patients	patient	NOUN
cana-3690	73	7	,	,	PUNCT
cana-3690	73	8	especially	especially	ADV
cana-3690	73	9	in	in	ADP
cana-3690	73	10	the	the	DET
cana-3690	73	11	case	case	NOUN
cana-3690	73	12	of	of	ADP
cana-3690	73	13	heart	heart	NOUN
cana-3690	73	14	disease	disease	NOUN
cana-3690	73	15	,	,	PUNCT
cana-3690	73	16	one	one	NUM
cana-3690	73	17	of	of	ADP
cana-3690	73	18	the	the	DET
cana-3690	73	19	most	most	ADV
cana-3690	73	20	dangerous	dangerous	ADJ
cana-3690	73	21	and	and	CCONJ
cana-3690	73	22	difficult	difficult	ADJ
cana-3690	73	23	diseases	disease	NOUN
cana-3690	73	24	to	to	PART
cana-3690	73	25	diagnose	diagnose	VERB
cana-3690	73	26	today	today	NOUN
cana-3690	73	27	.	.	PUNCT
cana-3690	74	1	machine	machine	NOUN
cana-3690	74	2	learning	learn	VERB
cana-3690	74	3	classification	classification	NOUN
cana-3690	74	4	methods	method	NOUN
cana-3690	74	5	and	and	CCONJ
cana-3690	74	6	image	image	NOUN
cana-3690	74	7	fusion	fusion	NOUN
cana-3690	74	8	techniques	technique	NOUN
cana-3690	74	9	that	that	PRON
cana-3690	74	10	have	have	AUX
cana-3690	74	11	been	be	AUX
cana-3690	74	12	proven	prove	VERB
cana-3690	74	13	to	to	PART
cana-3690	74	14	assist	assist	VERB
cana-3690	74	15	healthcare	healthcare	NOUN
cana-3690	74	16	professionals	professional	NOUN
cana-3690	74	17	in	in	ADP
cana-3690	74	18	identifying	identify	VERB
cana-3690	74	19	heart	heart	NOUN
cana-3690	74	20	disease	disease	NOUN
cana-3690	74	21	.	.	PUNCT
cana-3690	75	1	it	it	PRON
cana-3690	75	2	begins	begin	VERB
cana-3690	75	3	with	with	ADP
cana-3690	75	4	a	a	DET
cana-3690	75	5	brief	brief	ADJ
cana-3690	75	6	overview	overview	NOUN
cana-3690	75	7	of	of	ADP
cana-3690	75	8	machine	machine	NOUN
cana-3690	75	9	learning	learning	NOUN
cana-3690	75	10	and	and	CCONJ
cana-3690	75	11	summarizes	summarize	VERB
cana-3690	75	12	the	the	DET
cana-3690	75	13	key	key	ADJ
cana-3690	75	14	classification	classification	NOUN
cana-3690	75	15	techniques	technique	NOUN
cana-3690	75	16	for	for	ADP
cana-3690	75	17	diagnosing	diagnose	VERB
cana-3690	75	18	heart	heart	NOUN
cana-3690	75	19	disease	disease	NOUN
cana-3690	75	20	.	.	PUNCT
cana-3690	76	1	in	in	ADP
cana-3690	76	2	addition	addition	NOUN
cana-3690	76	3	,	,	PUNCT
cana-3690	76	4	it	it	PRON
cana-3690	76	5	examines	examine	VERB
cana-3690	76	6	the	the	DET
cana-3690	76	7	application	application	NOUN
cana-3690	76	8	of	of	ADP
cana-3690	76	9	machine	machine	NOUN
cana-3690	76	10	learning	learning	NOUN
cana-3690	76	11	and	and	CCONJ
cana-3690	76	12	image	image	NOUN
cana-3690	76	13	fusion	fusion	NOUN
cana-3690	76	14	techniques	technique	NOUN
cana-3690	76	15	in	in	ADP
cana-3690	76	16	this	this	DET
cana-3690	76	17	area	area	NOUN
cana-3690	76	18	,	,	PUNCT
cana-3690	76	19	outlines	outline	VERB
cana-3690	76	20	the	the	DET
cana-3690	76	21	working	work	VERB
cana-3690	76	22	algorithms	algorithm	NOUN
cana-3690	76	23	and	and	CCONJ
cana-3690	76	24	provides	provide	VERB
cana-3690	76	25	an	an	DET
cana-3690	76	26	overview	overview	NOUN
cana-3690	76	27	of	of	ADP
cana-3690	76	28	current	current	ADJ
cana-3690	76	29	research	research	NOUN
cana-3690	76	30	.	.	PUNCT
cana-3690	77	1	tougui	tougui	VERB
cana-3690	77	2	et	et	PROPN
cana-3690	77	3	al	al	PROPN
cana-3690	77	4	.	.	PROPN
cana-3690	78	1	(	(	PUNCT
cana-3690	78	2	2020	2020	NUM
cana-3690	78	3	)	)	PUNCT
cana-3690	78	4	discussed	discuss	VERB
cana-3690	78	5	data	datum	NOUN
cana-3690	78	6	analytics	analytic	NOUN
cana-3690	78	7	in	in	ADP
cana-3690	78	8	healthcare	healthcare	NOUN
cana-3690	78	9	can	can	AUX
cana-3690	78	10	save	save	VERB
cana-3690	78	11	lives	life	NOUN
cana-3690	78	12	by	by	ADP
cana-3690	78	13	improving	improve	VERB
cana-3690	78	14	medical	medical	ADJ
cana-3690	78	15	diagnoses	diagnosis	NOUN
cana-3690	78	16	.	.	PUNCT
cana-3690	79	1	significant	significant	ADJ
cana-3690	79	2	advances	advance	NOUN
cana-3690	79	3	in	in	ADP
cana-3690	79	4	software	software	NOUN
cana-3690	79	5	engineering	engineering	NOUN
cana-3690	79	6	have	have	AUX
cana-3690	79	7	been	be	AUX
cana-3690	79	8	made	make	VERB
cana-3690	79	9	in	in	ADP
cana-3690	79	10	this	this	DET
cana-3690	79	11	area	area	NOUN
cana-3690	79	12	,	,	PUNCT
cana-3690	79	13	resulting	result	VERB
cana-3690	79	14	in	in	ADP
cana-3690	79	15	various	various	ADJ
cana-3690	79	16	data	datum	NOUN
cana-3690	79	17	mining	mining	NOUN
cana-3690	79	18	tools	tool	NOUN
cana-3690	79	19	used	use	VERB
cana-3690	79	20	by	by	ADP
cana-3690	79	21	researchers	researcher	NOUN
cana-3690	79	22	to	to	PART
cana-3690	79	23	conduct	conduct	VERB
cana-3690	79	24	studies	study	NOUN
cana-3690	79	25	and	and	CCONJ
cana-3690	79	26	experiments	experiment	NOUN
cana-3690	79	27	.	.	PUNCT
cana-3690	80	1	this	this	DET
cana-3690	80	2	study	study	NOUN
cana-3690	80	3	compared	compare	VERB
cana-3690	80	4	six	six	NUM
cana-3690	80	5	popular	popular	ADJ
cana-3690	80	6	data	datum	NOUN
cana-3690	80	7	mining	mining	NOUN
cana-3690	80	8	tools	tool	NOUN
cana-3690	80	9	,	,	PUNCT
cana-3690	80	10	including	include	VERB
cana-3690	80	11	orange	orange	PROPN
cana-3690	80	12	,	,	PUNCT
cana-3690	80	13	weka	weka	PROPN
cana-3690	80	14	,	,	PUNCT
cana-3690	80	15	rapidminer	rapidminer	NOUN
cana-3690	80	16	,	,	PUNCT
cana-3690	80	17	knime	knime	ADJ
cana-3690	80	18	,	,	PUNCT
cana-3690	80	19	matlab	matlab	PROPN
cana-3690	80	20	,	,	PUNCT
cana-3690	80	21	and	and	CCONJ
cana-3690	80	22	scikit	scikit	NOUN
cana-3690	80	23	-	-	PUNCT
cana-3690	80	24	learn	learn	VERB
cana-3690	80	25	,	,	PUNCT
cana-3690	80	26	using	use	VERB
cana-3690	80	27	six	six	NUM
cana-3690	80	28	machine	machine	NOUN
cana-3690	80	29	learning	learn	VERB
cana-3690	80	30	techniques	technique	NOUN
cana-3690	80	31	to	to	PART
cana-3690	80	32	classify	classify	VERB
cana-3690	80	33	heart	heart	NOUN
cana-3690	80	34	diseases	disease	NOUN
cana-3690	80	35	.	.	PUNCT
cana-3690	81	1	the	the	DET
cana-3690	81	2	dataset	dataset	NOUN
cana-3690	81	3	contains	contain	VERB
cana-3690	81	4	13	13	NUM
cana-3690	81	5	features	feature	NOUN
cana-3690	81	6	,	,	PUNCT
cana-3690	81	7	one	one	NUM
cana-3690	81	8	target	target	NOUN
cana-3690	81	9	variable	variable	NOUN
cana-3690	81	10	and	and	CCONJ
cana-3690	81	11	303	303	NUM
cana-3690	81	12	cases	case	NOUN
cana-3690	81	13	with	with	ADP
cana-3690	81	14	139	139	NUM
cana-3690	81	15	people	people	NOUN
cana-3690	81	16	suffering	suffer	VERB
cana-3690	81	17	from	from	ADP
cana-3690	81	18	cardiovascular	cardiovascular	ADJ
cana-3690	81	19	diseases	disease	NOUN
cana-3690	81	20	and	and	CCONJ
cana-3690	81	21	164	164	NUM
cana-3690	81	22	healthy	healthy	ADJ
cana-3690	81	23	subjects	subject	NOUN
cana-3690	81	24	.	.	PUNCT
cana-3690	82	1	three	three	NUM
cana-3690	82	2	performance	performance	NOUN
cana-3690	82	3	measures	measure	NOUN
cana-3690	82	4	were	be	AUX
cana-3690	82	5	used	use	VERB
cana-3690	82	6	to	to	PART
cana-3690	82	7	assess	assess	VERB
cana-3690	82	8	the	the	DET
cana-3690	82	9	performance	performance	NOUN
cana-3690	82	10	of	of	ADP
cana-3690	82	11	the	the	DET
cana-3690	82	12	tool	tool	NOUN
cana-3690	82	13	:	:	PUNCT
cana-3690	82	14	accuracy	accuracy	NOUN
cana-3690	82	15	,	,	PUNCT
cana-3690	82	16	sensitivity	sensitivity	NOUN
cana-3690	82	17	and	and	CCONJ
cana-3690	82	18	specificity	specificity	NOUN
cana-3690	82	19	.	.	PUNCT
cana-3690	83	1	the	the	DET
cana-3690	83	2	results	result	NOUN
cana-3690	83	3	show	show	VERB
cana-3690	83	4	that	that	SCONJ
cana-3690	83	5	matlab	matlab	PROPN
cana-3690	83	6	is	be	AUX
cana-3690	83	7	the	the	DET
cana-3690	83	8	most	most	ADV
cana-3690	83	9	powerful	powerful	ADJ
cana-3690	83	10	tool	tool	NOUN
cana-3690	83	11	,	,	PUNCT
cana-3690	83	12	with	with	ADP
cana-3690	83	13	matlab	matlab	PROPN
cana-3690	83	14	's	's	PART
cana-3690	83	15	artificial	artificial	ADJ
cana-3690	83	16	neural	neural	ADJ
cana-3690	83	17	network	network	NOUN
cana-3690	83	18	model	model	NOUN
cana-3690	83	19	being	be	AUX
cana-3690	83	20	the	the	DET
cana-3690	83	21	most	most	ADV
cana-3690	83	22	powerful	powerful	ADJ
cana-3690	83	23	technique	technique	NOUN
cana-3690	83	24	.	.	PUNCT
cana-3690	84	1	the	the	DET
cana-3690	84	2	research	research	NOUN
cana-3690	84	3	ends	end	VERB
cana-3690	84	4	with	with	ADP
cana-3690	84	5	the	the	DET
cana-3690	84	6	presentation	presentation	NOUN
cana-3690	84	7	of	of	ADP
cana-3690	84	8	the	the	DET
cana-3690	84	9	receiver	receiver	NOUN
cana-3690	84	10	operating	operate	VERB
cana-3690	84	11	characteristic	characteristic	ADJ
cana-3690	84	12	curve	curve	NOUN
cana-3690	84	13	for	for	ADP
cana-3690	84	14	matlab	matlab	NOUN
cana-3690	84	15	and	and	CCONJ
cana-3690	84	16	recommendations	recommendation	NOUN
cana-3690	84	17	for	for	ADP
cana-3690	84	18	tool	tool	NOUN
cana-3690	84	19	selection	selection	NOUN
cana-3690	84	20	based	base	VERB
cana-3690	84	21	on	on	ADP
cana-3690	84	22	user	user	NOUN
cana-3690	84	23	experiences	experience	NOUN
cana-3690	84	24	in	in	ADP
cana-3690	84	25	data	datum	NOUN
cana-3690	84	26	mining	mining	NOUN
cana-3690	84	27	.	.	PUNCT
cana-3690	85	1	sitar	sitar	NOUN
cana-3690	85	2	-	-	PUNCT
cana-3690	85	3	taut	taut	NOUN
cana-3690	85	4	et	et	PROPN
cana-3690	85	5	al	al	PROPN
cana-3690	85	6	.	.	PROPN
cana-3690	85	7	(	(	PUNCT
cana-3690	85	8	2009	2009	NUM
cana-3690	85	9	)	)	PUNCT
cana-3690	85	10	explored	explore	VERB
cana-3690	85	11	that	that	PRON
cana-3690	85	12	medicine	medicine	NOUN
cana-3690	85	13	and	and	CCONJ
cana-3690	85	14	computer	computer	NOUN
cana-3690	85	15	science	science	NOUN
cana-3690	85	16	may	may	AUX
cana-3690	85	17	seem	seem	VERB
cana-3690	85	18	different	different	ADJ
cana-3690	85	19	,	,	PUNCT
cana-3690	85	20	they	they	PRON
cana-3690	85	21	have	have	AUX
cana-3690	85	22	been	be	AUX
cana-3690	85	23	working	work	VERB
cana-3690	85	24	together	together	ADV
cana-3690	85	25	for	for	ADP
cana-3690	85	26	several	several	ADJ
cana-3690	85	27	decades	decade	NOUN
cana-3690	85	28	,	,	PUNCT
cana-3690	85	29	with	with	ADP
cana-3690	85	30	data	datum	NOUN
cana-3690	85	31	mining	mining	NOUN
cana-3690	85	32	being	be	AUX
cana-3690	85	33	a	a	DET
cana-3690	85	34	notable	notable	ADJ
cana-3690	85	35	example	example	NOUN
cana-3690	85	36	of	of	ADP
cana-3690	85	37	this	this	DET
cana-3690	85	38	collaboration	collaboration	NOUN
cana-3690	85	39	.	.	PUNCT
cana-3690	86	1	however	however	ADV
cana-3690	86	2	,	,	PUNCT
cana-3690	86	3	data	data	NOUN
cana-3690	86	4	mining	mining	NOUN
cana-3690	86	5	has	have	AUX
cana-3690	86	6	been	be	AUX
cana-3690	86	7	insufficiently	insufficiently	ADV
cana-3690	86	8	used	use	VERB
cana-3690	86	9	in	in	ADP
cana-3690	86	10	cardiology	cardiology	NOUN
cana-3690	86	11	studies	study	NOUN
cana-3690	86	12	.	.	PUNCT
cana-3690	87	1	this	this	DET
cana-3690	87	2	article	article	NOUN
cana-3690	87	3	aims	aim	VERB
cana-3690	87	4	to	to	PART
cana-3690	87	5	demonstrate	demonstrate	VERB
cana-3690	87	6	that	that	SCONJ
cana-3690	87	7	certain	certain	ADJ
cana-3690	87	8	data	data	NOUN
cana-3690	87	9	mining	mining	NOUN
cana-3690	87	10	tools	tool	NOUN
cana-3690	87	11	can	can	AUX
cana-3690	87	12	replace	replace	VERB
cana-3690	87	13	complex	complex	ADJ
cana-3690	87	14	,	,	PUNCT
cana-3690	87	15	expensive	expensive	ADJ
cana-3690	87	16	,	,	PUNCT
cana-3690	87	17	time	time	NOUN
cana-3690	87	18	-	-	PUNCT
cana-3690	87	19	consuming	consume	VERB
cana-3690	87	20	and	and	CCONJ
cana-3690	87	21	potentially	potentially	ADV
cana-3690	87	22	risky	risky	ADJ
cana-3690	87	23	medical	medical	ADJ
cana-3690	87	24	examinations	examination	NOUN
cana-3690	87	25	to	to	PART
cana-3690	87	26	predict	predict	VERB
cana-3690	87	27	cardiovascular	cardiovascular	ADJ
cana-3690	87	28	disease	disease	NOUN
cana-3690	87	29	in	in	ADP
cana-3690	87	30	a	a	DET
cana-3690	87	31	non	non	ADJ
cana-3690	87	32	-	-	ADJ
cana-3690	87	33	invasive	invasive	ADJ
cana-3690	87	34	manner	manner	NOUN
cana-3690	87	35	.	.	PUNCT
cana-3690	87	36	bhatla	bhatla	PROPN
cana-3690	87	37	and	and	CCONJ
cana-3690	87	38	jyoti	jyoti	PROPN
cana-3690	87	39	(	(	PUNCT
cana-3690	87	40	2012	2012	NUM
cana-3690	87	41	)	)	PUNCT
cana-3690	87	42	discussed	discuss	VERB
cana-3690	87	43	the	the	DET
cana-3690	87	44	heart	heart	NOUN
cana-3690	87	45	disease	disease	NOUN
cana-3690	87	46	encompasses	encompass	VERB
cana-3690	87	47	a	a	DET
cana-3690	87	48	broad	broad	ADJ
cana-3690	87	49	spectrum	spectrum	NOUN
cana-3690	87	50	of	of	ADP
cana-3690	87	51	medical	medical	ADJ
cana-3690	87	52	conditions	condition	NOUN
cana-3690	87	53	that	that	PRON
cana-3690	87	54	directly	directly	ADV
cana-3690	87	55	affect	affect	VERB
cana-3690	87	56	the	the	DET
cana-3690	87	57	heart	heart	NOUN
cana-3690	87	58	and	and	CCONJ
cana-3690	87	59	its	its	PRON
cana-3690	87	60	components	component	NOUN
cana-3690	87	61	.	.	PUNCT
cana-3690	88	1	it	it	PRON
cana-3690	88	2	is	be	AUX
cana-3690	88	3	a	a	DET
cana-3690	88	4	significant	significant	ADJ
cana-3690	88	5	health	health	NOUN
cana-3690	88	6	problem	problem	NOUN
cana-3690	88	7	today	today	NOUN
cana-3690	88	8	.	.	PUNCT
cana-3690	89	1	this	this	DET
cana-3690	89	2	article	article	NOUN
cana-3690	89	3	analyzes	analyze	VERB
cana-3690	89	4	various	various	ADJ
cana-3690	89	5	data	datum	NOUN
cana-3690	89	6	mining	mining	NOUN
cana-3690	89	7	techniques	technique	NOUN
cana-3690	89	8	that	that	PRON
cana-3690	89	9	have	have	AUX
cana-3690	89	10	been	be	AUX
cana-3690	89	11	introduced	introduce	VERB
cana-3690	89	12	in	in	ADP
cana-3690	89	13	recent	recent	ADJ
cana-3690	89	14	years	year	NOUN
cana-3690	89	15	to	to	PART
cana-3690	89	16	predict	predict	VERB
cana-3690	89	17	heart	heart	NOUN
cana-3690	89	18	disease	disease	NOUN
cana-3690	89	19	.	.	PUNCT
cana-3690	90	1	the	the	DET
cana-3690	90	2	results	result	NOUN
cana-3690	90	3	suggest	suggest	VERB
cana-3690	90	4	that	that	SCONJ
cana-3690	90	5	15	15	NUM
cana-3690	90	6	-	-	PUNCT
cana-3690	90	7	attribute	attribute	NOUN
cana-3690	90	8	neural	neural	ADJ
cana-3690	90	9	networks	network	NOUN
cana-3690	90	10	outperform	outperform	VERB
cana-3690	90	11	all	all	DET
cana-3690	90	12	other	other	ADJ
cana-3690	90	13	data	data	NOUN
cana-3690	90	14	communications	communication	NOUN
cana-3690	90	15	on	on	ADP
cana-3690	90	16	applied	apply	VERB
cana-3690	90	17	nonlinear	nonlinear	ADJ
cana-3690	90	18	analysis	analysis	NOUN
cana-3690	90	19	issn	issn	NOUN
cana-3690	90	20	:	:	PUNCT
cana-3690	90	21	1074	1074	NUM
cana-3690	90	22	-	-	PUNCT
cana-3690	90	23	133x	133x	NUM
cana-3690	90	24	vol	vol	NOUN
cana-3690	90	25	32	32	NUM
cana-3690	90	26	no	no	NOUN
cana-3690	90	27	.	.	PUNCT
cana-3690	91	1	8s	8s	PROPN
cana-3690	91	2	(	(	PUNCT
cana-3690	91	3	2025	2025	NUM
cana-3690	91	4	)	)	PUNCT
cana-3690	91	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-3690	91	6	460	460	NUM
cana-3690	91	7	mining	mining	NOUN
cana-3690	91	8	techniques	technique	NOUN
cana-3690	91	9	.	.	PUNCT
cana-3690	92	1	furthermore	furthermore	ADV
cana-3690	92	2	,	,	PUNCT
cana-3690	92	3	the	the	DET
cana-3690	92	4	analysis	analysis	NOUN
cana-3690	92	5	shows	show	VERB
cana-3690	92	6	that	that	SCONJ
cana-3690	92	7	decision	decision	NOUN
cana-3690	92	8	trees	tree	NOUN
cana-3690	92	9	combined	combine	VERB
cana-3690	92	10	with	with	ADP
cana-3690	92	11	genetic	genetic	ADJ
cana-3690	92	12	algorithms	algorithm	NOUN
cana-3690	92	13	and	and	CCONJ
cana-3690	92	14	feature	feature	NOUN
cana-3690	92	15	subset	subset	NOUN
cana-3690	92	16	selection	selection	NOUN
cana-3690	92	17	also	also	ADV
cana-3690	92	18	provide	provide	VERB
cana-3690	92	19	high	high	ADJ
cana-3690	92	20	accuracy	accuracy	NOUN
cana-3690	92	21	.	.	PUNCT
cana-3690	93	1	rajesh	rajesh	PROPN
cana-3690	93	2	et	et	PROPN
cana-3690	93	3	al	al	PROPN
cana-3690	93	4	.	.	PROPN
cana-3690	94	1	(	(	PUNCT
cana-3690	94	2	2019	2019	NUM
cana-3690	94	3	)	)	PUNCT
cana-3690	94	4	explored	explore	VERB
cana-3690	94	5	chronic	chronic	ADJ
cana-3690	94	6	disease	disease	NOUN
cana-3690	94	7	data	datum	NOUN
cana-3690	94	8	is	be	AUX
cana-3690	94	9	analyzed	analyze	VERB
cana-3690	94	10	with	with	ADP
cana-3690	94	11	attributes	attribute	NOUN
cana-3690	94	12	representing	represent	VERB
cana-3690	94	13	topics	topic	NOUN
cana-3690	94	14	,	,	PUNCT
cana-3690	94	15	questions	question	NOUN
cana-3690	94	16	,	,	PUNCT
cana-3690	94	17	data	datum	NOUN
cana-3690	94	18	values	value	NOUN
cana-3690	94	19	,	,	PUNCT
cana-3690	94	20	low	low	ADJ
cana-3690	94	21	and	and	CCONJ
cana-3690	94	22	high	high	ADJ
cana-3690	94	23	confidence	confidence	NOUN
cana-3690	94	24	limits	limit	NOUN
cana-3690	94	25	.	.	PUNCT
cana-3690	95	1	five	five	NUM
cana-3690	95	2	classification	classification	NOUN
cana-3690	95	3	algorithms	algorithm	NOUN
cana-3690	95	4	are	be	AUX
cana-3690	95	5	used	use	VERB
cana-3690	95	6	to	to	PART
cana-3690	95	7	evaluate	evaluate	VERB
cana-3690	95	8	the	the	DET
cana-3690	95	9	data	datum	NOUN
cana-3690	95	10	.	.	PUNCT
cana-3690	96	1	the	the	DET
cana-3690	96	2	m5p	m5p	NOUN
cana-3690	96	3	decision	decision	NOUN
cana-3690	96	4	tree	tree	NOUN
cana-3690	96	5	approach	approach	NOUN
cana-3690	96	6	is	be	AUX
cana-3690	96	7	found	find	VERB
cana-3690	96	8	to	to	PART
cana-3690	96	9	be	be	AUX
cana-3690	96	10	the	the	DET
cana-3690	96	11	best	good	ADJ
cana-3690	96	12	algorithm	algorithm	NOUN
cana-3690	96	13	for	for	ADP
cana-3690	96	14	building	build	VERB
cana-3690	96	15	a	a	DET
cana-3690	96	16	model	model	NOUN
cana-3690	96	17	compared	compare	VERB
cana-3690	96	18	to	to	ADP
cana-3690	96	19	other	other	ADJ
cana-3690	96	20	decision	decision	NOUN
cana-3690	96	21	tree	tree	NOUN
cana-3690	96	22	approaches	approach	NOUN
cana-3690	96	23	.	.	PUNCT
cana-3690	97	1	heart	heart	NOUN
cana-3690	97	2	disease	disease	NOUN
cana-3690	97	3	receives	receive	VERB
cana-3690	97	4	considerable	considerable	ADJ
cana-3690	97	5	attention	attention	NOUN
cana-3690	97	6	in	in	ADP
cana-3690	97	7	medical	medical	ADJ
cana-3690	97	8	research	research	NOUN
cana-3690	97	9	due	due	ADP
cana-3690	97	10	to	to	ADP
cana-3690	97	11	its	its	PRON
cana-3690	97	12	impact	impact	NOUN
cana-3690	97	13	on	on	ADP
cana-3690	97	14	human	human	ADJ
cana-3690	97	15	health	health	NOUN
cana-3690	97	16	and	and	CCONJ
cana-3690	97	17	its	its	PRON
cana-3690	97	18	role	role	NOUN
cana-3690	97	19	as	as	ADP
cana-3690	97	20	a	a	DET
cana-3690	97	21	leading	lead	VERB
cana-3690	97	22	cause	cause	NOUN
cana-3690	97	23	of	of	ADP
cana-3690	97	24	death	death	NOUN
cana-3690	97	25	.	.	PUNCT
cana-3690	98	1	data	datum	NOUN
cana-3690	98	2	mining	mining	NOUN
cana-3690	98	3	with	with	ADP
cana-3690	98	4	its	its	PRON
cana-3690	98	5	various	various	ADJ
cana-3690	98	6	algorithms	algorithm	NOUN
cana-3690	98	7	has	have	AUX
cana-3690	98	8	contributed	contribute	VERB
cana-3690	98	9	significantly	significantly	ADV
cana-3690	98	10	to	to	ADP
cana-3690	98	11	medical	medical	ADJ
cana-3690	98	12	data	datum	NOUN
cana-3690	98	13	analysis	analysis	NOUN
cana-3690	98	14	.	.	PUNCT
cana-3690	99	1	this	this	DET
cana-3690	99	2	work	work	NOUN
cana-3690	99	3	leverages	leverage	NOUN
cana-3690	99	4	supervised	supervise	VERB
cana-3690	99	5	machine	machine	NOUN
cana-3690	99	6	learning	learn	VERB
cana-3690	99	7	algorithms	algorithm	NOUN
cana-3690	99	8	such	such	ADJ
cana-3690	99	9	as	as	ADP
cana-3690	99	10	svm	svm	PROPN
cana-3690	99	11	,	,	PUNCT
cana-3690	99	12	ann	ann	PROPN
cana-3690	99	13	and	and	CCONJ
cana-3690	99	14	naïve	naïve	ADJ
cana-3690	99	15	bayes	bayes	PROPN
cana-3690	99	16	implemented	implement	VERB
cana-3690	99	17	using	use	VERB
cana-3690	99	18	r	r	NOUN
cana-3690	99	19	programming	programming	NOUN
cana-3690	99	20	to	to	PART
cana-3690	99	21	predict	predict	VERB
cana-3690	99	22	heart	heart	NOUN
cana-3690	99	23	disease	disease	NOUN
cana-3690	99	24	.	.	PUNCT
cana-3690	100	1	the	the	DET
cana-3690	100	2	performance	performance	NOUN
cana-3690	100	3	of	of	ADP
cana-3690	100	4	the	the	DET
cana-3690	100	5	algorithms	algorithms	NOUN
cana-3690	100	6	is	be	AUX
cana-3690	100	7	measured	measure	VERB
cana-3690	100	8	based	base	VERB
cana-3690	100	9	on	on	ADP
cana-3690	100	10	their	their	PRON
cana-3690	100	11	accuracy	accuracy	NOUN
cana-3690	100	12	and	and	CCONJ
cana-3690	100	13	the	the	DET
cana-3690	100	14	results	result	NOUN
cana-3690	100	15	are	be	AUX
cana-3690	100	16	discussed	discuss	VERB
cana-3690	100	17	by	by	ADP
cana-3690	100	18	anitha	anitha	NOUN
cana-3690	100	19	and	and	CCONJ
cana-3690	100	20	sridevi	sridevi	NOUN
cana-3690	100	21	(	(	PUNCT
cana-3690	100	22	2019	2019	NUM
cana-3690	100	23	)	)	PUNCT
cana-3690	100	24	.	.	PUNCT
cana-3690	101	1	data	datum	NOUN
cana-3690	101	2	mining	mining	NOUN
cana-3690	101	3	is	be	AUX
cana-3690	101	4	a	a	DET
cana-3690	101	5	valuable	valuable	ADJ
cana-3690	101	6	tool	tool	NOUN
cana-3690	101	7	for	for	ADP
cana-3690	101	8	extracting	extract	VERB
cana-3690	101	9	useful	useful	ADJ
cana-3690	101	10	information	information	NOUN
cana-3690	101	11	from	from	ADP
cana-3690	101	12	large	large	ADJ
cana-3690	101	13	existing	exist	VERB
cana-3690	101	14	databases	database	NOUN
cana-3690	101	15	.	.	PUNCT
cana-3690	102	1	this	this	DET
cana-3690	102	2	study	study	NOUN
cana-3690	102	3	uses	use	VERB
cana-3690	102	4	a	a	DET
cana-3690	102	5	weather	weather	NOUN
cana-3690	102	6	dataset	dataset	VERB
cana-3690	102	7	whose	whose	DET
cana-3690	102	8	attributes	attribute	NOUN
cana-3690	102	9	represent	represent	VERB
cana-3690	102	10	the	the	DET
cana-3690	102	11	weather	weather	NOUN
cana-3690	102	12	conditions	condition	NOUN
cana-3690	102	13	and	and	CCONJ
cana-3690	102	14	the	the	DET
cana-3690	102	15	class	class	NOUN
cana-3690	102	16	variable	variable	NOUN
cana-3690	102	17	indicates	indicate	VERB
cana-3690	102	18	whether	whether	SCONJ
cana-3690	102	19	the	the	DET
cana-3690	102	20	conditions	condition	NOUN
cana-3690	102	21	are	be	AUX
cana-3690	102	22	suitable	suitable	ADJ
cana-3690	102	23	for	for	ADP
cana-3690	102	24	playing	play	VERB
cana-3690	102	25	golf	golf	NOUN
cana-3690	102	26	.	.	PUNCT
cana-3690	103	1	seven	seven	NUM
cana-3690	103	2	classification	classification	NOUN
cana-3690	103	3	algorithms	algorithm	NOUN
cana-3690	103	4	were	be	AUX
cana-3690	103	5	used	use	VERB
cana-3690	103	6	to	to	PART
cana-3690	103	7	measure	measure	VERB
cana-3690	103	8	accuracy	accuracy	NOUN
cana-3690	103	9	,	,	PUNCT
cana-3690	103	10	including	include	VERB
cana-3690	103	11	j48	j48	PROPN
cana-3690	103	12	,	,	PUNCT
cana-3690	103	13	random	random	ADJ
cana-3690	103	14	tree	tree	NOUN
cana-3690	103	15	,	,	PUNCT
cana-3690	103	16	decision	decision	NOUN
cana-3690	103	17	stump	stump	NOUN
cana-3690	103	18	,	,	PUNCT
cana-3690	103	19	logistic	logistic	ADJ
cana-3690	103	20	model	model	NOUN
cana-3690	103	21	tree	tree	NOUN
cana-3690	103	22	,	,	PUNCT
cana-3690	103	23	hoeffding	hoeffding	NOUN
cana-3690	103	24	tree	tree	NOUN
cana-3690	103	25	,	,	PUNCT
cana-3690	103	26	reduce	reduce	VERB
cana-3690	103	27	error	error	NOUN
cana-3690	103	28	pruning	pruning	NOUN
cana-3690	103	29	,	,	PUNCT
cana-3690	103	30	and	and	CCONJ
cana-3690	103	31	random	random	ADJ
cana-3690	103	32	forest	forest	NOUN
cana-3690	103	33	.	.	PUNCT
cana-3690	104	1	among	among	ADP
cana-3690	104	2	these	these	DET
cana-3690	104	3	algorithms	algorithm	NOUN
cana-3690	104	4	,	,	PUNCT
cana-3690	104	5	random	random	ADJ
cana-3690	104	6	tree	tree	NOUN
cana-3690	104	7	achieved	achieve	VERB
cana-3690	104	8	the	the	DET
cana-3690	104	9	highest	high	ADJ
cana-3690	104	10	accuracy	accuracy	NOUN
cana-3690	104	11	of	of	ADP
cana-3690	104	12	85.714	85.714	NUM
cana-3690	104	13	%	%	NOUN
cana-3690	104	14	discussed	discuss	VERB
cana-3690	104	15	by	by	ADP
cana-3690	104	16	rajesh	rajesh	PROPN
cana-3690	104	17	and	and	CCONJ
cana-3690	104	18	karthikeyan	karthikeyan	PROPN
cana-3690	104	19	(	(	PUNCT
cana-3690	104	20	2017	2017	NUM
cana-3690	104	21	)	)	PUNCT
cana-3690	104	22	.	.	PUNCT
cana-3690	105	1	the	the	DET
cana-3690	105	2	cleveland	cleveland	PROPN
cana-3690	105	3	heart	heart	NOUN
cana-3690	105	4	disease	disease	NOUN
cana-3690	105	5	dataset	dataset	VERB
cana-3690	105	6	from	from	ADP
cana-3690	105	7	the	the	DET
cana-3690	105	8	“	"	PUNCT
cana-3690	105	9	uci	uci	NOUN
cana-3690	105	10	machine	machine	NOUN
cana-3690	105	11	learning	learning	NOUN
cana-3690	105	12	(	(	PUNCT
cana-3690	105	13	ml	ml	NOUN
cana-3690	105	14	)	)	PUNCT
cana-3690	105	15	repository	repository	NOUN
cana-3690	105	16	”	"	PUNCT
cana-3690	105	17	examines	examine	VERB
cana-3690	105	18	various	various	ADJ
cana-3690	105	19	supervised	supervised	ADJ
cana-3690	105	20	machine	machine	NOUN
cana-3690	105	21	learning	learning	NOUN
cana-3690	105	22	and	and	CCONJ
cana-3690	105	23	data	datum	NOUN
cana-3690	105	24	mining	mining	NOUN
cana-3690	105	25	techniques	technique	NOUN
cana-3690	105	26	,	,	PUNCT
cana-3690	105	27	including	include	VERB
cana-3690	105	28	attributes	attribute	NOUN
cana-3690	105	29	related	relate	VERB
cana-3690	105	30	to	to	ADP
cana-3690	105	31	the	the	DET
cana-3690	105	32	causes	cause	NOUN
cana-3690	105	33	of	of	ADP
cana-3690	105	34	cardiovascular	cardiovascular	ADJ
cana-3690	105	35	heart	heart	NOUN
cana-3690	105	36	disease	disease	NOUN
cana-3690	105	37	such	such	ADJ
cana-3690	105	38	as	as	ADP
cana-3690	105	39	age	age	NOUN
cana-3690	105	40	,	,	PUNCT
cana-3690	105	41	gender	gender	NOUN
cana-3690	105	42	,	,	PUNCT
cana-3690	105	43	type	type	NOUN
cana-3690	105	44	of	of	ADP
cana-3690	105	45	chest	chest	NOUN
cana-3690	105	46	pain	pain	NOUN
cana-3690	105	47	,	,	PUNCT
cana-3690	105	48	cholesterol	cholesterol	NOUN
cana-3690	105	49	,	,	PUNCT
cana-3690	105	50	etc	etc	X
cana-3690	105	51	.	.	X
cana-3690	105	52	thalassemia	thalassemia	NOUN
cana-3690	105	53	.	.	PUNCT
cana-3690	106	1	the	the	DET
cana-3690	106	2	article	article	NOUN
cana-3690	106	3	discusses	discuss	VERB
cana-3690	106	4	the	the	DET
cana-3690	106	5	results	result	NOUN
cana-3690	106	6	of	of	ADP
cana-3690	106	7	modern	modern	ADJ
cana-3690	106	8	techniques	technique	NOUN
cana-3690	106	9	and	and	CCONJ
cana-3690	106	10	achieves	achieve	VERB
cana-3690	106	11	an	an	DET
cana-3690	106	12	accuracy	accuracy	NOUN
cana-3690	106	13	of	of	ADP
cana-3690	106	14	86.89	86.89	NUM
cana-3690	106	15	%	%	NOUN
cana-3690	106	16	using	use	VERB
cana-3690	106	17	a	a	DET
cana-3690	106	18	logistic	logistic	ADJ
cana-3690	106	19	regression	regression	NOUN
cana-3690	106	20	algorithm	algorithm	NOUN
cana-3690	106	21	discussed	discuss	VERB
cana-3690	106	22	by	by	ADP
cana-3690	106	23	younas	youna	NOUN
cana-3690	106	24	(	(	PUNCT
cana-3690	106	25	2021	2021	NUM
cana-3690	106	26	)	)	PUNCT
cana-3690	106	27	.	.	PUNCT
cana-3690	107	1	learning	learn	VERB
cana-3690	107	2	(	(	PUNCT
cana-3690	107	3	2017	2017	NUM
cana-3690	107	4	)	)	PUNCT
cana-3690	107	5	explored	explore	VERB
cana-3690	107	6	the	the	DET
cana-3690	107	7	“	"	PUNCT
cana-3690	107	8	information	information	NOUN
cana-3690	107	9	age	age	NOUN
cana-3690	107	10	”	"	PUNCT
cana-3690	107	11	,	,	PUNCT
cana-3690	107	12	huge	huge	ADJ
cana-3690	107	13	amounts	amount	NOUN
cana-3690	107	14	of	of	ADP
cana-3690	107	15	data	datum	NOUN
cana-3690	107	16	are	be	AUX
cana-3690	107	17	generated	generate	VERB
cana-3690	107	18	every	every	DET
cana-3690	107	19	day	day	NOUN
cana-3690	107	20	,	,	PUNCT
cana-3690	107	21	including	include	VERB
cana-3690	107	22	in	in	ADP
cana-3690	107	23	the	the	DET
cana-3690	107	24	healthcare	healthcare	NOUN
cana-3690	107	25	sector	sector	NOUN
cana-3690	107	26	.	.	PUNCT
cana-3690	108	1	however	however	ADV
cana-3690	108	2	,	,	PUNCT
cana-3690	108	3	much	much	ADJ
cana-3690	108	4	of	of	ADP
cana-3690	108	5	this	this	DET
cana-3690	108	6	data	data	NOUN
cana-3690	108	7	is	be	AUX
cana-3690	108	8	still	still	ADV
cana-3690	108	9	underused	underused	ADJ
cana-3690	108	10	.	.	PUNCT
cana-3690	109	1	summarizes	summarize	NOUN
cana-3690	109	2	current	current	ADJ
cana-3690	109	3	research	research	NOUN
cana-3690	109	4	on	on	ADP
cana-3690	109	5	heart	heart	NOUN
cana-3690	109	6	disease	disease	NOUN
cana-3690	109	7	prediction	prediction	NOUN
cana-3690	109	8	using	use	VERB
cana-3690	109	9	data	datum	NOUN
cana-3690	109	10	mining	mining	NOUN
cana-3690	109	11	techniques	technique	NOUN
cana-3690	109	12	,	,	PUNCT
cana-3690	109	13	evaluates	evaluate	VERB
cana-3690	109	14	combinations	combination	NOUN
cana-3690	109	15	of	of	ADP
cana-3690	109	16	mining	mining	NOUN
cana-3690	109	17	algorithms	algorithm	NOUN
cana-3690	109	18	,	,	PUNCT
cana-3690	109	19	and	and	CCONJ
cana-3690	109	20	provides	provide	VERB
cana-3690	109	21	insights	insight	NOUN
cana-3690	109	22	into	into	ADP
cana-3690	109	23	effective	effective	ADJ
cana-3690	109	24	and	and	CCONJ
cana-3690	109	25	efficient	efficient	ADJ
cana-3690	109	26	techniques	technique	NOUN
cana-3690	109	27	.	.	PUNCT
cana-3690	110	1	it	it	PRON
cana-3690	110	2	also	also	ADV
cana-3690	110	3	addresses	address	VERB
cana-3690	110	4	future	future	ADJ
cana-3690	110	5	directions	direction	NOUN
cana-3690	110	6	in	in	ADP
cana-3690	110	7	forecasting	forecasting	NOUN
cana-3690	110	8	systems	system	NOUN
cana-3690	110	9	.	.	PUNCT
cana-3690	111	1	disease	disease	NOUN
cana-3690	111	2	prediction	prediction	NOUN
cana-3690	111	3	and	and	CCONJ
cana-3690	111	4	control	control	NOUN
cana-3690	111	5	is	be	AUX
cana-3690	111	6	a	a	DET
cana-3690	111	7	crucial	crucial	ADJ
cana-3690	111	8	requirement	requirement	NOUN
cana-3690	111	9	in	in	ADP
cana-3690	111	10	the	the	DET
cana-3690	111	11	medical	medical	ADJ
cana-3690	111	12	field	field	NOUN
cana-3690	111	13	.	.	PUNCT
cana-3690	112	1	this	this	DET
cana-3690	112	2	article	article	NOUN
cana-3690	112	3	proposes	propose	VERB
cana-3690	112	4	a	a	DET
cana-3690	112	5	machine	machine	NOUN
cana-3690	112	6	learning	learn	VERB
cana-3690	112	7	framework	framework	NOUN
cana-3690	112	8	to	to	PART
cana-3690	112	9	predict	predict	VERB
cana-3690	112	10	the	the	DET
cana-3690	112	11	probability	probability	NOUN
cana-3690	112	12	of	of	ADP
cana-3690	112	13	heart	heart	NOUN
cana-3690	112	14	disease	disease	NOUN
cana-3690	112	15	using	use	VERB
cana-3690	112	16	various	various	ADJ
cana-3690	112	17	algorithms	algorithm	NOUN
cana-3690	112	18	including	include	VERB
cana-3690	112	19	random	random	ADJ
cana-3690	112	20	forest	forest	NOUN
cana-3690	112	21	,	,	PUNCT
cana-3690	112	22	naive	naive	ADJ
cana-3690	112	23	bayes	bayes	NOUN
cana-3690	112	24	,	,	PUNCT
cana-3690	112	25	support	support	VERB
cana-3690	112	26	vector	vector	NOUN
cana-3690	112	27	machine	machine	NOUN
cana-3690	112	28	,	,	PUNCT
cana-3690	112	29	hoeffding	hoeffding	NOUN
cana-3690	112	30	decision	decision	NOUN
cana-3690	112	31	tree	tree	NOUN
cana-3690	112	32	,	,	PUNCT
cana-3690	112	33	and	and	CCONJ
cana-3690	112	34	logistic	logistic	ADJ
cana-3690	112	35	model	model	NOUN
cana-3690	112	36	tree	tree	NOUN
cana-3690	112	37	.	.	PUNCT
cana-3690	113	1	the	the	DET
cana-3690	113	2	framework	framework	NOUN
cana-3690	113	3	is	be	AUX
cana-3690	113	4	trained	train	VERB
cana-3690	113	5	and	and	CCONJ
cana-3690	113	6	tested	test	VERB
cana-3690	113	7	using	use	VERB
cana-3690	113	8	the	the	DET
cana-3690	113	9	cleveland	cleveland	PROPN
cana-3690	113	10	dataset	dataset	PROPN
cana-3690	113	11	.	.	PUNCT
cana-3690	114	1	the	the	DET
cana-3690	114	2	results	result	NOUN
cana-3690	114	3	show	show	VERB
cana-3690	114	4	that	that	SCONJ
cana-3690	114	5	random	random	ADJ
cana-3690	114	6	forest	forest	NOUN
cana-3690	114	7	achieves	achieve	VERB
cana-3690	114	8	the	the	DET
cana-3690	114	9	highest	high	ADJ
cana-3690	114	10	accuracy	accuracy	NOUN
cana-3690	114	11	discussed	discuss	VERB
cana-3690	114	12	by	by	ADP
cana-3690	114	13	motarwar	motarwar	PROPN
cana-3690	114	14	et	et	PROPN
cana-3690	114	15	al	al	PROPN
cana-3690	114	16	.	.	PROPN
cana-3690	114	17	(	(	PUNCT
cana-3690	114	18	2020	2020	NUM
cana-3690	114	19	)	)	PUNCT
cana-3690	114	20	.	.	PUNCT
cana-3690	115	1	data	datum	NOUN
cana-3690	115	2	mining	mining	NOUN
cana-3690	115	3	is	be	AUX
cana-3690	115	4	a	a	DET
cana-3690	115	5	powerful	powerful	ADJ
cana-3690	115	6	tool	tool	NOUN
cana-3690	115	7	for	for	ADP
cana-3690	115	8	discovering	discover	VERB
cana-3690	115	9	hidden	hide	VERB
cana-3690	115	10	information	information	NOUN
cana-3690	115	11	and	and	CCONJ
cana-3690	115	12	making	make	VERB
cana-3690	115	13	predictions	prediction	NOUN
cana-3690	115	14	based	base	VERB
cana-3690	115	15	on	on	ADP
cana-3690	115	16	stochastic	stochastic	ADJ
cana-3690	115	17	sensing	sense	VERB
cana-3690	115	18	concepts	concept	NOUN
cana-3690	115	19	.	.	PUNCT
cana-3690	116	1	this	this	DET
cana-3690	116	2	article	article	NOUN
cana-3690	116	3	evaluates	evaluate	VERB
cana-3690	116	4	groundwater	groundwater	NOUN
cana-3690	116	5	levels	level	NOUN
cana-3690	116	6	,	,	PUNCT
cana-3690	116	7	rainfall	rainfall	NOUN
cana-3690	116	8	,	,	PUNCT
cana-3690	116	9	population	population	NOUN
cana-3690	116	10	,	,	PUNCT
cana-3690	116	11	crop	crop	NOUN
cana-3690	116	12	data	datum	NOUN
cana-3690	116	13	,	,	PUNCT
cana-3690	116	14	and	and	CCONJ
cana-3690	116	15	businesses	business	NOUN
cana-3690	116	16	using	use	VERB
cana-3690	116	17	stochastic	stochastic	ADJ
cana-3690	116	18	modeling	modeling	NOUN
cana-3690	116	19	and	and	CCONJ
cana-3690	116	20	data	datum	NOUN
cana-3690	116	21	mining	mining	NOUN
cana-3690	116	22	.	.	PUNCT
cana-3690	117	1	the	the	DET
cana-3690	117	2	approach	approach	NOUN
cana-3690	117	3	includes	include	VERB
cana-3690	117	4	data	datum	NOUN
cana-3690	117	5	assimilation	assimilation	NOUN
cana-3690	117	6	analysis	analysis	NOUN
cana-3690	117	7	to	to	PART
cana-3690	117	8	effectively	effectively	ADV
cana-3690	117	9	predict	predict	VERB
cana-3690	117	10	groundwater	groundwater	NOUN
cana-3690	117	11	levels	level	NOUN
cana-3690	117	12	,	,	PUNCT
cana-3690	117	13	with	with	ADP
cana-3690	117	14	experimental	experimental	ADJ
cana-3690	117	15	results	result	NOUN
cana-3690	117	16	demonstrating	demonstrate	VERB
cana-3690	117	17	the	the	DET
cana-3690	117	18	effectiveness	effectiveness	NOUN
cana-3690	117	19	of	of	ADP
cana-3690	117	20	the	the	DET
cana-3690	117	21	method	method	NOUN
cana-3690	117	22	discussed	discuss	VERB
cana-3690	117	23	by	by	ADP
cana-3690	117	24	rajesh	rajesh	NOUN
cana-3690	117	25	and	and	CCONJ
cana-3690	117	26	karthikeyan	karthikeyan	PROPN
cana-3690	117	27	(	(	PUNCT
cana-3690	117	28	2019	2019	NUM
cana-3690	117	29	)	)	PUNCT
cana-3690	117	30	and	and	CCONJ
cana-3690	117	31	the	the	DET
cana-3690	117	32	similar	similar	ADJ
cana-3690	117	33	approaches	approach	NOUN
cana-3690	117	34	by	by	ADP
cana-3690	117	35	rajesh	rajesh	NOUN
cana-3690	117	36	and	and	CCONJ
cana-3690	117	37	karthikeyan	karthikeyan	PROPN
cana-3690	117	38	(	(	PUNCT
cana-3690	117	39	2019	2019	NUM
cana-3690	117	40	)	)	PUNCT
cana-3690	117	41	.	.	PUNCT
cana-3690	118	1	communications	communication	NOUN
cana-3690	118	2	on	on	ADP
cana-3690	118	3	applied	apply	VERB
cana-3690	118	4	nonlinear	nonlinear	ADJ
cana-3690	118	5	analysis	analysis	NOUN
cana-3690	118	6	issn	issn	NOUN
cana-3690	118	7	:	:	PUNCT
cana-3690	118	8	1074	1074	NUM
cana-3690	118	9	-	-	PUNCT
cana-3690	118	10	133x	133x	NUM
cana-3690	118	11	vol	vol	NOUN
cana-3690	118	12	32	32	NUM
cana-3690	118	13	no	no	NOUN
cana-3690	118	14	.	.	PUNCT
cana-3690	119	1	8s	8s	PROPN
cana-3690	119	2	(	(	PUNCT
cana-3690	119	3	2025	2025	NUM
cana-3690	119	4	)	)	PUNCT
cana-3690	119	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-3690	119	6	461	461	NUM
cana-3690	119	7	2	2	NUM
cana-3690	119	8	.	.	PUNCT
cana-3690	119	9	backgrounds	background	NOUN
cana-3690	119	10	and	and	CCONJ
cana-3690	119	11	methodologies	methodology	NOUN
cana-3690	119	12	2.1	2.1	NUM
cana-3690	119	13	linear	linear	NOUN
cana-3690	119	14	regression	regression	NOUN
cana-3690	119	15	linear	linear	PROPN
cana-3690	119	16	regression	regression	NOUN
cana-3690	119	17	is	be	AUX
cana-3690	119	18	a	a	DET
cana-3690	119	19	statistical	statistical	ADJ
cana-3690	119	20	technique	technique	NOUN
cana-3690	119	21	used	use	VERB
cana-3690	119	22	to	to	PART
cana-3690	119	23	understand	understand	VERB
cana-3690	119	24	and	and	CCONJ
cana-3690	119	25	predict	predict	VERB
cana-3690	119	26	the	the	DET
cana-3690	119	27	relationship	relationship	NOUN
cana-3690	119	28	between	between	ADP
cana-3690	119	29	two	two	NUM
cana-3690	119	30	variables	variable	NOUN
cana-3690	119	31	by	by	ADP
cana-3690	119	32	finding	find	VERB
cana-3690	119	33	the	the	DET
cana-3690	119	34	optimal	optimal	ADJ
cana-3690	119	35	straight	straight	ADJ
cana-3690	119	36	line	line	NOUN
cana-3690	119	37	that	that	PRON
cana-3690	119	38	most	most	ADV
cana-3690	119	39	effectively	effectively	ADV
cana-3690	119	40	fits	fit	VERB
cana-3690	119	41	the	the	DET
cana-3690	119	42	data	data	NOUN
cana-3690	119	43	points	point	NOUN
cana-3690	119	44	.	.	PUNCT
cana-3690	120	1	it	it	PRON
cana-3690	120	2	helps	help	VERB
cana-3690	120	3	determine	determine	VERB
cana-3690	120	4	how	how	SCONJ
cana-3690	120	5	changes	change	NOUN
cana-3690	120	6	in	in	ADP
cana-3690	120	7	one	one	NUM
cana-3690	120	8	variable	variable	NOUN
cana-3690	120	9	correspond	correspond	NOUN
cana-3690	120	10	to	to	ADP
cana-3690	120	11	changes	change	NOUN
cana-3690	120	12	in	in	ADP
cana-3690	120	13	another	another	DET
cana-3690	120	14	variable	variable	NOUN
cana-3690	120	15	and	and	CCONJ
cana-3690	120	16	proves	prove	VERB
cana-3690	120	17	valuable	valuable	ADJ
cana-3690	120	18	for	for	ADP
cana-3690	120	19	forecasting	forecasting	NOUN
cana-3690	120	20	and	and	CCONJ
cana-3690	120	21	trend	trend	NOUN
cana-3690	120	22	detection	detection	NOUN
cana-3690	120	23	.	.	PUNCT
cana-3690	121	1	the	the	DET
cana-3690	121	2	core	core	ADJ
cana-3690	121	3	idea	idea	NOUN
cana-3690	121	4	of	of	ADP
cana-3690	121	5	linear	linear	PROPN
cana-3690	121	6	regression	regression	NOUN
cana-3690	121	7	is	be	AUX
cana-3690	121	8	to	to	PART
cana-3690	121	9	find	find	VERB
cana-3690	121	10	the	the	DET
cana-3690	121	11	best	well	ADV
cana-3690	121	12	fitting	fitting	ADJ
cana-3690	121	13	straight	straight	ADJ
cana-3690	121	14	line	line	NOUN
cana-3690	121	15	(	(	PUNCT
cana-3690	121	16	also	also	ADV
cana-3690	121	17	called	call	VERB
cana-3690	121	18	a	a	DET
cana-3690	121	19	“	"	PUNCT
cana-3690	121	20	regression	regression	NOUN
cana-3690	121	21	line	line	NOUN
cana-3690	121	22	”	"	PUNCT
cana-3690	121	23	)	)	PUNCT
cana-3690	121	24	through	through	ADP
cana-3690	121	25	a	a	DET
cana-3690	121	26	scatterplot	scatterplot	NOUN
cana-3690	121	27	of	of	ADP
cana-3690	121	28	data	datum	NOUN
cana-3690	121	29	points	point	NOUN
cana-3690	121	30	.	.	PUNCT
cana-3690	122	1	this	this	DET
cana-3690	122	2	line	line	NOUN
cana-3690	122	3	represents	represent	VERB
cana-3690	122	4	a	a	DET
cana-3690	122	5	linear	linear	ADJ
cana-3690	122	6	equation	equation	NOUN
cana-3690	122	7	of	of	ADP
cana-3690	122	8	the	the	DET
cana-3690	122	9	form	form	NOUN
cana-3690	122	10	:	:	PUNCT
cana-3690	122	11	y	y	PROPN
cana-3690	122	12	=	=	SYM
cana-3690	122	13	mx+b	mx+b	PROPN
cana-3690	122	14	…	…	PUNCT
cana-3690	122	15	(	(	PUNCT
cana-3690	122	16	1	1	X
cana-3690	122	17	)	)	PUNCT
cana-3690	122	18	where	where	SCONJ
cana-3690	122	19	y	y	PROPN
cana-3690	122	20	is	be	AUX
cana-3690	122	21	the	the	DET
cana-3690	122	22	dependent	dependent	ADJ
cana-3690	122	23	variable	variable	NOUN
cana-3690	122	24	(	(	PUNCT
cana-3690	122	25	the	the	DET
cana-3690	122	26	one	one	NOUN
cana-3690	122	27	you	you	PRON
cana-3690	122	28	want	want	VERB
cana-3690	122	29	to	to	PART
cana-3690	122	30	predict	predict	VERB
cana-3690	122	31	or	or	CCONJ
cana-3690	122	32	explain	explain	VERB
cana-3690	122	33	)	)	PUNCT
cana-3690	122	34	.	.	PUNCT
cana-3690	123	1	x	x	PUNCT
cana-3690	123	2	is	be	AUX
cana-3690	123	3	the	the	DET
cana-3690	123	4	independent	independent	ADJ
cana-3690	123	5	variable	variable	NOUN
cana-3690	123	6	(	(	PUNCT
cana-3690	123	7	the	the	DET
cana-3690	123	8	one	one	NOUN
cana-3690	123	9	you	you	PRON
cana-3690	123	10	're	be	AUX
cana-3690	123	11	using	use	VERB
cana-3690	123	12	to	to	PART
cana-3690	123	13	make	make	VERB
cana-3690	123	14	predictions	prediction	NOUN
cana-3690	123	15	or	or	CCONJ
cana-3690	123	16	explanations	explanation	NOUN
cana-3690	123	17	)	)	PUNCT
cana-3690	123	18	.	.	PUNCT
cana-3690	124	1	m	m	PROPN
cana-3690	124	2	is	be	AUX
cana-3690	124	3	the	the	DET
cana-3690	124	4	slope	slope	NOUN
cana-3690	124	5	of	of	ADP
cana-3690	124	6	the	the	DET
cana-3690	124	7	line	line	NOUN
cana-3690	124	8	,	,	PUNCT
cana-3690	124	9	representing	represent	VERB
cana-3690	124	10	how	how	SCONJ
cana-3690	124	11	much	much	ADJ
cana-3690	124	12	y	y	PROPN
cana-3690	124	13	changes	change	VERB
cana-3690	124	14	for	for	ADP
cana-3690	124	15	a	a	DET
cana-3690	124	16	unit	unit	NOUN
cana-3690	124	17	change	change	NOUN
cana-3690	124	18	in	in	ADP
cana-3690	124	19	x.	x.	PROPN
cana-3690	124	20	b	b	PROPN
cana-3690	124	21	is	be	AUX
cana-3690	124	22	the	the	DET
cana-3690	124	23	y	y	PROPN
cana-3690	124	24	-	-	PUNCT
cana-3690	124	25	intercept	intercept	NOUN
cana-3690	124	26	,	,	PUNCT
cana-3690	124	27	indicating	indicate	VERB
cana-3690	124	28	the	the	DET
cana-3690	124	29	value	value	NOUN
cana-3690	124	30	of	of	ADP
cana-3690	124	31	y	y	PRON
cana-3690	124	32	when	when	SCONJ
cana-3690	124	33	x	x	PRON
cana-3690	124	34	is	be	AUX
cana-3690	124	35	0	0	NUM
cana-3690	124	36	.	.	PUNCT
cana-3690	124	37	2.2	2.2	NUM
cana-3690	124	38	multilayer	multilayer	ADJ
cana-3690	124	39	perception	perception	NOUN
cana-3690	124	40	a	a	DET
cana-3690	124	41	multilayer	multilayer	ADJ
cana-3690	124	42	perceptron	perceptron	PROPN
cana-3690	124	43	(	(	PUNCT
cana-3690	124	44	mlp	mlp	PROPN
cana-3690	124	45	)	)	PUNCT
cana-3690	124	46	is	be	AUX
cana-3690	124	47	a	a	DET
cana-3690	124	48	type	type	NOUN
cana-3690	124	49	of	of	ADP
cana-3690	124	50	artificial	artificial	ADJ
cana-3690	124	51	neural	neural	ADJ
cana-3690	124	52	network	network	NOUN
cana-3690	124	53	that	that	PRON
cana-3690	124	54	is	be	AUX
cana-3690	124	55	comprised	comprise	VERB
cana-3690	124	56	of	of	ADP
cana-3690	124	57	numerous	numerous	ADJ
cana-3690	124	58	interconnected	interconnected	ADJ
cana-3690	124	59	layers	layer	NOUN
cana-3690	124	60	of	of	ADP
cana-3690	124	61	nodes	node	NOUN
cana-3690	124	62	or	or	CCONJ
cana-3690	124	63	neurons	neuron	NOUN
cana-3690	124	64	.	.	PUNCT
cana-3690	125	1	this	this	PRON
cana-3690	125	2	is	be	AUX
cana-3690	125	3	a	a	DET
cana-3690	125	4	core	core	ADJ
cana-3690	125	5	deep	deep	ADJ
cana-3690	125	6	learning	learning	NOUN
cana-3690	125	7	architecture	architecture	NOUN
cana-3690	125	8	that	that	PRON
cana-3690	125	9	can	can	AUX
cana-3690	125	10	be	be	AUX
cana-3690	125	11	applied	apply	VERB
cana-3690	125	12	to	to	ADP
cana-3690	125	13	a	a	DET
cana-3690	125	14	variety	variety	NOUN
cana-3690	125	15	of	of	ADP
cana-3690	125	16	tasks	task	NOUN
cana-3690	125	17	,	,	PUNCT
cana-3690	125	18	such	such	ADJ
cana-3690	125	19	as	as	ADP
cana-3690	125	20	regression	regression	NOUN
cana-3690	125	21	and	and	CCONJ
cana-3690	125	22	classification	classification	NOUN
cana-3690	125	23	,	,	PUNCT
cana-3690	125	24	as	as	ADV
cana-3690	125	25	well	well	ADV
cana-3690	125	26	as	as	ADP
cana-3690	125	27	more	more	ADV
cana-3690	125	28	difficult	difficult	ADJ
cana-3690	125	29	ones	one	NOUN
cana-3690	125	30	like	like	ADP
cana-3690	125	31	image	image	NOUN
cana-3690	125	32	recognition	recognition	NOUN
cana-3690	125	33	and	and	CCONJ
cana-3690	125	34	natural	natural	ADJ
cana-3690	125	35	language	language	NOUN
cana-3690	125	36	processing	processing	NOUN
cana-3690	125	37	.	.	PUNCT
cana-3690	126	1	an	an	DET
cana-3690	126	2	mlp	mlp	NOUN
cana-3690	126	3	's	's	PART
cana-3690	126	4	architecture	architecture	NOUN
cana-3690	126	5	normally	normally	ADV
cana-3690	126	6	consists	consist	VERB
cana-3690	126	7	of	of	ADP
cana-3690	126	8	three	three	NUM
cana-3690	126	9	different	different	ADJ
cana-3690	126	10	kinds	kind	NOUN
cana-3690	126	11	of	of	ADP
cana-3690	126	12	layers	layer	NOUN
cana-3690	126	13	namely	namely	ADV
cana-3690	126	14	input	input	VERB
cana-3690	126	15	layer	layer	NOUN
cana-3690	126	16	(	(	PUNCT
cana-3690	126	17	this	this	DET
cana-3690	126	18	layer	layer	NOUN
cana-3690	126	19	consists	consist	VERB
cana-3690	126	20	of	of	ADP
cana-3690	126	21	neurons	neuron	NOUN
cana-3690	126	22	receiving	receive	VERB
cana-3690	126	23	input	input	NOUN
cana-3690	126	24	data	datum	NOUN
cana-3690	126	25	and	and	CCONJ
cana-3690	126	26	each	each	DET
cana-3690	126	27	neuron	neuron	NOUN
cana-3690	126	28	corresponds	correspond	VERB
cana-3690	126	29	to	to	ADP
cana-3690	126	30	a	a	DET
cana-3690	126	31	feature	feature	NOUN
cana-3690	126	32	in	in	ADP
cana-3690	126	33	the	the	DET
cana-3690	126	34	input	input	NOUN
cana-3690	126	35	data	datum	NOUN
cana-3690	126	36	,	,	PUNCT
cana-3690	126	37	and	and	CCONJ
cana-3690	126	38	the	the	DET
cana-3690	126	39	values	value	NOUN
cana-3690	126	40	of	of	ADP
cana-3690	126	41	these	these	DET
cana-3690	126	42	neurons	neuron	NOUN
cana-3690	126	43	pass	pass	VERB
cana-3690	126	44	through	through	ADP
cana-3690	126	45	the	the	DET
cana-3690	126	46	network	network	NOUN
cana-3690	126	47	)	)	PUNCT
cana-3690	126	48	,	,	PUNCT
cana-3690	126	49	hidden	hide	VERB
cana-3690	126	50	layers	layer	NOUN
cana-3690	126	51	(	(	PUNCT
cana-3690	126	52	come	come	VERB
cana-3690	126	53	after	after	ADP
cana-3690	126	54	the	the	DET
cana-3690	126	55	input	input	NOUN
cana-3690	126	56	layer	layer	NOUN
cana-3690	126	57	and	and	CCONJ
cana-3690	126	58	precede	precede	VERB
cana-3690	126	59	the	the	DET
cana-3690	126	60	output	output	NOUN
cana-3690	126	61	layer	layer	NOUN
cana-3690	126	62	.	.	PUNCT
cana-3690	127	1	they	they	PRON
cana-3690	127	2	are	be	AUX
cana-3690	127	3	called	call	VERB
cana-3690	127	4	"	"	PUNCT
cana-3690	127	5	hidden	hide	VERB
cana-3690	127	6	"	"	PUNCT
cana-3690	127	7	because	because	SCONJ
cana-3690	127	8	their	their	PRON
cana-3690	127	9	activations	activation	NOUN
cana-3690	127	10	are	be	AUX
cana-3690	127	11	not	not	PART
cana-3690	127	12	directly	directly	ADV
cana-3690	127	13	observed	observe	VERB
cana-3690	127	14	in	in	ADP
cana-3690	127	15	the	the	DET
cana-3690	127	16	final	final	ADJ
cana-3690	127	17	output	output	NOUN
cana-3690	127	18	)	)	PUNCT
cana-3690	127	19	,	,	PUNCT
cana-3690	127	20	and	and	CCONJ
cana-3690	127	21	output	output	NOUN
cana-3690	127	22	layer	layer	NOUN
cana-3690	127	23	(	(	PUNCT
cana-3690	127	24	produces	produce	VERB
cana-3690	127	25	the	the	DET
cana-3690	127	26	network	network	NOUN
cana-3690	127	27	's	's	PART
cana-3690	127	28	final	final	ADJ
cana-3690	127	29	output	output	NOUN
cana-3690	127	30	and	and	CCONJ
cana-3690	127	31	the	the	DET
cana-3690	127	32	number	number	NOUN
cana-3690	127	33	of	of	ADP
cana-3690	127	34	neurons	neuron	NOUN
cana-3690	127	35	in	in	ADP
cana-3690	127	36	the	the	DET
cana-3690	127	37	output	output	NOUN
cana-3690	127	38	layer	layer	NOUN
cana-3690	127	39	depends	depend	VERB
cana-3690	127	40	on	on	ADP
cana-3690	127	41	the	the	DET
cana-3690	127	42	problem	problem	NOUN
cana-3690	127	43	type	type	NOUN
cana-3690	127	44	)	)	PUNCT
cana-3690	127	45	.	.	PUNCT
cana-3690	128	1	2.3	2.3	NUM
cana-3690	128	2	smo	smo	PROPN
cana-3690	128	3	smo	smo	PROPN
cana-3690	128	4	stands	stand	VERB
cana-3690	128	5	for	for	ADP
cana-3690	128	6	sequential	sequential	ADJ
cana-3690	128	7	minimal	minimal	ADJ
cana-3690	128	8	optimization	optimization	NOUN
cana-3690	128	9	,	,	PUNCT
cana-3690	128	10	an	an	DET
cana-3690	128	11	algorithm	algorithm	NOUN
cana-3690	128	12	for	for	ADP
cana-3690	128	13	training	training	NOUN
cana-3690	128	14	support	support	NOUN
cana-3690	128	15	vector	vector	NOUN
cana-3690	128	16	machines	machine	NOUN
cana-3690	128	17	(	(	PUNCT
cana-3690	128	18	svms	svms	NOUN
cana-3690	128	19	)	)	PUNCT
cana-3690	128	20	,	,	PUNCT
cana-3690	128	21	machine	machine	NOUN
cana-3690	128	22	learning	learning	NOUN
cana-3690	128	23	models	model	NOUN
cana-3690	128	24	commonly	commonly	ADV
cana-3690	128	25	used	use	VERB
cana-3690	128	26	for	for	ADP
cana-3690	128	27	classification	classification	NOUN
cana-3690	128	28	and	and	CCONJ
cana-3690	128	29	regression	regression	NOUN
cana-3690	128	30	tasks	task	NOUN
cana-3690	128	31	.	.	PUNCT
cana-3690	129	1	the	the	DET
cana-3690	129	2	smo	smo	PROPN
cana-3690	129	3	algorithm	algorithm	NOUN
cana-3690	129	4	is	be	AUX
cana-3690	129	5	particularly	particularly	ADV
cana-3690	129	6	suitable	suitable	ADJ
cana-3690	129	7	for	for	ADP
cana-3690	129	8	solving	solve	VERB
cana-3690	129	9	the	the	DET
cana-3690	129	10	quadratic	quadratic	ADJ
cana-3690	129	11	programming	programming	NOUN
cana-3690	129	12	optimization	optimization	NOUN
cana-3690	129	13	problem	problem	NOUN
cana-3690	129	14	encountered	encounter	VERB
cana-3690	129	15	when	when	SCONJ
cana-3690	129	16	training	training	NOUN
cana-3690	129	17	svms	svms	NOUN
cana-3690	129	18	.	.	PUNCT
cana-3690	130	1	various	various	ADJ
cana-3690	130	2	steps	step	NOUN
cana-3690	130	3	involved	involve	VERB
cana-3690	130	4	in	in	ADP
cana-3690	130	5	this	this	DET
cana-3690	130	6	approach	approach	NOUN
cana-3690	130	7	namely	namely	ADV
cana-3690	130	8	initialization	initialization	NOUN
cana-3690	130	9	,	,	PUNCT
cana-3690	130	10	selection	selection	NOUN
cana-3690	130	11	of	of	ADP
cana-3690	130	12	two	two	NUM
cana-3690	130	13	lagrange	lagrange	NOUN
cana-3690	130	14	multipliers	multiplier	NOUN
cana-3690	130	15	,	,	PUNCT
cana-3690	130	16	optimize	optimize	VERB
cana-3690	130	17	the	the	DET
cana-3690	130	18	pair	pair	NOUN
cana-3690	130	19	of	of	ADP
cana-3690	130	20	lagrange	lagrange	NOUN
cana-3690	130	21	,	,	PUNCT
cana-3690	130	22	multipliers	multiplier	NOUN
cana-3690	130	23	,	,	PUNCT
cana-3690	130	24	update	update	VERB
cana-3690	130	25	the	the	DET
cana-3690	130	26	model	model	NOUN
cana-3690	130	27	,	,	PUNCT
cana-3690	130	28	convergence	convergence	NOUN
cana-3690	130	29	checking	checking	NOUN
cana-3690	130	30	,	,	PUNCT
cana-3690	130	31	and	and	CCONJ
cana-3690	130	32	repeat	repeat	VERB
cana-3690	130	33	if	if	SCONJ
cana-3690	130	34	convergence	convergence	NOUN
cana-3690	130	35	has	have	AUX
cana-3690	130	36	n't	not	PART
cana-3690	130	37	been	be	AUX
cana-3690	130	38	reached	reach	VERB
cana-3690	130	39	,	,	PUNCT
cana-3690	130	40	repeat	repeat	VERB
cana-3690	130	41	the	the	DET
cana-3690	130	42	above	above	ADJ
cana-3690	130	43	steps	step	NOUN
cana-3690	130	44	until	until	SCONJ
cana-3690	130	45	it	it	PRON
cana-3690	130	46	is	be	AUX
cana-3690	130	47	.	.	PUNCT
cana-3690	131	1	2.4	2.4	NUM
cana-3690	131	2	random	random	ADJ
cana-3690	131	3	forest	forest	NOUN
cana-3690	131	4	random	random	ADJ
cana-3690	131	5	forest	forest	NOUN
cana-3690	131	6	is	be	AUX
cana-3690	131	7	a	a	DET
cana-3690	131	8	popular	popular	ADJ
cana-3690	131	9	ensemble	ensemble	ADJ
cana-3690	131	10	machine	machine	NOUN
cana-3690	131	11	learning	learning	NOUN
cana-3690	131	12	method	method	NOUN
cana-3690	131	13	for	for	ADP
cana-3690	131	14	classification	classification	NOUN
cana-3690	131	15	and	and	CCONJ
cana-3690	131	16	regression	regression	NOUN
cana-3690	131	17	tasks	task	NOUN
cana-3690	131	18	.	.	PUNCT
cana-3690	132	1	it	it	PRON
cana-3690	132	2	is	be	AUX
cana-3690	132	3	an	an	DET
cana-3690	132	4	extension	extension	NOUN
cana-3690	132	5	of	of	ADP
cana-3690	132	6	decision	decision	NOUN
cana-3690	132	7	trees	tree	NOUN
cana-3690	132	8	and	and	CCONJ
cana-3690	132	9	is	be	AUX
cana-3690	132	10	known	know	VERB
cana-3690	132	11	for	for	ADP
cana-3690	132	12	its	its	PRON
cana-3690	132	13	high	high	ADJ
cana-3690	132	14	accuracy	accuracy	NOUN
cana-3690	132	15	,	,	PUNCT
cana-3690	132	16	robustness	robustness	NOUN
cana-3690	132	17	,	,	PUNCT
cana-3690	132	18	and	and	CCONJ
cana-3690	132	19	ability	ability	NOUN
cana-3690	132	20	to	to	PART
cana-3690	132	21	handle	handle	VERB
cana-3690	132	22	complex	complex	ADJ
cana-3690	132	23	data	datum	NOUN
cana-3690	132	24	sets	set	NOUN
cana-3690	132	25	.	.	PUNCT
cana-3690	133	1	random	random	ADJ
cana-3690	133	2	forest	forest	NOUN
cana-3690	133	3	is	be	AUX
cana-3690	133	4	widely	widely	ADV
cana-3690	133	5	used	use	VERB
cana-3690	133	6	in	in	ADP
cana-3690	133	7	various	various	ADJ
cana-3690	133	8	fields	field	NOUN
cana-3690	133	9	including	include	VERB
cana-3690	133	10	data	datum	NOUN
cana-3690	133	11	science	science	NOUN
cana-3690	133	12	,	,	PUNCT
cana-3690	133	13	machine	machine	NOUN
cana-3690	133	14	learning	learning	NOUN
cana-3690	133	15	,	,	PUNCT
cana-3690	133	16	and	and	CCONJ
cana-3690	133	17	pattern	pattern	NOUN
cana-3690	133	18	recognition	recognition	NOUN
cana-3690	133	19	.	.	PUNCT
cana-3690	134	1	the	the	DET
cana-3690	134	2	main	main	ADJ
cana-3690	134	3	idea	idea	NOUN
cana-3690	134	4	of	of	ADP
cana-3690	134	5	random	random	ADJ
cana-3690	134	6	forest	forest	NOUN
cana-3690	134	7	is	be	AUX
cana-3690	134	8	to	to	PART
cana-3690	134	9	create	create	VERB
cana-3690	134	10	an	an	DET
cana-3690	134	11	ensemble	ensemble	ADJ
cana-3690	134	12	(	(	PUNCT
cana-3690	134	13	collection	collection	NOUN
cana-3690	134	14	)	)	PUNCT
cana-3690	134	15	of	of	ADP
cana-3690	134	16	decision	decision	NOUN
cana-3690	134	17	trees	tree	NOUN
cana-3690	134	18	and	and	CCONJ
cana-3690	134	19	combine	combine	VERB
cana-3690	134	20	their	their	PRON
cana-3690	134	21	predictions	prediction	NOUN
cana-3690	134	22	to	to	PART
cana-3690	134	23	make	make	VERB
cana-3690	134	24	more	more	ADV
cana-3690	134	25	accurate	accurate	ADJ
cana-3690	134	26	and	and	CCONJ
cana-3690	134	27	stable	stable	ADJ
cana-3690	134	28	predictions	prediction	NOUN
cana-3690	134	29	.	.	PUNCT
cana-3690	135	1	the	the	DET
cana-3690	135	2	following	follow	VERB
cana-3690	135	3	steps	step	NOUN
cana-3690	135	4	describe	describe	VERB
cana-3690	135	5	how	how	SCONJ
cana-3690	135	6	random	random	ADJ
cana-3690	135	7	forest	forest	NOUN
cana-3690	135	8	works	work	NOUN
cana-3690	135	9	namely	namely	ADV
cana-3690	135	10	bootstrap	bootstrap	VERB
cana-3690	135	11	aggregating	aggregate	VERB
cana-3690	135	12	(	(	PUNCT
cana-3690	135	13	bagging	bagging	NOUN
cana-3690	135	14	)	)	PUNCT
cana-3690	135	15	,	,	PUNCT
cana-3690	135	16	communications	communication	NOUN
cana-3690	135	17	on	on	ADP
cana-3690	135	18	applied	apply	VERB
cana-3690	135	19	nonlinear	nonlinear	ADJ
cana-3690	135	20	analysis	analysis	NOUN
cana-3690	135	21	issn	issn	NOUN
cana-3690	135	22	:	:	PUNCT
cana-3690	135	23	1074	1074	NUM
cana-3690	135	24	-	-	PUNCT
cana-3690	135	25	133x	133x	NUM
cana-3690	135	26	vol	vol	NOUN
cana-3690	135	27	32	32	NUM
cana-3690	135	28	no	no	NOUN
cana-3690	135	29	.	.	PUNCT
cana-3690	136	1	8s	8s	PROPN
cana-3690	136	2	(	(	PUNCT
cana-3690	136	3	2025	2025	NUM
cana-3690	136	4	)	)	PUNCT
cana-3690	137	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-3690	137	2	462	462	NUM
cana-3690	137	3	decision	decision	NOUN
cana-3690	137	4	tree	tree	NOUN
cana-3690	137	5	construction	construction	NOUN
cana-3690	137	6	,	,	PUNCT
cana-3690	137	7	voting	vote	VERB
cana-3690	137	8	for	for	ADP
cana-3690	137	9	classification	classification	NOUN
cana-3690	137	10	,	,	PUNCT
cana-3690	137	11	and	and	CCONJ
cana-3690	137	12	averaging	average	VERB
cana-3690	137	13	for	for	ADP
cana-3690	137	14	regression	regression	NOUN
cana-3690	137	15	.	.	PUNCT
cana-3690	138	1	the	the	DET
cana-3690	138	2	key	key	ADJ
cana-3690	138	3	advantages	advantage	NOUN
cana-3690	138	4	of	of	ADP
cana-3690	138	5	random	random	ADJ
cana-3690	138	6	forest	forest	NOUN
cana-3690	138	7	are	be	AUX
cana-3690	138	8	reduced	reduce	VERB
cana-3690	138	9	overfitting	overfitte	VERB
cana-3690	138	10	,	,	PUNCT
cana-3690	138	11	robustness	robustness	NOUN
cana-3690	138	12	,	,	PUNCT
cana-3690	138	13	and	and	CCONJ
cana-3690	138	14	feature	feature	NOUN
cana-3690	138	15	importance	importance	NOUN
cana-3690	138	16	.	.	PUNCT
cana-3690	139	1	steps	step	NOUN
cana-3690	139	2	involved	involve	VERB
cana-3690	139	3	in	in	ADP
cana-3690	139	4	random	random	ADJ
cana-3690	139	5	forest	forest	NOUN
cana-3690	139	6	step	step	NOUN
cana-3690	139	7	1	1	NUM
cana-3690	139	8	.	.	PUNCT
cana-3690	140	1	data	datum	NOUN
cana-3690	140	2	bootstrapping	bootstrappe	VERB
cana-3690	140	3	step	step	NOUN
cana-3690	140	4	2	2	NUM
cana-3690	140	5	.	.	PUNCT
cana-3690	140	6	random	random	ADJ
cana-3690	140	7	feature	feature	NOUN
cana-3690	140	8	subset	subset	NOUN
cana-3690	140	9	selection	selection	NOUN
cana-3690	140	10	step	step	NOUN
cana-3690	140	11	3	3	NUM
cana-3690	140	12	.	.	PUNCT
cana-3690	141	1	decision	decision	NOUN
cana-3690	141	2	tree	tree	NOUN
cana-3690	141	3	construction	construction	NOUN
cana-3690	141	4	step	step	NOUN
cana-3690	141	5	4	4	NUM
cana-3690	141	6	.	.	PUNCT
cana-3690	141	7	ensemble	ensemble	ADJ
cana-3690	141	8	of	of	ADP
cana-3690	141	9	decision	decision	NOUN
cana-3690	141	10	trees	tree	NOUN
cana-3690	141	11	step	step	VERB
cana-3690	141	12	5	5	NUM
cana-3690	141	13	.	.	PUNCT
cana-3690	142	1	out	out	ADP
cana-3690	142	2	-	-	PUNCT
cana-3690	142	3	of	of	ADP
cana-3690	142	4	-	-	PUNCT
cana-3690	142	5	bag	bag	NOUN
cana-3690	142	6	(	(	PUNCT
cana-3690	142	7	oob	oob	NOUN
cana-3690	142	8	)	)	PUNCT
cana-3690	142	9	evaluation	evaluation	NOUN
cana-3690	142	10	step	step	NOUN
cana-3690	142	11	6	6	NUM
cana-3690	142	12	.	.	PUNCT
cana-3690	143	1	hyperparameter	hyperparameter	NOUN
cana-3690	143	2	tuning	tuning	NOUN
cana-3690	143	3	(	(	PUNCT
cana-3690	143	4	optional	optional	ADJ
cana-3690	143	5	)	)	PUNCT
cana-3690	143	6	2.5	2.5	NUM
cana-3690	143	7	random	random	ADJ
cana-3690	143	8	tree	tree	NOUN
cana-3690	143	9	in	in	ADP
cana-3690	143	10	machine	machine	NOUN
cana-3690	143	11	learning	learning	NOUN
cana-3690	143	12	,	,	PUNCT
cana-3690	143	13	a	a	DET
cana-3690	143	14	random	random	ADJ
cana-3690	143	15	tree	tree	NOUN
cana-3690	143	16	is	be	AUX
cana-3690	143	17	a	a	DET
cana-3690	143	18	specific	specific	ADJ
cana-3690	143	19	type	type	NOUN
cana-3690	143	20	of	of	ADP
cana-3690	143	21	decision	decision	NOUN
cana-3690	143	22	tree	tree	NOUN
cana-3690	143	23	variant	variant	NOUN
cana-3690	143	24	that	that	PRON
cana-3690	143	25	introduces	introduce	VERB
cana-3690	143	26	randomness	randomness	NOUN
cana-3690	143	27	when	when	SCONJ
cana-3690	143	28	constructed	construct	VERB
cana-3690	143	29	.	.	PUNCT
cana-3690	144	1	random	random	ADJ
cana-3690	144	2	trees	tree	NOUN
cana-3690	144	3	are	be	AUX
cana-3690	144	4	similar	similar	ADJ
cana-3690	144	5	to	to	ADP
cana-3690	144	6	traditional	traditional	ADJ
cana-3690	144	7	decision	decision	NOUN
cana-3690	144	8	trees	tree	NOUN
cana-3690	144	9	,	,	PUNCT
cana-3690	144	10	but	but	CCONJ
cana-3690	144	11	differ	differ	VERB
cana-3690	144	12	in	in	ADP
cana-3690	144	13	how	how	SCONJ
cana-3690	144	14	they	they	PRON
cana-3690	144	15	select	select	VERB
cana-3690	144	16	the	the	DET
cana-3690	144	17	split	split	NOUN
cana-3690	144	18	features	feature	NOUN
cana-3690	144	19	and	and	CCONJ
cana-3690	144	20	thresholds	threshold	NOUN
cana-3690	144	21	at	at	ADP
cana-3690	144	22	each	each	DET
cana-3690	144	23	node	node	NOUN
cana-3690	144	24	.	.	PUNCT
cana-3690	145	1	the	the	DET
cana-3690	145	2	main	main	ADJ
cana-3690	145	3	goal	goal	NOUN
cana-3690	145	4	of	of	ADP
cana-3690	145	5	introducing	introduce	VERB
cana-3690	145	6	randomness	randomness	NOUN
cana-3690	145	7	is	be	AUX
cana-3690	145	8	to	to	PART
cana-3690	145	9	create	create	VERB
cana-3690	145	10	a	a	DET
cana-3690	145	11	more	more	ADV
cana-3690	145	12	diverse	diverse	ADJ
cana-3690	145	13	set	set	NOUN
cana-3690	145	14	of	of	ADP
cana-3690	145	15	decision	decision	NOUN
cana-3690	145	16	trees	tree	NOUN
cana-3690	145	17	,	,	PUNCT
cana-3690	145	18	which	which	PRON
cana-3690	145	19	can	can	AUX
cana-3690	145	20	help	help	VERB
cana-3690	145	21	reduce	reduce	VERB
cana-3690	145	22	overfitting	overfitte	VERB
cana-3690	145	23	and	and	CCONJ
cana-3690	145	24	improve	improve	VERB
cana-3690	145	25	the	the	DET
cana-3690	145	26	generalization	generalization	NOUN
cana-3690	145	27	performance	performance	NOUN
cana-3690	145	28	of	of	ADP
cana-3690	145	29	the	the	DET
cana-3690	145	30	model	model	NOUN
cana-3690	145	31	.	.	PUNCT
cana-3690	146	1	random	random	ADJ
cana-3690	146	2	trees	tree	NOUN
cana-3690	146	3	are	be	AUX
cana-3690	146	4	often	often	ADV
cana-3690	146	5	used	use	VERB
cana-3690	146	6	as	as	ADP
cana-3690	146	7	building	build	VERB
cana-3690	146	8	blocks	block	NOUN
cana-3690	146	9	in	in	ADP
cana-3690	146	10	ensemble	ensemble	ADJ
cana-3690	146	11	methods	method	NOUN
cana-3690	146	12	such	such	ADJ
cana-3690	146	13	as	as	ADP
cana-3690	146	14	random	random	ADJ
cana-3690	146	15	forests	forest	NOUN
cana-3690	146	16	.	.	PUNCT
cana-3690	147	1	the	the	DET
cana-3690	147	2	crucial	crucial	ADJ
cana-3690	147	3	features	feature	NOUN
cana-3690	147	4	of	of	ADP
cana-3690	147	5	random	random	ADJ
cana-3690	147	6	trees	tree	NOUN
cana-3690	147	7	are	be	AUX
cana-3690	147	8	as	as	SCONJ
cana-3690	147	9	follows	follow	VERB
cana-3690	147	10	:	:	PUNCT
cana-3690	147	11	❖	❖	NUM
cana-3690	147	12	random	random	ADJ
cana-3690	147	13	feature	feature	NOUN
cana-3690	147	14	subset	subset	NOUN
cana-3690	147	15	❖	❖	NUM
cana-3690	147	16	random	random	ADJ
cana-3690	147	17	threshold	threshold	NOUN
cana-3690	147	18	selection	selection	NOUN
cana-3690	147	19	❖	❖	AUX
cana-3690	147	20	no	no	DET
cana-3690	147	21	pruning	prune	VERB
cana-3690	147	22	❖	❖	NUM
cana-3690	147	23	ensemble	ensemble	ADJ
cana-3690	147	24	methods	method	NOUN
cana-3690	147	25	steps	step	NOUN
cana-3690	147	26	involved	involve	VERB
cana-3690	147	27	in	in	ADP
cana-3690	147	28	random	random	ADJ
cana-3690	147	29	tree	tree	NOUN
cana-3690	147	30	step	step	NOUN
cana-3690	147	31	1	1	NUM
cana-3690	147	32	.	.	PUNCT
cana-3690	148	1	data	datum	NOUN
cana-3690	148	2	bootstrapping	bootstrappe	VERB
cana-3690	148	3	:	:	PUNCT
cana-3690	148	4	step	step	NOUN
cana-3690	148	5	2	2	NUM
cana-3690	148	6	.	.	PUNCT
cana-3690	148	7	random	random	ADJ
cana-3690	148	8	subset	subset	NOUN
cana-3690	148	9	selection	selection	NOUN
cana-3690	148	10	for	for	ADP
cana-3690	148	11	features	feature	NOUN
cana-3690	148	12	:	:	PUNCT
cana-3690	148	13	step	step	NOUN
cana-3690	148	14	3	3	NUM
cana-3690	148	15	.	.	PUNCT
cana-3690	148	16	decision	decision	NOUN
cana-3690	148	17	tree	tree	NOUN
cana-3690	148	18	construction	construction	NOUN
cana-3690	148	19	:	:	PUNCT
cana-3690	148	20	step	step	NOUN
cana-3690	148	21	4	4	NUM
cana-3690	148	22	.	.	PUNCT
cana-3690	149	1	voting	voting	NOUN
cana-3690	149	2	(	(	PUNCT
cana-3690	149	3	classification	classification	NOUN
cana-3690	149	4	)	)	PUNCT
cana-3690	149	5	or	or	CCONJ
cana-3690	149	6	averaging	average	VERB
cana-3690	149	7	(	(	PUNCT
cana-3690	149	8	regression	regression	NOUN
cana-3690	149	9	):	):	PUNCT
cana-3690	149	10	2.6	2.6	NUM
cana-3690	149	11	rep	rep	NOUN
cana-3690	149	12	tree	tree	PROPN
cana-3690	149	13	rep	rep	PROPN
cana-3690	149	14	(	(	PUNCT
cana-3690	149	15	repeated	repeat	VERB
cana-3690	149	16	incremental	incremental	ADJ
cana-3690	149	17	pruning	pruning	NOUN
cana-3690	149	18	to	to	PART
cana-3690	149	19	produce	produce	VERB
cana-3690	149	20	error	error	NOUN
cana-3690	149	21	reduction	reduction	NOUN
cana-3690	149	22	)	)	PUNCT
cana-3690	149	23	tree	tree	NOUN
cana-3690	149	24	is	be	AUX
cana-3690	149	25	a	a	DET
cana-3690	149	26	machine	machine	NOUN
cana-3690	149	27	learning	learn	VERB
cana-3690	149	28	algorithm	algorithm	NOUN
cana-3690	149	29	for	for	ADP
cana-3690	149	30	classification	classification	NOUN
cana-3690	149	31	and	and	CCONJ
cana-3690	149	32	regression	regression	NOUN
cana-3690	149	33	tasks	task	NOUN
cana-3690	149	34	.	.	PUNCT
cana-3690	150	1	a	a	DET
cana-3690	150	2	decision	decision	NOUN
cana-3690	150	3	tree	tree	NOUN
cana-3690	150	4	-	-	PUNCT
cana-3690	150	5	based	base	VERB
cana-3690	150	6	algorithm	algorithm	NOUN
cana-3690	150	7	creates	create	VERB
cana-3690	150	8	a	a	DET
cana-3690	150	9	decision	decision	NOUN
cana-3690	150	10	tree	tree	NOUN
cana-3690	150	11	using	use	VERB
cana-3690	150	12	a	a	DET
cana-3690	150	13	combination	combination	NOUN
cana-3690	150	14	of	of	ADP
cana-3690	150	15	incremental	incremental	ADJ
cana-3690	150	16	pruning	pruning	NOUN
cana-3690	150	17	and	and	CCONJ
cana-3690	150	18	error	error	NOUN
cana-3690	150	19	reduction	reduction	NOUN
cana-3690	150	20	techniques	technique	NOUN
cana-3690	150	21	.	.	PUNCT
cana-3690	151	1	the	the	DET
cana-3690	151	2	main	main	ADJ
cana-3690	151	3	steps	step	NOUN
cana-3690	151	4	in	in	ADP
cana-3690	151	5	building	build	VERB
cana-3690	151	6	a	a	DET
cana-3690	151	7	rep	rep	NOUN
cana-3690	151	8	tree	tree	NOUN
cana-3690	151	9	are	be	AUX
cana-3690	151	10	namely	namely	ADV
cana-3690	151	11	recursive	recursive	ADJ
cana-3690	151	12	binary	binary	ADJ
cana-3690	151	13	splitting	splitting	NOUN
cana-3690	151	14	,	,	PUNCT
cana-3690	151	15	pruning	pruning	NOUN
cana-3690	151	16	,	,	PUNCT
cana-3690	151	17	and	and	CCONJ
cana-3690	151	18	repeated	repeat	VERB
cana-3690	151	19	pruning	pruning	NOUN
cana-3690	151	20	and	and	CCONJ
cana-3690	151	21	error	error	NOUN
cana-3690	151	22	reduction	reduction	NOUN
cana-3690	151	23	steps	step	NOUN
cana-3690	151	24	involved	involve	VERB
cana-3690	151	25	in	in	ADP
cana-3690	151	26	rep	rep	NOUN
cana-3690	151	27	tree	tree	NOUN
cana-3690	151	28	step	step	NOUN
cana-3690	151	29	1	1	NUM
cana-3690	151	30	.	.	PUNCT
cana-3690	152	1	recursive	recursive	ADJ
cana-3690	152	2	binary	binary	ADJ
cana-3690	152	3	splitting	splitting	NOUN
cana-3690	152	4	step	step	NOUN
cana-3690	152	5	2	2	NUM
cana-3690	152	6	.	.	PUNCT
cana-3690	152	7	pruning	prune	VERB
cana-3690	152	8	step	step	NOUN
cana-3690	152	9	3	3	NUM
cana-3690	152	10	.	.	PUNCT
cana-3690	152	11	repeated	repeat	VERB
cana-3690	152	12	pruning	pruning	NOUN
cana-3690	152	13	and	and	CCONJ
cana-3690	152	14	error	error	NOUN
cana-3690	152	15	reduction	reduction	NOUN
cana-3690	152	16	step	step	NOUN
cana-3690	152	17	4	4	NUM
cana-3690	152	18	.	.	PUNCT
cana-3690	152	19	model	model	NOUN
cana-3690	152	20	evaluation	evaluation	NOUN
cana-3690	152	21	communications	communication	NOUN
cana-3690	152	22	on	on	ADP
cana-3690	152	23	applied	apply	VERB
cana-3690	152	24	nonlinear	nonlinear	ADJ
cana-3690	152	25	analysis	analysis	NOUN
cana-3690	152	26	issn	issn	NOUN
cana-3690	152	27	:	:	PUNCT
cana-3690	152	28	1074	1074	NUM
cana-3690	152	29	-	-	PUNCT
cana-3690	152	30	133x	133x	NUM
cana-3690	152	31	vol	vol	NOUN
cana-3690	152	32	32	32	NUM
cana-3690	152	33	no	no	NOUN
cana-3690	152	34	.	.	PUNCT
cana-3690	153	1	8s	8s	PROPN
cana-3690	153	2	(	(	PUNCT
cana-3690	153	3	2025	2025	NUM
cana-3690	153	4	)	)	PUNCT
cana-3690	153	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-3690	153	6	463	463	NUM
cana-3690	153	7	2.7	2.7	NUM
cana-3690	153	8	accuracy	accuracy	NOUN
cana-3690	153	9	metrics	metric	NOUN
cana-3690	153	10	the	the	DET
cana-3690	153	11	error	error	NOUN
cana-3690	153	12	rate	rate	NOUN
cana-3690	153	13	of	of	ADP
cana-3690	153	14	the	the	DET
cana-3690	153	15	prediction	prediction	NOUN
cana-3690	153	16	model	model	NOUN
cana-3690	153	17	can	can	AUX
cana-3690	153	18	be	be	AUX
cana-3690	153	19	evaluated	evaluate	VERB
cana-3690	153	20	by	by	ADP
cana-3690	153	21	applying	apply	VERB
cana-3690	153	22	various	various	ADJ
cana-3690	153	23	accuracy	accuracy	NOUN
cana-3690	153	24	metrics	metric	NOUN
cana-3690	153	25	in	in	ADP
cana-3690	153	26	machine	machine	NOUN
cana-3690	153	27	learning	learning	NOUN
cana-3690	153	28	and	and	CCONJ
cana-3690	153	29	statistics	statistic	NOUN
cana-3690	153	30	.	.	PUNCT
cana-3690	154	1	the	the	DET
cana-3690	154	2	basic	basic	ADJ
cana-3690	154	3	concept	concept	NOUN
cana-3690	154	4	of	of	ADP
cana-3690	154	5	accuracy	accuracy	NOUN
cana-3690	154	6	assessment	assessment	NOUN
cana-3690	154	7	in	in	ADP
cana-3690	154	8	regression	regression	NOUN
cana-3690	154	9	analysis	analysis	NOUN
cana-3690	154	10	is	be	AUX
cana-3690	154	11	to	to	PART
cana-3690	154	12	compare	compare	VERB
cana-3690	154	13	the	the	DET
cana-3690	154	14	original	original	ADJ
cana-3690	154	15	target	target	NOUN
cana-3690	154	16	with	with	ADP
cana-3690	154	17	the	the	DET
cana-3690	154	18	predicted	predict	VERB
cana-3690	154	19	one	one	NUM
cana-3690	154	20	,	,	PUNCT
cana-3690	154	21	and	and	CCONJ
cana-3690	154	22	use	use	VERB
cana-3690	154	23	metrics	metric	NOUN
cana-3690	154	24	such	such	ADJ
cana-3690	154	25	as	as	ADP
cana-3690	154	26	correlation	correlation	NOUN
cana-3690	154	27	coefficient	coefficient	NOUN
cana-3690	154	28	,	,	PUNCT
cana-3690	154	29	mae	mae	PROPN
cana-3690	154	30	,	,	PUNCT
cana-3690	154	31	mse	mse	PROPN
cana-3690	154	32	and	and	CCONJ
cana-3690	154	33	rmse	rmse	NOUN
cana-3690	154	34	to	to	PART
cana-3690	154	35	explain	explain	VERB
cana-3690	154	36	the	the	DET
cana-3690	154	37	errors	error	NOUN
cana-3690	154	38	and	and	CCONJ
cana-3690	154	39	predictive	predictive	ADJ
cana-3690	154	40	ability	ability	NOUN
cana-3690	154	41	of	of	ADP
cana-3690	154	42	the	the	DET
cana-3690	154	43	model	model	NOUN
cana-3690	154	44	discussed	discuss	VERB
cana-3690	154	45	by	by	ADP
cana-3690	154	46	akusok	akusok	NOUN
cana-3690	154	47	(	(	PUNCT
cana-3690	154	48	2020	2020	NUM
cana-3690	154	49	)	)	PUNCT
cana-3690	154	50	.	.	PUNCT
cana-3690	155	1	the	the	DET
cana-3690	155	2	correlation	correlation	NOUN
cana-3690	155	3	coefficient	coefficient	PROPN
cana-3690	155	4	mse	mse	PROPN
cana-3690	155	5	,	,	PUNCT
cana-3690	155	6	mae	mae	PROPN
cana-3690	155	7	and	and	CCONJ
cana-3690	155	8	rmse	rmse	NOUN
cana-3690	155	9	are	be	AUX
cana-3690	155	10	metrics	metric	NOUN
cana-3690	155	11	used	use	VERB
cana-3690	155	12	to	to	PART
cana-3690	155	13	evaluate	evaluate	VERB
cana-3690	155	14	prediction	prediction	NOUN
cana-3690	155	15	error	error	NOUN
cana-3690	155	16	rates	rate	NOUN
cana-3690	155	17	and	and	CCONJ
cana-3690	155	18	model	model	NOUN
cana-3690	155	19	performance	performance	NOUN
cana-3690	155	20	in	in	ADP
cana-3690	155	21	analysis	analysis	NOUN
cana-3690	155	22	and	and	CCONJ
cana-3690	155	23	prediction	prediction	NOUN
cana-3690	155	24	and	and	CCONJ
cana-3690	155	25	its	its	PRON
cana-3690	155	26	related	related	ADJ
cana-3690	155	27	concepts	concept	NOUN
cana-3690	155	28	discussed	discuss	VERB
cana-3690	155	29	by	by	ADP
cana-3690	155	30	hosseini	hosseini	PROPN
cana-3690	155	31	et	et	PROPN
cana-3690	155	32	al	al	PROPN
cana-3690	155	33	.	.	PROPN
cana-3690	156	1	(	(	PUNCT
cana-3690	156	2	2019	2019	NUM
cana-3690	156	3	)	)	PUNCT
cana-3690	156	4	and	and	CCONJ
cana-3690	156	5	chi	chi	NOUN
cana-3690	156	6	(	(	PUNCT
cana-3690	156	7	2020	2020	NUM
cana-3690	156	8	)	)	PUNCT
cana-3690	156	9	.	.	PUNCT
cana-3690	157	1	the	the	DET
cana-3690	157	2	coefficient	coefficient	NOUN
cana-3690	157	3	of	of	ADP
cana-3690	157	4	determination	determination	NOUN
cana-3690	157	5	is	be	AUX
cana-3690	157	6	a	a	DET
cana-3690	157	7	dimension	dimension	NOUN
cana-3690	157	8	used	use	VERB
cana-3690	157	9	to	to	PART
cana-3690	157	10	explain	explain	VERB
cana-3690	157	11	how	how	SCONJ
cana-3690	157	12	important	important	ADJ
cana-3690	157	13	the	the	DET
cana-3690	157	14	variability	variability	NOUN
cana-3690	157	15	of	of	ADP
cana-3690	157	16	a	a	DET
cana-3690	157	17	factor	factor	NOUN
cana-3690	157	18	can	can	AUX
cana-3690	157	19	be	be	AUX
cana-3690	157	20	through	through	ADP
cana-3690	157	21	its	its	PRON
cana-3690	157	22	relationship	relationship	NOUN
cana-3690	157	23	to	to	ADP
cana-3690	157	24	another	another	DET
cana-3690	157	25	related	relate	VERB
cana-3690	157	26	factor	factor	NOUN
cana-3690	157	27	.	.	PUNCT
cana-3690	158	1	this	this	DET
cana-3690	158	2	correlation	correlation	NOUN
cana-3690	158	3	,	,	PUNCT
cana-3690	158	4	known	know	VERB
cana-3690	158	5	as	as	ADP
cana-3690	158	6	goodness	goodness	NOUN
cana-3690	158	7	of	of	ADP
cana-3690	158	8	fit	fit	NOUN
cana-3690	158	9	,	,	PUNCT
cana-3690	158	10	is	be	AUX
cana-3690	158	11	represented	represent	VERB
cana-3690	158	12	by	by	ADP
cana-3690	158	13	values	value	NOUN
cana-3690	158	14	between	between	ADP
cana-3690	158	15	0.0	0.0	NUM
cana-3690	158	16	and	and	CCONJ
cana-3690	158	17	1.0	1.0	NUM
cana-3690	158	18	.	.	PUNCT
cana-3690	159	1	a	a	DET
cana-3690	159	2	value	value	NOUN
cana-3690	159	3	of	of	ADP
cana-3690	159	4	1.0	1.0	NUM
cana-3690	159	5	indicates	indicate	VERB
cana-3690	159	6	a	a	DET
cana-3690	159	7	perfect	perfect	ADJ
cana-3690	159	8	fit	fit	NOUN
cana-3690	159	9	and	and	CCONJ
cana-3690	159	10	is	be	AUX
cana-3690	159	11	therefore	therefore	ADV
cana-3690	159	12	a	a	DET
cana-3690	159	13	largely	largely	ADV
cana-3690	159	14	reliable	reliable	ADJ
cana-3690	159	15	model	model	NOUN
cana-3690	159	16	for	for	ADP
cana-3690	159	17	future	future	ADJ
cana-3690	159	18	predictions	prediction	NOUN
cana-3690	159	19	,	,	PUNCT
cana-3690	159	20	while	while	SCONJ
cana-3690	159	21	a	a	DET
cana-3690	159	22	value	value	NOUN
cana-3690	159	23	of	of	ADP
cana-3690	159	24	0.0	0.0	NUM
cana-3690	159	25	would	would	AUX
cana-3690	159	26	indicate	indicate	VERB
cana-3690	159	27	that	that	SCONJ
cana-3690	159	28	the	the	DET
cana-3690	159	29	calculation	calculation	NOUN
cana-3690	159	30	can	can	AUX
cana-3690	159	31	not	not	PART
cana-3690	159	32	accurately	accurately	ADV
cana-3690	159	33	model	model	VERB
cana-3690	159	34	the	the	DET
cana-3690	159	35	data	datum	NOUN
cana-3690	159	36	at	at	ADP
cana-3690	159	37	all	all	ADV
cana-3690	159	38	𝒓	𝒓	NOUN
cana-3690	159	39	=	=	PUNCT
cana-3690	159	40	𝒏(∑	𝒏(∑	PROPN
cana-3690	159	41	𝒙𝒚)−(∑	𝒙𝒚)−(∑	PROPN
cana-3690	159	42	𝒙	𝒙	NOUN
cana-3690	159	43	)	)	PUNCT
cana-3690	159	44	(	(	PUNCT
cana-3690	159	45	∑	∑	PROPN
cana-3690	159	46	𝒚	𝒚	X
cana-3690	159	47	)	)	PUNCT
cana-3690	159	48	√[𝒏	√[𝒏	PROPN
cana-3690	159	49	∑	∑	PROPN
cana-3690	159	50	𝒙𝟐−(∑	𝒙𝟐−(∑	PUNCT
cana-3690	159	51	𝒙)𝟐]−[𝒏	𝒙)𝟐]−[𝒏	PROPN
cana-3690	159	52	∑	∑	PUNCT
cana-3690	159	53	𝒚𝟐−(∑	𝒚𝟐−(∑	X
cana-3690	159	54	𝒚)𝟐	𝒚)𝟐	PROPN
cana-3690	159	55	]	]	X
cana-3690	159	56	…	…	PUNCT
cana-3690	159	57	(	(	PUNCT
cana-3690	159	58	2	2	X
cana-3690	159	59	)	)	PUNCT
cana-3690	159	60	mae	mae	PROPN
cana-3690	159	61	(	(	PUNCT
cana-3690	159	62	mean	mean	ADJ
cana-3690	159	63	absolute	absolute	ADJ
cana-3690	159	64	error	error	NOUN
cana-3690	159	65	)	)	PUNCT
cana-3690	159	66	represents	represent	VERB
cana-3690	159	67	the	the	DET
cana-3690	159	68	difference	difference	NOUN
cana-3690	159	69	between	between	ADP
cana-3690	159	70	the	the	DET
cana-3690	159	71	original	original	ADJ
cana-3690	159	72	and	and	CCONJ
cana-3690	159	73	predicted	predict	VERB
cana-3690	159	74	values	value	NOUN
cana-3690	159	75	extracted	extract	VERB
cana-3690	159	76	by	by	ADP
cana-3690	159	77	averaging	average	VERB
cana-3690	159	78	the	the	DET
cana-3690	159	79	absolute	absolute	ADJ
cana-3690	159	80	difference	difference	NOUN
cana-3690	159	81	over	over	ADP
cana-3690	159	82	the	the	DET
cana-3690	159	83	data	datum	NOUN
cana-3690	159	84	set	set	VERB
cana-3690	159	85	.	.	PUNCT
cana-3690	160	1	𝑀𝐴𝐸	𝑀𝐴𝐸	NOUN
cana-3690	160	2	=	=	SYM
cana-3690	160	3	1	1	NUM
cana-3690	160	4	𝑁	𝑁	PROPN
cana-3690	160	5	∑	∑	PUNCT
cana-3690	160	6	|𝑦𝑖	|𝑦𝑖	X
cana-3690	160	7	−	−	PROPN
cana-3690	160	8	�	�	PROPN
cana-3690	160	9	̂	̂	SYM
cana-3690	160	10	�	�	PROPN
cana-3690	160	11	|𝑁	|𝑁	NUM
cana-3690	160	12	𝑖=1	𝑖=1	PROPN
cana-3690	160	13	...	...	PUNCT
cana-3690	161	1	(	(	PUNCT
cana-3690	161	2	3	3	X
cana-3690	161	3	)	)	PUNCT
cana-3690	161	4	rmse	rmse	NOUN
cana-3690	161	5	(	(	PUNCT
cana-3690	161	6	root	root	NOUN
cana-3690	161	7	mean	mean	VERB
cana-3690	161	8	squared	square	VERB
cana-3690	161	9	error	error	NOUN
cana-3690	161	10	)	)	PUNCT
cana-3690	161	11	is	be	AUX
cana-3690	161	12	the	the	DET
cana-3690	161	13	error	error	NOUN
cana-3690	161	14	rate	rate	NOUN
cana-3690	161	15	by	by	ADP
cana-3690	161	16	the	the	DET
cana-3690	161	17	square	square	ADJ
cana-3690	161	18	root	root	NOUN
cana-3690	161	19	of	of	ADP
cana-3690	161	20	mse	mse	PROPN
cana-3690	161	21	.	.	PUNCT
cana-3690	162	1	𝑅𝑀𝑆𝐸	𝑅𝑀𝑆𝐸	PROPN
cana-3690	162	2	=	=	PRON
cana-3690	162	3	√	√	ADP
cana-3690	162	4	1	1	NUM
cana-3690	163	1	𝑁	𝑁	PROPN
cana-3690	163	2	∑	∑	PROPN
cana-3690	163	3	(	(	PUNCT
cana-3690	163	4	𝑦𝑖	𝑦𝑖	PROPN
cana-3690	163	5	−	−	PROPN
cana-3690	163	6	�	�	PROPN
cana-3690	163	7	̂	̂	NOUN
cana-3690	163	8	�	�	NOUN
cana-3690	163	9	)2𝑁	)2𝑁	PROPN
cana-3690	163	10	𝑖=1	𝑖=1	PUNCT
cana-3690	163	11	...	...	PUNCT
cana-3690	163	12	(	(	PUNCT
cana-3690	163	13	4	4	X
cana-3690	163	14	)	)	PUNCT
cana-3690	163	15	relative	relative	ADJ
cana-3690	163	16	absolute	absolute	ADJ
cana-3690	163	17	error	error	NOUN
cana-3690	163	18	(	(	PUNCT
cana-3690	163	19	rae	rae	NOUN
cana-3690	163	20	)	)	PUNCT
cana-3690	163	21	is	be	AUX
cana-3690	163	22	a	a	DET
cana-3690	163	23	metric	metric	NOUN
cana-3690	163	24	used	use	VERB
cana-3690	163	25	in	in	ADP
cana-3690	163	26	statistics	statistic	NOUN
cana-3690	163	27	and	and	CCONJ
cana-3690	163	28	data	datum	NOUN
cana-3690	163	29	analysis	analysis	NOUN
cana-3690	163	30	to	to	PART
cana-3690	163	31	measure	measure	VERB
cana-3690	163	32	the	the	DET
cana-3690	163	33	accuracy	accuracy	NOUN
cana-3690	163	34	of	of	ADP
cana-3690	163	35	a	a	DET
cana-3690	163	36	forecasting	forecasting	NOUN
cana-3690	163	37	or	or	CCONJ
cana-3690	163	38	predictive	predictive	ADJ
cana-3690	163	39	model	model	NOUN
cana-3690	163	40	's	's	PART
cana-3690	163	41	predictions	prediction	NOUN
cana-3690	163	42	.	.	PUNCT
cana-3690	164	1	it	it	PRON
cana-3690	164	2	is	be	AUX
cana-3690	164	3	particularly	particularly	ADV
cana-3690	164	4	useful	useful	ADJ
cana-3690	164	5	when	when	SCONJ
cana-3690	164	6	dealing	deal	VERB
cana-3690	164	7	with	with	ADP
cana-3690	164	8	numerical	numerical	ADJ
cana-3690	164	9	data	datum	NOUN
cana-3690	164	10	,	,	PUNCT
cana-3690	164	11	such	such	ADJ
cana-3690	164	12	as	as	ADP
cana-3690	164	13	in	in	ADP
cana-3690	164	14	regression	regression	NOUN
cana-3690	164	15	analysis	analysis	NOUN
cana-3690	164	16	or	or	CCONJ
cana-3690	164	17	time	time	NOUN
cana-3690	164	18	series	series	PROPN
cana-3690	164	19	forecasting	forecasting	NOUN
cana-3690	164	20	.	.	PUNCT
cana-3690	165	1	𝑅𝐴𝐸	𝑅𝐴𝐸	NOUN
cana-3690	165	2	=	=	SYM
cana-3690	165	3	∑|𝑦𝑖−	∑|𝑦𝑖−	PROPN
cana-3690	165	4	�	�	PROPN
cana-3690	165	5	̂	̂	NUM
cana-3690	165	6	�	�	NOUN
cana-3690	165	7	𝑖|	𝑖|	PROPN
cana-3690	165	8	∑|𝑦𝑖−	∑|𝑦𝑖−	ADJ
cana-3690	165	9	�	�	PROPN
cana-3690	165	10	̅	̅	NOUN
cana-3690	165	11	�	�	NOUN
cana-3690	165	12	|	|	CCONJ
cana-3690	165	13	…	…	PUNCT
cana-3690	165	14	(	(	PUNCT
cana-3690	165	15	5	5	X
cana-3690	165	16	)	)	PUNCT
cana-3690	165	17	root	root	NOUN
cana-3690	165	18	relative	relative	ADJ
cana-3690	165	19	squared	square	VERB
cana-3690	165	20	error	error	NOUN
cana-3690	165	21	(	(	PUNCT
cana-3690	165	22	rrse	rrse	NOUN
cana-3690	165	23	)	)	PUNCT
cana-3690	165	24	is	be	AUX
cana-3690	165	25	another	another	DET
cana-3690	165	26	metric	metric	NOUN
cana-3690	165	27	used	use	VERB
cana-3690	165	28	in	in	ADP
cana-3690	165	29	statistics	statistic	NOUN
cana-3690	165	30	and	and	CCONJ
cana-3690	165	31	data	datum	NOUN
cana-3690	165	32	analysis	analysis	NOUN
cana-3690	165	33	to	to	PART
cana-3690	165	34	evaluate	evaluate	VERB
cana-3690	165	35	the	the	DET
cana-3690	165	36	accuracy	accuracy	NOUN
cana-3690	165	37	of	of	ADP
cana-3690	165	38	predictive	predictive	ADJ
cana-3690	165	39	models	model	NOUN
cana-3690	165	40	,	,	PUNCT
cana-3690	165	41	especially	especially	ADV
cana-3690	165	42	in	in	ADP
cana-3690	165	43	the	the	DET
cana-3690	165	44	context	context	NOUN
cana-3690	165	45	of	of	ADP
cana-3690	165	46	regression	regression	NOUN
cana-3690	165	47	analysis	analysis	NOUN
cana-3690	165	48	or	or	CCONJ
cana-3690	165	49	time	time	NOUN
cana-3690	165	50	series	series	PROPN
cana-3690	165	51	forecasting	forecasting	NOUN
cana-3690	165	52	.	.	PUNCT
cana-3690	166	1	𝑅𝑅𝑆𝐸	𝑅𝑅𝑆𝐸	ADJ
cana-3690	166	2	=	=	SYM
cana-3690	166	3	√	√	NUM
cana-3690	166	4	∑(𝑦𝑖−	∑(𝑦𝑖−	VERB
cana-3690	166	5	�	�	PROPN
cana-3690	166	6	̂	̂	SYM
cana-3690	166	7	�	�	PROPN
cana-3690	166	8	𝑖)2	𝑖)2	ADJ
cana-3690	166	9	∑(𝑦𝑖−	∑(𝑦𝑖−	NOUN
cana-3690	166	10	�	�	PROPN
cana-3690	166	11	̅	̅	NOUN
cana-3690	166	12	�	�	PROPN
cana-3690	166	13	)2	)2	PUNCT
cana-3690	166	14	…	…	PUNCT
cana-3690	166	15	(	(	PUNCT
cana-3690	166	16	6	6	X
cana-3690	166	17	)	)	PUNCT
cana-3690	166	18	equations	equation	NOUN
cana-3690	166	19	2	2	NUM
cana-3690	166	20	to	to	PART
cana-3690	166	21	6	6	NUM
cana-3690	166	22	are	be	AUX
cana-3690	166	23	used	use	VERB
cana-3690	166	24	to	to	PART
cana-3690	166	25	find	find	VERB
cana-3690	166	26	the	the	DET
cana-3690	166	27	model	model	NOUN
cana-3690	166	28	accuracy	accuracy	NOUN
cana-3690	166	29	,	,	PUNCT
cana-3690	166	30	which	which	PRON
cana-3690	166	31	is	be	AUX
cana-3690	166	32	used	use	VERB
cana-3690	166	33	to	to	PART
cana-3690	166	34	find	find	VERB
cana-3690	166	35	the	the	DET
cana-3690	166	36	model	model	NOUN
cana-3690	166	37	performance	performance	NOUN
cana-3690	166	38	and	and	CCONJ
cana-3690	166	39	error	error	NOUN
cana-3690	166	40	.	.	PUNCT
cana-3690	167	1	where	where	SCONJ
cana-3690	167	2	yi	yi	PROPN
cana-3690	167	3	represents	represent	VERB
cana-3690	167	4	the	the	DET
cana-3690	167	5	individual	individual	ADJ
cana-3690	167	6	observed	observe	VERB
cana-3690	167	7	(	(	PUNCT
cana-3690	167	8	actual	actual	ADJ
cana-3690	167	9	)	)	PUNCT
cana-3690	167	10	values	value	NOUN
cana-3690	167	11	,	,	PUNCT
cana-3690	167	12	ŷi	ŷi	X
cana-3690	167	13	represents	represent	VERB
cana-3690	167	14	the	the	DET
cana-3690	167	15	corresponding	corresponding	ADJ
cana-3690	167	16	individual	individual	ADJ
cana-3690	167	17	predicted	predict	VERB
cana-3690	167	18	values	value	NOUN
cana-3690	167	19	,	,	PUNCT
cana-3690	167	20	ȳ	ȳ	PROPN
cana-3690	167	21	represents	represent	VERB
cana-3690	167	22	the	the	DET
cana-3690	167	23	mean	mean	ADJ
cana-3690	167	24	(	(	PUNCT
cana-3690	167	25	average	average	ADJ
cana-3690	167	26	)	)	PUNCT
cana-3690	167	27	of	of	ADP
cana-3690	167	28	the	the	DET
cana-3690	167	29	observed	observed	ADJ
cana-3690	167	30	values	value	NOUN
cana-3690	167	31	and	and	CCONJ
cana-3690	167	32	σ	σ	NOUN
cana-3690	167	33	represents	represent	VERB
cana-3690	167	34	the	the	DET
cana-3690	167	35	summation	summation	NOUN
cana-3690	167	36	symbol	symbol	NOUN
cana-3690	167	37	,	,	PUNCT
cana-3690	167	38	indicating	indicate	VERB
cana-3690	167	39	that	that	SCONJ
cana-3690	167	40	you	you	PRON
cana-3690	167	41	should	should	AUX
cana-3690	167	42	sum	sum	VERB
cana-3690	167	43	the	the	DET
cana-3690	167	44	absolute	absolute	ADJ
cana-3690	167	45	differences	difference	NOUN
cana-3690	167	46	for	for	ADP
cana-3690	167	47	all	all	DET
cana-3690	167	48	data	datum	NOUN
cana-3690	167	49	points	point	NOUN
cana-3690	167	50	.	.	PUNCT
cana-3690	168	1	3.0	3.0	NUM
cana-3690	168	2	numerical	numerical	ADJ
cana-3690	168	3	illustrations	illustration	NOUN
cana-3690	168	4	communications	communication	NOUN
cana-3690	168	5	on	on	ADP
cana-3690	168	6	applied	apply	VERB
cana-3690	168	7	nonlinear	nonlinear	ADJ
cana-3690	168	8	analysis	analysis	NOUN
cana-3690	168	9	issn	issn	NOUN
cana-3690	168	10	:	:	PUNCT
cana-3690	168	11	1074	1074	NUM
cana-3690	168	12	-	-	PUNCT
cana-3690	168	13	133x	133x	NUM
cana-3690	168	14	vol	vol	NOUN
cana-3690	168	15	32	32	NUM
cana-3690	168	16	no	no	NOUN
cana-3690	168	17	.	.	PUNCT
cana-3690	169	1	8s	8s	PROPN
cana-3690	169	2	(	(	PUNCT
cana-3690	169	3	2025	2025	NUM
cana-3690	169	4	)	)	PUNCT
cana-3690	170	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-3690	170	2	464	464	NUM
cana-3690	171	1	this	this	DET
cana-3690	171	2	dataset	dataset	NOUN
cana-3690	171	3	dates	date	VERB
cana-3690	171	4	back	back	ADV
cana-3690	171	5	to	to	ADP
cana-3690	171	6	1988	1988	NUM
cana-3690	171	7	and	and	CCONJ
cana-3690	171	8	consists	consist	VERB
cana-3690	171	9	of	of	ADP
cana-3690	171	10	four	four	NUM
cana-3690	171	11	databases	database	NOUN
cana-3690	171	12	:	:	PUNCT
cana-3690	171	13	cleveland	cleveland	PROPN
cana-3690	171	14	,	,	PUNCT
cana-3690	171	15	hungary	hungary	PROPN
cana-3690	171	16	,	,	PUNCT
cana-3690	171	17	switzerland	switzerland	PROPN
cana-3690	171	18	and	and	CCONJ
cana-3690	171	19	long	long	ADJ
cana-3690	171	20	beach	beach	NOUN
cana-3690	172	1	v.	v.	CCONJ
cana-3690	172	2	it	it	PRON
cana-3690	172	3	contains	contain	VERB
cana-3690	172	4	76	76	NUM
cana-3690	172	5	attributes	attribute	NOUN
cana-3690	172	6	,	,	PUNCT
cana-3690	172	7	including	include	VERB
cana-3690	172	8	the	the	DET
cana-3690	172	9	predicted	predict	VERB
cana-3690	172	10	attribute	attribute	NOUN
cana-3690	172	11	,	,	PUNCT
cana-3690	172	12	but	but	CCONJ
cana-3690	172	13	all	all	DET
cana-3690	172	14	published	publish	VERB
cana-3690	172	15	experiments	experiment	NOUN
cana-3690	172	16	involve	involve	VERB
cana-3690	172	17	using	use	VERB
cana-3690	172	18	a	a	DET
cana-3690	172	19	subset	subset	NOUN
cana-3690	172	20	of	of	ADP
cana-3690	172	21	14	14	NUM
cana-3690	172	22	of	of	ADP
cana-3690	172	23	them	they	PRON
cana-3690	172	24	.	.	PUNCT
cana-3690	173	1	the	the	DET
cana-3690	173	2	“	"	PUNCT
cana-3690	173	3	target	target	NOUN
cana-3690	173	4	”	"	PUNCT
cana-3690	173	5	field	field	NOUN
cana-3690	173	6	refers	refer	VERB
cana-3690	173	7	to	to	ADP
cana-3690	173	8	the	the	DET
cana-3690	173	9	presence	presence	NOUN
cana-3690	173	10	of	of	ADP
cana-3690	173	11	heart	heart	NOUN
cana-3690	173	12	disease	disease	NOUN
cana-3690	173	13	in	in	ADP
cana-3690	173	14	the	the	DET
cana-3690	173	15	patient	patient	NOUN
cana-3690	173	16	.	.	PUNCT
cana-3690	174	1	the	the	DET
cana-3690	174	2	integer	integer	NOUN
cana-3690	174	3	value	value	NOUN
cana-3690	174	4	is	be	AUX
cana-3690	174	5	0	0	NUM
cana-3690	174	6	=	=	SYM
cana-3690	174	7	no	no	DET
cana-3690	174	8	disease	disease	NOUN
cana-3690	174	9	and	and	CCONJ
cana-3690	174	10	1	1	NUM
cana-3690	174	11	=	=	NOUN
cana-3690	174	12	disease	disease	NOUN
cana-3690	174	13	kaggle	kaggle	NOUN
cana-3690	174	14	(	(	PUNCT
cana-3690	174	15	2024	2024	NUM
cana-3690	174	16	)	)	PUNCT
cana-3690	174	17	.	.	PUNCT
cana-3690	175	1	table	table	NOUN
cana-3690	175	2	1	1	NUM
cana-3690	175	3	.	.	PUNCT
cana-3690	176	1	heart	heart	NOUN
cana-3690	176	2	disease	disease	NOUN
cana-3690	176	3	sample	sample	NOUN
cana-3690	176	4	dataset	dataset	VERB
cana-3690	176	5	ag	ag	PROPN
cana-3690	176	6	e	e	PROPN
cana-3690	176	7	se	se	X
cana-3690	176	8	x	x	PROPN
cana-3690	176	9	c	c	NOUN
cana-3690	176	10	p	p	NOUN
cana-3690	176	11	trestbp	trestbp	NOUN
cana-3690	176	12	s	s	PART
cana-3690	176	13	cho	cho	NOUN
cana-3690	176	14	l	l	NOUN
cana-3690	176	15	fb	fb	NOUN
cana-3690	176	16	s	s	PART
cana-3690	176	17	restec	restec	NOUN
cana-3690	176	18	g	g	PROPN
cana-3690	176	19	thalac	thalac	PROPN
cana-3690	176	20	h	h	PROPN
cana-3690	176	21	exan	exan	PROPN
cana-3690	176	22	g	g	PROPN
cana-3690	176	23	oldpea	oldpea	PROPN
cana-3690	176	24	k	k	PROPN
cana-3690	176	25	slop	slop	PROPN
cana-3690	177	1	e	e	PROPN
cana-3690	177	2	c	c	PROPN
cana-3690	177	3	a	a	DET
cana-3690	177	4	tha	tha	NOUN
cana-3690	177	5	l	l	NOUN
cana-3690	177	6	targe	targe	NOUN
cana-3690	177	7	t	t	PROPN
cana-3690	177	8	52	52	NUM
cana-3690	177	9	1	1	NUM
cana-3690	177	10	0	0	NUM
cana-3690	177	11	125	125	NUM
cana-3690	177	12	212	212	NUM
cana-3690	177	13	0	0	NUM
cana-3690	177	14	1	1	NUM
cana-3690	177	15	168	168	NUM
cana-3690	177	16	0	0	NUM
cana-3690	177	17	1	1	NUM
cana-3690	177	18	2	2	NUM
cana-3690	177	19	2	2	NUM
cana-3690	177	20	3	3	NUM
cana-3690	177	21	0	0	NUM
cana-3690	177	22	53	53	NUM
cana-3690	177	23	1	1	NUM
cana-3690	177	24	0	0	NUM
cana-3690	177	25	140	140	NUM
cana-3690	177	26	203	203	NUM
cana-3690	177	27	1	1	NUM
cana-3690	177	28	0	0	NUM
cana-3690	177	29	155	155	NUM
cana-3690	177	30	1	1	NUM
cana-3690	177	31	3.1	3.1	NUM
cana-3690	177	32	0	0	NUM
cana-3690	177	33	0	0	NUM
cana-3690	177	34	3	3	NUM
cana-3690	177	35	0	0	NUM
cana-3690	177	36	70	70	NUM
cana-3690	177	37	1	1	NUM
cana-3690	177	38	0	0	NUM
cana-3690	177	39	145	145	NUM
cana-3690	177	40	174	174	NUM
cana-3690	177	41	0	0	NUM
cana-3690	177	42	1	1	NUM
cana-3690	177	43	125	125	NUM
cana-3690	177	44	1	1	NUM
cana-3690	177	45	2.6	2.6	NUM
cana-3690	177	46	0	0	NUM
cana-3690	177	47	0	0	NUM
cana-3690	177	48	3	3	NUM
cana-3690	177	49	0	0	NUM
cana-3690	177	50	61	61	NUM
cana-3690	177	51	1	1	NUM
cana-3690	177	52	0	0	NUM
cana-3690	177	53	148	148	NUM
cana-3690	177	54	203	203	NUM
cana-3690	177	55	0	0	NUM
cana-3690	177	56	1	1	NUM
cana-3690	177	57	161	161	NUM
cana-3690	177	58	0	0	NUM
cana-3690	177	59	0	0	NUM
cana-3690	177	60	2	2	NUM
cana-3690	177	61	1	1	NUM
cana-3690	177	62	3	3	NUM
cana-3690	177	63	0	0	NUM
cana-3690	177	64	62	62	NUM
cana-3690	177	65	0	0	NUM
cana-3690	177	66	0	0	NUM
cana-3690	177	67	138	138	NUM
cana-3690	177	68	294	294	NUM
cana-3690	177	69	1	1	NUM
cana-3690	177	70	1	1	NUM
cana-3690	177	71	106	106	NUM
cana-3690	177	72	0	0	NUM
cana-3690	177	73	1.9	1.9	NUM
cana-3690	177	74	1	1	NUM
cana-3690	177	75	3	3	NUM
cana-3690	177	76	2	2	NUM
cana-3690	177	77	0	0	NUM
cana-3690	177	78	51	51	NUM
cana-3690	177	79	1	1	NUM
cana-3690	177	80	0	0	NUM
cana-3690	177	81	140	140	NUM
cana-3690	177	82	298	298	NUM
cana-3690	177	83	0	0	NUM
cana-3690	177	84	1	1	NUM
cana-3690	177	85	122	122	NUM
cana-3690	177	86	1	1	NUM
cana-3690	177	87	4.2	4.2	NUM
cana-3690	177	88	1	1	NUM
cana-3690	177	89	3	3	NUM
cana-3690	177	90	3	3	NUM
cana-3690	177	91	0	0	NUM
cana-3690	177	92	52	52	NUM
cana-3690	177	93	1	1	NUM
cana-3690	177	94	0	0	NUM
cana-3690	177	95	128	128	NUM
cana-3690	177	96	204	204	NUM
cana-3690	177	97	1	1	NUM
cana-3690	177	98	1	1	NUM
cana-3690	177	99	156	156	NUM
cana-3690	177	100	1	1	NUM
cana-3690	177	101	1	1	NUM
cana-3690	177	102	1	1	NUM
cana-3690	177	103	0	0	NUM
cana-3690	177	104	0	0	NUM
cana-3690	177	105	0	0	NUM
cana-3690	177	106	34	34	NUM
cana-3690	177	107	0	0	NUM
cana-3690	177	108	1	1	NUM
cana-3690	177	109	118	118	NUM
cana-3690	177	110	210	210	NUM
cana-3690	177	111	0	0	NUM
cana-3690	177	112	1	1	NUM
cana-3690	177	113	192	192	NUM
cana-3690	177	114	0	0	NUM
cana-3690	178	1	0.7	0.7	NUM
cana-3690	178	2	2	2	NUM
cana-3690	178	3	0	0	NUM
cana-3690	178	4	2	2	NUM
cana-3690	178	5	1	1	NUM
cana-3690	178	6	51	51	NUM
cana-3690	178	7	0	0	NUM
cana-3690	178	8	2	2	NUM
cana-3690	178	9	140	140	NUM
cana-3690	178	10	308	308	NUM
cana-3690	178	11	0	0	NUM
cana-3690	178	12	0	0	NUM
cana-3690	178	13	142	142	NUM
cana-3690	178	14	0	0	NUM
cana-3690	178	15	1.5	1.5	NUM
cana-3690	178	16	2	2	NUM
cana-3690	178	17	1	1	NUM
cana-3690	178	18	2	2	NUM
cana-3690	178	19	1	1	NUM
cana-3690	178	20	54	54	NUM
cana-3690	178	21	1	1	NUM
cana-3690	178	22	0	0	NUM
cana-3690	178	23	124	124	NUM
cana-3690	178	24	266	266	NUM
cana-3690	178	25	0	0	NUM
cana-3690	178	26	0	0	NUM
cana-3690	178	27	109	109	NUM
cana-3690	178	28	1	1	NUM
cana-3690	178	29	2.2	2.2	NUM
cana-3690	178	30	1	1	NUM
cana-3690	178	31	1	1	NUM
cana-3690	178	32	3	3	NUM
cana-3690	178	33	0	0	NUM
cana-3690	178	34	50	50	NUM
cana-3690	178	35	0	0	NUM
cana-3690	178	36	1	1	NUM
cana-3690	178	37	120	120	NUM
cana-3690	178	38	244	244	NUM
cana-3690	178	39	0	0	NUM
cana-3690	178	40	1	1	NUM
cana-3690	178	41	162	162	NUM
cana-3690	178	42	0	0	NUM
cana-3690	178	43	1.1	1.1	NUM
cana-3690	178	44	2	2	NUM
cana-3690	178	45	0	0	NUM
cana-3690	178	46	2	2	NUM
cana-3690	178	47	1	1	NUM
cana-3690	178	48	58	58	NUM
cana-3690	178	49	1	1	NUM
cana-3690	178	50	2	2	NUM
cana-3690	178	51	140	140	NUM
cana-3690	178	52	211	211	NUM
cana-3690	178	53	1	1	NUM
cana-3690	178	54	0	0	NUM
cana-3690	178	55	165	165	NUM
cana-3690	178	56	0	0	NUM
cana-3690	178	57	0	0	NUM
cana-3690	178	58	2	2	NUM
cana-3690	178	59	0	0	NUM
cana-3690	178	60	2	2	NUM
cana-3690	178	61	1	1	NUM
cana-3690	178	62	60	60	NUM
cana-3690	178	63	1	1	NUM
cana-3690	178	64	2	2	NUM
cana-3690	178	65	140	140	NUM
cana-3690	178	66	185	185	NUM
cana-3690	178	67	0	0	NUM
cana-3690	178	68	0	0	NUM
cana-3690	178	69	155	155	NUM
cana-3690	178	70	0	0	NUM
cana-3690	178	71	3	3	NUM
cana-3690	178	72	1	1	NUM
cana-3690	178	73	0	0	NUM
cana-3690	178	74	2	2	NUM
cana-3690	178	75	0	0	NUM
cana-3690	178	76	67	67	NUM
cana-3690	178	77	0	0	NUM
cana-3690	178	78	0	0	NUM
cana-3690	178	79	106	106	NUM
cana-3690	178	80	223	223	NUM
cana-3690	178	81	0	0	NUM
cana-3690	178	82	1	1	NUM
cana-3690	178	83	142	142	NUM
cana-3690	178	84	0	0	NUM
cana-3690	178	85	0.3	0.3	NUM
cana-3690	178	86	2	2	NUM
cana-3690	178	87	2	2	NUM
cana-3690	178	88	2	2	NUM
cana-3690	178	89	1	1	NUM
cana-3690	178	90	45	45	NUM
cana-3690	178	91	1	1	NUM
cana-3690	178	92	0	0	NUM
cana-3690	178	93	104	104	NUM
cana-3690	178	94	208	208	NUM
cana-3690	178	95	0	0	NUM
cana-3690	178	96	0	0	NUM
cana-3690	178	97	148	148	NUM
cana-3690	178	98	1	1	NUM
cana-3690	178	99	3	3	NUM
cana-3690	178	100	1	1	NUM
cana-3690	178	101	0	0	NUM
cana-3690	178	102	2	2	NUM
cana-3690	178	103	1	1	NUM
cana-3690	178	104	63	63	NUM
cana-3690	178	105	0	0	NUM
cana-3690	178	106	2	2	NUM
cana-3690	178	107	135	135	NUM
cana-3690	178	108	252	252	NUM
cana-3690	178	109	0	0	NUM
cana-3690	178	110	0	0	NUM
cana-3690	178	111	172	172	NUM
cana-3690	178	112	0	0	NUM
cana-3690	178	113	0	0	NUM
cana-3690	178	114	2	2	NUM
cana-3690	178	115	0	0	NUM
cana-3690	178	116	2	2	NUM
cana-3690	178	117	1	1	NUM
cana-3690	178	118	42	42	NUM
cana-3690	178	119	0	0	NUM
cana-3690	178	120	2	2	NUM
cana-3690	178	121	120	120	NUM
cana-3690	178	122	209	209	NUM
cana-3690	178	123	0	0	NUM
cana-3690	178	124	1	1	NUM
cana-3690	178	125	173	173	NUM
cana-3690	178	126	0	0	NUM
cana-3690	178	127	0	0	NUM
cana-3690	178	128	1	1	NUM
cana-3690	178	129	0	0	NUM
cana-3690	178	130	2	2	NUM
cana-3690	178	131	1	1	NUM
cana-3690	178	132	61	61	NUM
cana-3690	178	133	0	0	NUM
cana-3690	178	134	0	0	NUM
cana-3690	178	135	145	145	NUM
cana-3690	178	136	307	307	NUM
cana-3690	178	137	0	0	NUM
cana-3690	178	138	0	0	NUM
cana-3690	178	139	146	146	NUM
cana-3690	178	140	1	1	NUM
cana-3690	178	141	1	1	NUM
cana-3690	178	142	1	1	NUM
cana-3690	178	143	0	0	NUM
cana-3690	178	144	3	3	NUM
cana-3690	178	145	0	0	NUM
cana-3690	178	146	table	table	NOUN
cana-3690	178	147	2	2	NUM
cana-3690	178	148	:	:	PUNCT
cana-3690	178	149	machine	machine	NOUN
cana-3690	178	150	learning	learning	NOUN
cana-3690	178	151	models	model	NOUN
cana-3690	178	152	with	with	ADP
cana-3690	178	153	correlation	correlation	NOUN
cana-3690	178	154	coefficient	coefficient	NOUN
cana-3690	178	155	ml	ml	ADP
cana-3690	178	156	approaches	approach	VERB
cana-3690	178	157	correlation	correlation	NOUN
cana-3690	178	158	coefficient	coefficient	NOUN
cana-3690	178	159	linear	linear	PROPN
cana-3690	178	160	regression	regression	NOUN
cana-3690	178	161	0.7063	0.7063	NUM
cana-3690	178	162	multilayer	multilayer	ADJ
cana-3690	178	163	perceptron	perceptron	PROPN
cana-3690	178	164	0.8296	0.8296	NUM
cana-3690	178	165	smoreg	smoreg	NOUN
cana-3690	178	166	0.6788	0.6788	NUM
cana-3690	178	167	random	random	ADJ
cana-3690	178	168	forest	forest	NOUN
cana-3690	178	169	0.9954	0.9954	NUM
cana-3690	178	170	random	random	ADJ
cana-3690	178	171	tree	tree	NOUN
cana-3690	178	172	0.9942	0.9942	NUM
cana-3690	178	173	rep	rep	NOUN
cana-3690	178	174	tree	tree	NOUN
cana-3690	178	175	0.8733	0.8733	NUM
cana-3690	178	176	table	table	NOUN
cana-3690	178	177	3	3	NUM
cana-3690	178	178	:	:	PUNCT
cana-3690	178	179	machine	machine	NOUN
cana-3690	178	180	learning	learning	NOUN
cana-3690	178	181	models	model	NOUN
cana-3690	178	182	with	with	ADP
cana-3690	178	183	mae	mae	PROPN
cana-3690	178	184	and	and	CCONJ
cana-3690	178	185	rmse	rmse	PROPN
cana-3690	178	186	ml	ml	AUX
cana-3690	178	187	approaches	approach	VERB
cana-3690	178	188	mae	mae	PROPN
cana-3690	178	189	rmse	rmse	PROPN
cana-3690	178	190	linear	linear	PROPN
cana-3690	178	191	regression	regression	VERB
cana-3690	178	192	0.2869	0.2869	NUM
cana-3690	178	193	0.3539	0.3539	NUM
cana-3690	178	194	multilayer	multilayer	ADJ
cana-3690	178	195	perceptron	perceptron	NOUN
cana-3690	179	1	0.1771	0.1771	NUM
cana-3690	179	2	0.2899	0.2899	NUM
cana-3690	179	3	smoreg	smoreg	VERB
cana-3690	179	4	0.2859	0.2859	NUM
cana-3690	179	5	0.3763	0.3763	NUM
cana-3690	179	6	random	random	ADJ
cana-3690	179	7	forest	forest	NOUN
cana-3690	179	8	0.0356	0.0356	NUM
cana-3690	179	9	0.0572	0.0572	NUM
cana-3690	179	10	random	random	ADJ
cana-3690	179	11	tree	tree	NOUN
cana-3690	179	12	0.0029	0.0029	NUM
cana-3690	179	13	0.0541	0.0541	NUM
cana-3690	180	1	communications	communication	NOUN
cana-3690	180	2	on	on	ADP
cana-3690	180	3	applied	apply	VERB
cana-3690	180	4	nonlinear	nonlinear	ADJ
cana-3690	180	5	analysis	analysis	NOUN
cana-3690	180	6	issn	issn	NOUN
cana-3690	180	7	:	:	PUNCT
cana-3690	180	8	1074	1074	NUM
cana-3690	180	9	-	-	PUNCT
cana-3690	180	10	133x	133x	NUM
cana-3690	180	11	vol	vol	NOUN
cana-3690	180	12	32	32	NUM
cana-3690	180	13	no	no	NOUN
cana-3690	180	14	.	.	PUNCT
cana-3690	181	1	8s	8s	PROPN
cana-3690	181	2	(	(	PUNCT
cana-3690	181	3	2025	2025	NUM
cana-3690	181	4	)	)	PUNCT
cana-3690	181	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-3690	181	6	465	465	NUM
cana-3690	181	7	rep	rep	NOUN
cana-3690	181	8	tree	tree	NOUN
cana-3690	181	9	0.0986	0.0986	NUM
cana-3690	181	10	0.2446	0.2446	NUM
cana-3690	181	11	table	table	NOUN
cana-3690	181	12	4	4	NUM
cana-3690	181	13	:	:	PUNCT
cana-3690	181	14	machine	machine	NOUN
cana-3690	181	15	learning	learning	NOUN
cana-3690	181	16	models	model	NOUN
cana-3690	181	17	with	with	ADP
cana-3690	181	18	rae	rae	PROPN
cana-3690	181	19	(	(	PUNCT
cana-3690	181	20	%	%	INTJ
cana-3690	181	21	)	)	PUNCT
cana-3690	181	22	and	and	CCONJ
cana-3690	181	23	rrse	rrse	NOUN
cana-3690	181	24	(	(	PUNCT
cana-3690	181	25	%	%	INTJ
cana-3690	181	26	)	)	PUNCT
cana-3690	181	27	ml	ml	ADP
cana-3690	181	28	approaches	approach	VERB
cana-3690	181	29	rae	rae	PROPN
cana-3690	181	30	(	(	PUNCT
cana-3690	181	31	%	%	INTJ
cana-3690	181	32	)	)	PUNCT
cana-3690	181	33	rrse	rrse	NOUN
cana-3690	181	34	(	(	PUNCT
cana-3690	181	35	%	%	INTJ
cana-3690	181	36	)	)	PUNCT
cana-3690	181	37	linear	linear	ADJ
cana-3690	181	38	regression	regression	VERB
cana-3690	181	39	57.3619	57.3619	NUM
cana-3690	181	40	70.7130	70.7130	NUM
cana-3690	181	41	multilayer	multilayer	ADJ
cana-3690	181	42	perceptron	perceptron	NOUN
cana-3690	181	43	35.3973	35.3973	NUM
cana-3690	181	44	57.9381	57.9381	NUM
cana-3690	181	45	smoreg	smoreg	NOUN
cana-3690	181	46	57.1569	57.1569	NUM
cana-3690	181	47	75.1948	75.1948	NUM
cana-3690	181	48	random	random	ADJ
cana-3690	181	49	forest	forest	NOUN
cana-3690	181	50	7.1107	7.1107	NUM
cana-3690	181	51	11.4300	11.4300	NUM
cana-3690	181	52	random	random	ADJ
cana-3690	181	53	tree	tree	NOUN
cana-3690	181	54	0.5851	0.5851	NUM
cana-3690	181	55	10.8104	10.8104	NUM
cana-3690	181	56	rep	rep	NOUN
cana-3690	181	57	tree	tree	NOUN
cana-3690	181	58	19.7053	19.7053	NUM
cana-3690	181	59	48.8679	48.8679	NUM
cana-3690	181	60	table	table	NOUN
cana-3690	181	61	5	5	NUM
cana-3690	181	62	:	:	PUNCT
cana-3690	181	63	machine	machine	NOUN
cana-3690	181	64	learning	learning	NOUN
cana-3690	181	65	models	model	NOUN
cana-3690	181	66	with	with	ADP
cana-3690	181	67	time	time	NOUN
cana-3690	181	68	taken	take	VERB
cana-3690	181	69	to	to	PART
cana-3690	181	70	build	build	VERB
cana-3690	181	71	model	model	NOUN
cana-3690	181	72	(	(	PUNCT
cana-3690	181	73	seconds	second	NOUN
cana-3690	181	74	)	)	PUNCT
cana-3690	181	75	ml	ml	VERB
cana-3690	181	76	approaches	approach	NOUN
cana-3690	181	77	time	time	NOUN
cana-3690	181	78	taken	take	VERB
cana-3690	181	79	(	(	PUNCT
cana-3690	181	80	seconds	second	NOUN
cana-3690	181	81	)	)	PUNCT
cana-3690	182	1	linear	linear	PROPN
cana-3690	182	2	regression	regression	VERB
cana-3690	182	3	0.2500	0.2500	NUM
cana-3690	182	4	multilayer	multilayer	ADJ
cana-3690	182	5	perceptron	perceptron	PROPN
cana-3690	182	6	1.6700	1.6700	NUM
cana-3690	182	7	smoreg	smoreg	NOUN
cana-3690	182	8	0.7000	0.7000	NUM
cana-3690	182	9	random	random	ADJ
cana-3690	182	10	forest	forest	NOUN
cana-3690	182	11	0.7800	0.7800	NUM
cana-3690	182	12	random	random	ADJ
cana-3690	182	13	tree	tree	NOUN
cana-3690	182	14	0.0400	0.0400	NUM
cana-3690	182	15	rep	rep	NOUN
cana-3690	182	16	tree	tree	NOUN
cana-3690	182	17	0.0800	0.0800	NUM
cana-3690	182	18	fig	fig	NOUN
cana-3690	182	19	.	.	PUNCT
cana-3690	183	1	1	1	X
cana-3690	183	2	.	.	X
cana-3690	183	3	r2	r2	NOUN
cana-3690	183	4	score	score	NOUN
cana-3690	183	5	for	for	ADP
cana-3690	183	6	machine	machine	NOUN
cana-3690	183	7	learning	learning	NOUN
cana-3690	183	8	approaches	approach	VERB
cana-3690	183	9	fig	fig	NOUN
cana-3690	183	10	.	.	PUNCT
cana-3690	184	1	2	2	X
cana-3690	184	2	.	.	X
cana-3690	184	3	machine	machine	NOUN
cana-3690	184	4	learning	learning	NOUN
cana-3690	184	5	models	model	NOUN
cana-3690	184	6	with	with	ADP
cana-3690	184	7	mae	mae	PROPN
cana-3690	184	8	and	and	CCONJ
cana-3690	184	9	rmse	rmse	ADJ
cana-3690	184	10	fig	fig	NOUN
cana-3690	184	11	.	.	PUNCT
cana-3690	185	1	3	3	X
cana-3690	185	2	.	.	X
cana-3690	185	3	machine	machine	NOUN
cana-3690	185	4	learning	learning	NOUN
cana-3690	185	5	models	model	NOUN
cana-3690	185	6	with	with	ADP
cana-3690	185	7	rae	rae	PROPN
cana-3690	185	8	(	(	PUNCT
cana-3690	185	9	%	%	INTJ
cana-3690	185	10	)	)	PUNCT
cana-3690	185	11	and	and	CCONJ
cana-3690	185	12	rrse	rrse	NOUN
cana-3690	185	13	(	(	PUNCT
cana-3690	185	14	%	%	INTJ
cana-3690	185	15	)	)	PUNCT
cana-3690	185	16	fig	fig	NOUN
cana-3690	185	17	.	.	PUNCT
cana-3690	186	1	4	4	X
cana-3690	186	2	.	.	X
cana-3690	186	3	machine	machine	NOUN
cana-3690	186	4	learning	learning	NOUN
cana-3690	186	5	models	model	NOUN
cana-3690	186	6	and	and	CCONJ
cana-3690	186	7	its	its	PRON
cana-3690	186	8	time	time	NOUN
cana-3690	186	9	taken	take	VERB
cana-3690	186	10	to	to	PART
cana-3690	186	11	build	build	VERB
cana-3690	186	12	the	the	DET
cana-3690	186	13	model	model	NOUN
cana-3690	186	14	(	(	PUNCT
cana-3690	186	15	seconds	second	NOUN
cana-3690	186	16	)	)	PUNCT
cana-3690	186	17	communications	communication	NOUN
cana-3690	186	18	on	on	ADP
cana-3690	186	19	applied	apply	VERB
cana-3690	186	20	nonlinear	nonlinear	ADJ
cana-3690	186	21	analysis	analysis	NOUN
cana-3690	186	22	issn	issn	NOUN
cana-3690	186	23	:	:	PUNCT
cana-3690	186	24	1074	1074	NUM
cana-3690	186	25	-	-	PUNCT
cana-3690	186	26	133x	133x	NUM
cana-3690	186	27	vol	vol	NOUN
cana-3690	186	28	32	32	NUM
cana-3690	186	29	no	no	NOUN
cana-3690	186	30	.	.	PUNCT
cana-3690	187	1	8s	8s	PROPN
cana-3690	187	2	(	(	PUNCT
cana-3690	187	3	2025	2025	NUM
cana-3690	187	4	)	)	PUNCT
cana-3690	187	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-3690	187	6	466	466	NUM
cana-3690	187	7	3	3	NUM
cana-3690	187	8	.	.	PUNCT
cana-3690	187	9	results	result	NOUN
cana-3690	187	10	and	and	CCONJ
cana-3690	187	11	discussion	discussion	VERB
cana-3690	187	12	the	the	DET
cana-3690	187	13	evaluation	evaluation	NOUN
cana-3690	187	14	of	of	ADP
cana-3690	187	15	six	six	NUM
cana-3690	187	16	machine	machine	NOUN
cana-3690	187	17	learning	learning	NOUN
cana-3690	187	18	models	model	NOUN
cana-3690	187	19	—	—	PUNCT
cana-3690	187	20	linear	linear	ADJ
cana-3690	187	21	regression	regression	NOUN
cana-3690	187	22	,	,	PUNCT
cana-3690	187	23	multilayer	multilayer	PROPN
cana-3690	187	24	perceptron	perceptron	PROPN
cana-3690	187	25	(	(	PUNCT
cana-3690	187	26	mlp	mlp	PROPN
cana-3690	187	27	)	)	PUNCT
cana-3690	187	28	,	,	PUNCT
cana-3690	187	29	smoreg	smoreg	NOUN
cana-3690	187	30	,	,	PUNCT
cana-3690	187	31	random	random	ADJ
cana-3690	187	32	forest	forest	NOUN
cana-3690	187	33	,	,	PUNCT
cana-3690	187	34	random	random	ADJ
cana-3690	187	35	tree	tree	NOUN
cana-3690	187	36	,	,	PUNCT
cana-3690	187	37	and	and	CCONJ
cana-3690	187	38	rep	rep	PROPN
cana-3690	187	39	tree	tree	NOUN
cana-3690	187	40	—	—	PUNCT
cana-3690	187	41	revealed	reveal	VERB
cana-3690	187	42	distinct	distinct	ADJ
cana-3690	187	43	differences	difference	NOUN
cana-3690	187	44	in	in	ADP
cana-3690	187	45	their	their	PRON
cana-3690	187	46	predictive	predictive	ADJ
cana-3690	187	47	capabilities	capability	NOUN
cana-3690	187	48	when	when	SCONJ
cana-3690	187	49	assessed	assess	VERB
cana-3690	187	50	using	use	VERB
cana-3690	187	51	clinical	clinical	ADJ
cana-3690	187	52	features	feature	NOUN
cana-3690	187	53	and	and	CCONJ
cana-3690	187	54	various	various	ADJ
cana-3690	187	55	performance	performance	NOUN
cana-3690	187	56	metrics	metric	NOUN
cana-3690	187	57	.	.	PUNCT
cana-3690	188	1	notably	notably	ADV
cana-3690	188	2	,	,	PUNCT
cana-3690	188	3	ensemble	ensemble	ADJ
cana-3690	188	4	methods	method	NOUN
cana-3690	188	5	and	and	CCONJ
cana-3690	188	6	neural	neural	ADJ
cana-3690	188	7	networks	network	NOUN
cana-3690	188	8	demonstrated	demonstrate	VERB
cana-3690	188	9	a	a	DET
cana-3690	188	10	clear	clear	ADJ
cana-3690	188	11	edge	edge	NOUN
cana-3690	188	12	in	in	ADP
cana-3690	188	13	delivering	deliver	VERB
cana-3690	188	14	superior	superior	ADJ
cana-3690	188	15	predictive	predictive	ADJ
cana-3690	188	16	accuracy	accuracy	NOUN
cana-3690	188	17	compared	compare	VERB
cana-3690	188	18	to	to	ADP
cana-3690	188	19	other	other	ADJ
cana-3690	188	20	models	model	NOUN
cana-3690	188	21	.	.	PUNCT
cana-3690	189	1	as	as	SCONJ
cana-3690	189	2	depicted	depict	VERB
cana-3690	189	3	in	in	ADP
cana-3690	189	4	table	table	NOUN
cana-3690	189	5	2	2	NUM
cana-3690	189	6	and	and	CCONJ
cana-3690	189	7	figure	figure	NOUN
cana-3690	189	8	1	1	NUM
cana-3690	189	9	,	,	PUNCT
cana-3690	189	10	the	the	DET
cana-3690	189	11	correlation	correlation	NOUN
cana-3690	189	12	coefficients	coefficient	VERB
cana-3690	189	13	highlight	highlight	VERB
cana-3690	189	14	the	the	DET
cana-3690	189	15	predictive	predictive	ADJ
cana-3690	189	16	strength	strength	NOUN
cana-3690	189	17	of	of	ADP
cana-3690	189	18	the	the	DET
cana-3690	189	19	models	model	NOUN
cana-3690	189	20	.	.	PUNCT
cana-3690	190	1	random	random	ADJ
cana-3690	190	2	forest	forest	NOUN
cana-3690	190	3	achieved	achieve	VERB
cana-3690	190	4	the	the	DET
cana-3690	190	5	highest	high	ADJ
cana-3690	190	6	r²	r²	NOUN
cana-3690	190	7	value	value	NOUN
cana-3690	190	8	(	(	PUNCT
cana-3690	190	9	0.9954	0.9954	NUM
cana-3690	190	10	)	)	PUNCT
cana-3690	190	11	,	,	PUNCT
cana-3690	190	12	followed	follow	VERB
cana-3690	190	13	closely	closely	ADV
cana-3690	190	14	by	by	ADP
cana-3690	190	15	random	random	ADJ
cana-3690	190	16	tree	tree	NOUN
cana-3690	190	17	(	(	PUNCT
cana-3690	190	18	0.9942	0.9942	NUM
cana-3690	190	19	)	)	PUNCT
cana-3690	190	20	and	and	CCONJ
cana-3690	190	21	rep	rep	PROPN
cana-3690	190	22	tree	tree	NOUN
cana-3690	190	23	(	(	PUNCT
cana-3690	190	24	0.8733	0.8733	NUM
cana-3690	190	25	)	)	PUNCT
cana-3690	190	26	.	.	PUNCT
cana-3690	191	1	the	the	DET
cana-3690	191	2	mlp	mlp	PROPN
cana-3690	191	3	model	model	NOUN
cana-3690	191	4	also	also	ADV
cana-3690	191	5	showed	show	VERB
cana-3690	191	6	promising	promising	ADJ
cana-3690	191	7	results	result	NOUN
cana-3690	191	8	with	with	ADP
cana-3690	191	9	an	an	DET
cana-3690	191	10	r²	r²	NOUN
cana-3690	191	11	value	value	NOUN
cana-3690	191	12	of	of	ADP
cana-3690	191	13	0.8296	0.8296	NUM
cana-3690	191	14	,	,	PUNCT
cana-3690	191	15	whereas	whereas	SCONJ
cana-3690	191	16	linear	linear	ADJ
cana-3690	191	17	regression	regression	NOUN
cana-3690	191	18	(	(	PUNCT
cana-3690	191	19	0.7063	0.7063	NUM
cana-3690	191	20	)	)	PUNCT
cana-3690	191	21	and	and	CCONJ
cana-3690	191	22	smoreg	smoreg	NOUN
cana-3690	191	23	(	(	PUNCT
cana-3690	191	24	0.6788	0.6788	NUM
cana-3690	191	25	)	)	PUNCT
cana-3690	191	26	exhibited	exhibit	VERB
cana-3690	191	27	weaker	weak	ADJ
cana-3690	191	28	correlations	correlation	NOUN
cana-3690	191	29	,	,	PUNCT
cana-3690	191	30	reflecting	reflect	VERB
cana-3690	191	31	their	their	PRON
cana-3690	191	32	limited	limited	ADJ
cana-3690	191	33	ability	ability	NOUN
cana-3690	191	34	to	to	PART
cana-3690	191	35	model	model	VERB
cana-3690	191	36	complex	complex	ADJ
cana-3690	191	37	feature	feature	NOUN
cana-3690	191	38	interactions	interaction	NOUN
cana-3690	191	39	effectively	effectively	ADV
cana-3690	191	40	.	.	PUNCT
cana-3690	192	1	the	the	DET
cana-3690	192	2	mean	mean	ADJ
cana-3690	192	3	absolute	absolute	ADJ
cana-3690	192	4	error	error	NOUN
cana-3690	192	5	(	(	PUNCT
cana-3690	192	6	mae	mae	PROPN
cana-3690	192	7	)	)	PUNCT
cana-3690	192	8	and	and	CCONJ
cana-3690	192	9	root	root	NOUN
cana-3690	192	10	mean	mean	VERB
cana-3690	192	11	squared	square	VERB
cana-3690	192	12	error	error	NOUN
cana-3690	192	13	(	(	PUNCT
cana-3690	192	14	rmse	rmse	ADJ
cana-3690	192	15	)	)	PUNCT
cana-3690	192	16	metrics	metric	NOUN
cana-3690	192	17	,	,	PUNCT
cana-3690	192	18	shown	show	VERB
cana-3690	192	19	in	in	ADP
cana-3690	192	20	table	table	NOUN
cana-3690	192	21	3	3	NUM
cana-3690	192	22	and	and	CCONJ
cana-3690	192	23	figure	figure	NOUN
cana-3690	192	24	2	2	NUM
cana-3690	192	25	,	,	PUNCT
cana-3690	192	26	provide	provide	VERB
cana-3690	192	27	insights	insight	NOUN
cana-3690	192	28	into	into	ADP
cana-3690	192	29	the	the	DET
cana-3690	192	30	accuracy	accuracy	NOUN
cana-3690	192	31	of	of	ADP
cana-3690	192	32	the	the	DET
cana-3690	192	33	models	model	NOUN
cana-3690	192	34	'	'	PART
cana-3690	192	35	predictions	prediction	NOUN
cana-3690	192	36	.	.	PUNCT
cana-3690	193	1	random	random	ADJ
cana-3690	193	2	tree	tree	NOUN
cana-3690	193	3	demonstrated	demonstrate	VERB
cana-3690	193	4	the	the	DET
cana-3690	193	5	lowest	low	ADJ
cana-3690	193	6	mae	mae	PROPN
cana-3690	193	7	(	(	PUNCT
cana-3690	193	8	0.0029	0.0029	NUM
cana-3690	193	9	)	)	PUNCT
cana-3690	193	10	and	and	CCONJ
cana-3690	193	11	rmse	rmse	PROPN
cana-3690	193	12	(	(	PUNCT
cana-3690	193	13	0.0541	0.0541	NUM
cana-3690	193	14	)	)	PUNCT
cana-3690	193	15	,	,	PUNCT
cana-3690	193	16	indicating	indicate	VERB
cana-3690	193	17	its	its	PRON
cana-3690	193	18	superior	superior	ADJ
cana-3690	193	19	point	point	NOUN
cana-3690	193	20	prediction	prediction	NOUN
cana-3690	193	21	accuracy	accuracy	NOUN
cana-3690	193	22	.	.	PUNCT
cana-3690	194	1	random	random	ADJ
cana-3690	194	2	forest	forest	NOUN
cana-3690	194	3	also	also	ADV
cana-3690	194	4	performed	perform	VERB
cana-3690	194	5	exceptionally	exceptionally	ADV
cana-3690	194	6	well	well	ADV
cana-3690	194	7	(	(	PUNCT
cana-3690	194	8	mae	mae	PROPN
cana-3690	194	9	=	=	SYM
cana-3690	194	10	0.0356	0.0356	NUM
cana-3690	194	11	,	,	PUNCT
cana-3690	194	12	rmse	rmse	NOUN
cana-3690	194	13	=	=	SYM
cana-3690	194	14	0.0572	0.0572	NUM
cana-3690	194	15	)	)	PUNCT
cana-3690	194	16	,	,	PUNCT
cana-3690	194	17	followed	follow	VERB
cana-3690	194	18	by	by	ADP
cana-3690	194	19	rep	rep	PROPN
cana-3690	194	20	tree	tree	NOUN
cana-3690	194	21	(	(	PUNCT
cana-3690	194	22	mae	mae	PROPN
cana-3690	194	23	=	=	PROPN
cana-3690	194	24	0.0986	0.0986	PROPN
cana-3690	194	25	,	,	PUNCT
cana-3690	194	26	rmse	rmse	NOUN
cana-3690	194	27	=	=	PROPN
cana-3690	194	28	0.2446	0.2446	NUM
cana-3690	194	29	)	)	PUNCT
cana-3690	194	30	.	.	PUNCT
cana-3690	195	1	in	in	ADP
cana-3690	195	2	contrast	contrast	NOUN
cana-3690	195	3	,	,	PUNCT
cana-3690	195	4	linear	linear	ADJ
cana-3690	195	5	regression	regression	NOUN
cana-3690	195	6	and	and	CCONJ
cana-3690	195	7	smoreg	smoreg	NOUN
cana-3690	195	8	produced	produce	VERB
cana-3690	195	9	higher	high	ADJ
cana-3690	195	10	error	error	NOUN
cana-3690	195	11	rates	rate	NOUN
cana-3690	195	12	,	,	PUNCT
cana-3690	195	13	underscoring	underscore	VERB
cana-3690	195	14	their	their	PRON
cana-3690	195	15	challenges	challenge	NOUN
cana-3690	195	16	in	in	ADP
cana-3690	195	17	capturing	capture	VERB
cana-3690	195	18	non	non	ADJ
cana-3690	195	19	-	-	ADJ
cana-3690	195	20	linear	linear	ADJ
cana-3690	195	21	relationships	relationship	NOUN
cana-3690	195	22	within	within	ADP
cana-3690	195	23	the	the	DET
cana-3690	195	24	dataset	dataset	NOUN
cana-3690	195	25	.	.	PUNCT
cana-3690	196	1	the	the	DET
cana-3690	196	2	evaluation	evaluation	NOUN
cana-3690	196	3	of	of	ADP
cana-3690	196	4	relative	relative	ADJ
cana-3690	196	5	absolute	absolute	ADJ
cana-3690	196	6	error	error	NOUN
cana-3690	196	7	(	(	PUNCT
cana-3690	196	8	rae	rae	NOUN
cana-3690	196	9	)	)	PUNCT
cana-3690	196	10	and	and	CCONJ
cana-3690	196	11	root	root	VERB
cana-3690	196	12	relative	relative	ADJ
cana-3690	196	13	squared	square	VERB
cana-3690	196	14	error	error	NOUN
cana-3690	196	15	(	(	PUNCT
cana-3690	196	16	rrse	rrse	NOUN
cana-3690	196	17	)	)	PUNCT
cana-3690	196	18	metrics	metric	NOUN
cana-3690	196	19	,	,	PUNCT
cana-3690	196	20	as	as	SCONJ
cana-3690	196	21	presented	present	VERB
cana-3690	196	22	in	in	ADP
cana-3690	196	23	table	table	NOUN
cana-3690	196	24	4	4	NUM
cana-3690	196	25	and	and	CCONJ
cana-3690	196	26	figure	figure	VERB
cana-3690	196	27	3	3	NUM
cana-3690	196	28	,	,	PUNCT
cana-3690	196	29	further	far	ADV
cana-3690	196	30	validates	validate	VERB
cana-3690	196	31	the	the	DET
cana-3690	196	32	performance	performance	NOUN
cana-3690	196	33	hierarchy	hierarchy	NOUN
cana-3690	196	34	.	.	PUNCT
cana-3690	197	1	random	random	ADJ
cana-3690	197	2	tree	tree	NOUN
cana-3690	197	3	(	(	PUNCT
cana-3690	197	4	rae	rae	NOUN
cana-3690	197	5	=	=	SYM
cana-3690	198	1	0.5851	0.5851	NUM
cana-3690	198	2	%	%	NOUN
cana-3690	198	3	,	,	PUNCT
cana-3690	198	4	rrse	rrse	NOUN
cana-3690	198	5	=	=	SYM
cana-3690	198	6	10.8104	10.8104	NUM
cana-3690	198	7	%	%	NOUN
cana-3690	198	8	)	)	PUNCT
cana-3690	198	9	and	and	CCONJ
cana-3690	198	10	random	random	ADJ
cana-3690	198	11	forest	forest	NOUN
cana-3690	198	12	(	(	PUNCT
cana-3690	198	13	rae	rae	NOUN
cana-3690	198	14	=	=	PROPN
cana-3690	198	15	7.1107	7.1107	NUM
cana-3690	198	16	%	%	NOUN
cana-3690	198	17	,	,	PUNCT
cana-3690	198	18	rrse	rrse	NOUN
cana-3690	198	19	=	=	SYM
cana-3690	198	20	11.4300	11.4300	NUM
cana-3690	198	21	%	%	NOUN
cana-3690	198	22	)	)	PUNCT
cana-3690	198	23	exhibited	exhibit	VERB
cana-3690	198	24	minimal	minimal	ADJ
cana-3690	198	25	errors	error	NOUN
cana-3690	198	26	,	,	PUNCT
cana-3690	198	27	reinforcing	reinforce	VERB
cana-3690	198	28	their	their	PRON
cana-3690	198	29	reliability	reliability	NOUN
cana-3690	198	30	.	.	PUNCT
cana-3690	199	1	on	on	ADP
cana-3690	199	2	the	the	DET
cana-3690	199	3	other	other	ADJ
cana-3690	199	4	hand	hand	NOUN
cana-3690	199	5	,	,	PUNCT
cana-3690	199	6	linear	linear	ADJ
cana-3690	199	7	regression	regression	NOUN
cana-3690	199	8	(	(	PUNCT
cana-3690	199	9	rae	rae	NOUN
cana-3690	199	10	=	=	PUNCT
cana-3690	199	11	57.3619	57.3619	NUM
cana-3690	199	12	%	%	NOUN
cana-3690	199	13	,	,	PUNCT
cana-3690	199	14	rrse	rrse	NOUN
cana-3690	199	15	=	=	SYM
cana-3690	199	16	70.7130	70.7130	NUM
cana-3690	199	17	%	%	NOUN
cana-3690	199	18	)	)	PUNCT
cana-3690	199	19	and	and	CCONJ
cana-3690	199	20	smoreg	smoreg	NOUN
cana-3690	199	21	(	(	PUNCT
cana-3690	199	22	rae	rae	NOUN
cana-3690	199	23	=	=	PROPN
cana-3690	199	24	57.1569	57.1569	NUM
cana-3690	199	25	%	%	NOUN
cana-3690	199	26	,	,	PUNCT
cana-3690	199	27	rrse	rrse	NOUN
cana-3690	199	28	=	=	SYM
cana-3690	199	29	75.1948	75.1948	NUM
cana-3690	199	30	%	%	NOUN
cana-3690	199	31	)	)	PUNCT
cana-3690	199	32	showed	show	VERB
cana-3690	199	33	significant	significant	ADJ
cana-3690	199	34	deviations	deviation	NOUN
cana-3690	199	35	,	,	PUNCT
cana-3690	199	36	reflecting	reflect	VERB
cana-3690	199	37	their	their	PRON
cana-3690	199	38	relative	relative	ADJ
cana-3690	199	39	inefficiency	inefficiency	NOUN
cana-3690	199	40	in	in	ADP
cana-3690	199	41	handling	handle	VERB
cana-3690	199	42	complex	complex	ADJ
cana-3690	199	43	datasets	dataset	NOUN
cana-3690	199	44	.	.	PUNCT
cana-3690	200	1	table	table	NOUN
cana-3690	200	2	5	5	NUM
cana-3690	200	3	and	and	CCONJ
cana-3690	200	4	figure	figure	VERB
cana-3690	200	5	4	4	NUM
cana-3690	200	6	highlight	highlight	VERB
cana-3690	200	7	the	the	DET
cana-3690	200	8	time	time	NOUN
cana-3690	200	9	required	require	VERB
cana-3690	200	10	to	to	PART
cana-3690	200	11	train	train	VERB
cana-3690	200	12	each	each	DET
cana-3690	200	13	model	model	NOUN
cana-3690	200	14	.	.	PUNCT
cana-3690	201	1	random	random	ADJ
cana-3690	201	2	tree	tree	NOUN
cana-3690	201	3	(	(	PUNCT
cana-3690	201	4	0.0400	0.0400	NUM
cana-3690	201	5	seconds	second	NOUN
cana-3690	201	6	)	)	PUNCT
cana-3690	201	7	and	and	CCONJ
cana-3690	201	8	rep	rep	PROPN
cana-3690	201	9	tree	tree	NOUN
cana-3690	201	10	(	(	PUNCT
cana-3690	201	11	0.0800	0.0800	NUM
cana-3690	201	12	seconds	second	NOUN
cana-3690	201	13	)	)	PUNCT
cana-3690	201	14	emerged	emerge	VERB
cana-3690	201	15	as	as	ADP
cana-3690	201	16	the	the	DET
cana-3690	201	17	most	most	ADJ
cana-3690	201	18	time	time	NOUN
cana-3690	201	19	-	-	PUNCT
cana-3690	201	20	efficient	efficient	ADJ
cana-3690	201	21	models	model	NOUN
cana-3690	201	22	,	,	PUNCT
cana-3690	201	23	making	make	VERB
cana-3690	201	24	them	they	PRON
cana-3690	201	25	ideal	ideal	ADJ
cana-3690	201	26	for	for	ADP
cana-3690	201	27	rapid	rapid	ADJ
cana-3690	201	28	decision	decision	NOUN
cana-3690	201	29	-	-	PUNCT
cana-3690	201	30	making	make	VERB
cana-3690	201	31	scenarios	scenario	NOUN
cana-3690	201	32	.	.	PUNCT
cana-3690	202	1	in	in	ADP
cana-3690	202	2	contrast	contrast	NOUN
cana-3690	202	3	,	,	PUNCT
cana-3690	202	4	mlp	mlp	PROPN
cana-3690	202	5	required	require	VERB
cana-3690	202	6	the	the	DET
cana-3690	202	7	longest	long	ADJ
cana-3690	202	8	training	training	NOUN
cana-3690	202	9	time	time	NOUN
cana-3690	202	10	(	(	PUNCT
cana-3690	202	11	1.6700	1.6700	NUM
cana-3690	202	12	seconds	second	NOUN
cana-3690	202	13	)	)	PUNCT
cana-3690	202	14	,	,	PUNCT
cana-3690	202	15	emphasizing	emphasize	VERB
cana-3690	202	16	the	the	DET
cana-3690	202	17	computational	computational	ADJ
cana-3690	202	18	intensity	intensity	NOUN
cana-3690	202	19	of	of	ADP
cana-3690	202	20	neural	neural	ADJ
cana-3690	202	21	networks	network	NOUN
cana-3690	202	22	.	.	PUNCT
cana-3690	203	1	the	the	DET
cana-3690	203	2	numerical	numerical	PROPN
cana-3690	203	3	illustrations	illustration	NOUN
cana-3690	203	4	conclude	conclude	VERB
cana-3690	203	5	the	the	DET
cana-3690	203	6	following	following	NOUN
cana-3690	203	7	.	.	PUNCT
cana-3690	204	1	superior	superior	ADJ
cana-3690	204	2	models	model	NOUN
cana-3690	204	3	:	:	PUNCT
cana-3690	204	4	random	random	ADJ
cana-3690	204	5	forest	forest	NOUN
cana-3690	204	6	and	and	CCONJ
cana-3690	204	7	random	random	ADJ
cana-3690	204	8	tree	tree	NOUN
cana-3690	204	9	consistently	consistently	ADV
cana-3690	204	10	achieved	achieve	VERB
cana-3690	204	11	high	high	ADJ
cana-3690	204	12	accuracy	accuracy	NOUN
cana-3690	204	13	and	and	CCONJ
cana-3690	204	14	low	low	ADJ
cana-3690	204	15	error	error	NOUN
cana-3690	204	16	rates	rate	NOUN
cana-3690	204	17	,	,	PUNCT
cana-3690	204	18	making	make	VERB
cana-3690	204	19	them	they	PRON
cana-3690	204	20	reliable	reliable	ADJ
cana-3690	204	21	choices	choice	NOUN
cana-3690	204	22	for	for	ADP
cana-3690	204	23	predictive	predictive	ADJ
cana-3690	204	24	modeling	modeling	NOUN
cana-3690	204	25	.	.	PUNCT
cana-3690	205	1	neural	neural	ADJ
cana-3690	205	2	networks	network	NOUN
cana-3690	205	3	:	:	PUNCT
cana-3690	205	4	although	although	SCONJ
cana-3690	205	5	mlp	mlp	NOUN
cana-3690	205	6	demonstrated	demonstrate	VERB
cana-3690	205	7	robust	robust	ADJ
cana-3690	205	8	predictive	predictive	ADJ
cana-3690	205	9	capabilities	capability	NOUN
cana-3690	205	10	,	,	PUNCT
cana-3690	205	11	it	it	PRON
cana-3690	205	12	required	require	VERB
cana-3690	205	13	significant	significant	ADJ
cana-3690	205	14	computational	computational	ADJ
cana-3690	205	15	resources	resource	NOUN
cana-3690	205	16	,	,	PUNCT
cana-3690	205	17	which	which	PRON
cana-3690	205	18	could	could	AUX
cana-3690	205	19	limit	limit	VERB
cana-3690	205	20	its	its	PRON
cana-3690	205	21	applicability	applicability	NOUN
cana-3690	205	22	in	in	ADP
cana-3690	205	23	time	time	NOUN
cana-3690	205	24	-	-	PUNCT
cana-3690	205	25	sensitive	sensitive	ADJ
cana-3690	205	26	environments	environment	NOUN
cana-3690	205	27	.	.	PUNCT
cana-3690	206	1	traditional	traditional	ADJ
cana-3690	206	2	models	model	NOUN
cana-3690	206	3	:	:	PUNCT
cana-3690	206	4	linear	linear	ADJ
cana-3690	206	5	regression	regression	NOUN
cana-3690	206	6	and	and	CCONJ
cana-3690	206	7	smoreg	smoreg	NOUN
cana-3690	206	8	struggled	struggle	VERB
cana-3690	206	9	with	with	ADP
cana-3690	206	10	high	high	ADJ
cana-3690	206	11	-	-	PUNCT
cana-3690	206	12	dimensional	dimensional	ADJ
cana-3690	206	13	and	and	CCONJ
cana-3690	206	14	non	non	ADJ
cana-3690	206	15	-	-	ADJ
cana-3690	206	16	linear	linear	ADJ
cana-3690	206	17	data	datum	NOUN
cana-3690	206	18	,	,	PUNCT
cana-3690	206	19	indicating	indicate	VERB
cana-3690	206	20	the	the	DET
cana-3690	206	21	necessity	necessity	NOUN
cana-3690	206	22	of	of	ADP
cana-3690	206	23	advanced	advanced	ADJ
cana-3690	206	24	modeling	modeling	NOUN
cana-3690	206	25	approaches	approach	NOUN
cana-3690	206	26	for	for	ADP
cana-3690	206	27	such	such	ADJ
cana-3690	206	28	datasets	dataset	NOUN
cana-3690	206	29	.	.	PUNCT
cana-3690	207	1	4	4	X
cana-3690	207	2	.	.	X
cana-3690	207	3	conclusion	conclusion	NOUN
cana-3690	207	4	this	this	DET
cana-3690	207	5	analysis	analysis	NOUN
cana-3690	207	6	demonstrates	demonstrate	VERB
cana-3690	207	7	the	the	DET
cana-3690	207	8	effectiveness	effectiveness	NOUN
cana-3690	207	9	of	of	ADP
cana-3690	207	10	machine	machine	NOUN
cana-3690	207	11	learning	learning	NOUN
cana-3690	207	12	models	model	NOUN
cana-3690	207	13	in	in	ADP
cana-3690	207	14	predicting	predict	VERB
cana-3690	207	15	heart	heart	NOUN
cana-3690	207	16	disease	disease	NOUN
cana-3690	207	17	risk	risk	NOUN
cana-3690	207	18	using	use	VERB
cana-3690	207	19	clinical	clinical	ADJ
cana-3690	207	20	features	feature	NOUN
cana-3690	207	21	.	.	PUNCT
cana-3690	208	1	random	random	ADJ
cana-3690	208	2	forest	forest	NOUN
cana-3690	208	3	emerged	emerge	VERB
cana-3690	208	4	as	as	ADP
cana-3690	208	5	the	the	DET
cana-3690	208	6	most	most	ADV
cana-3690	208	7	reliable	reliable	ADJ
cana-3690	208	8	algorithm	algorithm	NOUN
cana-3690	208	9	,	,	PUNCT
cana-3690	208	10	offering	offer	VERB
cana-3690	208	11	high	high	ADJ
cana-3690	208	12	communications	communication	NOUN
cana-3690	208	13	on	on	ADP
cana-3690	208	14	applied	apply	VERB
cana-3690	208	15	nonlinear	nonlinear	ADJ
cana-3690	208	16	analysis	analysis	NOUN
cana-3690	208	17	issn	issn	NOUN
cana-3690	208	18	:	:	PUNCT
cana-3690	208	19	1074	1074	NUM
cana-3690	208	20	-	-	PUNCT
cana-3690	208	21	133x	133x	NUM
cana-3690	208	22	vol	vol	NOUN
cana-3690	208	23	32	32	NUM
cana-3690	208	24	no	no	NOUN
cana-3690	208	25	.	.	PUNCT
cana-3690	209	1	8s	8s	PROPN
cana-3690	209	2	(	(	PUNCT
cana-3690	209	3	2025	2025	NUM
cana-3690	209	4	)	)	PUNCT
cana-3690	209	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-3690	209	6	467	467	NUM
cana-3690	209	7	accuracy	accuracy	NOUN
cana-3690	209	8	and	and	CCONJ
cana-3690	209	9	generalizability	generalizability	NOUN
cana-3690	209	10	.	.	PUNCT
cana-3690	210	1	random	random	ADJ
cana-3690	210	2	tree	tree	NOUN
cana-3690	210	3	provided	provide	VERB
cana-3690	210	4	comparable	comparable	ADJ
cana-3690	210	5	performance	performance	NOUN
cana-3690	210	6	with	with	ADP
cana-3690	210	7	reduced	reduced	ADJ
cana-3690	210	8	computational	computational	ADJ
cana-3690	210	9	requirements	requirement	NOUN
cana-3690	210	10	,	,	PUNCT
cana-3690	210	11	making	make	VERB
cana-3690	210	12	it	it	PRON
cana-3690	210	13	suitable	suitable	ADJ
cana-3690	210	14	for	for	ADP
cana-3690	210	15	resource	resource	NOUN
cana-3690	210	16	-	-	PUNCT
cana-3690	210	17	constrained	constrain	VERB
cana-3690	210	18	scenarios	scenario	NOUN
cana-3690	210	19	.	.	PUNCT
cana-3690	211	1	mlp	mlp	NOUN
cana-3690	211	2	excelled	excel	VERB
cana-3690	211	3	in	in	ADP
cana-3690	211	4	modelling	model	VERB
cana-3690	211	5	non	non	ADJ
cana-3690	211	6	-	-	ADJ
cana-3690	211	7	linear	linear	ADJ
cana-3690	211	8	relationships	relationship	NOUN
cana-3690	211	9	but	but	CCONJ
cana-3690	211	10	required	require	VERB
cana-3690	211	11	extensive	extensive	ADJ
cana-3690	211	12	computational	computational	ADJ
cana-3690	211	13	resources	resource	NOUN
cana-3690	211	14	,	,	PUNCT
cana-3690	211	15	presenting	present	VERB
cana-3690	211	16	a	a	DET
cana-3690	211	17	tradeoff	tradeoff	NOUN
cana-3690	211	18	between	between	ADP
cana-3690	211	19	accuracy	accuracy	NOUN
cana-3690	211	20	and	and	CCONJ
cana-3690	211	21	efficiency	efficiency	NOUN
cana-3690	211	22	.	.	PUNCT
cana-3690	212	1	the	the	DET
cana-3690	212	2	study	study	NOUN
cana-3690	212	3	underscores	underscore	VERB
cana-3690	212	4	the	the	DET
cana-3690	212	5	importance	importance	NOUN
cana-3690	212	6	of	of	ADP
cana-3690	212	7	selecting	select	VERB
cana-3690	212	8	appropriate	appropriate	ADJ
cana-3690	212	9	algorithms	algorithm	NOUN
cana-3690	212	10	tailored	tailor	VERB
cana-3690	212	11	to	to	ADP
cana-3690	212	12	specific	specific	ADJ
cana-3690	212	13	datasets	dataset	NOUN
cana-3690	212	14	and	and	CCONJ
cana-3690	212	15	prediction	prediction	NOUN
cana-3690	212	16	requirements	requirement	NOUN
cana-3690	212	17	.	.	PUNCT
cana-3690	213	1	metrics	metric	NOUN
cana-3690	213	2	such	such	ADJ
cana-3690	213	3	as	as	ADP
cana-3690	213	4	mae	mae	PROPN
cana-3690	213	5	,	,	PUNCT
cana-3690	213	6	rmse	rmse	PROPN
cana-3690	213	7	,	,	PUNCT
cana-3690	213	8	rae	rae	PROPN
cana-3690	213	9	,	,	PUNCT
cana-3690	213	10	and	and	CCONJ
cana-3690	213	11	rrse	rrse	NOUN
cana-3690	213	12	offer	offer	VERB
cana-3690	213	13	comprehensive	comprehensive	ADJ
cana-3690	213	14	insights	insight	NOUN
cana-3690	213	15	into	into	ADP
cana-3690	213	16	model	model	NOUN
cana-3690	213	17	performance	performance	NOUN
cana-3690	213	18	,	,	PUNCT
cana-3690	213	19	enabling	enable	VERB
cana-3690	213	20	informed	informed	ADJ
cana-3690	213	21	decisionmaking	decisionmaking	NOUN
cana-3690	213	22	in	in	ADP
cana-3690	213	23	clinical	clinical	ADJ
cana-3690	213	24	settings	setting	NOUN
cana-3690	213	25	.	.	PUNCT
cana-3690	214	1	future	future	ADJ
cana-3690	214	2	research	research	NOUN
cana-3690	214	3	to	to	PART
cana-3690	214	4	build	build	VERB
cana-3690	214	5	upon	upon	SCONJ
cana-3690	214	6	these	these	DET
cana-3690	214	7	findings	finding	NOUN
cana-3690	214	8	,	,	PUNCT
cana-3690	214	9	the	the	DET
cana-3690	214	10	following	follow	VERB
cana-3690	214	11	research	research	NOUN
cana-3690	214	12	directions	direction	NOUN
cana-3690	214	13	are	be	AUX
cana-3690	214	14	proposed	propose	VERB
cana-3690	214	15	:	:	PUNCT
cana-3690	214	16	real	real	ADJ
cana-3690	214	17	-	-	PUNCT
cana-3690	214	18	time	time	NOUN
cana-3690	214	19	data	datum	NOUN
cana-3690	214	20	integration	integration	NOUN
cana-3690	214	21	:	:	PUNCT
cana-3690	214	22	incorporating	incorporate	VERB
cana-3690	214	23	real	real	ADJ
cana-3690	214	24	-	-	PUNCT
cana-3690	214	25	time	time	NOUN
cana-3690	214	26	data	datum	NOUN
cana-3690	214	27	from	from	ADP
cana-3690	214	28	wearable	wearable	ADJ
cana-3690	214	29	devices	device	NOUN
cana-3690	214	30	could	could	AUX
cana-3690	214	31	improve	improve	VERB
cana-3690	214	32	the	the	DET
cana-3690	214	33	models	model	NOUN
cana-3690	214	34	'	'	PART
cana-3690	214	35	applicability	applicability	NOUN
cana-3690	214	36	and	and	CCONJ
cana-3690	214	37	responsiveness	responsiveness	NOUN
cana-3690	214	38	.	.	PUNCT
cana-3690	215	1	hybrid	hybrid	ADJ
cana-3690	215	2	approaches	approach	NOUN
cana-3690	215	3	:	:	PUNCT
cana-3690	215	4	combining	combine	VERB
cana-3690	215	5	ensemble	ensemble	ADJ
cana-3690	215	6	techniques	technique	NOUN
cana-3690	215	7	(	(	PUNCT
cana-3690	215	8	e.g.	e.g.	ADV
cana-3690	215	9	,	,	PUNCT
cana-3690	215	10	random	random	ADJ
cana-3690	215	11	forest	forest	NOUN
cana-3690	215	12	)	)	PUNCT
cana-3690	215	13	with	with	ADP
cana-3690	215	14	neural	neural	ADJ
cana-3690	215	15	networks	network	NOUN
cana-3690	215	16	may	may	AUX
cana-3690	215	17	enhance	enhance	VERB
cana-3690	215	18	accuracy	accuracy	NOUN
cana-3690	215	19	for	for	ADP
cana-3690	215	20	complex	complex	ADJ
cana-3690	215	21	and	and	CCONJ
cana-3690	215	22	high	high	ADJ
cana-3690	215	23	-	-	PUNCT
cana-3690	215	24	dimensional	dimensional	ADJ
cana-3690	215	25	datasets	dataset	NOUN
cana-3690	215	26	.	.	PUNCT
cana-3690	216	1	feature	feature	NOUN
cana-3690	216	2	optimization	optimization	NOUN
cana-3690	216	3	:	:	PUNCT
cana-3690	216	4	leveraging	leverage	VERB
cana-3690	216	5	advanced	advanced	ADJ
cana-3690	216	6	feature	feature	NOUN
cana-3690	216	7	selection	selection	NOUN
cana-3690	216	8	techniques	technique	NOUN
cana-3690	216	9	to	to	PART
cana-3690	216	10	identify	identify	VERB
cana-3690	216	11	critical	critical	ADJ
cana-3690	216	12	predictors	predictor	NOUN
cana-3690	216	13	can	can	AUX
cana-3690	216	14	reduce	reduce	VERB
cana-3690	216	15	computational	computational	ADJ
cana-3690	216	16	overhead	overhead	NOUN
cana-3690	216	17	while	while	SCONJ
cana-3690	216	18	maintaining	maintain	VERB
cana-3690	216	19	precision	precision	NOUN
cana-3690	216	20	.	.	PUNCT
cana-3690	217	1	dataset	dataset	ADJ
cana-3690	217	2	diversity	diversity	NOUN
cana-3690	217	3	:	:	PUNCT
cana-3690	217	4	expanding	expand	VERB
cana-3690	217	5	the	the	DET
cana-3690	217	6	analysis	analysis	NOUN
cana-3690	217	7	to	to	PART
cana-3690	217	8	include	include	VERB
cana-3690	217	9	diverse	diverse	ADJ
cana-3690	217	10	demographic	demographic	ADJ
cana-3690	217	11	and	and	CCONJ
cana-3690	217	12	clinical	clinical	ADJ
cana-3690	217	13	datasets	dataset	NOUN
cana-3690	217	14	would	would	AUX
cana-3690	217	15	improve	improve	VERB
cana-3690	217	16	the	the	DET
cana-3690	217	17	models	model	NOUN
cana-3690	217	18	'	'	PART
cana-3690	217	19	generalizability	generalizability	NOUN
cana-3690	217	20	.	.	PUNCT
cana-3690	218	1	explainable	explainable	ADJ
cana-3690	218	2	ai	ai	NOUN
cana-3690	218	3	:	:	PUNCT
cana-3690	218	4	developing	develop	VERB
cana-3690	218	5	interpretable	interpretable	ADJ
cana-3690	218	6	models	model	NOUN
cana-3690	218	7	to	to	PART
cana-3690	218	8	provide	provide	VERB
cana-3690	218	9	actionable	actionable	ADJ
cana-3690	218	10	insights	insight	NOUN
cana-3690	218	11	will	will	AUX
cana-3690	218	12	increase	increase	VERB
cana-3690	218	13	trust	trust	NOUN
cana-3690	218	14	and	and	CCONJ
cana-3690	218	15	usability	usability	NOUN
cana-3690	218	16	in	in	ADP
cana-3690	218	17	healthcare	healthcare	NOUN
cana-3690	218	18	applications	application	NOUN
cana-3690	218	19	.	.	PUNCT
cana-3690	219	1	addressing	address	VERB
cana-3690	219	2	these	these	DET
cana-3690	219	3	areas	area	NOUN
cana-3690	219	4	can	can	AUX
cana-3690	219	5	advance	advance	VERB
cana-3690	219	6	predictive	predictive	ADJ
cana-3690	219	7	frameworks	framework	NOUN
cana-3690	219	8	,	,	PUNCT
cana-3690	219	9	contributing	contribute	VERB
cana-3690	219	10	to	to	ADP
cana-3690	219	11	improved	improve	VERB
cana-3690	219	12	heart	heart	NOUN
cana-3690	219	13	disease	disease	NOUN
cana-3690	219	14	risk	risk	NOUN
cana-3690	219	15	assessment	assessment	NOUN
cana-3690	219	16	and	and	CCONJ
cana-3690	219	17	personalized	personalize	VERB
cana-3690	219	18	healthcare	healthcare	NOUN
cana-3690	219	19	solutions	solution	NOUN
cana-3690	219	20	.	.	PUNCT
cana-3690	220	1	5	5	X
cana-3690	220	2	.	.	NUM
cana-3690	220	3	references	reference	NOUN
cana-3690	220	4	[	[	X
cana-3690	220	5	1	1	NUM
cana-3690	220	6	]	]	X
cana-3690	220	7	diwakar	diwakar	NOUN
cana-3690	220	8	,	,	PUNCT
cana-3690	220	9	m.	m.	NOUN
cana-3690	220	10	,	,	PUNCT
cana-3690	220	11	tripathi	tripathi	PROPN
cana-3690	220	12	,	,	PUNCT
cana-3690	220	13	a.	a.	PROPN
cana-3690	220	14	,	,	PUNCT
cana-3690	220	15	joshi	joshi	PROPN
cana-3690	220	16	,	,	PUNCT
cana-3690	220	17	k.	k.	PROPN
cana-3690	220	18	,	,	PUNCT
cana-3690	220	19	memoria	memoria	PROPN
cana-3690	220	20	,	,	PUNCT
cana-3690	220	21	m.	m.	NOUN
cana-3690	220	22	and	and	CCONJ
cana-3690	220	23	singh	singh	NOUN
cana-3690	220	24	,	,	PUNCT
cana-3690	220	25	p.	p.	PROPN
cana-3690	220	26	,	,	PUNCT
cana-3690	220	27	(	(	PUNCT
cana-3690	220	28	2021	2021	NUM
cana-3690	220	29	)	)	PUNCT
cana-3690	220	30	.	.	PUNCT
cana-3690	221	1	latest	late	ADJ
cana-3690	221	2	trends	trend	NOUN
cana-3690	221	3	on	on	ADP
cana-3690	221	4	heart	heart	NOUN
cana-3690	221	5	disease	disease	NOUN
cana-3690	221	6	prediction	prediction	NOUN
cana-3690	221	7	using	use	VERB
cana-3690	221	8	machine	machine	NOUN
cana-3690	221	9	learning	learning	NOUN
cana-3690	221	10	and	and	CCONJ
cana-3690	221	11	image	image	NOUN
cana-3690	221	12	fusion	fusion	NOUN
cana-3690	221	13	.	.	PUNCT
cana-3690	222	1	materials	material	NOUN
cana-3690	222	2	today	today	NOUN
cana-3690	222	3	:	:	PUNCT
cana-3690	222	4	proceedings	proceeding	NOUN
cana-3690	222	5	,	,	PUNCT
cana-3690	222	6	37	37	NUM
cana-3690	222	7	,	,	PUNCT
cana-3690	222	8	3213	3213	NUM
cana-3690	222	9	-	-	SYM
cana-3690	222	10	3218	3218	NUM
cana-3690	222	11	.	.	PUNCT
cana-3690	223	1	[	[	X
cana-3690	223	2	2	2	NUM
cana-3690	223	3	]	]	PUNCT
cana-3690	223	4	tougui	tougui	NOUN
cana-3690	223	5	,	,	PUNCT
cana-3690	223	6	i.	i.	PROPN
cana-3690	223	7	,	,	PUNCT
cana-3690	223	8	jilbab	jilbab	PROPN
cana-3690	223	9	,	,	PUNCT
cana-3690	223	10	a.	a.	NOUN
cana-3690	223	11	and	and	CCONJ
cana-3690	223	12	el	el	PROPN
cana-3690	223	13	mhamdi	mhamdi	PROPN
cana-3690	223	14	,	,	PUNCT
cana-3690	223	15	j.	j.	PROPN
cana-3690	223	16	,	,	PUNCT
cana-3690	223	17	(	(	PUNCT
cana-3690	223	18	2020	2020	NUM
cana-3690	223	19	)	)	PUNCT
cana-3690	223	20	.	.	PUNCT
cana-3690	224	1	heart	heart	NOUN
cana-3690	224	2	disease	disease	NOUN
cana-3690	224	3	classification	classification	NOUN
cana-3690	224	4	using	use	VERB
cana-3690	224	5	data	datum	NOUN
cana-3690	224	6	mining	mining	NOUN
cana-3690	224	7	tools	tool	NOUN
cana-3690	224	8	and	and	CCONJ
cana-3690	224	9	machine	machine	NOUN
cana-3690	224	10	learning	learn	VERB
cana-3690	224	11	techniques	technique	NOUN
cana-3690	224	12	.	.	PUNCT
cana-3690	225	1	health	health	NOUN
cana-3690	225	2	and	and	CCONJ
cana-3690	225	3	technology	technology	NOUN
cana-3690	225	4	,	,	PUNCT
cana-3690	225	5	10	10	NUM
cana-3690	225	6	,	,	PUNCT
cana-3690	225	7	1137	1137	NUM
cana-3690	225	8	-	-	SYM
cana-3690	225	9	1144	1144	NUM
cana-3690	225	10	.	.	PUNCT
cana-3690	226	1	[	[	X
cana-3690	226	2	3	3	NUM
cana-3690	226	3	]	]	SYM
cana-3690	226	4	sitar	sitar	NOUN
cana-3690	226	5	-	-	PUNCT
cana-3690	226	6	tăut	tăut	NOUN
cana-3690	226	7	,	,	PUNCT
cana-3690	226	8	a.	a.	NOUN
cana-3690	226	9	,	,	PUNCT
cana-3690	226	10	zdrenghea	zdrenghea	PROPN
cana-3690	226	11	,	,	PUNCT
cana-3690	226	12	d.	d.	PROPN
cana-3690	226	13	,	,	PUNCT
cana-3690	226	14	pop	pop	PROPN
cana-3690	226	15	,	,	PUNCT
cana-3690	226	16	d.	d.	NOUN
cana-3690	226	17	and	and	CCONJ
cana-3690	226	18	sitar	sitar	NOUN
cana-3690	226	19	-	-	PUNCT
cana-3690	226	20	tăut	tăut	PROPN
cana-3690	226	21	,	,	PUNCT
cana-3690	226	22	d.	d.	PROPN
cana-3690	226	23	,	,	PUNCT
cana-3690	226	24	(	(	PUNCT
cana-3690	226	25	2009	2009	NUM
cana-3690	226	26	)	)	PUNCT
cana-3690	226	27	.	.	PUNCT
cana-3690	227	1	using	use	VERB
cana-3690	227	2	machine	machine	NOUN
cana-3690	227	3	learning	learn	VERB
cana-3690	227	4	algorithms	algorithm	NOUN
cana-3690	227	5	in	in	ADP
cana-3690	227	6	cardiovascular	cardiovascular	ADJ
cana-3690	227	7	disease	disease	NOUN
cana-3690	227	8	risk	risk	NOUN
cana-3690	227	9	evaluation	evaluation	NOUN
cana-3690	227	10	.	.	PUNCT
cana-3690	228	1	age	age	NOUN
cana-3690	228	2	,	,	PUNCT
cana-3690	228	3	1(4	1(4	NUM
cana-3690	228	4	)	)	PUNCT
cana-3690	228	5	,	,	PUNCT
cana-3690	228	6	4	4	X
cana-3690	228	7	.	.	PUNCT
cana-3690	229	1	[	[	X
cana-3690	229	2	4	4	NUM
cana-3690	229	3	]	]	SYM
cana-3690	229	4	bhatla	bhatla	NOUN
cana-3690	229	5	,	,	PUNCT
cana-3690	229	6	n.	n.	NOUN
cana-3690	229	7	and	and	CCONJ
cana-3690	229	8	jyoti	jyoti	PROPN
cana-3690	229	9	,	,	PUNCT
cana-3690	229	10	k.	k.	PROPN
cana-3690	229	11	,	,	PUNCT
cana-3690	229	12	(	(	PUNCT
cana-3690	229	13	2012	2012	NUM
cana-3690	229	14	)	)	PUNCT
cana-3690	229	15	.	.	PUNCT
cana-3690	230	1	an	an	DET
cana-3690	230	2	analysis	analysis	NOUN
cana-3690	230	3	of	of	ADP
cana-3690	230	4	heart	heart	NOUN
cana-3690	230	5	disease	disease	NOUN
cana-3690	230	6	prediction	prediction	NOUN
cana-3690	230	7	using	use	VERB
cana-3690	230	8	different	different	ADJ
cana-3690	230	9	data	datum	NOUN
cana-3690	230	10	mining	mining	NOUN
cana-3690	230	11	techniques	technique	NOUN
cana-3690	230	12	.	.	PUNCT
cana-3690	231	1	international	international	ADJ
cana-3690	231	2	journal	journal	PROPN
cana-3690	231	3	of	of	ADP
cana-3690	231	4	engineering	engineering	NOUN
cana-3690	231	5	,	,	PUNCT
cana-3690	231	6	1(8	1(8	NUM
cana-3690	231	7	)	)	PUNCT
cana-3690	231	8	,	,	PUNCT
cana-3690	231	9	1	1	NUM
cana-3690	231	10	-	-	SYM
cana-3690	231	11	4	4	NUM
cana-3690	231	12	.	.	PUNCT
cana-3690	232	1	[	[	X
cana-3690	232	2	5	5	NUM
cana-3690	232	3	]	]	PUNCT
cana-3690	232	4	anitha	anitha	PROPN
cana-3690	232	5	,	,	PUNCT
cana-3690	232	6	s.	s.	PROPN
cana-3690	232	7	and	and	CCONJ
cana-3690	232	8	sridevi	sridevi	PROPN
cana-3690	232	9	,	,	PUNCT
cana-3690	232	10	n.	n.	NOUN
cana-3690	232	11	,	,	PUNCT
cana-3690	232	12	(	(	PUNCT
cana-3690	232	13	2019	2019	NUM
cana-3690	232	14	)	)	PUNCT
cana-3690	232	15	.	.	PUNCT
cana-3690	233	1	heart	heart	NOUN
cana-3690	233	2	disease	disease	NOUN
cana-3690	233	3	prediction	prediction	NOUN
cana-3690	233	4	using	use	VERB
cana-3690	233	5	data	datum	NOUN
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cana-3690	234	8	)	)	PUNCT
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cana-3690	237	2	in	in	ADP
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cana-3690	237	9	)	)	PUNCT
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cana-3690	247	6	)	)	PUNCT
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cana-3690	248	2	9	9	NUM
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cana-3690	251	5	.	.	PUNCT
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cana-3690	253	2	10	10	NUM
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cana-3690	258	10	)	)	PUNCT
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cana-3690	268	10	)	)	PUNCT
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cana-3690	269	10	.	.	PUNCT
cana-3690	270	1	relative	relative	ADJ
cana-3690	270	2	absolute	absolute	ADJ
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cana-3690	270	4	(	(	PUNCT
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cana-3690	270	6	)	)	PUNCT
cana-3690	270	7	–	–	PUNCT
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cana-3690	270	9	and	and	CCONJ
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cana-3690	270	11	.	.	PUNCT
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cana-3690	271	2	.	.	PUNCT
cana-3690	271	3	https://medium.com/@wchi/relative-absolute-error-rae-definition-and-examples-e37a24c1b566	https://medium.com/@wchi/relative-absolute-error-rae-definition-and-examples-e37a24c1b566	X
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cana-3690	272	3	]	]	X
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cana-3690	273	1	[	[	X
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cana-3690	273	3	]	]	X
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cana-3690	274	9	a	a	DET
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cana-3690	274	12	.	.	PUNCT
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cana-3690	275	5	,	,	PUNCT
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cana-3690	275	7	)	)	PUNCT
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cana-3690	277	10	.	.	PUNCT
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cana-3690	278	10	)	)	PUNCT
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cana-3690	279	3	]	]	X
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cana-3690	284	9	)	)	PUNCT
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cana-3690	287	8	)	)	PUNCT
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cana-3690	296	10	)	)	PUNCT
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cana-3690	299	8	)	)	PUNCT
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cana-3690	301	6	and	and	CCONJ
cana-3690	301	7	ensemble	ensemble	ADJ
cana-3690	301	8	methods	method	NOUN
cana-3690	301	9	for	for	ADP
cana-3690	301	10	heart	heart	NOUN
cana-3690	301	11	disease	disease	NOUN
cana-3690	301	12	classification	classification	NOUN
cana-3690	301	13	.	.	PUNCT
cana-3690	302	1	journal	journal	NOUN
cana-3690	302	2	of	of	ADP
cana-3690	302	3	computational	computational	ADJ
cana-3690	302	4	medicine	medicine	NOUN
cana-3690	302	5	,	,	PUNCT
cana-3690	302	6	18(3	18(3	NUM
cana-3690	302	7	)	)	PUNCT
cana-3690	302	8	,	,	PUNCT
cana-3690	302	9	222	222	NUM
cana-3690	302	10	-	-	SYM
cana-3690	302	11	235	235	NUM
cana-3690	302	12	.	.	PUNCT
cana-3690	303	1	https://machinelearningmastery.com/mean-absolute-error-mae-for-machine-learning/	https://machinelearningmastery.com/mean-absolute-error-mae-for-machine-learning/	PROPN
cana-3690	304	1	https://machinelearningmastery.com/mean-absolute-error-mae-for-machine-learning/	https://machinelearningmastery.com/mean-absolute-error-mae-for-machine-learning/	PROPN
cana-3690	304	2	https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset?resource=download	https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset?resource=download	ADV