id	sid	tid	token	lemma	pos
cana-6186	1	1	communications	communication	NOUN
cana-6186	1	2	on	on	ADP
cana-6186	1	3	applied	apply	VERB
cana-6186	1	4	nonlinear	nonlinear	ADJ
cana-6186	1	5	analysis	analysis	NOUN
cana-6186	1	6	issn	issn	NOUN
cana-6186	1	7	:	:	PUNCT
cana-6186	1	8	1074	1074	NUM
cana-6186	1	9	-	-	PUNCT
cana-6186	1	10	133x	133x	NUM
cana-6186	1	11	vol	vol	NOUN
cana-6186	1	12	32	32	NUM
cana-6186	1	13	no	no	NOUN
cana-6186	1	14	.	.	NOUN
cana-6186	1	15	1	1	NUM
cana-6186	1	16	(	(	PUNCT
cana-6186	1	17	2025	2025	NUM
cana-6186	1	18	)	)	PUNCT
cana-6186	1	19	656	656	NUM
cana-6186	1	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-6186	1	21	cardiovascular	cardiovascular	ADJ
cana-6186	1	22	disease	disease	NOUN
cana-6186	1	23	classification	classification	NOUN
cana-6186	1	24	using	use	VERB
cana-6186	1	25	advanced	advanced	ADJ
cana-6186	1	26	machine	machine	NOUN
cana-6186	1	27	learning	learn	VERB
cana-6186	1	28	techniques	technique	NOUN
cana-6186	1	29	:	:	PUNCT
cana-6186	1	30	a	a	DET
cana-6186	1	31	comparative	comparative	ADJ
cana-6186	1	32	study	study	NOUN
cana-6186	1	33	ms	ms	PROPN
cana-6186	1	34	.	.	PROPN
cana-6186	1	35	pritibala	pritibala	PROPN
cana-6186	1	36	sudhakar	sudhakar	PROPN
cana-6186	1	37	ingle	ingle	PROPN
cana-6186	1	38	phd	phd	PROPN
cana-6186	1	39	scholar	scholar	NOUN
cana-6186	1	40	,	,	PUNCT
cana-6186	1	41	computer	computer	NOUN
cana-6186	1	42	management	management	NOUN
cana-6186	1	43	,	,	PUNCT
cana-6186	1	44	imcc	imcc	ADJ
cana-6186	1	45	,	,	PUNCT
cana-6186	1	46	pune	pune	NOUN
cana-6186	1	47	,	,	PUNCT
cana-6186	1	48	india	india	PROPN
cana-6186	1	49	prity.ingle@gmail.com	prity.ingle@gmail.com	PROPN
cana-6186	1	50	dr	dr	PROPN
cana-6186	1	51	.	.	PROPN
cana-6186	1	52	santosh	santosh	PROPN
cana-6186	1	53	deshpande	deshpande	PROPN
cana-6186	1	54	phd	phd	PROPN
cana-6186	1	55	guide	guide	NOUN
cana-6186	1	56	,	,	PUNCT
cana-6186	1	57	computer	computer	NOUN
cana-6186	1	58	management	management	NOUN
cana-6186	1	59	,	,	PUNCT
cana-6186	1	60	imcc	imcc	ADJ
cana-6186	1	61	,	,	PUNCT
cana-6186	1	62	pune	pune	NOUN
cana-6186	1	63	,	,	PUNCT
cana-6186	1	64	india	india	PROPN
cana-6186	1	65	director.imcc@mespune.in	director.imcc@mespune.in	NOUN
cana-6186	1	66	article	article	NOUN
cana-6186	1	67	history	history	NOUN
cana-6186	1	68	:	:	PUNCT
cana-6186	1	69	received	receive	VERB
cana-6186	1	70	:	:	PUNCT
cana-6186	1	71	02/02/2025	02/02/2025	NUM
cana-6186	1	72	revised	revise	VERB
cana-6186	1	73	:	:	PUNCT
cana-6186	1	74	15/03/2025	15/03/2025	NUM
cana-6186	1	75	accepted	accept	VERB
cana-6186	1	76	:	:	PUNCT
cana-6186	1	77	25/03/2025	25/03/2025	NUM
cana-6186	1	78	abstract	abstract	NOUN
cana-6186	1	79	:	:	PUNCT
cana-6186	1	80	cardiovascular	cardiovascular	ADJ
cana-6186	1	81	disease	disease	NOUN
cana-6186	1	82	is	be	AUX
cana-6186	1	83	still	still	ADV
cana-6186	1	84	a	a	DET
cana-6186	1	85	leading	lead	VERB
cana-6186	1	86	cause	cause	NOUN
cana-6186	1	87	of	of	ADP
cana-6186	1	88	death	death	NOUN
cana-6186	1	89	and	and	CCONJ
cana-6186	1	90	requires	require	VERB
cana-6186	1	91	accurate	accurate	ADJ
cana-6186	1	92	diagnostic	diagnostic	ADJ
cana-6186	1	93	tools	tool	NOUN
cana-6186	1	94	.	.	PUNCT
cana-6186	2	1	this	this	DET
cana-6186	2	2	research	research	NOUN
cana-6186	2	3	presents	present	VERB
cana-6186	2	4	a	a	DET
cana-6186	2	5	machine	machine	NOUN
cana-6186	2	6	learning	learning	NOUN
cana-6186	2	7	-	-	PUNCT
cana-6186	2	8	based	base	VERB
cana-6186	2	9	approach	approach	NOUN
cana-6186	2	10	using	use	VERB
cana-6186	2	11	algorithms	algorithm	NOUN
cana-6186	2	12	like	like	ADP
cana-6186	2	13	svm	svm	PROPN
cana-6186	2	14	,	,	PUNCT
cana-6186	2	15	knn	knn	PROPN
cana-6186	2	16	,	,	PUNCT
cana-6186	2	17	decision	decision	NOUN
cana-6186	2	18	tree	tree	NOUN
cana-6186	2	19	,	,	PUNCT
cana-6186	2	20	random	random	ADJ
cana-6186	2	21	forest	forest	NOUN
cana-6186	2	22	,	,	PUNCT
cana-6186	2	23	and	and	CCONJ
cana-6186	2	24	gradient	gradient	NOUN
cana-6186	2	25	boosting	boost	VERB
cana-6186	2	26	for	for	ADP
cana-6186	2	27	disease	disease	NOUN
cana-6186	2	28	classification	classification	NOUN
cana-6186	2	29	.	.	PUNCT
cana-6186	3	1	the	the	DET
cana-6186	3	2	dataset	dataset	NOUN
cana-6186	3	3	was	be	AUX
cana-6186	3	4	preprocessed	preprocesse	VERB
cana-6186	3	5	by	by	ADP
cana-6186	3	6	tackling	tackle	VERB
cana-6186	3	7	the	the	DET
cana-6186	3	8	missing	miss	VERB
cana-6186	3	9	values	value	NOUN
cana-6186	3	10	problem	problem	NOUN
cana-6186	3	11	and	and	CCONJ
cana-6186	3	12	feature	feature	NOUN
cana-6186	3	13	standardization	standardization	NOUN
cana-6186	3	14	to	to	PART
cana-6186	3	15	obtain	obtain	VERB
cana-6186	3	16	critical	critical	ADJ
cana-6186	3	17	predictors	predictor	NOUN
cana-6186	3	18	using	use	VERB
cana-6186	3	19	feature	feature	NOUN
cana-6186	3	20	selection	selection	NOUN
cana-6186	3	21	methods	method	NOUN
cana-6186	3	22	.	.	PUNCT
cana-6186	4	1	these	these	DET
cana-6186	4	2	algorithms	algorithm	NOUN
cana-6186	4	3	were	be	AUX
cana-6186	4	4	assessed	assess	VERB
cana-6186	4	5	by	by	ADP
cana-6186	4	6	the	the	DET
cana-6186	4	7	following	follow	VERB
cana-6186	4	8	metrics	metric	NOUN
cana-6186	4	9	such	such	ADJ
cana-6186	4	10	as	as	ADP
cana-6186	4	11	precision	precision	NOUN
cana-6186	4	12	,	,	PUNCT
cana-6186	4	13	recall	recall	NOUN
cana-6186	4	14	,	,	PUNCT
cana-6186	4	15	f1	f1	NOUN
cana-6186	4	16	-	-	PUNCT
cana-6186	4	17	score	score	NOUN
cana-6186	4	18	,	,	PUNCT
cana-6186	4	19	and	and	CCONJ
cana-6186	4	20	accuracy	accuracy	NOUN
cana-6186	4	21	.	.	PUNCT
cana-6186	5	1	after	after	ADP
cana-6186	5	2	pre	pre	ADJ
cana-6186	5	3	-	-	ADJ
cana-6186	5	4	processing	process	VERB
cana-6186	5	5	the	the	DET
cana-6186	5	6	dataset	dataset	NOUN
cana-6186	5	7	,	,	PUNCT
cana-6186	5	8	gradient	gradient	NOUN
cana-6186	5	9	boosting	boosting	NOUN
cana-6186	5	10	achieved	achieve	VERB
cana-6186	5	11	the	the	DET
cana-6186	5	12	highest	high	ADJ
cana-6186	5	13	accuracy	accuracy	NOUN
cana-6186	5	14	(	(	PUNCT
cana-6186	5	15	92	92	NUM
cana-6186	5	16	%	%	NOUN
cana-6186	5	17	)	)	PUNCT
cana-6186	5	18	,	,	PUNCT
cana-6186	5	19	followed	follow	VERB
cana-6186	5	20	by	by	ADP
cana-6186	5	21	random	random	ADJ
cana-6186	5	22	forest	forest	NOUN
cana-6186	5	23	(	(	PUNCT
cana-6186	5	24	89	89	NUM
cana-6186	5	25	%	%	NOUN
cana-6186	5	26	)	)	PUNCT
cana-6186	5	27	.	.	PUNCT
cana-6186	6	1	key	key	ADJ
cana-6186	6	2	predictors	predictor	NOUN
cana-6186	6	3	like	like	ADP
cana-6186	6	4	cholesterol	cholesterol	NOUN
cana-6186	6	5	,	,	PUNCT
cana-6186	6	6	blood	blood	NOUN
cana-6186	6	7	pressure	pressure	NOUN
cana-6186	6	8	,	,	PUNCT
cana-6186	6	9	and	and	CCONJ
cana-6186	6	10	age	age	NOUN
cana-6186	6	11	were	be	AUX
cana-6186	6	12	identified	identify	VERB
cana-6186	6	13	.	.	PUNCT
cana-6186	7	1	the	the	DET
cana-6186	7	2	study	study	NOUN
cana-6186	7	3	shows	show	VERB
cana-6186	7	4	that	that	SCONJ
cana-6186	7	5	ensemble	ensemble	ADJ
cana-6186	7	6	methods	method	NOUN
cana-6186	7	7	have	have	VERB
cana-6186	7	8	potential	potential	NOUN
cana-6186	7	9	in	in	ADP
cana-6186	7	10	medical	medical	ADJ
cana-6186	7	11	datasets	dataset	NOUN
cana-6186	7	12	but	but	CCONJ
cana-6186	7	13	identifies	identify	VERB
cana-6186	7	14	the	the	DET
cana-6186	7	15	challenges	challenge	NOUN
cana-6186	7	16	of	of	ADP
cana-6186	7	17	data	datum	NOUN
cana-6186	7	18	imbalance	imbalance	NOUN
cana-6186	7	19	and	and	CCONJ
cana-6186	7	20	limited	limited	ADJ
cana-6186	7	21	generalizability	generalizability	NOUN
cana-6186	7	22	and	and	CCONJ
cana-6186	7	23	encourages	encourage	VERB
cana-6186	7	24	future	future	ADJ
cana-6186	7	25	work	work	NOUN
cana-6186	7	26	with	with	ADP
cana-6186	7	27	deep	deep	ADJ
cana-6186	7	28	learning	learning	NOUN
cana-6186	7	29	and	and	CCONJ
cana-6186	7	30	larger	large	ADJ
cana-6186	7	31	datasets	dataset	NOUN
cana-6186	7	32	to	to	PART
cana-6186	7	33	improve	improve	VERB
cana-6186	7	34	early	early	ADJ
cana-6186	7	35	diagnosis	diagnosis	NOUN
cana-6186	7	36	and	and	CCONJ
cana-6186	7	37	patient	patient	ADJ
cana-6186	7	38	outcomes	outcome	NOUN
cana-6186	7	39	.	.	PUNCT
cana-6186	8	1	keywords	keyword	NOUN
cana-6186	8	2	—	—	PUNCT
cana-6186	8	3	facial	facial	ADJ
cana-6186	8	4	paralysis	paralysis	NOUN
cana-6186	8	5	recognition	recognition	NOUN
cana-6186	8	6	,	,	PUNCT
cana-6186	8	7	deep	deep	ADJ
cana-6186	8	8	learning	learning	NOUN
cana-6186	8	9	,	,	PUNCT
cana-6186	8	10	machine	machine	NOUN
cana-6186	8	11	learning	learning	NOUN
cana-6186	8	12	,	,	PUNCT
cana-6186	8	13	facial	facial	ADJ
cana-6186	8	14	landmarks	landmark	NOUN
cana-6186	8	15	,	,	PUNCT
cana-6186	8	16	generative	generative	ADJ
cana-6186	8	17	adversarial	adversarial	ADJ
cana-6186	8	18	networks	network	NOUN
cana-6186	8	19	,	,	PUNCT
cana-6186	8	20	convolutional	convolutional	ADJ
cana-6186	8	21	neural	neural	ADJ
cana-6186	8	22	networks	network	NOUN
cana-6186	8	23	introduction	introduction	NOUN
cana-6186	8	24	heart	heart	NOUN
cana-6186	8	25	disease	disease	NOUN
cana-6186	8	26	includes	include	VERB
cana-6186	8	27	all	all	DET
cana-6186	8	28	diseases	disease	NOUN
cana-6186	8	29	of	of	ADP
cana-6186	8	30	the	the	DET
cana-6186	8	31	heart	heart	NOUN
cana-6186	8	32	and	and	CCONJ
cana-6186	8	33	the	the	DET
cana-6186	8	34	larger	large	ADJ
cana-6186	8	35	blood	blood	NOUN
cana-6186	8	36	vessels	vessel	NOUN
cana-6186	8	37	,	,	PUNCT
cana-6186	8	38	and	and	CCONJ
cana-6186	8	39	it	it	PRON
cana-6186	8	40	is	be	AUX
cana-6186	8	41	an	an	DET
cana-6186	8	42	immense	immense	ADJ
cana-6186	8	43	challenge	challenge	NOUN
cana-6186	8	44	to	to	ADP
cana-6186	8	45	health	health	NOUN
cana-6186	8	46	on	on	ADP
cana-6186	8	47	earth	earth	NOUN
cana-6186	8	48	.	.	PUNCT
cana-6186	9	1	according	accord	VERB
cana-6186	9	2	to	to	ADP
cana-6186	9	3	statistics	statistic	NOUN
cana-6186	9	4	,	,	PUNCT
cana-6186	9	5	cvds	cvds	NOUN
cana-6186	9	6	by	by	ADP
cana-6186	9	7	the	the	DET
cana-6186	9	8	world	world	PROPN
cana-6186	9	9	health	health	PROPN
cana-6186	9	10	organization	organization	NOUN
cana-6186	9	11	happen	happen	VERB
cana-6186	9	12	to	to	PART
cana-6186	9	13	be	be	AUX
cana-6186	9	14	the	the	DET
cana-6186	9	15	leading	lead	VERB
cana-6186	9	16	causes	cause	NOUN
cana-6186	9	17	of	of	ADP
cana-6186	9	18	death	death	NOUN
cana-6186	9	19	,	,	PUNCT
cana-6186	9	20	accounting	account	VERB
cana-6186	9	21	nearly	nearly	ADV
cana-6186	9	22	17.9	17.9	NUM
cana-6186	9	23	million	million	NUM
cana-6186	9	24	lives	life	NOUN
cana-6186	9	25	lost	lose	VERB
cana-6186	9	26	every	every	DET
cana-6186	9	27	year	year	NOUN
cana-6186	9	28	,	,	PUNCT
cana-6186	9	29	including	include	VERB
cana-6186	9	30	coronary	coronary	ADJ
cana-6186	9	31	artery	artery	NOUN
cana-6186	9	32	disease	disease	NOUN
cana-6186	9	33	,	,	PUNCT
cana-6186	9	34	arrhythmias	arrhythmia	NOUN
cana-6186	9	35	,	,	PUNCT
cana-6186	9	36	heart	heart	NOUN
cana-6186	9	37	failure	failure	NOUN
cana-6186	9	38	,	,	PUNCT
cana-6186	9	39	and	and	CCONJ
cana-6186	9	40	others.[2	others.[2	ADJ
cana-6186	9	41	]	]	X
cana-6186	9	42	this	this	PRON
cana-6186	9	43	is	be	AUX
cana-6186	9	44	alarming	alarming	ADJ
cana-6186	9	45	and	and	CCONJ
cana-6186	9	46	mostly	mostly	ADV
cana-6186	9	47	due	due	ADJ
cana-6186	9	48	to	to	ADP
cana-6186	9	49	a	a	DET
cana-6186	9	50	sedentary	sedentary	ADJ
cana-6186	9	51	lifestyle	lifestyle	NOUN
cana-6186	9	52	,	,	PUNCT
cana-6186	9	53	poor	poor	ADJ
cana-6186	9	54	dietary	dietary	ADJ
cana-6186	9	55	intake	intake	NOUN
cana-6186	9	56	,	,	PUNCT
cana-6186	9	57	aging	age	VERB
cana-6186	9	58	populations	population	NOUN
cana-6186	9	59	,	,	PUNCT
cana-6186	9	60	and	and	CCONJ
cana-6186	9	61	hereditary	hereditary	ADJ
cana-6186	9	62	influence	influence	NOUN
cana-6186	9	63	.	.	PUNCT
cana-6186	10	1	it	it	PRON
cana-6186	10	2	is	be	AUX
cana-6186	10	3	with	with	ADP
cana-6186	10	4	these	these	DET
cana-6186	10	5	improvements	improvement	NOUN
cana-6186	10	6	in	in	ADP
cana-6186	10	7	the	the	DET
cana-6186	10	8	medical	medical	ADJ
cana-6186	10	9	sciences	science	NOUN
cana-6186	10	10	that	that	PRON
cana-6186	10	11	early	early	ADJ
cana-6186	10	12	detection	detection	NOUN
cana-6186	10	13	and	and	CCONJ
cana-6186	10	14	treatment	treatment	NOUN
cana-6186	10	15	become	become	VERB
cana-6186	10	16	critical	critical	ADJ
cana-6186	10	17	issues	issue	NOUN
cana-6186	10	18	for	for	ADP
cana-6186	10	19	improving	improve	VERB
cana-6186	10	20	patient	patient	ADJ
cana-6186	10	21	care	care	NOUN
cana-6186	10	22	as	as	ADV
cana-6186	10	23	well	well	ADV
cana-6186	10	24	as	as	ADP
cana-6186	10	25	mortality	mortality	NOUN
cana-6186	10	26	rates	rate	NOUN
cana-6186	10	27	.	.	PUNCT
cana-6186	11	1	traditional	traditional	ADJ
cana-6186	11	2	diagnostic	diagnostic	ADJ
cana-6186	11	3	methods	method	NOUN
cana-6186	11	4	,	,	PUNCT
cana-6186	11	5	though	though	ADV
cana-6186	11	6	,	,	PUNCT
cana-6186	11	7	often	often	ADV
cana-6186	11	8	fail	fail	VERB
cana-6186	11	9	because	because	SCONJ
cana-6186	11	10	of	of	ADP
cana-6186	11	11	reliance	reliance	NOUN
cana-6186	11	12	on	on	ADP
cana-6186	11	13	human	human	ADJ
cana-6186	11	14	judgment	judgment	NOUN
cana-6186	11	15	,	,	PUNCT
cana-6186	11	16	expense	expense	NOUN
cana-6186	11	17	,	,	PUNCT
cana-6186	11	18	and	and	CCONJ
cana-6186	11	19	the	the	DET
cana-6186	11	20	extensive	extensive	ADJ
cana-6186	11	21	time	time	NOUN
cana-6186	11	22	required.[2	required.[2	X
cana-6186	11	23	]	]	PUNCT
cana-6186	12	1	this	this	PRON
cana-6186	12	2	all	all	DET
cana-6186	12	3	the	the	PRON
cana-6186	12	4	more	more	ADV
cana-6186	12	5	necessitates	necessitate	VERB
cana-6186	12	6	automation	automation	NOUN
cana-6186	12	7	,	,	PUNCT
cana-6186	12	8	efficiency	efficiency	NOUN
cana-6186	12	9	,	,	PUNCT
cana-6186	12	10	and	and	CCONJ
cana-6186	12	11	scalability	scalability	NOUN
cana-6186	12	12	of	of	ADP
cana-6186	12	13	the	the	DET
cana-6186	12	14	solutions	solution	NOUN
cana-6186	12	15	.	.	PUNCT
cana-6186	13	1	mailto:prity.ingle@gmail.com	mailto:prity.ingle@gmail.com	X
cana-6186	13	2	mailto:director.imcc@mespune.in	mailto:director.imcc@mespune.in	PROPN
cana-6186	13	3	communications	communication	NOUN
cana-6186	13	4	on	on	ADP
cana-6186	13	5	applied	apply	VERB
cana-6186	13	6	nonlinear	nonlinear	ADJ
cana-6186	13	7	analysis	analysis	NOUN
cana-6186	13	8	issn	issn	NOUN
cana-6186	13	9	:	:	PUNCT
cana-6186	13	10	1074	1074	NUM
cana-6186	13	11	-	-	PUNCT
cana-6186	13	12	133x	133x	NUM
cana-6186	13	13	vol	vol	NOUN
cana-6186	13	14	32	32	NUM
cana-6186	13	15	no	no	NOUN
cana-6186	13	16	.	.	NOUN
cana-6186	13	17	1	1	NUM
cana-6186	13	18	(	(	PUNCT
cana-6186	13	19	2025	2025	NUM
cana-6186	13	20	)	)	PUNCT
cana-6186	13	21	657	657	NUM
cana-6186	13	22	https://internationalpubls.com	https://internationalpubls.com	X
cana-6186	13	23	of	of	ADP
cana-6186	13	24	late	late	ADV
cana-6186	13	25	,	,	PUNCT
cana-6186	13	26	ai	ai	VERB
cana-6186	13	27	has	have	AUX
cana-6186	13	28	been	be	AUX
cana-6186	13	29	incorporated	incorporate	VERB
cana-6186	13	30	with	with	ADP
cana-6186	13	31	the	the	DET
cana-6186	13	32	health	health	NOUN
cana-6186	13	33	sector	sector	NOUN
cana-6186	13	34	,	,	PUNCT
cana-6186	13	35	changing	change	VERB
cana-6186	13	36	the	the	DET
cana-6186	13	37	very	very	ADJ
cana-6186	13	38	framework	framework	NOUN
cana-6186	13	39	of	of	ADP
cana-6186	13	40	medical	medical	ADJ
cana-6186	13	41	diagnosis	diagnosis	NOUN
cana-6186	13	42	.	.	PUNCT
cana-6186	14	1	machine	machine	NOUN
cana-6186	14	2	learning	learning	NOUN
cana-6186	14	3	has	have	AUX
cana-6186	14	4	revealed	reveal	VERB
cana-6186	14	5	tremendous	tremendous	ADJ
cana-6186	14	6	potential	potential	NOUN
cana-6186	14	7	in	in	ADP
cana-6186	14	8	terms	term	NOUN
cana-6186	14	9	of	of	ADP
cana-6186	14	10	predictions	prediction	NOUN
cana-6186	14	11	and	and	CCONJ
cana-6186	14	12	disease	disease	NOUN
cana-6186	14	13	identification	identification	NOUN
cana-6186	14	14	from	from	ADP
cana-6186	14	15	analysing	analyse	VERB
cana-6186	14	16	complex	complex	ADJ
cana-6186	14	17	datasets	dataset	NOUN
cana-6186	14	18	.	.	PUNCT
cana-6186	15	1	such	such	ADJ
cana-6186	15	2	models	model	NOUN
cana-6186	15	3	of	of	ADP
cana-6186	15	4	ml	ml	PUNCT
cana-6186	15	5	can	can	AUX
cana-6186	15	6	identify	identify	VERB
cana-6186	15	7	patterns	pattern	NOUN
cana-6186	15	8	and	and	CCONJ
cana-6186	15	9	relationships	relationship	NOUN
cana-6186	15	10	within	within	ADP
cana-6186	15	11	medical	medical	ADJ
cana-6186	15	12	data	datum	NOUN
cana-6186	15	13	that	that	PRON
cana-6186	15	14	are	be	AUX
cana-6186	15	15	often	often	ADV
cana-6186	15	16	missed	miss	VERB
cana-6186	15	17	out	out	ADP
cana-6186	15	18	on	on	ADP
cana-6186	15	19	by	by	ADP
cana-6186	15	20	traditional	traditional	ADJ
cana-6186	15	21	approaches	approach	NOUN
cana-6186	15	22	.	.	PUNCT
cana-6186	16	1	the	the	DET
cana-6186	16	2	use	use	NOUN
cana-6186	16	3	of	of	ADP
cana-6186	16	4	machine	machine	NOUN
cana-6186	16	5	learning	learn	VERB
cana-6186	16	6	algorithms	algorithm	NOUN
cana-6186	16	7	in	in	ADP
cana-6186	16	8	the	the	DET
cana-6186	16	9	classification	classification	NOUN
cana-6186	16	10	of	of	ADP
cana-6186	16	11	heart	heart	NOUN
cana-6186	16	12	disease	disease	NOUN
cana-6186	16	13	utilizes	utilize	VERB
cana-6186	16	14	patient	patient	ADJ
cana-6186	16	15	details	detail	NOUN
cana-6186	16	16	,	,	PUNCT
cana-6186	16	17	including	include	VERB
cana-6186	16	18	age	age	NOUN
cana-6186	16	19	,	,	PUNCT
cana-6186	16	20	levels	level	NOUN
cana-6186	16	21	of	of	ADP
cana-6186	16	22	cholesterol	cholesterol	NOUN
cana-6186	16	23	,	,	PUNCT
cana-6186	16	24	blood	blood	NOUN
cana-6186	16	25	pressure	pressure	NOUN
cana-6186	16	26	,	,	PUNCT
cana-6186	16	27	and	and	CCONJ
cana-6186	16	28	other	other	ADJ
cana-6186	16	29	clinical	clinical	ADJ
cana-6186	16	30	conditions	condition	NOUN
cana-6186	16	31	,	,	PUNCT
cana-6186	16	32	to	to	PART
cana-6186	16	33	predict	predict	VERB
cana-6186	16	34	the	the	DET
cana-6186	16	35	chance	chance	NOUN
cana-6186	16	36	of	of	ADP
cana-6186	16	37	having	have	VERB
cana-6186	16	38	a	a	DET
cana-6186	16	39	cardiovascular	cardiovascular	ADJ
cana-6186	16	40	illness.[10	illness.[10	NOUN
cana-6186	16	41	]	]	PUNCT
cana-6186	16	42	in	in	ADP
cana-6186	16	43	this	this	DET
cana-6186	16	44	way	way	NOUN
cana-6186	16	45	,	,	PUNCT
cana-6186	16	46	automation	automation	NOUN
cana-6186	16	47	improves	improve	VERB
cana-6186	16	48	the	the	DET
cana-6186	16	49	speed	speed	NOUN
cana-6186	16	50	and	and	CCONJ
cana-6186	16	51	efficiency	efficiency	NOUN
cana-6186	16	52	of	of	ADP
cana-6186	16	53	the	the	DET
cana-6186	16	54	diagnostic	diagnostic	ADJ
cana-6186	16	55	process	process	NOUN
cana-6186	16	56	while	while	SCONJ
cana-6186	16	57	increasing	increase	VERB
cana-6186	16	58	accuracy	accuracy	NOUN
cana-6186	16	59	and	and	CCONJ
cana-6186	16	60	replicability	replicability	NOUN
cana-6186	16	61	dramatically	dramatically	ADV
cana-6186	16	62	,	,	PUNCT
cana-6186	16	63	making	make	VERB
cana-6186	16	64	this	this	PRON
cana-6186	16	65	an	an	DET
cana-6186	16	66	indispensable	indispensable	ADJ
cana-6186	16	67	tool	tool	NOUN
cana-6186	16	68	in	in	ADP
cana-6186	16	69	conquering	conquer	VERB
cana-6186	16	70	the	the	DET
cana-6186	16	71	weaknesses	weakness	NOUN
cana-6186	16	72	of	of	ADP
cana-6186	16	73	classical	classical	ADJ
cana-6186	16	74	methods	method	NOUN
cana-6186	16	75	.	.	PUNCT
cana-6186	17	1	[	[	X
cana-6186	17	2	10	10	NUM
cana-6186	17	3	]	]	X
cana-6186	17	4	the	the	DET
cana-6186	17	5	early	early	ADJ
cana-6186	17	6	identification	identification	NOUN
cana-6186	17	7	of	of	ADP
cana-6186	17	8	cardiac	cardiac	ADJ
cana-6186	17	9	diseases	disease	NOUN
cana-6186	17	10	is	be	AUX
cana-6186	17	11	crucial	crucial	ADJ
cana-6186	17	12	to	to	PART
cana-6186	17	13	prevent	prevent	VERB
cana-6186	17	14	severe	severe	ADJ
cana-6186	17	15	complications	complication	NOUN
cana-6186	17	16	and	and	CCONJ
cana-6186	17	17	enable	enable	VERB
cana-6186	17	18	appropriate	appropriate	ADJ
cana-6186	17	19	interventions	intervention	NOUN
cana-6186	17	20	.	.	PUNCT
cana-6186	18	1	however	however	ADV
cana-6186	18	2	,	,	PUNCT
cana-6186	18	3	traditional	traditional	ADJ
cana-6186	18	4	methods	method	NOUN
cana-6186	18	5	like	like	ADP
cana-6186	18	6	echocardiography	echocardiography	NOUN
cana-6186	18	7	,	,	PUNCT
cana-6186	18	8	stress	stress	NOUN
cana-6186	18	9	testing	testing	NOUN
cana-6186	18	10	,	,	PUNCT
cana-6186	18	11	and	and	CCONJ
cana-6186	18	12	invasive	invasive	ADJ
cana-6186	18	13	procedures	procedure	NOUN
cana-6186	18	14	require	require	VERB
cana-6186	18	15	sophisticated	sophisticated	ADJ
cana-6186	18	16	equipment	equipment	NOUN
cana-6186	18	17	and	and	CCONJ
cana-6186	18	18	expertise	expertise	NOUN
cana-6186	18	19	and	and	CCONJ
cana-6186	18	20	are	be	AUX
cana-6186	18	21	often	often	ADV
cana-6186	18	22	unavailable	unavailable	ADJ
cana-6186	18	23	in	in	ADP
cana-6186	18	24	resource	resource	NOUN
cana-6186	18	25	-	-	PUNCT
cana-6186	18	26	poor	poor	ADJ
cana-6186	18	27	settings	setting	NOUN
cana-6186	18	28	.	.	PUNCT
cana-6186	19	1	such	such	ADJ
cana-6186	19	2	methods	method	NOUN
cana-6186	19	3	can	can	AUX
cana-6186	19	4	be	be	AUX
cana-6186	19	5	time	time	NOUN
cana-6186	19	6	-	-	PUNCT
cana-6186	19	7	consuming	consume	VERB
cana-6186	19	8	and	and	CCONJ
cana-6186	19	9	costly	costly	ADJ
cana-6186	19	10	and	and	CCONJ
cana-6186	19	11	,	,	PUNCT
cana-6186	19	12	therefore	therefore	ADV
cana-6186	19	13	,	,	PUNCT
cana-6186	19	14	are	be	AUX
cana-6186	19	15	a	a	DET
cana-6186	19	16	barrier	barrier	NOUN
cana-6186	19	17	to	to	ADP
cana-6186	19	18	mass	mass	ADJ
cana-6186	19	19	screening	screening	NOUN
cana-6186	19	20	and	and	CCONJ
cana-6186	19	21	early	early	ADJ
cana-6186	19	22	detection	detection	NOUN
cana-6186	19	23	.	.	PUNCT
cana-6186	20	1	the	the	DET
cana-6186	20	2	machine	machine	NOUN
cana-6186	20	3	learning	learning	NOUN
cana-6186	20	4	-	-	PUNCT
cana-6186	20	5	based	base	VERB
cana-6186	20	6	automated	automate	VERB
cana-6186	20	7	systems	system	NOUN
cana-6186	20	8	are	be	AUX
cana-6186	20	9	offering	offer	VERB
cana-6186	20	10	nondisruptive	nondisruptive	ADJ
cana-6186	20	11	,	,	PUNCT
cana-6186	20	12	cost	cost	NOUN
cana-6186	20	13	-	-	PUNCT
cana-6186	20	14	effective	effective	ADJ
cana-6186	20	15	,	,	PUNCT
cana-6186	20	16	and	and	CCONJ
cana-6186	20	17	highly	highly	ADV
cana-6186	20	18	accurate	accurate	ADJ
cana-6186	20	19	solutions	solution	NOUN
cana-6186	20	20	to	to	PART
cana-6186	20	21	predict	predict	VERB
cana-6186	20	22	heart	heart	NOUN
cana-6186	20	23	disease	disease	NOUN
cana-6186	20	24	.	.	PUNCT
cana-6186	21	1	machine	machine	NOUN
cana-6186	21	2	learning	learning	NOUN
cana-6186	21	3	models	model	NOUN
cana-6186	21	4	are	be	AUX
cana-6186	21	5	helping	help	VERB
cana-6186	21	6	healthcare	healthcare	NOUN
cana-6186	21	7	providers	provider	NOUN
cana-6186	21	8	take	take	VERB
cana-6186	21	9	proactive	proactive	ADJ
cana-6186	21	10	steps	step	NOUN
cana-6186	21	11	and	and	CCONJ
cana-6186	21	12	enable	enable	VERB
cana-6186	21	13	early	early	ADJ
cana-6186	21	14	intervention	intervention	NOUN
cana-6186	21	15	for	for	ADP
cana-6186	21	16	better	well	ADJ
cana-6186	21	17	patient	patient	ADJ
cana-6186	21	18	care	care	NOUN
cana-6186	21	19	.	.	PUNCT
cana-6186	22	1	despite	despite	SCONJ
cana-6186	22	2	its	its	PRON
cana-6186	22	3	promising	promising	ADJ
cana-6186	22	4	future	future	NOUN
cana-6186	22	5	,	,	PUNCT
cana-6186	22	6	applying	apply	VERB
cana-6186	22	7	machine	machine	NOUN
cana-6186	22	8	learning	learn	VERB
cana-6186	22	9	techniques	technique	NOUN
cana-6186	22	10	in	in	ADP
cana-6186	22	11	the	the	DET
cana-6186	22	12	task	task	NOUN
cana-6186	22	13	of	of	ADP
cana-6186	22	14	heart	heart	NOUN
cana-6186	22	15	disease	disease	NOUN
cana-6186	22	16	classification	classification	NOUN
cana-6186	22	17	presents	present	VERB
cana-6186	22	18	various	various	ADJ
cana-6186	22	19	challenges	challenge	NOUN
cana-6186	22	20	.	.	PUNCT
cana-6186	23	1	one	one	NUM
cana-6186	23	2	key	key	ADJ
cana-6186	23	3	challenge	challenge	NOUN
cana-6186	23	4	is	be	AUX
cana-6186	23	5	the	the	DET
cana-6186	23	6	quality	quality	NOUN
cana-6186	23	7	and	and	CCONJ
cana-6186	23	8	diversity	diversity	NOUN
cana-6186	23	9	of	of	ADP
cana-6186	23	10	the	the	DET
cana-6186	23	11	training	training	NOUN
cana-6186	23	12	data	datum	NOUN
cana-6186	23	13	used	use	VERB
cana-6186	23	14	in	in	ADP
cana-6186	23	15	these	these	DET
cana-6186	23	16	models	model	NOUN
cana-6186	23	17	.	.	PUNCT
cana-6186	24	1	poor	poor	ADJ
cana-6186	24	2	or	or	CCONJ
cana-6186	24	3	biased	biased	ADJ
cana-6186	24	4	training	training	NOUN
cana-6186	24	5	datasets	dataset	NOUN
cana-6186	24	6	will	will	AUX
cana-6186	24	7	result	result	VERB
cana-6186	24	8	in	in	ADP
cana-6186	24	9	models	model	NOUN
cana-6186	24	10	that	that	PRON
cana-6186	24	11	do	do	AUX
cana-6186	24	12	not	not	PART
cana-6186	24	13	generalize	generalize	VERB
cana-6186	24	14	well	well	ADV
cana-6186	24	15	across	across	ADP
cana-6186	24	16	populations	population	NOUN
cana-6186	24	17	,	,	PUNCT
cana-6186	24	18	thereby	thereby	ADV
cana-6186	24	19	limiting	limit	VERB
cana-6186	24	20	their	their	PRON
cana-6186	24	21	potential	potential	NOUN
cana-6186	24	22	for	for	ADP
cana-6186	24	23	deployment	deployment	NOUN
cana-6186	24	24	in	in	ADP
cana-6186	24	25	real	real	ADJ
cana-6186	24	26	-	-	PUNCT
cana-6186	24	27	world	world	NOUN
cana-6186	24	28	settings	setting	NOUN
cana-6186	24	29	.	.	PUNCT
cana-6186	25	1	second	second	ADV
cana-6186	25	2	,	,	PUNCT
cana-6186	25	3	the	the	DET
cana-6186	25	4	choice	choice	NOUN
cana-6186	25	5	of	of	ADP
cana-6186	25	6	features	feature	NOUN
cana-6186	25	7	as	as	ADV
cana-6186	25	8	well	well	ADV
cana-6186	25	9	as	as	ADP
cana-6186	25	10	algorithms	algorithm	NOUN
cana-6186	25	11	is	be	AUX
cana-6186	25	12	vital	vital	ADJ
cana-6186	25	13	for	for	ADP
cana-6186	25	14	the	the	DET
cana-6186	25	15	performance	performance	NOUN
cana-6186	25	16	of	of	ADP
cana-6186	25	17	the	the	DET
cana-6186	25	18	model	model	NOUN
cana-6186	25	19	.	.	PUNCT
cana-6186	26	1	erroneous	erroneous	ADJ
cana-6186	26	2	predictions	prediction	NOUN
cana-6186	26	3	might	might	AUX
cana-6186	26	4	arise	arise	VERB
cana-6186	26	5	from	from	ADP
cana-6186	26	6	poorly	poorly	ADV
cana-6186	26	7	selected	select	VERB
cana-6186	26	8	features	feature	NOUN
cana-6186	26	9	or	or	CCONJ
cana-6186	26	10	less	less	ADJ
cana-6186	26	11	than	than	ADP
cana-6186	26	12	ideal	ideal	ADJ
cana-6186	26	13	algorithms.[24	algorithms.[24	PROPN
cana-6186	26	14	]	]	PUNCT
cana-6186	26	15	the	the	DET
cana-6186	26	16	second	second	ADJ
cana-6186	26	17	major	major	ADJ
cana-6186	26	18	barrier	barrier	NOUN
cana-6186	26	19	of	of	ADP
cana-6186	26	20	interest	interest	NOUN
cana-6186	26	21	is	be	AUX
cana-6186	26	22	the	the	DET
cana-6186	26	23	interpretability	interpretability	NOUN
cana-6186	26	24	of	of	ADP
cana-6186	26	25	machine	machine	NOUN
cana-6186	26	26	learning	learning	NOUN
cana-6186	26	27	models	model	NOUN
cana-6186	26	28	themselves	themselves	PRON
cana-6186	26	29	.	.	PUNCT
cana-6186	27	1	clinicians	clinician	NOUN
cana-6186	27	2	frequently	frequently	ADV
cana-6186	27	3	crave	crave	VERB
cana-6186	27	4	transparency	transparency	NOUN
cana-6186	27	5	in	in	ADP
cana-6186	27	6	the	the	DET
cana-6186	27	7	decision	decision	NOUN
cana-6186	27	8	making	make	VERB
cana-6186	27	9	process	process	NOUN
cana-6186	27	10	that	that	SCONJ
cana-6186	27	11	such	such	ADJ
cana-6186	27	12	complicated	complicated	ADJ
cana-6186	27	13	"	"	PUNCT
cana-6186	27	14	black	black	ADJ
cana-6186	27	15	-	-	PUNCT
cana-6186	27	16	box	box	NOUN
cana-6186	27	17	"	"	PUNCT
cana-6186	27	18	models	model	NOUN
cana-6186	27	19	have	have	AUX
cana-6186	27	20	been	be	AUX
cana-6186	27	21	known	know	VERB
cana-6186	27	22	not	not	PART
cana-6186	27	23	to	to	PART
cana-6186	27	24	dependably	dependably	ADV
cana-6186	27	25	deliver	deliver	VERB
cana-6186	27	26	.	.	PUNCT
cana-6186	28	1	the	the	DET
cana-6186	28	2	primary	primary	ADJ
cana-6186	28	3	aim	aim	NOUN
cana-6186	28	4	of	of	ADP
cana-6186	28	5	this	this	DET
cana-6186	28	6	research	research	NOUN
cana-6186	28	7	work	work	NOUN
cana-6186	28	8	is	be	AUX
cana-6186	28	9	to	to	PART
cana-6186	28	10	develop	develop	VERB
cana-6186	28	11	an	an	DET
cana-6186	28	12	efficient	efficient	ADJ
cana-6186	28	13	and	and	CCONJ
cana-6186	28	14	robust	robust	ADJ
cana-6186	28	15	machine	machine	NOUN
cana-6186	28	16	learning	learning	NOUN
cana-6186	28	17	-	-	PUNCT
cana-6186	28	18	based	base	VERB
cana-6186	28	19	approach	approach	NOUN
cana-6186	28	20	for	for	ADP
cana-6186	28	21	the	the	DET
cana-6186	28	22	classification	classification	NOUN
cana-6186	28	23	of	of	ADP
cana-6186	28	24	heart	heart	NOUN
cana-6186	28	25	disease	disease	NOUN
cana-6186	28	26	.	.	PUNCT
cana-6186	29	1	this	this	DET
cana-6186	29	2	study	study	NOUN
cana-6186	29	3	mainly	mainly	ADV
cana-6186	29	4	focuses	focus	VERB
cana-6186	29	5	on	on	ADP
cana-6186	29	6	pre	pre	ADJ
cana-6186	29	7	-	-	ADJ
cana-6186	29	8	processing	process	VERB
cana-6186	29	9	the	the	DET
cana-6186	29	10	dataset	dataset	NOUN
cana-6186	29	11	to	to	PART
cana-6186	29	12	ensure	ensure	VERB
cana-6186	29	13	high	high	ADJ
cana-6186	29	14	quality	quality	NOUN
cana-6186	29	15	input	input	NOUN
cana-6186	29	16	,	,	PUNCT
cana-6186	29	17	selecting	select	VERB
cana-6186	29	18	features	feature	NOUN
cana-6186	29	19	for	for	ADP
cana-6186	29	20	precise	precise	ADJ
cana-6186	29	21	predictions	prediction	NOUN
cana-6186	29	22	and	and	CCONJ
cana-6186	29	23	assessing	assess	VERB
cana-6186	29	24	several	several	ADJ
cana-6186	29	25	machine	machine	NOUN
cana-6186	29	26	learning	learn	VERB
cana-6186	29	27	techniques	technique	NOUN
cana-6186	29	28	to	to	PART
cana-6186	29	29	identify	identify	VERB
cana-6186	29	30	the	the	DET
cana-6186	29	31	best	good	ADJ
cana-6186	29	32	model	model	NOUN
cana-6186	29	33	.	.	PUNCT
cana-6186	30	1	the	the	DET
cana-6186	30	2	methodology	methodology	NOUN
cana-6186	30	3	proposed	propose	VERB
cana-6186	30	4	in	in	ADP
cana-6186	30	5	this	this	DET
cana-6186	30	6	study	study	NOUN
cana-6186	30	7	would	would	AUX
cana-6186	30	8	overcome	overcome	VERB
cana-6186	30	9	the	the	DET
cana-6186	30	10	deficiencies	deficiency	NOUN
cana-6186	30	11	of	of	ADP
cana-6186	30	12	the	the	DET
cana-6186	30	13	existing	exist	VERB
cana-6186	30	14	techniques	technique	NOUN
cana-6186	30	15	by	by	ADP
cana-6186	30	16	giving	give	VERB
cana-6186	30	17	more	more	ADJ
cana-6186	30	18	importance	importance	NOUN
cana-6186	30	19	to	to	ADP
cana-6186	30	20	interpretability	interpretability	NOUN
cana-6186	30	21	,	,	PUNCT
cana-6186	30	22	scalability	scalability	NOUN
cana-6186	30	23	,	,	PUNCT
cana-6186	30	24	and	and	CCONJ
cana-6186	30	25	adaptability	adaptability	NOUN
cana-6186	30	26	to	to	ADP
cana-6186	30	27	varied	varied	ADJ
cana-6186	30	28	datasets	dataset	NOUN
cana-6186	30	29	.	.	PUNCT
cana-6186	31	1	thus	thus	ADV
cana-6186	31	2	,	,	PUNCT
cana-6186	31	3	this	this	DET
cana-6186	31	4	study	study	NOUN
cana-6186	31	5	also	also	ADV
cana-6186	31	6	aims	aim	VERB
cana-6186	31	7	for	for	ADP
cana-6186	31	8	the	the	DET
cana-6186	31	9	way	way	NOUN
cana-6186	31	10	to	to	PART
cana-6186	31	11	bridge	bridge	VERB
cana-6186	31	12	advanced	advanced	ADJ
cana-6186	31	13	technological	technological	ADJ
cana-6186	31	14	inputs	input	NOUN
cana-6186	31	15	with	with	ADP
cana-6186	31	16	real	real	ADJ
cana-6186	31	17	-	-	PUNCT
cana-6186	31	18	world	world	NOUN
cana-6186	31	19	applications	application	NOUN
cana-6186	31	20	by	by	ADP
cana-6186	31	21	supporting	support	VERB
cana-6186	31	22	innovative	innovative	ADJ
cana-6186	31	23	healthcare	healthcare	NOUN
cana-6186	31	24	services	service	NOUN
cana-6186	31	25	that	that	PRON
cana-6186	31	26	may	may	AUX
cana-6186	31	27	bring	bring	VERB
cana-6186	31	28	excellence	excellence	NOUN
cana-6186	31	29	among	among	ADP
cana-6186	31	30	patients	patient	NOUN
cana-6186	31	31	and	and	CCONJ
cana-6186	31	32	their	their	PRON
cana-6186	31	33	families	family	NOUN
cana-6186	31	34	.	.	PUNCT
cana-6186	32	1	the	the	DET
cana-6186	32	2	significance	significance	NOUN
cana-6186	32	3	of	of	ADP
cana-6186	32	4	this	this	DET
cana-6186	32	5	research	research	NOUN
cana-6186	32	6	is	be	AUX
cana-6186	32	7	multi	multi	ADJ
cana-6186	32	8	fold	fold	ADV
cana-6186	32	9	.	.	PUNCT
cana-6186	33	1	first	first	ADV
cana-6186	33	2	,	,	PUNCT
cana-6186	33	3	the	the	DET
cana-6186	33	4	proposed	propose	VERB
cana-6186	33	5	approach	approach	NOUN
cana-6186	33	6	does	do	VERB
cana-6186	33	7	a	a	DET
cana-6186	33	8	comprehensive	comprehensive	ADJ
cana-6186	33	9	evaluation	evaluation	NOUN
cana-6186	33	10	of	of	ADP
cana-6186	33	11	a	a	DET
cana-6186	33	12	set	set	NOUN
cana-6186	33	13	of	of	ADP
cana-6186	33	14	machine	machine	NOUN
cana-6186	33	15	learning	learn	VERB
cana-6186	33	16	algorithms	algorithm	NOUN
cana-6186	33	17	used	use	VERB
cana-6186	33	18	for	for	ADP
cana-6186	33	19	heart	heart	NOUN
cana-6186	33	20	disease	disease	NOUN
cana-6186	33	21	classification	classification	NOUN
cana-6186	33	22	.	.	PUNCT
cana-6186	34	1	therefore	therefore	ADV
cana-6186	34	2	,	,	PUNCT
cana-6186	34	3	it	it	PRON
cana-6186	34	4	provides	provide	VERB
cana-6186	34	5	insights	insight	NOUN
cana-6186	34	6	about	about	ADP
cana-6186	34	7	their	their	PRON
cana-6186	34	8	comparative	comparative	ADJ
cana-6186	34	9	performance	performance	NOUN
cana-6186	34	10	.	.	PUNCT
cana-6186	35	1	secondly	secondly	ADV
cana-6186	35	2	,	,	PUNCT
cana-6186	35	3	it	it	PRON
cana-6186	35	4	emphasizes	emphasize	VERB
cana-6186	35	5	the	the	DET
cana-6186	35	6	critical	critical	ADJ
cana-6186	35	7	role	role	NOUN
cana-6186	35	8	of	of	ADP
cana-6186	35	9	feature	feature	NOUN
cana-6186	35	10	selection	selection	NOUN
cana-6186	35	11	and	and	CCONJ
cana-6186	35	12	engineering	engineering	NOUN
cana-6186	35	13	in	in	ADP
cana-6186	35	14	improving	improve	VERB
cana-6186	35	15	model	model	NOUN
cana-6186	35	16	accuracy	accuracy	NOUN
cana-6186	35	17	and	and	CCONJ
cana-6186	35	18	reliability	reliability	NOUN
cana-6186	35	19	.	.	PUNCT
cana-6186	36	1	finally	finally	ADV
cana-6186	36	2	,	,	PUNCT
cana-6186	36	3	it	it	PRON
cana-6186	36	4	draws	draw	VERB
cana-6186	36	5	attention	attention	NOUN
cana-6186	36	6	to	to	ADP
cana-6186	36	7	the	the	DET
cana-6186	36	8	issue	issue	NOUN
cana-6186	36	9	of	of	ADP
cana-6186	36	10	scalability	scalability	NOUN
cana-6186	36	11	,	,	PUNCT
cana-6186	36	12	ensuring	ensure	VERB
cana-6186	36	13	that	that	SCONJ
cana-6186	36	14	the	the	DET
cana-6186	36	15	system	system	NOUN
cana-6186	36	16	being	be	AUX
cana-6186	36	17	proposed	propose	VERB
cana-6186	36	18	should	should	AUX
cana-6186	36	19	effectively	effectively	ADV
cana-6186	36	20	be	be	AUX
cana-6186	36	21	deployable	deployable	ADJ
cana-6186	36	22	in	in	ADP
cana-6186	36	23	the	the	DET
cana-6186	36	24	clinical	clinical	ADJ
cana-6186	36	25	setup	setup	NOUN
cana-6186	36	26	.	.	PUNCT
cana-6186	37	1	finally	finally	ADV
cana-6186	37	2	,	,	PUNCT
cana-6186	37	3	by	by	ADP
cana-6186	37	4	identifying	identify	VERB
cana-6186	37	5	limitations	limitation	NOUN
cana-6186	37	6	and	and	CCONJ
cana-6186	37	7	proposing	propose	VERB
cana-6186	37	8	future	future	ADJ
cana-6186	37	9	directions	direction	NOUN
cana-6186	37	10	,	,	PUNCT
cana-6186	37	11	the	the	DET
cana-6186	37	12	developed	develop	VERB
cana-6186	37	13	research	research	NOUN
cana-6186	37	14	will	will	AUX
cana-6186	37	15	set	set	VERB
cana-6186	37	16	the	the	DET
cana-6186	37	17	basis	basis	NOUN
cana-6186	37	18	for	for	ADP
cana-6186	37	19	further	further	ADJ
cana-6186	37	20	advancements	advancement	NOUN
cana-6186	37	21	into	into	ADP
cana-6186	37	22	ai	ai	ADJ
cana-6186	37	23	-	-	PUNCT
cana-6186	37	24	driven	drive	VERB
cana-6186	37	25	healthcare	healthcare	NOUN
cana-6186	37	26	solutions	solution	NOUN
cana-6186	37	27	.	.	PUNCT
cana-6186	38	1	communications	communication	NOUN
cana-6186	38	2	on	on	ADP
cana-6186	38	3	applied	apply	VERB
cana-6186	38	4	nonlinear	nonlinear	ADJ
cana-6186	38	5	analysis	analysis	NOUN
cana-6186	38	6	issn	issn	NOUN
cana-6186	38	7	:	:	PUNCT
cana-6186	38	8	1074	1074	NUM
cana-6186	38	9	-	-	PUNCT
cana-6186	38	10	133x	133x	NUM
cana-6186	38	11	vol	vol	NOUN
cana-6186	38	12	32	32	NUM
cana-6186	38	13	no	no	NOUN
cana-6186	38	14	.	.	NOUN
cana-6186	38	15	1	1	NUM
cana-6186	38	16	(	(	PUNCT
cana-6186	38	17	2025	2025	NUM
cana-6186	38	18	)	)	PUNCT
cana-6186	38	19	658	658	NUM
cana-6186	38	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-6186	38	21	several	several	ADJ
cana-6186	38	22	challenges	challenge	NOUN
cana-6186	38	23	are	be	AUX
cana-6186	38	24	inherent	inherent	ADJ
cana-6186	38	25	in	in	ADP
cana-6186	38	26	the	the	DET
cana-6186	38	27	development	development	NOUN
cana-6186	38	28	of	of	ADP
cana-6186	38	29	a	a	DET
cana-6186	38	30	machine	machine	NOUN
cana-6186	38	31	learning	learning	NOUN
cana-6186	38	32	-	-	PUNCT
cana-6186	38	33	based	base	VERB
cana-6186	38	34	heart	heart	NOUN
cana-6186	38	35	disease	disease	NOUN
cana-6186	38	36	classification	classification	NOUN
cana-6186	38	37	system	system	NOUN
cana-6186	38	38	.	.	PUNCT
cana-6186	39	1	ensuring	ensure	VERB
cana-6186	39	2	the	the	DET
cana-6186	39	3	quality	quality	NOUN
cana-6186	39	4	and	and	CCONJ
cana-6186	39	5	diversity	diversity	NOUN
cana-6186	39	6	of	of	ADP
cana-6186	39	7	the	the	DET
cana-6186	39	8	training	training	NOUN
cana-6186	39	9	dataset	dataset	NOUN
cana-6186	39	10	is	be	AUX
cana-6186	39	11	paramount	paramount	ADJ
cana-6186	39	12	because	because	SCONJ
cana-6186	39	13	poor	poor	ADJ
cana-6186	39	14	or	or	CCONJ
cana-6186	39	15	noisy	noisy	ADJ
cana-6186	39	16	data	datum	NOUN
cana-6186	39	17	can	can	AUX
cana-6186	39	18	adversely	adversely	ADV
cana-6186	39	19	affect	affect	VERB
cana-6186	39	20	model	model	NOUN
cana-6186	39	21	performance	performance	NOUN
cana-6186	39	22	.	.	PUNCT
cana-6186	40	1	effective	effective	ADJ
cana-6186	40	2	feature	feature	NOUN
cana-6186	40	3	selection	selection	NOUN
cana-6186	40	4	is	be	AUX
cana-6186	40	5	also	also	ADV
cana-6186	40	6	important	important	ADJ
cana-6186	40	7	since	since	SCONJ
cana-6186	40	8	redundant	redundant	ADJ
cana-6186	40	9	or	or	CCONJ
cana-6186	40	10	unnecessary	unnecessary	ADJ
cana-6186	40	11	features	feature	NOUN
cana-6186	40	12	may	may	AUX
cana-6186	40	13	result	result	VERB
cana-6186	40	14	in	in	ADP
cana-6186	40	15	noise	noise	NOUN
cana-6186	40	16	and	and	CCONJ
cana-6186	40	17	overfitting	overfitting	NOUN
cana-6186	40	18	.	.	PUNCT
cana-6186	41	1	the	the	DET
cana-6186	41	2	choice	choice	NOUN
cana-6186	41	3	of	of	ADP
cana-6186	41	4	algorithm	algorithm	NOUN
cana-6186	41	5	is	be	AUX
cana-6186	41	6	also	also	ADV
cana-6186	41	7	a	a	DET
cana-6186	41	8	deciding	decide	VERB
cana-6186	41	9	factor	factor	NOUN
cana-6186	41	10	;	;	PUNCT
cana-6186	41	11	while	while	SCONJ
cana-6186	41	12	some	some	DET
cana-6186	41	13	algorithms	algorithm	NOUN
cana-6186	41	14	are	be	AUX
cana-6186	41	15	excellent	excellent	ADJ
cana-6186	41	16	in	in	ADP
cana-6186	41	17	high	high	ADJ
cana-6186	41	18	-	-	PUNCT
cana-6186	41	19	dimensional	dimensional	ADJ
cana-6186	41	20	data	datum	NOUN
cana-6186	41	21	,	,	PUNCT
cana-6186	41	22	others	other	NOUN
cana-6186	41	23	are	be	AUX
cana-6186	41	24	geared	gear	VERB
cana-6186	41	25	for	for	ADP
cana-6186	41	26	simple	simple	ADJ
cana-6186	41	27	tasks	task	NOUN
cana-6186	41	28	.	.	PUNCT
cana-6186	42	1	balance	balance	NOUN
cana-6186	42	2	between	between	ADP
cana-6186	42	3	accuracy	accuracy	NOUN
cana-6186	42	4	and	and	CCONJ
cana-6186	42	5	interpretability	interpretability	NOUN
cana-6186	42	6	still	still	ADV
cana-6186	42	7	persists	persist	VERB
cana-6186	42	8	because	because	SCONJ
cana-6186	42	9	clinicians	clinician	NOUN
cana-6186	42	10	need	need	VERB
cana-6186	42	11	explicit	explicit	ADJ
cana-6186	42	12	explanations	explanation	NOUN
cana-6186	42	13	for	for	ADP
cana-6186	42	14	model	model	NOUN
cana-6186	42	15	predictions	prediction	NOUN
cana-6186	42	16	to	to	PART
cana-6186	42	17	introduce	introduce	VERB
cana-6186	42	18	such	such	ADJ
cana-6186	42	19	tools	tool	NOUN
cana-6186	42	20	into	into	ADP
cana-6186	42	21	their	their	PRON
cana-6186	42	22	practices	practice	NOUN
cana-6186	42	23	.	.	PUNCT
cana-6186	43	1	generalizability	generalizability	NOUN
cana-6186	43	2	to	to	ADP
cana-6186	43	3	various	various	ADJ
cana-6186	43	4	populations	population	NOUN
cana-6186	43	5	is	be	AUX
cana-6186	43	6	necessary	necessary	ADJ
cana-6186	43	7	to	to	PART
cana-6186	43	8	gain	gain	VERB
cana-6186	43	9	wider	wide	ADJ
cana-6186	43	10	acceptance	acceptance	NOUN
cana-6186	43	11	of	of	ADP
cana-6186	43	12	such	such	ADJ
cana-6186	43	13	systems	system	NOUN
cana-6186	43	14	.	.	PUNCT
cana-6186	44	1	this	this	DET
cana-6186	44	2	research	research	NOUN
cana-6186	44	3	is	be	AUX
cana-6186	44	4	important	important	ADJ
cana-6186	44	5	because	because	SCONJ
cana-6186	44	6	it	it	PRON
cana-6186	44	7	deals	deal	VERB
cana-6186	44	8	with	with	ADP
cana-6186	44	9	the	the	DET
cana-6186	44	10	long	long	ADV
cana-6186	44	11	-	-	PUNCT
cana-6186	44	12	needed	need	VERB
cana-6186	44	13	demand	demand	NOUN
cana-6186	44	14	for	for	ADP
cana-6186	44	15	automated	automate	VERB
cana-6186	44	16	,	,	PUNCT
cana-6186	44	17	trustworthy	trustworthy	ADJ
cana-6186	44	18	,	,	PUNCT
cana-6186	44	19	and	and	CCONJ
cana-6186	44	20	scalable	scalable	ADJ
cana-6186	44	21	diagnostic	diagnostic	ADJ
cana-6186	44	22	applications	application	NOUN
cana-6186	44	23	in	in	ADP
cana-6186	44	24	cardiology	cardiology	NOUN
cana-6186	44	25	.	.	PUNCT
cana-6186	45	1	since	since	SCONJ
cana-6186	45	2	it	it	PRON
cana-6186	45	3	uses	use	VERB
cana-6186	45	4	advanced	advanced	ADJ
cana-6186	45	5	machine	machine	NOUN
cana-6186	45	6	learning	learn	VERB
cana-6186	45	7	techniques	technique	NOUN
cana-6186	45	8	,	,	PUNCT
cana-6186	45	9	the	the	DET
cana-6186	45	10	proposed	propose	VERB
cana-6186	45	11	solution	solution	NOUN
cana-6186	45	12	presents	present	VERB
cana-6186	45	13	a	a	DET
cana-6186	45	14	way	way	NOUN
cana-6186	45	15	toward	toward	ADP
cana-6186	45	16	making	make	VERB
cana-6186	45	17	better	well	ADJ
cana-6186	45	18	and	and	CCONJ
cana-6186	45	19	more	more	ADV
cana-6186	45	20	efficient	efficient	ADJ
cana-6186	45	21	prediction	prediction	NOUN
cana-6186	45	22	of	of	ADP
cana-6186	45	23	heart	heart	NOUN
cana-6186	45	24	disease	disease	NOUN
cana-6186	45	25	.	.	PUNCT
cana-6186	46	1	through	through	ADP
cana-6186	46	2	this	this	DET
cana-6186	46	3	research	research	NOUN
cana-6186	46	4	,	,	PUNCT
cana-6186	46	5	it	it	PRON
cana-6186	46	6	could	could	AUX
cana-6186	46	7	not	not	PART
cana-6186	46	8	only	only	ADV
cana-6186	46	9	show	show	VERB
cana-6186	46	10	the	the	DET
cana-6186	46	11	importance	importance	NOUN
cana-6186	46	12	of	of	ADP
cana-6186	46	13	ml	ml	NOUN
cana-6186	46	14	in	in	ADP
cana-6186	46	15	medical	medical	ADJ
cana-6186	46	16	diagnosis	diagnosis	NOUN
cana-6186	46	17	but	but	CCONJ
cana-6186	46	18	also	also	ADV
cana-6186	46	19	provide	provide	VERB
cana-6186	46	20	practical	practical	ADJ
cana-6186	46	21	ways	way	NOUN
cana-6186	46	22	that	that	PRON
cana-6186	46	23	can	can	AUX
cana-6186	46	24	be	be	AUX
cana-6186	46	25	used	use	VERB
cana-6186	46	26	by	by	ADP
cana-6186	46	27	others	other	NOUN
cana-6186	46	28	for	for	ADP
cana-6186	46	29	future	future	ADJ
cana-6186	46	30	studies	study	NOUN
cana-6186	46	31	in	in	ADP
cana-6186	46	32	aiming	aim	VERB
cana-6186	46	33	to	to	PART
cana-6186	46	34	integrate	integrate	VERB
cana-6186	46	35	ai	ai	VERB
cana-6186	46	36	into	into	ADP
cana-6186	46	37	their	their	PRON
cana-6186	46	38	healthcare	healthcare	NOUN
cana-6186	46	39	applications	application	NOUN
cana-6186	46	40	.	.	PUNCT
cana-6186	47	1	this	this	DET
cana-6186	47	2	paper	paper	NOUN
cana-6186	47	3	is	be	AUX
cana-6186	47	4	divided	divide	VERB
cana-6186	47	5	into	into	ADP
cana-6186	47	6	the	the	DET
cana-6186	47	7	following	follow	VERB
cana-6186	47	8	sections	section	NOUN
cana-6186	47	9	:	:	PUNCT
cana-6186	47	10	introduction	introduction	NOUN
cana-6186	47	11	.	.	PUNCT
cana-6186	48	1	the	the	DET
cana-6186	48	2	introduction	introduction	NOUN
cana-6186	48	3	presents	present	VERB
cana-6186	48	4	the	the	DET
cana-6186	48	5	relevance	relevance	NOUN
cana-6186	48	6	of	of	ADP
cana-6186	48	7	heart	heart	NOUN
cana-6186	48	8	disease	disease	NOUN
cana-6186	48	9	diagnosis	diagnosis	NOUN
cana-6186	48	10	and	and	CCONJ
cana-6186	48	11	machine	machine	NOUN
cana-6186	48	12	learning	learning	NOUN
cana-6186	48	13	.	.	PUNCT
cana-6186	49	1	literature	literature	NOUN
cana-6186	49	2	survey	survey	NOUN
cana-6186	49	3	.	.	PUNCT
cana-6186	50	1	related	related	ADJ
cana-6186	50	2	work	work	NOUN
cana-6186	50	3	has	have	AUX
cana-6186	50	4	been	be	AUX
cana-6186	50	5	discussed	discuss	VERB
cana-6186	50	6	along	along	ADP
cana-6186	50	7	with	with	ADP
cana-6186	50	8	identification	identification	NOUN
cana-6186	50	9	of	of	ADP
cana-6186	50	10	gaps	gap	NOUN
cana-6186	50	11	in	in	ADP
cana-6186	50	12	research	research	NOUN
cana-6186	50	13	.	.	PUNCT
cana-6186	51	1	methodology	methodology	NOUN
cana-6186	51	2	.	.	PUNCT
cana-6186	52	1	dataset	dataset	NOUN
cana-6186	52	2	,	,	PUNCT
cana-6186	52	3	preprocessing	preprocessing	NOUN
cana-6186	52	4	,	,	PUNCT
cana-6186	52	5	feature	feature	NOUN
cana-6186	52	6	extraction	extraction	NOUN
cana-6186	52	7	,	,	PUNCT
cana-6186	52	8	and	and	CCONJ
cana-6186	52	9	implementation	implementation	NOUN
cana-6186	52	10	of	of	ADP
cana-6186	52	11	svm	svm	PROPN
cana-6186	52	12	,	,	PUNCT
cana-6186	52	13	knn	knn	PROPN
cana-6186	52	14	,	,	PUNCT
cana-6186	52	15	decision	decision	NOUN
cana-6186	52	16	tree	tree	NOUN
cana-6186	52	17	,	,	PUNCT
cana-6186	52	18	random	random	ADJ
cana-6186	52	19	forest	forest	NOUN
cana-6186	52	20	,	,	PUNCT
cana-6186	52	21	and	and	CCONJ
cana-6186	52	22	gradient	gradient	ADJ
cana-6186	52	23	boosting	boosting	NOUN
cana-6186	52	24	.	.	PUNCT
cana-6186	53	1	results	result	NOUN
cana-6186	53	2	and	and	CCONJ
cana-6186	53	3	discussion	discussion	NOUN
cana-6186	53	4	.	.	PUNCT
cana-6186	54	1	it	it	PRON
cana-6186	54	2	has	have	AUX
cana-6186	54	3	been	be	AUX
cana-6186	54	4	done	do	VERB
cana-6186	54	5	on	on	ADP
cana-6186	54	6	the	the	DET
cana-6186	54	7	basis	basis	NOUN
cana-6186	54	8	of	of	ADP
cana-6186	54	9	the	the	DET
cana-6186	54	10	comparison	comparison	NOUN
cana-6186	54	11	of	of	ADP
cana-6186	54	12	classifier	classifier	NOUN
cana-6186	54	13	performances	performance	NOUN
cana-6186	54	14	by	by	ADP
cana-6186	54	15	precision	precision	NOUN
cana-6186	54	16	,	,	PUNCT
cana-6186	54	17	recall	recall	NOUN
cana-6186	54	18	,	,	PUNCT
cana-6186	54	19	f1	f1	NOUN
cana-6186	54	20	-	-	PUNCT
cana-6186	54	21	score	score	NOUN
cana-6186	54	22	,	,	PUNCT
cana-6186	54	23	and	and	CCONJ
cana-6186	54	24	accuracy	accuracy	NOUN
cana-6186	54	25	.	.	PUNCT
cana-6186	55	1	lastly	lastly	ADV
cana-6186	55	2	,	,	PUNCT
cana-6186	55	3	conclusion	conclusion	NOUN
cana-6186	55	4	and	and	CCONJ
cana-6186	55	5	future	future	ADJ
cana-6186	55	6	scope	scope	NOUN
cana-6186	55	7	summarizes	summarize	VERB
cana-6186	55	8	the	the	DET
cana-6186	55	9	findings	finding	NOUN
cana-6186	55	10	and	and	CCONJ
cana-6186	55	11	proposes	propose	VERB
cana-6186	55	12	improvements	improvement	NOUN
cana-6186	55	13	such	such	ADJ
cana-6186	55	14	as	as	ADP
cana-6186	55	15	integration	integration	NOUN
cana-6186	55	16	of	of	ADP
cana-6186	55	17	deep	deep	ADJ
cana-6186	55	18	learning	learning	NOUN
cana-6186	55	19	and	and	CCONJ
cana-6186	55	20	handling	handle	VERB
cana-6186	55	21	data	datum	NOUN
cana-6186	55	22	imbalance	imbalance	NOUN
cana-6186	55	23	to	to	PART
cana-6186	55	24	ensure	ensure	VERB
cana-6186	55	25	scalability	scalability	NOUN
cana-6186	55	26	and	and	CCONJ
cana-6186	55	27	applicability	applicability	NOUN
cana-6186	55	28	.	.	PUNCT
cana-6186	56	1	i.	i.	PROPN
cana-6186	56	2	literature	literature	PROPN
cana-6186	56	3	survey	survey	PROPN
cana-6186	56	4	pattekari	pattekari	NOUN
cana-6186	56	5	[	[	X
cana-6186	56	6	26	26	NUM
cana-6186	56	7	]	]	PUNCT
cana-6186	56	8	discussed	discuss	VERB
cana-6186	56	9	a	a	DET
cana-6186	56	10	model	model	NOUN
cana-6186	56	11	based	base	VERB
cana-6186	56	12	on	on	ADP
cana-6186	56	13	a	a	DET
cana-6186	56	14	naive	naive	ADJ
cana-6186	56	15	bayesian	bayesian	NOUN
cana-6186	56	16	data	data	NOUN
cana-6186	56	17	mining	mining	NOUN
cana-6186	56	18	approach	approach	NOUN
cana-6186	56	19	,	,	PUNCT
cana-6186	56	20	implemented	implement	VERB
cana-6186	56	21	as	as	ADP
cana-6186	56	22	a	a	DET
cana-6186	56	23	user	user	NOUN
cana-6186	56	24	-	-	PUNCT
cana-6186	56	25	computer	computer	NOUN
cana-6186	56	26	application	application	NOUN
cana-6186	56	27	where	where	SCONJ
cana-6186	56	28	subjects	subject	NOUN
cana-6186	56	29	respond	respond	VERB
cana-6186	56	30	to	to	AUX
cana-6186	56	31	pre	pre	VERB
cana-6186	56	32	-	-	ADJ
cana-6186	56	33	designed	design	VERB
cana-6186	56	34	query	query	NOUN
cana-6186	56	35	sets	set	NOUN
cana-6186	56	36	.	.	PUNCT
cana-6186	57	1	this	this	DET
cana-6186	57	2	system	system	NOUN
cana-6186	57	3	unveils	unveil	VERB
cana-6186	57	4	hidden	hidden	ADJ
cana-6186	57	5	patterns	pattern	NOUN
cana-6186	57	6	from	from	ADP
cana-6186	57	7	data	datum	NOUN
cana-6186	57	8	and	and	CCONJ
cana-6186	57	9	compares	compare	VERB
cana-6186	57	10	the	the	DET
cana-6186	57	11	inputs	input	NOUN
cana-6186	57	12	fed	feed	VERB
cana-6186	57	13	by	by	ADP
cana-6186	57	14	the	the	DET
cana-6186	57	15	user	user	NOUN
cana-6186	57	16	with	with	ADP
cana-6186	57	17	the	the	DET
cana-6186	57	18	well	well	ADV
cana-6186	57	19	-	-	PUNCT
cana-6186	57	20	predefined	predefine	VERB
cana-6186	57	21	data	datum	NOUN
cana-6186	57	22	and	and	CCONJ
cana-6186	57	23	therefore	therefore	ADV
cana-6186	57	24	provides	provide	VERB
cana-6186	57	25	solutions	solution	NOUN
cana-6186	57	26	to	to	ADP
cana-6186	57	27	complex	complex	ADJ
cana-6186	57	28	heart	heart	NOUN
cana-6186	57	29	disease	disease	NOUN
cana-6186	57	30	diagnostic	diagnostic	ADJ
cana-6186	57	31	challenges	challenge	NOUN
cana-6186	57	32	.	.	PUNCT
cana-6186	58	1	further	far	ADV
cana-6186	58	2	,	,	PUNCT
cana-6186	58	3	it	it	PRON
cana-6186	58	4	helps	help	VERB
cana-6186	58	5	healthcare	healthcare	NOUN
cana-6186	58	6	experts	expert	NOUN
cana-6186	58	7	make	make	VERB
cana-6186	58	8	better	well	ADJ
cana-6186	58	9	clinical	clinical	ADJ
cana-6186	58	10	decisions	decision	NOUN
cana-6186	58	11	than	than	ADP
cana-6186	58	12	regular	regular	ADJ
cana-6186	58	13	systems	system	NOUN
cana-6186	58	14	and	and	CCONJ
cana-6186	58	15	reduces	reduce	VERB
cana-6186	58	16	treatment	treatment	NOUN
cana-6186	58	17	expenses	expense	NOUN
cana-6186	58	18	by	by	ADP
cana-6186	58	19	suggesting	suggest	VERB
cana-6186	58	20	appropriate	appropriate	ADJ
cana-6186	58	21	interventions	intervention	NOUN
cana-6186	58	22	at	at	ADP
cana-6186	58	23	the	the	DET
cana-6186	58	24	right	right	ADJ
cana-6186	58	25	time	time	NOUN
cana-6186	58	26	.	.	PUNCT
cana-6186	59	1	tran	tran	PROPN
cana-6186	60	1	[	[	X
cana-6186	60	2	2	2	X
cana-6186	60	3	]	]	PUNCT
cana-6186	60	4	designed	design	VERB
cana-6186	60	5	an	an	DET
cana-6186	60	6	intelligent	intelligent	ADJ
cana-6186	60	7	web	web	NOUN
cana-6186	60	8	-	-	PUNCT
cana-6186	60	9	based	base	VERB
cana-6186	60	10	system	system	NOUN
cana-6186	60	11	using	use	VERB
cana-6186	60	12	naive	naive	ADJ
cana-6186	60	13	bayes	bayes	NOUN
cana-6186	60	14	modeling	model	VERB
cana-6186	60	15	approach	approach	NOUN
cana-6186	60	16	.	.	PUNCT
cana-6186	61	1	users	user	NOUN
cana-6186	61	2	answer	answer	VERB
cana-6186	61	3	structured	structured	ADJ
cana-6186	61	4	questions	question	NOUN
cana-6186	61	5	.	.	PUNCT
cana-6186	62	1	the	the	DET
cana-6186	62	2	system	system	NOUN
cana-6186	62	3	analyzes	analyze	VERB
cana-6186	62	4	a	a	DET
cana-6186	62	5	hidden	hidden	ADJ
cana-6186	62	6	information	information	NOUN
cana-6186	62	7	database	database	NOUN
cana-6186	62	8	,	,	PUNCT
cana-6186	62	9	matches	match	VERB
cana-6186	62	10	up	up	ADP
cana-6186	62	11	responses	response	NOUN
cana-6186	62	12	with	with	ADP
cana-6186	62	13	a	a	DET
cana-6186	62	14	learned	learn	VERB
cana-6186	62	15	data	datum	NOUN
cana-6186	62	16	set	set	VERB
cana-6186	62	17	.	.	PUNCT
cana-6186	63	1	this	this	PRON
cana-6186	63	2	helps	help	VERB
cana-6186	63	3	ensure	ensure	VERB
cana-6186	63	4	accurate	accurate	ADJ
cana-6186	63	5	diagnoses	diagnosis	NOUN
cana-6186	63	6	of	of	ADP
cana-6186	63	7	cardiac	cardiac	ADJ
cana-6186	63	8	diseases	disease	NOUN
cana-6186	63	9	,	,	PUNCT
cana-6186	63	10	thereby	thereby	ADV
cana-6186	63	11	helping	help	VERB
cana-6186	63	12	medical	medical	ADJ
cana-6186	63	13	professionals	professional	NOUN
cana-6186	63	14	with	with	ADP
cana-6186	63	15	informed	informed	ADJ
cana-6186	63	16	decisions	decision	NOUN
cana-6186	63	17	while	while	SCONJ
cana-6186	63	18	reducing	reduce	VERB
cana-6186	63	19	health	health	NOUN
cana-6186	63	20	care	care	NOUN
cana-6186	63	21	costs	cost	NOUN
cana-6186	63	22	.	.	PUNCT
cana-6186	64	1	gnaneswar	gnaneswar	NOUN
cana-6186	64	2	[	[	X
cana-6186	64	3	3	3	NUM
cana-6186	64	4	]	]	PUNCT
cana-6186	64	5	worked	work	VERB
cana-6186	64	6	on	on	ADP
cana-6186	64	7	wearables	wearable	NOUN
cana-6186	64	8	to	to	PART
cana-6186	64	9	monitor	monitor	VERB
cana-6186	64	10	heart	heart	NOUN
cana-6186	64	11	rate	rate	NOUN
cana-6186	64	12	,	,	PUNCT
cana-6186	64	13	while	while	SCONJ
cana-6186	64	14	cycling	cycling	NOUN
cana-6186	64	15	.	.	PUNCT
cana-6186	65	1	parameters	parameter	NOUN
cana-6186	65	2	,	,	PUNCT
cana-6186	65	3	like	like	ADP
cana-6186	65	4	cadence	cadence	NOUN
cana-6186	65	5	,	,	PUNCT
cana-6186	65	6	now	now	ADV
cana-6186	65	7	help	help	VERB
cana-6186	65	8	cyclists	cyclist	NOUN
cana-6186	65	9	to	to	PART
cana-6186	65	10	track	track	VERB
cana-6186	65	11	over	over	ADP
cana-6186	65	12	-	-	PUNCT
cana-6186	65	13	training	training	NOUN
cana-6186	65	14	risk	risk	NOUN
cana-6186	65	15	and	and	CCONJ
cana-6186	65	16	heart	heart	NOUN
cana-6186	65	17	-	-	PUNCT
cana-6186	65	18	related	relate	VERB
cana-6186	65	19	complications	complication	NOUN
cana-6186	65	20	also	also	ADV
cana-6186	65	21	.	.	PUNCT
cana-6186	66	1	in	in	ADP
cana-6186	66	2	wearable	wearable	ADJ
cana-6186	66	3	technology	technology	NOUN
cana-6186	66	4	arises	arise	VERB
cana-6186	66	5	the	the	DET
cana-6186	66	6	problem	problem	NOUN
cana-6186	66	7	of	of	ADP
cana-6186	66	8	continuous	continuous	ADJ
cana-6186	66	9	loggings	logging	NOUN
cana-6186	66	10	of	of	ADP
cana-6186	66	11	data	datum	NOUN
cana-6186	66	12	.	.	PUNCT
cana-6186	67	1	such	such	ADJ
cana-6186	67	2	issues	issue	NOUN
cana-6186	67	3	bring	bring	VERB
cana-6186	67	4	into	into	ADP
cana-6186	67	5	the	the	DET
cana-6186	67	6	arena	arena	NOUN
cana-6186	67	7	the	the	DET
cana-6186	67	8	construction	construction	NOUN
cana-6186	67	9	of	of	ADP
cana-6186	67	10	models	model	NOUN
cana-6186	67	11	filling	fill	VERB
cana-6186	67	12	up	up	ADP
cana-6186	67	13	missing	miss	VERB
cana-6186	67	14	points	point	NOUN
cana-6186	67	15	for	for	ADP
cana-6186	67	16	better	well	ADJ
cana-6186	67	17	forecasts	forecast	NOUN
cana-6186	67	18	.	.	PUNCT
cana-6186	68	1	mutijarsa	mutijarsa	PROPN
cana-6186	69	1	[	[	X
cana-6186	69	2	27	27	NUM
cana-6186	69	3	]	]	PUNCT
cana-6186	69	4	researched	research	VERB
cana-6186	69	5	the	the	DET
cana-6186	69	6	development	development	NOUN
cana-6186	69	7	of	of	ADP
cana-6186	69	8	remote	remote	ADJ
cana-6186	69	9	communication	communication	NOUN
cana-6186	69	10	technologies	technology	NOUN
cana-6186	69	11	for	for	ADP
cana-6186	69	12	cardiac	cardiac	ADJ
cana-6186	69	13	disease	disease	NOUN
cana-6186	69	14	management	management	NOUN
cana-6186	69	15	.	.	PUNCT
cana-6186	70	1	the	the	DET
cana-6186	70	2	study	study	NOUN
cana-6186	70	3	utilized	utilize	VERB
cana-6186	70	4	data	datum	NOUN
cana-6186	70	5	mining	mining	NOUN
cana-6186	70	6	techniques	technique	NOUN
cana-6186	70	7	to	to	PART
cana-6186	70	8	identify	identify	VERB
cana-6186	70	9	and	and	CCONJ
cana-6186	70	10	map	map	VERB
cana-6186	70	11	coronary	coronary	ADJ
cana-6186	70	12	diseases	disease	NOUN
cana-6186	70	13	.	.	PUNCT
cana-6186	71	1	communications	communication	NOUN
cana-6186	71	2	on	on	ADP
cana-6186	71	3	applied	apply	VERB
cana-6186	71	4	nonlinear	nonlinear	ADJ
cana-6186	71	5	analysis	analysis	NOUN
cana-6186	71	6	issn	issn	NOUN
cana-6186	71	7	:	:	PUNCT
cana-6186	71	8	1074	1074	NUM
cana-6186	71	9	-	-	PUNCT
cana-6186	71	10	133x	133x	NUM
cana-6186	71	11	vol	vol	NOUN
cana-6186	71	12	32	32	NUM
cana-6186	71	13	no	no	NOUN
cana-6186	71	14	.	.	NOUN
cana-6186	71	15	1	1	NUM
cana-6186	71	16	(	(	PUNCT
cana-6186	71	17	2025	2025	NUM
cana-6186	71	18	)	)	PUNCT
cana-6186	71	19	659	659	NUM
cana-6186	71	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-6186	71	21	comparative	comparative	ADJ
cana-6186	71	22	studies	study	NOUN
cana-6186	71	23	of	of	ADP
cana-6186	71	24	different	different	ADJ
cana-6186	71	25	algorithms	algorithm	NOUN
cana-6186	71	26	determined	determine	VERB
cana-6186	71	27	the	the	DET
cana-6186	71	28	methods	method	NOUN
cana-6186	71	29	that	that	PRON
cana-6186	71	30	best	well	ADV
cana-6186	71	31	predicted	predict	VERB
cana-6186	71	32	the	the	DET
cana-6186	71	33	onset	onset	NOUN
cana-6186	71	34	of	of	ADP
cana-6186	71	35	coronary	coronary	ADJ
cana-6186	71	36	diseases	disease	NOUN
cana-6186	71	37	.	.	PUNCT
cana-6186	72	1	devansh	devansh	ADJ
cana-6186	72	2	[	[	X
cana-6186	72	3	16	16	NUM
cana-6186	72	4	]	]	PUNCT
cana-6186	72	5	emphasized	emphasize	VERB
cana-6186	72	6	the	the	DET
cana-6186	72	7	increasing	increase	VERB
cana-6186	72	8	role	role	NOUN
cana-6186	72	9	of	of	ADP
cana-6186	72	10	artificial	artificial	ADJ
cana-6186	72	11	intelligence	intelligence	NOUN
cana-6186	72	12	in	in	ADP
cana-6186	72	13	disease	disease	NOUN
cana-6186	72	14	prediction	prediction	NOUN
cana-6186	72	15	,	,	PUNCT
cana-6186	72	16	which	which	PRON
cana-6186	72	17	can	can	AUX
cana-6186	72	18	mimic	mimic	VERB
cana-6186	72	19	human	human	ADJ
cana-6186	72	20	-	-	PUNCT
cana-6186	72	21	like	like	ADJ
cana-6186	72	22	decision	decision	NOUN
cana-6186	72	23	-	-	PUNCT
cana-6186	72	24	making	making	NOUN
cana-6186	72	25	,	,	PUNCT
cana-6186	72	26	thus	thus	ADV
cana-6186	72	27	improving	improve	VERB
cana-6186	72	28	the	the	DET
cana-6186	72	29	accuracy	accuracy	NOUN
cana-6186	72	30	of	of	ADP
cana-6186	72	31	heart	heart	NOUN
cana-6186	72	32	disease	disease	NOUN
cana-6186	72	33	detection	detection	NOUN
cana-6186	72	34	.	.	PUNCT
cana-6186	73	1	manjula	manjula	NOUN
cana-6186	74	1	[	[	X
cana-6186	74	2	23	23	NUM
cana-6186	74	3	]	]	PUNCT
cana-6186	74	4	emphasized	emphasize	VERB
cana-6186	74	5	the	the	DET
cana-6186	74	6	importance	importance	NOUN
cana-6186	74	7	of	of	ADP
cana-6186	74	8	proper	proper	ADJ
cana-6186	74	9	diagnosis	diagnosis	NOUN
cana-6186	74	10	of	of	ADP
cana-6186	74	11	cardiac	cardiac	ADJ
cana-6186	74	12	diseases	disease	NOUN
cana-6186	74	13	using	use	VERB
cana-6186	74	14	machine	machine	NOUN
cana-6186	74	15	learning	learning	NOUN
cana-6186	74	16	methods	method	NOUN
cana-6186	74	17	such	such	ADJ
cana-6186	74	18	as	as	ADP
cana-6186	74	19	svm	svm	ADJ
cana-6186	74	20	,	,	PUNCT
cana-6186	74	21	naive	naive	ADJ
cana-6186	74	22	bayes	bayes	NOUN
cana-6186	74	23	,	,	PUNCT
cana-6186	74	24	and	and	CCONJ
cana-6186	74	25	decision	decision	NOUN
cana-6186	74	26	trees	tree	NOUN
cana-6186	74	27	,	,	PUNCT
cana-6186	74	28	which	which	PRON
cana-6186	74	29	support	support	VERB
cana-6186	74	30	more	more	ADV
cana-6186	74	31	reliable	reliable	ADJ
cana-6186	74	32	decisions	decision	NOUN
cana-6186	74	33	in	in	ADP
cana-6186	74	34	clinical	clinical	ADJ
cana-6186	74	35	scenarios	scenario	NOUN
cana-6186	74	36	.	.	PUNCT
cana-6186	75	1	tripoliti	tripoliti	NOUN
cana-6186	75	2	[	[	X
cana-6186	75	3	7	7	X
cana-6186	75	4	]	]	PUNCT
cana-6186	75	5	emphasized	emphasize	VERB
cana-6186	75	6	that	that	SCONJ
cana-6186	75	7	the	the	DET
cana-6186	75	8	diagnosis	diagnosis	NOUN
cana-6186	75	9	of	of	ADP
cana-6186	75	10	high	high	ADJ
cana-6186	75	11	-	-	PUNCT
cana-6186	75	12	prevalence	prevalence	NOUN
cana-6186	75	13	diseases	disease	NOUN
cana-6186	75	14	like	like	ADP
cana-6186	75	15	coronary	coronary	ADJ
cana-6186	75	16	disease	disease	NOUN
cana-6186	75	17	should	should	AUX
cana-6186	75	18	be	be	AUX
cana-6186	75	19	done	do	VERB
cana-6186	75	20	with	with	ADP
cana-6186	75	21	high	high	ADJ
cana-6186	75	22	-	-	PUNCT
cana-6186	75	23	tech	tech	NOUN
cana-6186	75	24	tools	tool	NOUN
cana-6186	75	25	for	for	ADP
cana-6186	75	26	biomedical	biomedical	ADJ
cana-6186	75	27	evaluation	evaluation	NOUN
cana-6186	75	28	.	.	PUNCT
cana-6186	76	1	gonsalves	gonsalve	NOUN
cana-6186	77	1	[	[	X
cana-6186	77	2	43	43	NUM
cana-6186	77	3	]	]	PUNCT
cana-6186	77	4	applied	apply	VERB
cana-6186	77	5	historical	historical	ADJ
cana-6186	77	6	medical	medical	ADJ
cana-6186	77	7	data	datum	NOUN
cana-6186	77	8	and	and	CCONJ
cana-6186	77	9	machine	machine	NOUN
cana-6186	77	10	learning	learn	VERB
cana-6186	77	11	to	to	PART
cana-6186	77	12	predict	predict	VERB
cana-6186	77	13	the	the	DET
cana-6186	77	14	onset	onset	NOUN
cana-6186	77	15	of	of	ADP
cana-6186	77	16	coronary	coronary	ADJ
cana-6186	77	17	diseases	disease	NOUN
cana-6186	77	18	.	.	PUNCT
cana-6186	78	1	oikonomou	oikonomou	PROPN
cana-6186	78	2	[	[	PUNCT
cana-6186	78	3	9	9	NUM
cana-6186	78	4	]	]	PUNCT
cana-6186	78	5	used	use	VERB
cana-6186	78	6	extreme	extreme	ADJ
cana-6186	78	7	value	value	NOUN
cana-6186	78	8	theory	theory	NOUN
cana-6186	78	9	and	and	CCONJ
cana-6186	78	10	machine	machine	NOUN
cana-6186	78	11	learning	learning	NOUN
cana-6186	78	12	methods	method	NOUN
cana-6186	78	13	to	to	PART
cana-6186	78	14	assess	assess	VERB
cana-6186	78	15	chronic	chronic	ADJ
cana-6186	78	16	diseases	disease	NOUN
cana-6186	78	17	in	in	ADP
cana-6186	78	18	terms	term	NOUN
cana-6186	78	19	of	of	ADP
cana-6186	78	20	severity	severity	NOUN
cana-6186	78	21	and	and	CCONJ
cana-6186	78	22	risk	risk	NOUN
cana-6186	78	23	.	.	PUNCT
cana-6186	79	1	hasnony	hasnony	NOUN
cana-6186	80	1	[	[	X
cana-6186	80	2	10	10	NUM
cana-6186	80	3	]	]	SYM
cana-6186	80	4	employed	employ	VERB
cana-6186	80	5	machine	machine	NOUN
cana-6186	80	6	learning	learning	NOUN
cana-6186	80	7	systems	system	NOUN
cana-6186	80	8	that	that	PRON
cana-6186	80	9	classified	classify	VERB
cana-6186	80	10	heart	heart	NOUN
cana-6186	80	11	disease	disease	NOUN
cana-6186	80	12	diagnosis	diagnosis	NOUN
cana-6186	80	13	incorporating	incorporate	VERB
cana-6186	80	14	feedback	feedback	NOUN
cana-6186	80	15	between	between	ADP
cana-6186	80	16	user	user	NOUN
cana-6186	80	17	and	and	CCONJ
cana-6186	80	18	expert	expert	NOUN
cana-6186	80	19	systems	system	NOUN
cana-6186	80	20	for	for	ADP
cana-6186	80	21	better	well	ADJ
cana-6186	80	22	accuracy	accuracy	NOUN
cana-6186	80	23	in	in	ADP
cana-6186	80	24	classification	classification	NOUN
cana-6186	80	25	.	.	PUNCT
cana-6186	81	1	pratiyush	pratiyush	PROPN
cana-6186	81	2	et	et	PROPN
cana-6186	81	3	al	al	PROPN
cana-6186	81	4	.	.	PUNCT
cana-6186	82	1	[	[	X
cana-6186	82	2	11	11	NUM
cana-6186	82	3	]	]	PUNCT
cana-6186	82	4	applied	apply	VERB
cana-6186	82	5	ensemble	ensemble	ADJ
cana-6186	82	6	classifiers	classifier	NOUN
cana-6186	82	7	under	under	ADP
cana-6186	82	8	an	an	DET
cana-6186	82	9	xai	xai	PROPN
cana-6186	82	10	framework	framework	NOUN
cana-6186	82	11	to	to	PART
cana-6186	82	12	classify	classify	VERB
cana-6186	82	13	the	the	DET
cana-6186	82	14	occurrence	occurrence	NOUN
cana-6186	82	15	of	of	ADP
cana-6186	82	16	heart	heart	NOUN
cana-6186	82	17	disease	disease	NOUN
cana-6186	82	18	in	in	ADP
cana-6186	82	19	the	the	DET
cana-6186	82	20	dataset	dataset	NOUN
cana-6186	82	21	using	use	VERB
cana-6186	82	22	algorithms	algorithm	NOUN
cana-6186	82	23	like	like	ADP
cana-6186	82	24	knn	knn	PROPN
cana-6186	82	25	,	,	PUNCT
cana-6186	82	26	svm	svm	ADJ
cana-6186	82	27	,	,	PUNCT
cana-6186	82	28	naive	naive	ADJ
cana-6186	82	29	bayes	bayes	NOUN
cana-6186	82	30	,	,	PUNCT
cana-6186	82	31	and	and	CCONJ
cana-6186	82	32	adaboost	adaboost	ADV
cana-6186	82	33	to	to	PART
cana-6186	82	34	predict	predict	VERB
cana-6186	82	35	highly	highly	ADV
cana-6186	82	36	accurately	accurately	ADV
cana-6186	82	37	.	.	PUNCT
cana-6186	83	1	ali	ali	PROPN
cana-6186	83	2	et	et	PROPN
cana-6186	83	3	al	al	PROPN
cana-6186	83	4	.	.	PUNCT
cana-6186	84	1	[	[	X
cana-6186	84	2	1	1	X
cana-6186	84	3	]	]	PUNCT
cana-6186	84	4	developed	develop	VERB
cana-6186	84	5	a	a	DET
cana-6186	84	6	dual	dual	ADJ
cana-6186	84	7	-	-	PUNCT
cana-6186	84	8	svm	svm	ADJ
cana-6186	84	9	model	model	NOUN
cana-6186	84	10	for	for	ADP
cana-6186	84	11	heart	heart	NOUN
cana-6186	84	12	disease	disease	NOUN
cana-6186	84	13	detection	detection	NOUN
cana-6186	84	14	.	.	PUNCT
cana-6186	85	1	one	one	NUM
cana-6186	85	2	svm	svm	NOUN
cana-6186	85	3	eliminated	eliminate	VERB
cana-6186	85	4	redundant	redundant	ADJ
cana-6186	85	5	features	feature	NOUN
cana-6186	85	6	,	,	PUNCT
cana-6186	85	7	while	while	SCONJ
cana-6186	85	8	the	the	DET
cana-6186	85	9	other	other	ADJ
cana-6186	85	10	performed	perform	VERB
cana-6186	85	11	predictions	prediction	NOUN
cana-6186	85	12	,	,	PUNCT
cana-6186	85	13	achieving	achieve	VERB
cana-6186	85	14	improved	improved	ADJ
cana-6186	85	15	accuracy	accuracy	NOUN
cana-6186	85	16	with	with	ADP
cana-6186	85	17	a	a	DET
cana-6186	85	18	hybrid	hybrid	ADJ
cana-6186	85	19	genetic	genetic	ADJ
cana-6186	85	20	simulated	simulated	ADJ
cana-6186	85	21	annealing	annealing	NOUN
cana-6186	85	22	(	(	PUNCT
cana-6186	85	23	hgsa	hgsa	NOUN
cana-6186	85	24	)	)	PUNCT
cana-6186	85	25	approach	approach	NOUN
cana-6186	85	26	.	.	PUNCT
cana-6186	86	1	javeed	javeed	PROPN
cana-6186	86	2	et	et	PROPN
cana-6186	86	3	al	al	PROPN
cana-6186	86	4	.	.	PUNCT
cana-6186	87	1	[	[	X
cana-6186	87	2	2	2	X
cana-6186	87	3	]	]	PUNCT
cana-6186	87	4	designed	design	VERB
cana-6186	87	5	a	a	DET
cana-6186	87	6	prototype	prototype	NOUN
cana-6186	87	7	combining	combine	VERB
cana-6186	87	8	random	random	ADJ
cana-6186	87	9	search	search	NOUN
cana-6186	87	10	and	and	CCONJ
cana-6186	87	11	random	random	ADJ
cana-6186	87	12	forest	forest	NOUN
cana-6186	87	13	algorithms	algorithm	NOUN
cana-6186	87	14	,	,	PUNCT
cana-6186	87	15	improving	improve	VERB
cana-6186	87	16	performance	performance	NOUN
cana-6186	87	17	by	by	ADP
cana-6186	87	18	3.3	3.3	NUM
cana-6186	87	19	%	%	NOUN
cana-6186	87	20	over	over	ADP
cana-6186	87	21	a	a	DET
cana-6186	87	22	standard	standard	ADJ
cana-6186	87	23	random	random	ADJ
cana-6186	87	24	forest	forest	NOUN
cana-6186	87	25	model	model	NOUN
cana-6186	87	26	.	.	PUNCT
cana-6186	88	1	santhana	santhana	PROPN
cana-6186	88	2	krishnan	krishnan	PROPN
cana-6186	88	3	.	.	PUNCT
cana-6186	89	1	j	j	PROPN
cana-6186	90	1	[	[	X
cana-6186	90	2	3	3	NUM
cana-6186	90	3	]	]	PUNCT
cana-6186	90	4	has	have	AUX
cana-6186	90	5	used	use	VERB
cana-6186	90	6	algorithms	algorithm	NOUN
cana-6186	90	7	of	of	ADP
cana-6186	90	8	naive	naive	ADJ
cana-6186	90	9	bayes	bayes	NOUN
cana-6186	90	10	and	and	CCONJ
cana-6186	90	11	decision	decision	NOUN
cana-6186	90	12	tree	tree	NOUN
cana-6186	90	13	for	for	ADP
cana-6186	90	14	prediction	prediction	NOUN
cana-6186	90	15	of	of	ADP
cana-6186	90	16	heart	heart	NOUN
cana-6186	90	17	attacks	attack	NOUN
cana-6186	90	18	.	.	PUNCT
cana-6186	91	1	decision	decision	NOUN
cana-6186	91	2	trees	tree	NOUN
cana-6186	91	3	had	have	AUX
cana-6186	91	4	already	already	ADV
cana-6186	91	5	attained	attain	VERB
cana-6186	91	6	a	a	DET
cana-6186	91	7	precision	precision	NOUN
cana-6186	91	8	rate	rate	NOUN
cana-6186	91	9	of	of	ADP
cana-6186	91	10	91	91	NUM
cana-6186	91	11	%	%	NOUN
cana-6186	91	12	,	,	PUNCT
cana-6186	91	13	while	while	SCONJ
cana-6186	91	14	naive	naive	ADJ
cana-6186	91	15	bayes	baye	NOUN
cana-6186	91	16	's	's	PART
cana-6186	91	17	accuracy	accuracy	NOUN
cana-6186	91	18	is	be	AUX
cana-6186	91	19	only	only	ADV
cana-6186	91	20	87	87	NUM
cana-6186	91	21	%	%	NOUN
cana-6186	91	22	.	.	PUNCT
cana-6186	92	1	aditi	aditi	PROPN
cana-6186	92	2	gavhane	gavhane	PROPN
cana-6186	92	3	et	et	PROPN
cana-6186	92	4	al	al	PROPN
cana-6186	92	5	.	.	PUNCT
cana-6186	93	1	[	[	X
cana-6186	93	2	4	4	NUM
cana-6186	93	3	]	]	PUNCT
cana-6186	93	4	applied	apply	VERB
cana-6186	93	5	supervised	supervised	ADJ
cana-6186	93	6	neural	neural	ADJ
cana-6186	93	7	networks	network	NOUN
cana-6186	93	8	for	for	ADP
cana-6186	93	9	heart	heart	NOUN
cana-6186	93	10	problems	problem	NOUN
cana-6186	93	11	prediction	prediction	NOUN
cana-6186	93	12	with	with	ADP
cana-6186	93	13	reliable	reliable	ADJ
cana-6186	93	14	results	result	NOUN
cana-6186	93	15	in	in	ADP
cana-6186	93	16	multilayer	multilayer	ADJ
cana-6186	93	17	perceptron	perceptron	PROPN
cana-6186	93	18	models	model	NOUN
cana-6186	93	19	.	.	PUNCT
cana-6186	94	1	devansh	devansh	ADJ
cana-6186	94	2	shah	shah	PROPN
cana-6186	94	3	et	et	PROPN
cana-6186	94	4	al	al	PROPN
cana-6186	94	5	.	.	PUNCT
cana-6186	95	1	[	[	X
cana-6186	95	2	5	5	NUM
cana-6186	95	3	]	]	PUNCT
cana-6186	95	4	used	use	VERB
cana-6186	95	5	decision	decision	NOUN
cana-6186	95	6	tree	tree	NOUN
cana-6186	95	7	,	,	PUNCT
cana-6186	95	8	random	random	ADJ
cana-6186	95	9	forest	forest	NOUN
cana-6186	95	10	,	,	PUNCT
cana-6186	95	11	knn	knn	PROPN
cana-6186	95	12	,	,	PUNCT
cana-6186	95	13	and	and	CCONJ
cana-6186	95	14	naive	naive	ADJ
cana-6186	95	15	bayes	bayes	NOUN
cana-6186	95	16	on	on	ADP
cana-6186	95	17	the	the	DET
cana-6186	95	18	cleveland	cleveland	PROPN
cana-6186	95	19	database	database	NOUN
cana-6186	95	20	from	from	ADP
cana-6186	95	21	the	the	DET
cana-6186	95	22	uci	uci	PROPN
cana-6186	95	23	repository	repository	NOUN
cana-6186	95	24	for	for	ADP
cana-6186	95	25	the	the	DET
cana-6186	95	26	heart	heart	NOUN
cana-6186	95	27	attack	attack	NOUN
cana-6186	95	28	prediction	prediction	NOUN
cana-6186	95	29	model	model	NOUN
cana-6186	95	30	.	.	PUNCT
cana-6186	96	1	14	14	NUM
cana-6186	96	2	important	important	ADJ
cana-6186	96	3	attributes	attribute	NOUN
cana-6186	96	4	were	be	AUX
cana-6186	96	5	taken	take	VERB
cana-6186	96	6	into	into	ADP
cana-6186	96	7	account	account	NOUN
cana-6186	96	8	.	.	PUNCT
cana-6186	97	1	archana	archana	PROPN
cana-6186	97	2	singh	singh	PROPN
cana-6186	97	3	et	et	PROPN
cana-6186	97	4	al	al	PROPN
cana-6186	97	5	.	.	PUNCT
cana-6186	98	1	[	[	X
cana-6186	98	2	6	6	NUM
cana-6186	98	3	]	]	PUNCT
cana-6186	98	4	presented	present	VERB
cana-6186	98	5	that	that	SCONJ
cana-6186	98	6	the	the	DET
cana-6186	98	7	knn	knn	PROPN
cana-6186	98	8	algorithm	algorithm	PROPN
cana-6186	98	9	performs	perform	VERB
cana-6186	98	10	better	well	ADJ
cana-6186	98	11	than	than	ADP
cana-6186	98	12	logistic	logistic	ADJ
cana-6186	98	13	regression	regression	NOUN
cana-6186	98	14	and	and	CCONJ
cana-6186	98	15	svm	svm	NOUN
cana-6186	98	16	in	in	ADP
cana-6186	98	17	the	the	DET
cana-6186	98	18	heart	heart	NOUN
cana-6186	98	19	disease	disease	NOUN
cana-6186	98	20	prediction	prediction	NOUN
cana-6186	98	21	task	task	NOUN
cana-6186	98	22	.	.	PUNCT
cana-6186	99	1	apurb	apurb	PROPN
cana-6186	99	2	rajdhan	rajdhan	PROPN
cana-6186	99	3	et	et	PROPN
cana-6186	99	4	al	al	PROPN
cana-6186	99	5	.	.	PUNCT
cana-6186	100	1	[	[	X
cana-6186	100	2	7	7	X
cana-6186	100	3	]	]	PUNCT
cana-6186	100	4	stated	state	VERB
cana-6186	100	5	that	that	SCONJ
cana-6186	100	6	the	the	DET
cana-6186	100	7	random	random	ADJ
cana-6186	100	8	forest	forest	NOUN
cana-6186	100	9	algorithm	algorithm	NOUN
cana-6186	100	10	achieved	achieve	VERB
cana-6186	100	11	an	an	DET
cana-6186	100	12	accuracy	accuracy	NOUN
cana-6186	100	13	of	of	ADP
cana-6186	100	14	90.16	90.16	NUM
cana-6186	100	15	%	%	NOUN
cana-6186	100	16	in	in	ADP
cana-6186	100	17	the	the	DET
cana-6186	100	18	heart	heart	NOUN
cana-6186	100	19	disease	disease	NOUN
cana-6186	100	20	diagnosis	diagnosis	NOUN
cana-6186	100	21	.	.	PUNCT
cana-6186	101	1	rati	rati	PROPN
cana-6186	101	2	goel	goel	PROPN
cana-6186	102	1	[	[	X
cana-6186	102	2	8	8	NUM
cana-6186	102	3	]	]	PUNCT
cana-6186	102	4	compared	compare	VERB
cana-6186	102	5	multialgorithms	multialgorithm	NOUN
cana-6186	102	6	,	,	PUNCT
cana-6186	102	7	including	include	VERB
cana-6186	102	8	logistic	logistic	ADJ
cana-6186	102	9	regression	regression	NOUN
cana-6186	102	10	,	,	PUNCT
cana-6186	102	11	svm	svm	PROPN
cana-6186	102	12	,	,	PUNCT
cana-6186	102	13	knn	knn	PROPN
cana-6186	102	14	and	and	CCONJ
cana-6186	102	15	naive	naive	ADJ
cana-6186	102	16	bayes	bayes	NOUN
cana-6186	102	17	,	,	PUNCT
cana-6186	102	18	concluding	conclude	VERB
cana-6186	102	19	the	the	DET
cana-6186	102	20	best	good	ADJ
cana-6186	102	21	prediction	prediction	NOUN
cana-6186	102	22	of	of	ADP
cana-6186	102	23	heart	heart	NOUN
cana-6186	102	24	diseases	disease	NOUN
cana-6186	102	25	based	base	VERB
cana-6186	102	26	on	on	ADP
cana-6186	102	27	attributes	attribute	NOUN
cana-6186	102	28	like	like	ADP
cana-6186	102	29	chest	chest	NOUN
cana-6186	102	30	pain	pain	NOUN
cana-6186	102	31	and	and	CCONJ
cana-6186	102	32	cholesterol	cholesterol	NOUN
cana-6186	102	33	.	.	PUNCT
cana-6186	103	1	further	far	ADV
cana-6186	103	2	,	,	PUNCT
cana-6186	103	3	ekta	ekta	PROPN
cana-6186	103	4	maini	maini	PROPN
cana-6186	103	5	et	et	PROPN
cana-6186	103	6	al	al	PROPN
cana-6186	103	7	.	.	PUNCT
cana-6186	104	1	[	[	X
cana-6186	104	2	9	9	NUM
cana-6186	104	3	]	]	PUNCT
cana-6186	104	4	highlighted	highlight	VERB
cana-6186	104	5	the	the	DET
cana-6186	104	6	ability	ability	NOUN
cana-6186	104	7	of	of	ADP
cana-6186	104	8	random	random	ADJ
cana-6186	104	9	forest	forest	NOUN
cana-6186	104	10	to	to	PART
cana-6186	104	11	outperform	outperform	VERB
cana-6186	104	12	logistic	logistic	ADJ
cana-6186	104	13	regression	regression	NOUN
cana-6186	104	14	and	and	CCONJ
cana-6186	104	15	other	other	ADJ
cana-6186	104	16	algorithms	algorithm	NOUN
cana-6186	104	17	using	use	VERB
cana-6186	104	18	ensemble	ensemble	ADJ
cana-6186	104	19	techniques	technique	NOUN
cana-6186	104	20	,	,	PUNCT
cana-6186	104	21	achieving	achieve	VERB
cana-6186	104	22	high	high	ADJ
cana-6186	104	23	sensitivity	sensitivity	NOUN
cana-6186	104	24	and	and	CCONJ
cana-6186	104	25	specificity	specificity	NOUN
cana-6186	104	26	.	.	PUNCT
cana-6186	105	1	apurv	apurv	PROPN
cana-6186	105	2	garg	garg	PROPN
cana-6186	105	3	et	et	PROPN
cana-6186	105	4	al	al	PROPN
cana-6186	105	5	.	.	PUNCT
cana-6186	106	1	[	[	X
cana-6186	106	2	10	10	NUM
cana-6186	106	3	]	]	PUNCT
cana-6186	106	4	analyzed	analyze	VERB
cana-6186	106	5	knn	knn	PROPN
cana-6186	106	6	and	and	CCONJ
cana-6186	106	7	random	random	ADJ
cana-6186	106	8	forest	forest	NOUN
cana-6186	106	9	,	,	PUNCT
cana-6186	106	10	concluding	conclude	VERB
cana-6186	106	11	that	that	PRON
cana-6186	106	12	knn	knn	PROPN
cana-6186	106	13	gave	give	VERB
cana-6186	106	14	better	well	ADJ
cana-6186	106	15	accuracy	accuracy	NOUN
cana-6186	106	16	(	(	PUNCT
cana-6186	106	17	86.89	86.89	NUM
cana-6186	106	18	%	%	NOUN
cana-6186	106	19	)	)	PUNCT
cana-6186	106	20	for	for	ADP
cana-6186	106	21	the	the	DET
cana-6186	106	22	heart	heart	NOUN
cana-6186	106	23	disease	disease	NOUN
cana-6186	106	24	prediction	prediction	NOUN
cana-6186	106	25	.	.	PUNCT
cana-6186	107	1	mahbubur	mahbubur	PROPN
cana-6186	107	2	rahman	rahman	PROPN
cana-6186	107	3	et	et	PROPN
cana-6186	107	4	al	al	PROPN
cana-6186	107	5	.	.	PUNCT
cana-6186	108	1	[	[	X
cana-6186	108	2	11	11	NUM
cana-6186	108	3	]	]	PUNCT
cana-6186	108	4	had	have	AUX
cana-6186	108	5	experimented	experiment	VERB
cana-6186	108	6	with	with	ADP
cana-6186	108	7	various	various	ADJ
cana-6186	108	8	algorithms	algorithm	NOUN
cana-6186	108	9	like	like	ADP
cana-6186	108	10	decision	decision	NOUN
cana-6186	108	11	tree	tree	NOUN
cana-6186	108	12	,	,	PUNCT
cana-6186	108	13	svm	svm	ADJ
cana-6186	108	14	,	,	PUNCT
cana-6186	108	15	naive	naive	ADJ
cana-6186	108	16	bayes	bayes	NOUN
cana-6186	108	17	,	,	PUNCT
cana-6186	108	18	and	and	CCONJ
cana-6186	108	19	random	random	ADJ
cana-6186	108	20	forest	forest	NOUN
cana-6186	108	21	.	.	PUNCT
cana-6186	109	1	it	it	PRON
cana-6186	109	2	found	find	VERB
cana-6186	109	3	that	that	SCONJ
cana-6186	109	4	the	the	DET
cana-6186	109	5	maximum	maximum	ADJ
cana-6186	109	6	accuracy	accuracy	NOUN
cana-6186	109	7	(	(	PUNCT
cana-6186	109	8	99	99	NUM
cana-6186	109	9	%	%	NOUN
cana-6186	109	10	)	)	PUNCT
cana-6186	109	11	and	and	CCONJ
cana-6186	109	12	sensitivity	sensitivity	NOUN
cana-6186	109	13	(	(	PUNCT
cana-6186	109	14	98	98	NUM
cana-6186	109	15	%	%	NOUN
cana-6186	109	16	)	)	PUNCT
cana-6186	109	17	were	be	AUX
cana-6186	109	18	achieved	achieve	VERB
cana-6186	109	19	by	by	ADP
cana-6186	109	20	the	the	DET
cana-6186	109	21	decision	decision	NOUN
cana-6186	109	22	trees	tree	NOUN
cana-6186	109	23	.	.	PUNCT
cana-6186	110	1	communications	communication	NOUN
cana-6186	110	2	on	on	ADP
cana-6186	110	3	applied	apply	VERB
cana-6186	110	4	nonlinear	nonlinear	ADJ
cana-6186	110	5	analysis	analysis	NOUN
cana-6186	110	6	issn	issn	NOUN
cana-6186	110	7	:	:	PUNCT
cana-6186	110	8	1074	1074	NUM
cana-6186	110	9	-	-	PUNCT
cana-6186	110	10	133x	133x	NUM
cana-6186	110	11	vol	vol	NOUN
cana-6186	110	12	32	32	NUM
cana-6186	110	13	no	no	NOUN
cana-6186	110	14	.	.	NOUN
cana-6186	110	15	1	1	NUM
cana-6186	110	16	(	(	PUNCT
cana-6186	110	17	2025	2025	NUM
cana-6186	110	18	)	)	PUNCT
cana-6186	110	19	660	660	NUM
cana-6186	110	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-6186	110	21	manjula	manjula	PROPN
cana-6186	110	22	p	p	X
cana-6186	110	23	et	et	PROPN
cana-6186	110	24	al	al	PROPN
cana-6186	110	25	.	.	PUNCT
cana-6186	111	1	[	[	X
cana-6186	111	2	12	12	NUM
cana-6186	111	3	]	]	PUNCT
cana-6186	111	4	have	have	AUX
cana-6186	111	5	used	use	VERB
cana-6186	111	6	different	different	ADJ
cana-6186	111	7	types	type	NOUN
cana-6186	111	8	of	of	ADP
cana-6186	111	9	machine	machine	NOUN
cana-6186	111	10	learning	learn	VERB
cana-6186	111	11	algorithms	algorithm	NOUN
cana-6186	111	12	for	for	ADP
cana-6186	111	13	the	the	DET
cana-6186	111	14	prediction	prediction	NOUN
cana-6186	111	15	of	of	ADP
cana-6186	111	16	heart	heart	NOUN
cana-6186	111	17	disease	disease	NOUN
cana-6186	111	18	,	,	PUNCT
cana-6186	111	19	with	with	ADP
cana-6186	111	20	excellent	excellent	ADJ
cana-6186	111	21	accuracy	accuracy	NOUN
cana-6186	111	22	for	for	ADP
cana-6186	111	23	the	the	DET
cana-6186	111	24	random	random	ADJ
cana-6186	111	25	forest	forest	NOUN
cana-6186	111	26	algorithm	algorithm	NOUN
cana-6186	111	27	.	.	PUNCT
cana-6186	112	1	feature	feature	NOUN
cana-6186	112	2	selection	selection	NOUN
cana-6186	112	3	and	and	CCONJ
cana-6186	112	4	ensemble	ensemble	ADJ
cana-6186	112	5	methods	method	NOUN
cana-6186	112	6	pavan	pavan	PROPN
cana-6186	112	7	kumar	kumar	PROPN
cana-6186	112	8	tadiparthi	tadiparthi	PROPN
cana-6186	112	9	et	et	PROPN
cana-6186	112	10	al	al	PROPN
cana-6186	112	11	.	.	PUNCT
cana-6186	113	1	[	[	X
cana-6186	113	2	13	13	NUM
cana-6186	113	3	]	]	PUNCT
cana-6186	113	4	reviewed	review	VERB
cana-6186	113	5	feature	feature	NOUN
cana-6186	113	6	selection	selection	NOUN
cana-6186	113	7	and	and	CCONJ
cana-6186	113	8	ensemble	ensemble	ADJ
cana-6186	113	9	methods	method	NOUN
cana-6186	113	10	.	.	PUNCT
cana-6186	114	1	in	in	ADP
cana-6186	114	2	their	their	PRON
cana-6186	114	3	research	research	NOUN
cana-6186	114	4	,	,	PUNCT
cana-6186	114	5	they	they	PRON
cana-6186	114	6	observed	observe	VERB
cana-6186	114	7	that	that	SCONJ
cana-6186	114	8	these	these	DET
cana-6186	114	9	methodologies	methodology	NOUN
cana-6186	114	10	significantly	significantly	ADV
cana-6186	114	11	contribute	contribute	VERB
cana-6186	114	12	to	to	ADP
cana-6186	114	13	improving	improve	VERB
cana-6186	114	14	the	the	DET
cana-6186	114	15	predictive	predictive	ADJ
cana-6186	114	16	accuracy	accuracy	NOUN
cana-6186	114	17	.	.	PUNCT
cana-6186	115	1	joloudari	joloudari	PROPN
cana-6186	115	2	et	et	PROPN
cana-6186	115	3	al	al	PROPN
cana-6186	115	4	.	.	PUNCT
cana-6186	116	1	[	[	X
cana-6186	116	2	14	14	NUM
cana-6186	116	3	]	]	PUNCT
cana-6186	116	4	obtained	obtain	VERB
cana-6186	116	5	an	an	DET
cana-6186	116	6	accuracy	accuracy	NOUN
cana-6186	116	7	of	of	ADP
cana-6186	116	8	91.47	91.47	NUM
cana-6186	116	9	%	%	NOUN
cana-6186	116	10	with	with	ADP
cana-6186	116	11	the	the	DET
cana-6186	116	12	random	random	ADJ
cana-6186	116	13	tree	tree	NOUN
cana-6186	116	14	model	model	NOUN
cana-6186	116	15	.	.	PUNCT
cana-6186	117	1	ii	ii	AUX
cana-6186	117	2	.	.	PUNCT
cana-6186	117	3	methodology	methodology	PROPN
cana-6186	117	4	the	the	DET
cana-6186	117	5	classification	classification	NOUN
cana-6186	117	6	system	system	NOUN
cana-6186	117	7	for	for	ADP
cana-6186	117	8	heart	heart	NOUN
cana-6186	117	9	disease	disease	NOUN
cana-6186	117	10	has	have	AUX
cana-6186	117	11	structured	structure	VERB
cana-6186	117	12	into	into	ADP
cana-6186	117	13	logical	logical	ADJ
cana-6186	117	14	blocks	block	NOUN
cana-6186	117	15	;	;	PUNCT
cana-6186	117	16	each	each	DET
cana-6186	117	17	block	block	NOUN
cana-6186	117	18	contributes	contribute	VERB
cana-6186	117	19	towards	towards	ADP
cana-6186	117	20	the	the	DET
cana-6186	117	21	overall	overall	ADJ
cana-6186	117	22	functionality	functionality	NOUN
cana-6186	117	23	of	of	ADP
cana-6186	117	24	the	the	DET
cana-6186	117	25	system	system	NOUN
cana-6186	117	26	with	with	ADP
cana-6186	117	27	a	a	DET
cana-6186	117	28	particular	particular	ADJ
cana-6186	117	29	role	role	NOUN
cana-6186	117	30	.	.	PUNCT
cana-6186	118	1	fig.1	fig.1	PROPN
cana-6186	118	2	presents	present	VERB
cana-6186	118	3	an	an	DET
cana-6186	118	4	extended	extended	ADJ
cana-6186	118	5	blockwise	blockwise	NOUN
cana-6186	118	6	explanation	explanation	NOUN
cana-6186	118	7	of	of	ADP
cana-6186	118	8	the	the	DET
cana-6186	118	9	architecture	architecture	NOUN
cana-6186	118	10	of	of	ADP
cana-6186	118	11	the	the	DET
cana-6186	118	12	system	system	NOUN
cana-6186	118	13	.	.	PUNCT
cana-6186	119	1	a.	a.	NOUN
cana-6186	119	2	user	user	PROPN
cana-6186	119	3	here	here	ADV
cana-6186	119	4	,	,	PUNCT
cana-6186	119	5	the	the	DET
cana-6186	119	6	primary	primary	ADJ
cana-6186	119	7	interacting	interact	VERB
cana-6186	119	8	entity	entity	NOUN
cana-6186	119	9	is	be	AUX
cana-6186	119	10	the	the	DET
cana-6186	119	11	person	person	NOUN
cana-6186	119	12	using	use	VERB
cana-6186	119	13	the	the	DET
cana-6186	119	14	cardiovascular	cardiovascular	ADJ
cana-6186	119	15	disease	disease	NOUN
cana-6186	119	16	classification	classification	NOUN
cana-6186	119	17	system	system	NOUN
cana-6186	119	18	.	.	PUNCT
cana-6186	120	1	he	he	PRON
cana-6186	120	2	can	can	AUX
cana-6186	120	3	be	be	AUX
cana-6186	120	4	a	a	DET
cana-6186	120	5	doctor	doctor	NOUN
cana-6186	120	6	,	,	PUNCT
cana-6186	120	7	researcher	researcher	NOUN
cana-6186	120	8	,	,	PUNCT
cana-6186	120	9	or	or	CCONJ
cana-6186	120	10	patient	patient	NOUN
cana-6186	120	11	seeking	seek	VERB
cana-6186	120	12	to	to	PART
cana-6186	120	13	know	know	VERB
cana-6186	120	14	something	something	PRON
cana-6186	120	15	about	about	ADP
cana-6186	120	16	heart	heart	NOUN
cana-6186	120	17	disease	disease	NOUN
cana-6186	120	18	diagnosis	diagnosis	NOUN
cana-6186	120	19	.	.	PUNCT
cana-6186	121	1	the	the	DET
cana-6186	121	2	system	system	NOUN
cana-6186	121	3	has	have	AUX
cana-6186	121	4	been	be	AUX
cana-6186	121	5	constructed	construct	VERB
cana-6186	121	6	in	in	ADP
cana-6186	121	7	a	a	DET
cana-6186	121	8	manner	manner	NOUN
cana-6186	121	9	that	that	SCONJ
cana-6186	121	10	its	its	PRON
cana-6186	121	11	use	use	NOUN
cana-6186	121	12	does	do	AUX
cana-6186	121	13	not	not	PART
cana-6186	121	14	require	require	VERB
cana-6186	121	15	very	very	ADV
cana-6186	121	16	technical	technical	ADJ
cana-6186	121	17	people	people	NOUN
cana-6186	121	18	.	.	PUNCT
cana-6186	122	1	the	the	DET
cana-6186	122	2	procedure	procedure	NOUN
cana-6186	122	3	starts	start	VERB
cana-6186	122	4	with	with	ADP
cana-6186	122	5	the	the	DET
cana-6186	122	6	necessity	necessity	NOUN
cana-6186	122	7	of	of	ADP
cana-6186	122	8	acquiring	acquire	VERB
cana-6186	122	9	input	input	NOUN
cana-6186	122	10	data	datum	NOUN
cana-6186	122	11	from	from	ADP
cana-6186	122	12	the	the	DET
cana-6186	122	13	user	user	NOUN
cana-6186	122	14	.	.	PUNCT
cana-6186	122	15	,	,	PUNCT
cana-6186	122	16	which	which	PRON
cana-6186	122	17	may	may	AUX
cana-6186	122	18	include	include	VERB
cana-6186	122	19	patient	patient	ADJ
cana-6186	122	20	information	information	NOUN
cana-6186	122	21	or	or	CCONJ
cana-6186	122	22	the	the	DET
cana-6186	122	23	use	use	NOUN
cana-6186	122	24	of	of	ADP
cana-6186	122	25	a	a	DET
cana-6186	122	26	ready	ready	ADJ
cana-6186	122	27	dataset	dataset	NOUN
cana-6186	122	28	for	for	ADP
cana-6186	122	29	analysis	analysis	NOUN
cana-6186	122	30	.	.	PUNCT
cana-6186	123	1	when	when	SCONJ
cana-6186	123	2	the	the	DET
cana-6186	123	3	user	user	NOUN
cana-6186	123	4	inputs	input	VERB
cana-6186	123	5	this	this	DET
cana-6186	123	6	information	information	NOUN
cana-6186	123	7	,	,	PUNCT
cana-6186	123	8	they	they	PRON
cana-6186	123	9	activate	activate	VERB
cana-6186	123	10	the	the	DET
cana-6186	123	11	classification	classification	NOUN
cana-6186	123	12	process	process	NOUN
cana-6186	123	13	,	,	PUNCT
cana-6186	123	14	enabling	enable	VERB
cana-6186	123	15	the	the	DET
cana-6186	123	16	system	system	NOUN
cana-6186	123	17	to	to	PART
cana-6186	123	18	run	run	VERB
cana-6186	123	19	the	the	DET
cana-6186	123	20	analysis	analysis	NOUN
cana-6186	123	21	and	and	CCONJ
cana-6186	123	22	generate	generate	VERB
cana-6186	123	23	outputs	output	NOUN
cana-6186	123	24	.	.	PUNCT
cana-6186	124	1	additionally	additionally	ADV
cana-6186	124	2	,	,	PUNCT
cana-6186	124	3	the	the	DET
cana-6186	124	4	interface	interface	NOUN
cana-6186	124	5	allows	allow	VERB
cana-6186	124	6	users	user	NOUN
cana-6186	124	7	to	to	PART
cana-6186	124	8	view	view	VERB
cana-6186	124	9	the	the	DET
cana-6186	124	10	outputs	output	NOUN
cana-6186	124	11	,	,	PUNCT
cana-6186	124	12	including	include	VERB
cana-6186	124	13	forecasts	forecast	NOUN
cana-6186	124	14	of	of	ADP
cana-6186	124	15	heart	heart	NOUN
cana-6186	124	16	disease	disease	NOUN
cana-6186	124	17	risk	risk	NOUN
cana-6186	124	18	and	and	CCONJ
cana-6186	124	19	accompanying	accompany	VERB
cana-6186	124	20	evaluation	evaluation	NOUN
cana-6186	124	21	metrics	metric	NOUN
cana-6186	124	22	such	such	ADJ
cana-6186	124	23	as	as	ADP
cana-6186	124	24	accuracy	accuracy	NOUN
cana-6186	124	25	and	and	CCONJ
cana-6186	124	26	precision	precision	NOUN
cana-6186	124	27	.	.	PUNCT
cana-6186	125	1	this	this	DET
cana-6186	125	2	interaction	interaction	NOUN
cana-6186	125	3	ensures	ensure	VERB
cana-6186	125	4	that	that	SCONJ
cana-6186	125	5	the	the	DET
cana-6186	125	6	system	system	NOUN
cana-6186	125	7	functions	function	NOUN
cana-6186	125	8	as	as	ADP
cana-6186	125	9	a	a	DET
cana-6186	125	10	ready	ready	ADJ
cana-6186	125	11	-	-	PUNCT
cana-6186	125	12	to	to	ADP
cana-6186	125	13	-	-	PUNCT
cana-6186	125	14	use	use	VERB
cana-6186	125	15	,	,	PUNCT
cana-6186	125	16	efficient	efficient	ADJ
cana-6186	125	17	,	,	PUNCT
cana-6186	125	18	and	and	CCONJ
cana-6186	125	19	user	user	NOUN
cana-6186	125	20	-	-	PUNCT
cana-6186	125	21	friendly	friendly	ADJ
cana-6186	125	22	tool	tool	NOUN
cana-6186	125	23	thereby	thereby	ADV
cana-6186	125	24	connecting	connect	VERB
cana-6186	125	25	advanced	advanced	ADJ
cana-6186	125	26	technology	technology	NOUN
cana-6186	125	27	with	with	ADP
cana-6186	125	28	its	its	PRON
cana-6186	125	29	application	application	NOUN
cana-6186	125	30	in	in	ADP
cana-6186	125	31	health	health	NOUN
cana-6186	125	32	care	care	NOUN
cana-6186	125	33	.	.	PUNCT
cana-6186	126	1	fig	fig	NOUN
cana-6186	126	2	.	.	PUNCT
cana-6186	127	1	1	1	X
cana-6186	127	2	.	.	X
cana-6186	127	3	architecture	architecture	NOUN
cana-6186	127	4	diagram	diagram	NOUN
cana-6186	127	5	of	of	ADP
cana-6186	127	6	the	the	DET
cana-6186	127	7	proposed	propose	VERB
cana-6186	127	8	cardiovascular	cardiovascular	ADJ
cana-6186	127	9	disease	disease	NOUN
cana-6186	127	10	recogntion	recogntion	NOUN
cana-6186	127	11	system	system	NOUN
cana-6186	127	12	communications	communication	NOUN
cana-6186	127	13	on	on	ADP
cana-6186	127	14	applied	apply	VERB
cana-6186	127	15	nonlinear	nonlinear	ADJ
cana-6186	127	16	analysis	analysis	NOUN
cana-6186	127	17	issn	issn	NOUN
cana-6186	127	18	:	:	PUNCT
cana-6186	127	19	1074	1074	NUM
cana-6186	127	20	-	-	PUNCT
cana-6186	127	21	133x	133x	NUM
cana-6186	127	22	vol	vol	NOUN
cana-6186	127	23	32	32	NUM
cana-6186	127	24	no	no	NOUN
cana-6186	127	25	.	.	NOUN
cana-6186	127	26	1	1	NUM
cana-6186	127	27	(	(	PUNCT
cana-6186	127	28	2025	2025	NUM
cana-6186	127	29	)	)	PUNCT
cana-6186	127	30	661	661	NUM
cana-6186	127	31	https://internationalpubls.com	https://internationalpubls.com	X
cana-6186	127	32	b.	b.	PROPN
cana-6186	127	33	input	input	PROPN
cana-6186	127	34	data	datum	NOUN
cana-6186	127	35	this	this	DET
cana-6186	127	36	study	study	NOUN
cana-6186	127	37	employed	employ	VERB
cana-6186	127	38	a	a	DET
cana-6186	127	39	dataset	dataset	NOUN
cana-6186	127	40	received	receive	VERB
cana-6186	127	41	from	from	ADP
cana-6186	127	42	the	the	DET
cana-6186	127	43	healthcare	healthcare	NOUN
cana-6186	127	44	centers	center	NOUN
cana-6186	127	45	approached	approach	VERB
cana-6186	127	46	for	for	ADP
cana-6186	127	47	this	this	DET
cana-6186	127	48	research	research	NOUN
cana-6186	127	49	work	work	NOUN
cana-6186	127	50	.the	.the	PRON
cana-6186	127	51	most	most	ADV
cana-6186	127	52	important	important	ADJ
cana-6186	127	53	attributes	attribute	NOUN
cana-6186	127	54	include	include	VERB
cana-6186	127	55	age	age	NOUN
cana-6186	127	56	,	,	PUNCT
cana-6186	127	57	sex	sex	NOUN
cana-6186	127	58	,	,	PUNCT
cana-6186	127	59	blood	blood	NOUN
cana-6186	127	60	pressure	pressure	NOUN
cana-6186	127	61	,	,	PUNCT
cana-6186	127	62	cholesterol	cholesterol	NOUN
cana-6186	127	63	level	level	NOUN
cana-6186	127	64	,	,	PUNCT
cana-6186	127	65	fasting	fast	VERB
cana-6186	127	66	blood	blood	NOUN
cana-6186	127	67	sugar	sugar	NOUN
cana-6186	127	68	,	,	PUNCT
cana-6186	127	69	and	and	CCONJ
cana-6186	127	70	the	the	DET
cana-6186	127	71	existence	existence	NOUN
cana-6186	127	72	of	of	ADP
cana-6186	127	73	conditions	condition	NOUN
cana-6186	127	74	such	such	ADJ
cana-6186	127	75	as	as	ADP
cana-6186	127	76	chest	chest	NOUN
cana-6186	127	77	pain	pain	NOUN
cana-6186	127	78	and	and	CCONJ
cana-6186	127	79	angina	angina	NOUN
cana-6186	127	80	during	during	ADP
cana-6186	127	81	exertion	exertion	NOUN
cana-6186	127	82	.	.	PUNCT
cana-6186	128	1	these	these	DET
cana-6186	128	2	attributes	attribute	NOUN
cana-6186	128	3	serve	serve	VERB
cana-6186	128	4	as	as	ADP
cana-6186	128	5	input	input	NOUN
cana-6186	128	6	data	datum	NOUN
cana-6186	128	7	essential	essential	ADJ
cana-6186	128	8	for	for	ADP
cana-6186	128	9	training	training	NOUN
cana-6186	128	10	and	and	CCONJ
cana-6186	128	11	testing	testing	NOUN
cana-6186	128	12	machine	machine	NOUN
cana-6186	128	13	learning	learning	NOUN
cana-6186	128	14	models	model	NOUN
cana-6186	128	15	.	.	PUNCT
cana-6186	129	1	data	datum	NOUN
cana-6186	129	2	set	set	NOUN
cana-6186	129	3	goes	go	VERB
cana-6186	129	4	through	through	ADP
cana-6186	129	5	preprocessing	preprocesse	VERB
cana-6186	129	6	to	to	PART
cana-6186	129	7	address	address	VERB
cana-6186	129	8	the	the	DET
cana-6186	129	9	existence	existence	NOUN
cana-6186	129	10	of	of	ADP
cana-6186	129	11	missing	miss	VERB
cana-6186	129	12	values	value	NOUN
cana-6186	129	13	,	,	PUNCT
cana-6186	129	14	normalization	normalization	NOUN
cana-6186	129	15	of	of	ADP
cana-6186	129	16	feature	feature	NOUN
cana-6186	129	17	distributions	distribution	NOUN
cana-6186	129	18	,	,	PUNCT
cana-6186	129	19	and	and	CCONJ
cana-6186	129	20	derivation	derivation	NOUN
cana-6186	129	21	of	of	ADP
cana-6186	129	22	relevant	relevant	ADJ
cana-6186	129	23	predictors	predictor	NOUN
cana-6186	129	24	;	;	PUNCT
cana-6186	129	25	this	this	DET
cana-6186	129	26	way	way	NOUN
cana-6186	129	27	,	,	PUNCT
cana-6186	129	28	it	it	PRON
cana-6186	129	29	is	be	AUX
cana-6186	129	30	fit	fit	ADJ
cana-6186	129	31	for	for	ADP
cana-6186	129	32	modeling	modeling	NOUN
cana-6186	129	33	and	and	CCONJ
cana-6186	129	34	testing	testing	NOUN
cana-6186	129	35	.	.	PUNCT
cana-6186	130	1	such	such	ADJ
cana-6186	130	2	variety	variety	NOUN
cana-6186	130	3	and	and	CCONJ
cana-6186	130	4	quality	quality	NOUN
cana-6186	130	5	of	of	ADP
cana-6186	130	6	this	this	DET
cana-6186	130	7	dataset	dataset	NOUN
cana-6186	130	8	make	make	VERB
cana-6186	130	9	it	it	PRON
cana-6186	130	10	an	an	DET
cana-6186	130	11	excellent	excellent	ADJ
cana-6186	130	12	material	material	NOUN
cana-6186	130	13	in	in	ADP
cana-6186	130	14	building	build	VERB
cana-6186	130	15	robust	robust	ADJ
cana-6186	130	16	predictive	predictive	ADJ
cana-6186	130	17	models	model	NOUN
cana-6186	130	18	with	with	ADP
cana-6186	130	19	the	the	DET
cana-6186	130	20	objective	objective	NOUN
cana-6186	130	21	of	of	ADP
cana-6186	130	22	identifying	identify	VERB
cana-6186	130	23	heart	heart	NOUN
cana-6186	130	24	disease	disease	NOUN
cana-6186	130	25	.	.	PUNCT
cana-6186	131	1	in	in	ADP
cana-6186	131	2	the	the	DET
cana-6186	131	3	following	follow	VERB
cana-6186	131	4	table	table	NOUN
cana-6186	131	5	i	i	PRON
cana-6186	131	6	,	,	PUNCT
cana-6186	131	7	the	the	DET
cana-6186	131	8	data	datum	NOUN
cana-6186	131	9	relating	relate	VERB
cana-6186	131	10	to	to	ADP
cana-6186	131	11	heart	heart	NOUN
cana-6186	131	12	disease	disease	NOUN
cana-6186	131	13	have	have	AUX
cana-6186	131	14	been	be	AUX
cana-6186	131	15	represented	represent	VERB
cana-6186	131	16	.	.	PUNCT
cana-6186	132	1	table	table	NOUN
cana-6186	132	2	i.	i.	PROPN
cana-6186	132	3	dataset	dataset	VERB
cana-6186	132	4	distribution	distribution	NOUN
cana-6186	132	5	label	label	NOUN
cana-6186	132	6	total	total	NOUN
cana-6186	132	7	samples	sample	NOUN
cana-6186	132	8	training	training	NOUN
cana-6186	132	9	samples	sample	NOUN
cana-6186	132	10	(	(	PUNCT
cana-6186	132	11	x_train	x_train	ADV
cana-6186	132	12	)	)	PUNCT
cana-6186	132	13	testing	test	VERB
cana-6186	132	14	samples	sample	NOUN
cana-6186	132	15	(	(	PUNCT
cana-6186	132	16	x_test	x_test	X
cana-6186	132	17	)	)	PUNCT
cana-6186	132	18	heart	heart	NOUN
cana-6186	132	19	disease	disease	NOUN
cana-6186	132	20	(	(	PUNCT
cana-6186	132	21	1	1	X
cana-6186	132	22	)	)	PUNCT
cana-6186	132	23	508	508	NUM
cana-6186	132	24	344	344	NUM
cana-6186	132	25	164	164	NUM
cana-6186	132	26	no	no	DET
cana-6186	132	27	heart	heart	NOUN
cana-6186	132	28	disease	disease	NOUN
cana-6186	132	29	(	(	PUNCT
cana-6186	132	30	0	0	NUM
cana-6186	132	31	)	)	PUNCT
cana-6186	132	32	410	410	NUM
cana-6186	132	33	298	298	NUM
cana-6186	132	34	112	112	NUM
cana-6186	132	35	total	total	NOUN
cana-6186	132	36	918	918	NUM
cana-6186	132	37	642	642	NUM
cana-6186	132	38	276	276	NUM
cana-6186	132	39	the	the	DET
cana-6186	132	40	dataset	dataset	NOUN
cana-6186	132	41	of	of	ADP
cana-6186	132	42	this	this	DET
cana-6186	132	43	study	study	NOUN
cana-6186	132	44	includes	include	VERB
cana-6186	132	45	demographic	demographic	ADJ
cana-6186	132	46	,	,	PUNCT
cana-6186	132	47	clinical	clinical	ADJ
cana-6186	132	48	,	,	PUNCT
cana-6186	132	49	and	and	CCONJ
cana-6186	132	50	diagnostic	diagnostic	ADJ
cana-6186	132	51	features	feature	NOUN
cana-6186	132	52	with	with	ADP
cana-6186	132	53	a	a	DET
cana-6186	132	54	total	total	NOUN
cana-6186	132	55	of	of	ADP
cana-6186	132	56	76	76	NUM
cana-6186	132	57	attributes	attribute	NOUN
cana-6186	132	58	.	.	PUNCT
cana-6186	133	1	however	however	ADV
cana-6186	133	2	,	,	PUNCT
cana-6186	133	3	most	most	ADJ
cana-6186	133	4	of	of	ADP
cana-6186	133	5	the	the	DET
cana-6186	133	6	published	publish	VERB
cana-6186	133	7	experiments	experiment	NOUN
cana-6186	133	8	,	,	PUNCT
cana-6186	133	9	and	and	CCONJ
cana-6186	133	10	as	as	ADP
cana-6186	133	11	per	per	ADP
cana-6186	133	12	the	the	DET
cana-6186	133	13	discussion	discussion	NOUN
cana-6186	133	14	with	with	ADP
cana-6186	133	15	health	health	NOUN
cana-6186	133	16	care	care	NOUN
cana-6186	133	17	practitioner	practitioner	NOUN
cana-6186	133	18	this	this	DET
cana-6186	133	19	study	study	NOUN
cana-6186	133	20	,	,	PUNCT
cana-6186	133	21	only	only	ADV
cana-6186	133	22	rely	rely	VERB
cana-6186	133	23	on	on	ADP
cana-6186	133	24	a	a	DET
cana-6186	133	25	subset	subset	NOUN
cana-6186	133	26	of	of	ADP
cana-6186	133	27	14	14	NUM
cana-6186	133	28	relevant	relevant	ADJ
cana-6186	133	29	attributes	attribute	NOUN
cana-6186	133	30	for	for	ADP
cana-6186	133	31	heart	heart	NOUN
cana-6186	133	32	disease	disease	NOUN
cana-6186	133	33	classification	classification	NOUN
cana-6186	133	34	.	.	PUNCT
cana-6186	134	1	these	these	PRON
cana-6186	134	2	include	include	VERB
cana-6186	134	3	age	age	NOUN
cana-6186	134	4	,	,	PUNCT
cana-6186	134	5	sex	sex	NOUN
cana-6186	134	6	,	,	PUNCT
cana-6186	134	7	type	type	NOUN
cana-6186	134	8	of	of	ADP
cana-6186	134	9	chest	chest	NOUN
cana-6186	134	10	pain	pain	NOUN
cana-6186	134	11	,	,	PUNCT
cana-6186	134	12	resting	rest	VERB
cana-6186	134	13	blood	blood	NOUN
cana-6186	134	14	pressure	pressure	NOUN
cana-6186	134	15	,	,	PUNCT
cana-6186	134	16	cholesterol	cholesterol	NOUN
cana-6186	134	17	level	level	NOUN
cana-6186	134	18	,	,	PUNCT
cana-6186	134	19	fasting	fast	VERB
cana-6186	134	20	blood	blood	NOUN
cana-6186	134	21	sugar	sugar	NOUN
cana-6186	134	22	,	,	PUNCT
cana-6186	134	23	resting	rest	VERB
cana-6186	134	24	electrocardiographic	electrocardiographic	ADJ
cana-6186	134	25	results	result	NOUN
cana-6186	134	26	,	,	PUNCT
cana-6186	134	27	maximum	maximum	ADJ
cana-6186	134	28	heart	heart	NOUN
cana-6186	134	29	rate	rate	NOUN
cana-6186	134	30	,	,	PUNCT
cana-6186	134	31	exercise	exercise	NOUN
cana-6186	134	32	-	-	PUNCT
cana-6186	134	33	induced	induce	VERB
cana-6186	134	34	angina	angina	NOUN
cana-6186	134	35	,	,	PUNCT
cana-6186	134	36	st	st	PROPN
cana-6186	134	37	depression	depression	NOUN
cana-6186	134	38	induced	induce	VERB
cana-6186	134	39	by	by	ADP
cana-6186	134	40	exercise	exercise	NOUN
cana-6186	134	41	,	,	PUNCT
cana-6186	134	42	and	and	CCONJ
cana-6186	134	43	so	so	ADV
cana-6186	134	44	on	on	ADV
cana-6186	134	45	.	.	PUNCT
cana-6186	135	1	the	the	DET
cana-6186	135	2	"	"	PUNCT
cana-6186	135	3	target	target	NOUN
cana-6186	135	4	"	"	PUNCT
cana-6186	135	5	attribute	attribute	NOUN
cana-6186	135	6	is	be	AUX
cana-6186	135	7	to	to	PART
cana-6186	135	8	be	be	AUX
cana-6186	135	9	predicted	predict	VERB
cana-6186	135	10	,	,	PUNCT
cana-6186	135	11	that	that	ADV
cana-6186	135	12	is	is	ADV
cana-6186	135	13	,	,	PUNCT
cana-6186	135	14	whether	whether	SCONJ
cana-6186	135	15	there	there	PRON
cana-6186	135	16	is	be	VERB
cana-6186	135	17	a	a	DET
cana-6186	135	18	heart	heart	NOUN
cana-6186	135	19	disease	disease	NOUN
cana-6186	135	20	or	or	CCONJ
cana-6186	135	21	not	not	PART
cana-6186	135	22	,	,	PUNCT
cana-6186	135	23	coded	code	VERB
cana-6186	135	24	as	as	ADP
cana-6186	135	25	0	0	NUM
cana-6186	135	26	(	(	PUNCT
cana-6186	135	27	absence	absence	NOUN
cana-6186	135	28	of	of	ADP
cana-6186	135	29	disease	disease	NOUN
cana-6186	135	30	)	)	PUNCT
cana-6186	135	31	or	or	CCONJ
cana-6186	135	32	1	1	NUM
cana-6186	135	33	(	(	PUNCT
cana-6186	135	34	presence	presence	NOUN
cana-6186	135	35	of	of	ADP
cana-6186	135	36	disease	disease	NOUN
cana-6186	135	37	)	)	PUNCT
cana-6186	135	38	.	.	PUNCT
cana-6186	136	1	this	this	DET
cana-6186	136	2	structured	structure	VERB
cana-6186	136	3	and	and	CCONJ
cana-6186	136	4	comprehensive	comprehensive	ADJ
cana-6186	136	5	dataset	dataset	NOUN
cana-6186	136	6	permits	permit	VERB
cana-6186	136	7	the	the	DET
cana-6186	136	8	effective	effective	ADJ
cana-6186	136	9	training	training	NOUN
cana-6186	136	10	and	and	CCONJ
cana-6186	136	11	testing	testing	NOUN
cana-6186	136	12	of	of	ADP
cana-6186	136	13	machine	machine	NOUN
cana-6186	136	14	learning	learning	NOUN
cana-6186	136	15	models	model	NOUN
cana-6186	136	16	,	,	PUNCT
cana-6186	136	17	whereby	whereby	SCONJ
cana-6186	136	18	correct	correct	ADJ
cana-6186	136	19	predictions	prediction	NOUN
cana-6186	136	20	and	and	CCONJ
cana-6186	136	21	actionable	actionable	ADJ
cana-6186	136	22	insights	insight	NOUN
cana-6186	136	23	are	be	AUX
cana-6186	136	24	delivered	deliver	VERB
cana-6186	136	25	in	in	ADP
cana-6186	136	26	the	the	DET
cana-6186	136	27	diagnosis	diagnosis	NOUN
cana-6186	136	28	of	of	ADP
cana-6186	136	29	heart	heart	NOUN
cana-6186	136	30	disease	disease	NOUN
cana-6186	136	31	.	.	PUNCT
cana-6186	137	1	c.	c.	PROPN
cana-6186	137	2	preprocessing	preprocesse	VERB
cana-6186	137	3	pre	pre	NOUN
cana-6186	137	4	-	-	ADJ
cana-6186	137	5	processing	processing	NOUN
cana-6186	137	6	is	be	AUX
cana-6186	137	7	the	the	DET
cana-6186	137	8	backbone	backbone	NOUN
cana-6186	137	9	of	of	ADP
cana-6186	137	10	the	the	DET
cana-6186	137	11	cvd	cvd	PROPN
cana-6186	137	12	disease	disease	PROPN
cana-6186	137	13	classification	classification	NOUN
cana-6186	137	14	model	model	NOUN
cana-6186	137	15	.	.	PUNCT
cana-6186	138	1	in	in	ADP
cana-6186	138	2	preprocessing	preprocessing	NOUN
cana-6186	138	3	,	,	PUNCT
cana-6186	138	4	raw	raw	ADJ
cana-6186	138	5	data	datum	NOUN
cana-6186	138	6	is	be	AUX
cana-6186	138	7	transformed	transform	VERB
cana-6186	138	8	into	into	ADP
cana-6186	138	9	standard	standard	ADJ
cana-6186	138	10	and	and	CCONJ
cana-6186	138	11	usable	usable	ADJ
cana-6186	138	12	data	datum	NOUN
cana-6186	138	13	suitable	suitable	ADJ
cana-6186	138	14	for	for	ADP
cana-6186	138	15	machine	machine	NOUN
cana-6186	138	16	learning	learning	NOUN
cana-6186	138	17	algorithms	algorithm	NOUN
cana-6186	138	18	.	.	PUNCT
cana-6186	139	1	the	the	DET
cana-6186	139	2	majority	majority	NOUN
cana-6186	139	3	of	of	ADP
cana-6186	139	4	medical	medical	ADJ
cana-6186	139	5	datasets	dataset	NOUN
cana-6186	139	6	include	include	VERB
cana-6186	139	7	noise	noise	NOUN
cana-6186	139	8	such	such	ADJ
cana-6186	139	9	as	as	ADP
cana-6186	139	10	missing	miss	VERB
cana-6186	139	11	values	value	NOUN
cana-6186	139	12	,	,	PUNCT
cana-6186	139	13	outliers	outlier	NOUN
cana-6186	139	14	,	,	PUNCT
cana-6186	139	15	and	and	CCONJ
cana-6186	139	16	irrelevant	irrelevant	ADJ
cana-6186	139	17	information	information	NOUN
cana-6186	139	18	that	that	PRON
cana-6186	139	19	might	might	AUX
cana-6186	139	20	impact	impact	VERB
cana-6186	139	21	the	the	DET
cana-6186	139	22	accuracy	accuracy	NOUN
cana-6186	139	23	and	and	CCONJ
cana-6186	139	24	effectiveness	effectiveness	NOUN
cana-6186	139	25	of	of	ADP
cana-6186	139	26	the	the	DET
cana-6186	139	27	classification	classification	NOUN
cana-6186	139	28	models	model	NOUN
cana-6186	139	29	.	.	PUNCT
cana-6186	140	1	preprocessing	preprocessing	NOUN
cana-6186	140	2	begins	begin	VERB
cana-6186	140	3	with	with	ADP
cana-6186	140	4	data	datum	NOUN
cana-6186	140	5	cleaning	cleaning	NOUN
cana-6186	140	6	,	,	PUNCT
cana-6186	140	7	which	which	PRON
cana-6186	140	8	deals	deal	VERB
cana-6186	140	9	with	with	ADP
cana-6186	140	10	removing	remove	VERB
cana-6186	140	11	errors	error	NOUN
cana-6186	140	12	,	,	PUNCT
cana-6186	140	13	redundancy	redundancy	NOUN
cana-6186	140	14	,	,	PUNCT
cana-6186	140	15	and	and	CCONJ
cana-6186	140	16	unnecessary	unnecessary	ADJ
cana-6186	140	17	records	record	NOUN
cana-6186	140	18	.	.	PUNCT
cana-6186	141	1	missing	miss	VERB
cana-6186	141	2	values	value	NOUN
cana-6186	141	3	are	be	AUX
cana-6186	141	4	filled	fill	VERB
cana-6186	141	5	up	up	ADP
cana-6186	141	6	using	use	VERB
cana-6186	141	7	mean	mean	ADJ
cana-6186	141	8	,	,	PUNCT
cana-6186	141	9	median	median	ADJ
cana-6186	141	10	imputation	imputation	NOUN
cana-6186	141	11	or	or	CCONJ
cana-6186	141	12	predictive	predictive	ADJ
cana-6186	141	13	methods	method	NOUN
cana-6186	141	14	for	for	ADP
cana-6186	141	15	making	make	VERB
cana-6186	141	16	the	the	DET
cana-6186	141	17	dataset	dataset	NOUN
cana-6186	141	18	complete	complete	ADJ
cana-6186	141	19	.	.	PUNCT
cana-6186	142	1	normalization	normalization	NOUN
cana-6186	142	2	and	and	CCONJ
cana-6186	142	3	standardization	standardization	NOUN
cana-6186	142	4	have	have	AUX
cana-6186	142	5	been	be	AUX
cana-6186	142	6	applied	apply	VERB
cana-6186	142	7	for	for	ADP
cana-6186	142	8	scaling	scale	VERB
cana-6186	142	9	all	all	DET
cana-6186	142	10	features	feature	NOUN
cana-6186	142	11	to	to	ADP
cana-6186	142	12	a	a	DET
cana-6186	142	13	uniform	uniform	ADJ
cana-6186	142	14	range	range	NOUN
cana-6186	142	15	,	,	PUNCT
cana-6186	142	16	allowing	allow	VERB
cana-6186	142	17	machine	machine	NOUN
cana-6186	142	18	learning	learning	NOUN
cana-6186	142	19	algorithms	algorithm	NOUN
cana-6186	142	20	to	to	PART
cana-6186	142	21	comprehend	comprehend	VERB
cana-6186	142	22	the	the	DET
cana-6186	142	23	data	datum	NOUN
cana-6186	142	24	better	well	ADV
cana-6186	142	25	.	.	PUNCT
cana-6186	143	1	for	for	ADP
cana-6186	143	2	instance	instance	NOUN
cana-6186	143	3	,	,	PUNCT
cana-6186	143	4	attributes	attribute	VERB
cana-6186	143	5	such	such	ADJ
cana-6186	143	6	as	as	ADP
cana-6186	143	7	cholesterol	cholesterol	NOUN
cana-6186	143	8	levels	level	NOUN
cana-6186	143	9	or	or	CCONJ
cana-6186	143	10	blood	blood	NOUN
cana-6186	143	11	pressure	pressure	NOUN
cana-6186	143	12	are	be	AUX
cana-6186	143	13	normalized	normalize	VERB
cana-6186	143	14	to	to	PART
cana-6186	143	15	avoid	avoid	VERB
cana-6186	143	16	one	one	NUM
cana-6186	143	17	feature	feature	NOUN
cana-6186	143	18	dominating	dominate	VERB
cana-6186	143	19	the	the	DET
cana-6186	143	20	model	model	NOUN
cana-6186	143	21	.	.	PUNCT
cana-6186	144	1	techniques	technique	NOUN
cana-6186	144	2	of	of	ADP
cana-6186	144	3	noise	noise	NOUN
cana-6186	144	4	elimination	elimination	NOUN
cana-6186	144	5	are	be	AUX
cana-6186	144	6	applied	apply	VERB
cana-6186	144	7	to	to	PART
cana-6186	144	8	remove	remove	VERB
cana-6186	144	9	unwanted	unwanted	ADJ
cana-6186	144	10	information	information	NOUN
cana-6186	144	11	or	or	CCONJ
cana-6186	144	12	errors	error	NOUN
cana-6186	144	13	that	that	PRON
cana-6186	144	14	may	may	AUX
cana-6186	144	15	skew	skew	VERB
cana-6186	144	16	the	the	DET
cana-6186	144	17	outcome	outcome	NOUN
cana-6186	144	18	.	.	PUNCT
cana-6186	145	1	this	this	DET
cana-6186	145	2	approach	approach	NOUN
cana-6186	145	3	ensures	ensure	VERB
cana-6186	145	4	that	that	SCONJ
cana-6186	145	5	the	the	DET
cana-6186	145	6	dataset	dataset	NOUN
cana-6186	145	7	remains	remain	VERB
cana-6186	145	8	consistent	consistent	ADJ
cana-6186	145	9	and	and	CCONJ
cana-6186	145	10	accurate	accurate	ADJ
cana-6186	145	11	,	,	PUNCT
cana-6186	145	12	free	free	ADJ
cana-6186	145	13	from	from	ADP
cana-6186	145	14	an	an	DET
cana-6186	145	15	anomaly	anomaly	NOUN
cana-6186	145	16	,	,	PUNCT
cana-6186	145	17	thus	thus	ADV
cana-6186	145	18	enhancing	enhance	VERB
cana-6186	145	19	reliability	reliability	NOUN
cana-6186	145	20	in	in	ADP
cana-6186	145	21	the	the	DET
cana-6186	145	22	classification	classification	NOUN
cana-6186	145	23	results	result	NOUN
cana-6186	145	24	.	.	PUNCT
cana-6186	146	1	given	give	VERB
cana-6186	146	2	that	that	DET
cana-6186	146	3	proper	proper	ADJ
cana-6186	146	4	preparation	preparation	NOUN
cana-6186	146	5	of	of	ADP
cana-6186	146	6	data	datum	NOUN
cana-6186	146	7	communications	communication	NOUN
cana-6186	146	8	on	on	ADP
cana-6186	146	9	applied	apply	VERB
cana-6186	146	10	nonlinear	nonlinear	ADJ
cana-6186	146	11	analysis	analysis	NOUN
cana-6186	146	12	issn	issn	NOUN
cana-6186	146	13	:	:	PUNCT
cana-6186	146	14	1074	1074	NUM
cana-6186	146	15	-	-	PUNCT
cana-6186	146	16	133x	133x	NUM
cana-6186	146	17	vol	vol	NOUN
cana-6186	146	18	32	32	NUM
cana-6186	146	19	no	no	NOUN
cana-6186	146	20	.	.	NOUN
cana-6186	146	21	1	1	NUM
cana-6186	146	22	(	(	PUNCT
cana-6186	146	23	2025	2025	NUM
cana-6186	146	24	)	)	PUNCT
cana-6186	146	25	662	662	NUM
cana-6186	146	26	https://internationalpubls.com	https://internationalpubls.com	X
cana-6186	146	27	during	during	ADP
cana-6186	146	28	preprocessing	preprocessing	NOUN
cana-6186	146	29	creates	create	VERB
cana-6186	146	30	a	a	DET
cana-6186	146	31	solid	solid	ADJ
cana-6186	146	32	foundation	foundation	NOUN
cana-6186	146	33	that	that	PRON
cana-6186	146	34	will	will	AUX
cana-6186	146	35	eventually	eventually	ADV
cana-6186	146	36	support	support	VERB
cana-6186	146	37	feature	feature	NOUN
cana-6186	146	38	extraction	extraction	NOUN
cana-6186	146	39	,	,	PUNCT
cana-6186	146	40	as	as	ADV
cana-6186	146	41	well	well	ADV
cana-6186	146	42	as	as	ADP
cana-6186	146	43	model	model	NOUN
cana-6186	146	44	development	development	NOUN
cana-6186	146	45	,	,	PUNCT
cana-6186	146	46	it	it	PRON
cana-6186	146	47	will	will	AUX
cana-6186	146	48	improve	improve	VERB
cana-6186	146	49	all	all	ADV
cana-6186	146	50	-	-	PUNCT
cana-6186	146	51	around	around	ADJ
cana-6186	146	52	performance	performance	NOUN
cana-6186	146	53	and	and	CCONJ
cana-6186	146	54	accuracy	accuracy	NOUN
cana-6186	146	55	.	.	PUNCT
cana-6186	147	1	d.	d.	PROPN
cana-6186	147	2	feature	feature	NOUN
cana-6186	147	3	extraction	extraction	NOUN
cana-6186	147	4	feature	feature	NOUN
cana-6186	147	5	extraction	extraction	NOUN
cana-6186	147	6	is	be	AUX
cana-6186	147	7	the	the	DET
cana-6186	147	8	key	key	ADJ
cana-6186	147	9	part	part	NOUN
cana-6186	147	10	of	of	ADP
cana-6186	147	11	the	the	DET
cana-6186	147	12	cardiovascular	cardiovascular	ADJ
cana-6186	147	13	disease	disease	NOUN
cana-6186	147	14	classification	classification	NOUN
cana-6186	147	15	system	system	NOUN
cana-6186	147	16	.	.	PUNCT
cana-6186	148	1	the	the	DET
cana-6186	148	2	process	process	NOUN
cana-6186	148	3	will	will	AUX
cana-6186	148	4	be	be	AUX
cana-6186	148	5	focused	focus	VERB
cana-6186	148	6	on	on	ADP
cana-6186	148	7	feature	feature	NOUN
cana-6186	148	8	selection	selection	NOUN
cana-6186	148	9	and	and	CCONJ
cana-6186	148	10	engineering	engineer	VERB
cana-6186	148	11	the	the	DET
cana-6186	148	12	most	most	ADV
cana-6186	148	13	relevant	relevant	ADJ
cana-6186	148	14	attributes	attribute	NOUN
cana-6186	148	15	from	from	ADP
cana-6186	148	16	the	the	DET
cana-6186	148	17	dataset	dataset	NOUN
cana-6186	148	18	in	in	ADP
cana-6186	148	19	order	order	NOUN
cana-6186	148	20	to	to	PART
cana-6186	148	21	make	make	VERB
cana-6186	148	22	the	the	DET
cana-6186	148	23	model	model	NOUN
cana-6186	148	24	efficient	efficient	ADJ
cana-6186	148	25	and	and	CCONJ
cana-6186	148	26	accurate	accurate	ADJ
cana-6186	148	27	.	.	PUNCT
cana-6186	149	1	medical	medical	ADJ
cana-6186	149	2	datasets	dataset	NOUN
cana-6186	149	3	contain	contain	VERB
cana-6186	149	4	a	a	DET
cana-6186	149	5	large	large	ADJ
cana-6186	149	6	number	number	NOUN
cana-6186	149	7	of	of	ADP
cana-6186	149	8	features	feature	NOUN
cana-6186	149	9	that	that	PRON
cana-6186	149	10	do	do	AUX
cana-6186	149	11	not	not	PART
cana-6186	149	12	contribute	contribute	VERB
cana-6186	149	13	much	much	ADJ
cana-6186	149	14	to	to	ADP
cana-6186	149	15	the	the	DET
cana-6186	149	16	predictive	predictive	ADJ
cana-6186	149	17	process	process	NOUN
cana-6186	149	18	.	.	PUNCT
cana-6186	150	1	this	this	PRON
cana-6186	150	2	helps	help	VERB
cana-6186	150	3	in	in	ADP
cana-6186	150	4	identifying	identify	VERB
cana-6186	150	5	the	the	DET
cana-6186	150	6	most	most	ADV
cana-6186	150	7	representative	representative	ADJ
cana-6186	150	8	features	feature	NOUN
cana-6186	150	9	of	of	ADP
cana-6186	150	10	the	the	DET
cana-6186	150	11	risk	risk	NOUN
cana-6186	150	12	associated	associate	VERB
cana-6186	150	13	with	with	ADP
cana-6186	150	14	heart	heart	NOUN
cana-6186	150	15	disease	disease	NOUN
cana-6186	150	16	,	,	PUNCT
cana-6186	150	17	such	such	ADJ
cana-6186	150	18	as	as	ADP
cana-6186	150	19	age	age	NOUN
cana-6186	150	20	,	,	PUNCT
cana-6186	150	21	cholesterol	cholesterol	NOUN
cana-6186	150	22	levels	level	NOUN
cana-6186	150	23	,	,	PUNCT
cana-6186	150	24	blood	blood	NOUN
cana-6186	150	25	pressure	pressure	NOUN
cana-6186	150	26	,	,	PUNCT
cana-6186	150	27	and	and	CCONJ
cana-6186	150	28	heart	heart	NOUN
cana-6186	150	29	rate	rate	NOUN
cana-6186	150	30	.	.	PUNCT
cana-6186	151	1	the	the	DET
cana-6186	151	2	feature	feature	NOUN
cana-6186	151	3	extraction	extraction	NOUN
cana-6186	151	4	process	process	NOUN
cana-6186	151	5	helps	help	VERB
cana-6186	151	6	in	in	ADP
cana-6186	151	7	reducing	reduce	VERB
cana-6186	151	8	the	the	DET
cana-6186	151	9	dimensionality	dimensionality	NOUN
cana-6186	151	10	of	of	ADP
cana-6186	151	11	the	the	DET
cana-6186	151	12	data	datum	NOUN
cana-6186	151	13	set	set	VERB
cana-6186	151	14	by	by	ADP
cana-6186	151	15	eliminating	eliminate	VERB
cana-6186	151	16	unnecessary	unnecessary	ADJ
cana-6186	151	17	or	or	CCONJ
cana-6186	151	18	irrelevant	irrelevant	ADJ
cana-6186	151	19	features	feature	NOUN
cana-6186	151	20	,	,	PUNCT
cana-6186	151	21	thus	thus	ADV
cana-6186	151	22	improving	improve	VERB
cana-6186	151	23	the	the	DET
cana-6186	151	24	input	input	NOUN
cana-6186	151	25	for	for	ADP
cana-6186	151	26	the	the	DET
cana-6186	151	27	classification	classification	NOUN
cana-6186	151	28	model	model	NOUN
cana-6186	151	29	.	.	PUNCT
cana-6186	152	1	e.	e.	PROPN
cana-6186	152	2	classification	classification	PROPN
cana-6186	152	3	model	model	NOUN
cana-6186	152	4	it	it	PRON
cana-6186	152	5	essentially	essentially	ADV
cana-6186	152	6	depends	depend	VERB
cana-6186	152	7	on	on	ADP
cana-6186	152	8	a	a	DET
cana-6186	152	9	classification	classification	NOUN
cana-6186	152	10	model	model	NOUN
cana-6186	152	11	that	that	PRON
cana-6186	152	12	uses	use	VERB
cana-6186	152	13	the	the	DET
cana-6186	152	14	drawn	draw	VERB
cana-6186	152	15	features	feature	NOUN
cana-6186	152	16	for	for	ADP
cana-6186	152	17	predicting	predict	VERB
cana-6186	152	18	whether	whether	SCONJ
cana-6186	152	19	a	a	DET
cana-6186	152	20	patient	patient	NOUN
cana-6186	152	21	has	have	VERB
cana-6186	152	22	heart	heart	NOUN
cana-6186	152	23	disease	disease	NOUN
cana-6186	152	24	or	or	CCONJ
cana-6186	152	25	not	not	PART
cana-6186	152	26	.	.	PUNCT
cana-6186	153	1	this	this	DET
cana-6186	153	2	block	block	NOUN
cana-6186	153	3	uses	use	VERB
cana-6186	153	4	the	the	DET
cana-6186	153	5	machine	machine	NOUN
cana-6186	153	6	learning	learn	VERB
cana-6186	153	7	algorithm	algorithm	NOUN
cana-6186	153	8	for	for	SCONJ
cana-6186	153	9	the	the	DET
cana-6186	153	10	data	datum	NOUN
cana-6186	153	11	to	to	PART
cana-6186	153	12	learn	learn	VERB
cana-6186	153	13	the	the	DET
cana-6186	153	14	patterns	pattern	NOUN
cana-6186	153	15	and	and	CCONJ
cana-6186	153	16	relationships	relationship	NOUN
cana-6186	153	17	so	so	SCONJ
cana-6186	153	18	that	that	SCONJ
cana-6186	153	19	it	it	PRON
cana-6186	153	20	could	could	AUX
cana-6186	153	21	make	make	VERB
cana-6186	153	22	accurate	accurate	ADJ
cana-6186	153	23	predictions	prediction	NOUN
cana-6186	153	24	.	.	PUNCT
cana-6186	154	1	the	the	DET
cana-6186	154	2	algorithm	algorithm	NOUN
cana-6186	154	3	implemented	implement	VERB
cana-6186	154	4	may	may	AUX
cana-6186	154	5	be	be	AUX
cana-6186	154	6	svm	svm	ADJ
cana-6186	154	7	,	,	PUNCT
cana-6186	154	8	knn	knn	PROPN
cana-6186	154	9	,	,	PUNCT
cana-6186	154	10	decision	decision	NOUN
cana-6186	154	11	tree	tree	NOUN
cana-6186	154	12	,	,	PUNCT
cana-6186	154	13	random	random	ADJ
cana-6186	154	14	forest	forest	NOUN
cana-6186	154	15	,	,	PUNCT
cana-6186	154	16	or	or	CCONJ
cana-6186	154	17	gradient	gradient	NOUN
cana-6186	154	18	boosting	boosting	NOUN
cana-6186	154	19	based	base	VERB
cana-6186	154	20	on	on	ADP
cana-6186	154	21	the	the	DET
cana-6186	154	22	complexity	complexity	NOUN
cana-6186	154	23	of	of	ADP
cana-6186	154	24	the	the	DET
cana-6186	154	25	dataset	dataset	NOUN
cana-6186	154	26	and	and	CCONJ
cana-6186	154	27	the	the	DET
cana-6186	154	28	desired	desire	VERB
cana-6186	154	29	outcomes	outcome	NOUN
cana-6186	154	30	.	.	PUNCT
cana-6186	155	1	this	this	PRON
cana-6186	155	2	is	be	AUX
cana-6186	155	3	because	because	SCONJ
cana-6186	155	4	each	each	PRON
cana-6186	155	5	of	of	ADP
cana-6186	155	6	them	they	PRON
cana-6186	155	7	is	be	AUX
cana-6186	155	8	chosen	choose	VERB
cana-6186	155	9	based	base	VERB
cana-6186	155	10	on	on	ADP
cana-6186	155	11	whether	whether	SCONJ
cana-6186	155	12	it	it	PRON
cana-6186	155	13	can	can	AUX
cana-6186	155	14	handle	handle	VERB
cana-6186	155	15	the	the	DET
cana-6186	155	16	characteristics	characteristic	NOUN
cana-6186	155	17	of	of	ADP
cana-6186	155	18	a	a	DET
cana-6186	155	19	dataset	dataset	NOUN
cana-6186	155	20	,	,	PUNCT
cana-6186	155	21	such	such	ADJ
cana-6186	155	22	as	as	ADP
cana-6186	155	23	dimensionality	dimensionality	NOUN
cana-6186	155	24	,	,	PUNCT
cana-6186	155	25	non	non	ADJ
cana-6186	155	26	-	-	ADJ
cana-6186	155	27	linearity	linearity	ADJ
cana-6186	155	28	,	,	PUNCT
cana-6186	155	29	or	or	CCONJ
cana-6186	155	30	feature	feature	NOUN
cana-6186	155	31	importance	importance	NOUN
cana-6186	155	32	.	.	PUNCT
cana-6186	156	1	the	the	DET
cana-6186	156	2	model	model	NOUN
cana-6186	156	3	will	will	AUX
cana-6186	156	4	be	be	AUX
cana-6186	156	5	trained	train	VERB
cana-6186	156	6	on	on	ADP
cana-6186	156	7	a	a	DET
cana-6186	156	8	labelled	label	VERB
cana-6186	156	9	dataset	dataset	NOUN
cana-6186	156	10	where	where	SCONJ
cana-6186	156	11	it	it	PRON
cana-6186	156	12	learns	learn	VERB
cana-6186	156	13	the	the	DET
cana-6186	156	14	correlation	correlation	NOUN
cana-6186	156	15	between	between	ADP
cana-6186	156	16	the	the	DET
cana-6186	156	17	input	input	NOUN
cana-6186	156	18	features	feature	VERB
cana-6186	156	19	such	such	ADJ
cana-6186	156	20	as	as	ADP
cana-6186	156	21	age	age	NOUN
cana-6186	156	22	,	,	PUNCT
cana-6186	156	23	cholesterol	cholesterol	NOUN
cana-6186	156	24	,	,	PUNCT
cana-6186	156	25	and	and	CCONJ
cana-6186	156	26	blood	blood	NOUN
cana-6186	156	27	pressure	pressure	NOUN
cana-6186	156	28	to	to	ADP
cana-6186	156	29	the	the	DET
cana-6186	156	30	output	output	NOUN
cana-6186	156	31	labels	label	NOUN
cana-6186	156	32	like	like	ADP
cana-6186	156	33	"	"	PUNCT
cana-6186	156	34	at	at	ADP
cana-6186	156	35	risk	risk	NOUN
cana-6186	156	36	"	"	PUNCT
cana-6186	156	37	or	or	CCONJ
cana-6186	156	38	"	"	PUNCT
cana-6186	156	39	no	no	DET
cana-6186	156	40	risk	risk	NOUN
cana-6186	156	41	.	.	PUNCT
cana-6186	156	42	"	"	PUNCT
cana-6186	157	1	during	during	ADP
cana-6186	157	2	training	training	NOUN
cana-6186	157	3	,	,	PUNCT
cana-6186	157	4	the	the	DET
cana-6186	157	5	best	good	ADJ
cana-6186	157	6	hyperparameters	hyperparameter	NOUN
cana-6186	157	7	would	would	AUX
cana-6186	157	8	be	be	AUX
cana-6186	157	9	learned	learn	VERB
cana-6186	157	10	like	like	ADP
cana-6186	157	11	learning	learn	VERB
cana-6186	157	12	rate	rate	NOUN
cana-6186	157	13	,	,	PUNCT
cana-6186	157	14	maximum	maximum	ADJ
cana-6186	157	15	tree	tree	NOUN
cana-6186	157	16	depth	depth	NOUN
cana-6186	157	17	,	,	PUNCT
cana-6186	157	18	or	or	CCONJ
cana-6186	157	19	kernel	kernel	NOUN
cana-6186	157	20	type	type	VERB
cana-6186	157	21	with	with	ADP
cana-6186	157	22	grid	grid	NOUN
cana-6186	157	23	search	search	NOUN
cana-6186	157	24	and	and	CCONJ
cana-6186	157	25	random	random	ADJ
cana-6186	157	26	search	search	NOUN
cana-6186	157	27	.	.	PUNCT
cana-6186	158	1	another	another	DET
cana-6186	158	2	application	application	NOUN
cana-6186	158	3	of	of	ADP
cana-6186	158	4	cross	cross	NOUN
cana-6186	158	5	-	-	ADJ
cana-6186	158	6	validation	validation	NOUN
cana-6186	158	7	is	be	AUX
cana-6186	158	8	to	to	PART
cana-6186	158	9	check	check	VERB
cana-6186	158	10	that	that	SCONJ
cana-6186	158	11	the	the	DET
cana-6186	158	12	model	model	NOUN
cana-6186	158	13	generalizes	generalize	VERB
cana-6186	158	14	well	well	ADV
cana-6186	158	15	over	over	ADP
cana-6186	158	16	unseen	unseen	ADJ
cana-6186	158	17	data	datum	NOUN
cana-6186	158	18	and	and	CCONJ
cana-6186	158	19	does	do	AUX
cana-6186	158	20	not	not	PART
cana-6186	158	21	overfit	overfit	VERB
cana-6186	158	22	the	the	DET
cana-6186	158	23	training	training	NOUN
cana-6186	158	24	set	set	NOUN
cana-6186	158	25	.	.	PUNCT
cana-6186	159	1	a	a	DET
cana-6186	159	2	)	)	PUNCT
cana-6186	159	3	svm	svm	NOUN
cana-6186	159	4	:	:	PUNCT
cana-6186	159	5	svm	svm	PROPN
cana-6186	159	6	is	be	AUX
cana-6186	159	7	a	a	DET
cana-6186	159	8	supervised	supervised	ADJ
cana-6186	159	9	learning	learn	VERB
cana-6186	159	10	algorithm	algorithm	NOUN
cana-6186	159	11	that	that	PRON
cana-6186	159	12	aims	aim	VERB
cana-6186	159	13	at	at	ADP
cana-6186	159	14	finding	find	VERB
cana-6186	159	15	the	the	DET
cana-6186	159	16	best	good	ADJ
cana-6186	159	17	hyperplane	hyperplane	NOUN
cana-6186	159	18	to	to	PART
cana-6186	159	19	separate	separate	VERB
cana-6186	159	20	data	datum	NOUN
cana-6186	159	21	into	into	ADP
cana-6186	159	22	different	different	ADJ
cana-6186	159	23	classes	class	NOUN
cana-6186	159	24	.	.	PUNCT
cana-6186	160	1	as	as	ADV
cana-6186	160	2	far	far	ADV
cana-6186	160	3	as	as	ADP
cana-6186	160	4	a	a	DET
cana-6186	160	5	binary	binary	ADJ
cana-6186	160	6	classification	classification	NOUN
cana-6186	160	7	problem	problem	NOUN
cana-6186	160	8	in	in	ADP
cana-6186	160	9	heart	heart	NOUN
cana-6186	160	10	disease	disease	NOUN
cana-6186	160	11	prediction	prediction	NOUN
cana-6186	160	12	,	,	PUNCT
cana-6186	160	13	an	an	DET
cana-6186	160	14	svm	svm	NOUN
cana-6186	160	15	works	work	VERB
cana-6186	160	16	by	by	ADP
cana-6186	160	17	maximizing	maximize	VERB
cana-6186	160	18	the	the	DET
cana-6186	160	19	margin	margin	NOUN
cana-6186	160	20	between	between	ADP
cana-6186	160	21	the	the	DET
cana-6186	160	22	nearest	near	ADJ
cana-6186	160	23	data	data	NOUN
cana-6186	160	24	points	point	NOUN
cana-6186	160	25	of	of	ADP
cana-6186	160	26	each	each	DET
cana-6186	160	27	class	class	NOUN
cana-6186	160	28	commonly	commonly	ADV
cana-6186	160	29	referred	refer	VERB
cana-6186	160	30	to	to	ADP
cana-6186	160	31	as	as	ADP
cana-6186	160	32	the	the	DET
cana-6186	160	33	support	support	NOUN
cana-6186	160	34	vectors	vector	NOUN
cana-6186	160	35	.	.	PUNCT
cana-6186	161	1	svm	svm	PROPN
cana-6186	161	2	is	be	AUX
cana-6186	161	3	particularly	particularly	ADV
cana-6186	161	4	useful	useful	ADJ
cana-6186	161	5	in	in	ADP
cana-6186	161	6	high	high	ADJ
cana-6186	161	7	-	-	PUNCT
cana-6186	161	8	dimensional	dimensional	ADJ
cana-6186	161	9	or	or	CCONJ
cana-6186	161	10	non	non	ADJ
cana-6186	161	11	-	-	ADJ
cana-6186	161	12	linear	linear	ADJ
cana-6186	161	13	separable	separable	ADJ
cana-6186	161	14	data	datum	NOUN
cana-6186	161	15	sets	set	NOUN
cana-6186	161	16	as	as	SCONJ
cana-6186	161	17	it	it	PRON
cana-6186	161	18	can	can	AUX
cana-6186	161	19	transform	transform	VERB
cana-6186	161	20	the	the	DET
cana-6186	161	21	data	datum	NOUN
cana-6186	161	22	into	into	ADP
cana-6186	161	23	an	an	DET
cana-6186	161	24	appropriate	appropriate	ADJ
cana-6186	161	25	space	space	NOUN
cana-6186	161	26	in	in	ADP
cana-6186	161	27	which	which	PRON
cana-6186	161	28	separation	separation	NOUN
cana-6186	161	29	is	be	AUX
cana-6186	161	30	linear	linear	ADJ
cana-6186	161	31	through	through	ADP
cana-6186	161	32	the	the	DET
cana-6186	161	33	utilization	utilization	NOUN
cana-6186	161	34	of	of	ADP
cana-6186	161	35	kernel	kernel	PROPN
cana-6186	161	36	functions	function	NOUN
cana-6186	161	37	.	.	PUNCT
cana-6186	162	1	naturally	naturally	ADV
cana-6186	162	2	resistant	resistant	ADJ
cana-6186	162	3	to	to	ADP
cana-6186	162	4	overfitting	overfitte	VERB
cana-6186	162	5	and	and	CCONJ
cana-6186	162	6	with	with	ADP
cana-6186	162	7	good	good	ADJ
cana-6186	162	8	behavior	behavior	NOUN
cana-6186	162	9	in	in	ADP
cana-6186	162	10	very	very	ADV
cana-6186	162	11	small	small	ADJ
cana-6186	162	12	datasets	dataset	NOUN
cana-6186	162	13	,	,	PUNCT
cana-6186	162	14	preparing	prepare	VERB
cana-6186	162	15	a	a	DET
cana-6186	162	16	system	system	NOUN
cana-6186	162	17	for	for	ADP
cana-6186	162	18	the	the	DET
cana-6186	162	19	potential	potential	NOUN
cana-6186	162	20	of	of	ADP
cana-6186	162	21	medical	medical	ADJ
cana-6186	162	22	applications	application	NOUN
cana-6186	162	23	like	like	ADP
cana-6186	162	24	the	the	DET
cana-6186	162	25	classification	classification	NOUN
cana-6186	162	26	heart	heart	NOUN
cana-6186	162	27	diseases	disease	NOUN
cana-6186	162	28	.	.	PUNCT
cana-6186	163	1	b	b	X
cana-6186	163	2	)	)	PUNCT
cana-6186	163	3	knn	knn	PROPN
cana-6186	163	4	:	:	PUNCT
cana-6186	163	5	knn	knn	PROPN
cana-6186	163	6	is	be	AUX
cana-6186	163	7	a	a	DET
cana-6186	163	8	simple	simple	ADJ
cana-6186	163	9	and	and	CCONJ
cana-6186	163	10	intuitive	intuitive	ADJ
cana-6186	163	11	machine	machine	NOUN
cana-6186	163	12	learning	learn	VERB
cana-6186	163	13	algorithm	algorithm	NOUN
cana-6186	163	14	that	that	PRON
cana-6186	163	15	works	work	VERB
cana-6186	163	16	on	on	ADP
cana-6186	163	17	the	the	DET
cana-6186	163	18	principle	principle	NOUN
cana-6186	163	19	of	of	ADP
cana-6186	163	20	classification	classification	NOUN
cana-6186	163	21	of	of	ADP
cana-6186	163	22	data	datum	NOUN
cana-6186	163	23	instances	instance	NOUN
cana-6186	163	24	based	base	VERB
cana-6186	163	25	on	on	ADP
cana-6186	163	26	its	its	PRON
cana-6186	163	27	similarity	similarity	NOUN
cana-6186	163	28	to	to	ADP
cana-6186	163	29	other	other	ADJ
cana-6186	163	30	data	datum	NOUN
cana-6186	163	31	instances	instance	NOUN
cana-6186	163	32	.	.	PUNCT
cana-6186	164	1	in	in	ADP
cana-6186	164	2	the	the	DET
cana-6186	164	3	case	case	NOUN
cana-6186	164	4	of	of	ADP
cana-6186	164	5	heart	heart	NOUN
cana-6186	164	6	disease	disease	NOUN
cana-6186	164	7	classification	classification	NOUN
cana-6186	164	8	,	,	PUNCT
cana-6186	164	9	knn	knn	PROPN
cana-6186	164	10	computes	compute	VERB
cana-6186	164	11	the	the	DET
cana-6186	164	12	distance	distance	NOUN
cana-6186	164	13	,	,	PUNCT
cana-6186	164	14	for	for	ADP
cana-6186	164	15	example	example	NOUN
cana-6186	164	16	,	,	PUNCT
cana-6186	164	17	euclidean	euclidean	ADJ
cana-6186	164	18	distance	distance	NOUN
cana-6186	164	19	between	between	ADP
cana-6186	164	20	the	the	DET
cana-6186	164	21	new	new	ADJ
cana-6186	164	22	data	datum	NOUN
cana-6186	164	23	instance	instance	NOUN
cana-6186	164	24	and	and	CCONJ
cana-6186	164	25	the	the	DET
cana-6186	164	26	existing	exist	VERB
cana-6186	164	27	labeled	label	VERB
cana-6186	164	28	data	data	NOUN
cana-6186	164	29	instances	instance	NOUN
cana-6186	164	30	.	.	PUNCT
cana-6186	165	1	then	then	ADV
cana-6186	165	2	,	,	PUNCT
cana-6186	165	3	it	it	PRON
cana-6186	165	4	assigns	assign	VERB
cana-6186	165	5	the	the	DET
cana-6186	165	6	most	most	ADV
cana-6186	165	7	prominent	prominent	ADJ
cana-6186	165	8	class	class	NOUN
cana-6186	165	9	of	of	ADP
cana-6186	165	10	the	the	DET
cana-6186	165	11	k	k	PROPN
cana-6186	165	12	nearest	near	ADJ
cana-6186	165	13	neighbors	neighbor	NOUN
cana-6186	165	14	to	to	ADP
cana-6186	165	15	the	the	DET
cana-6186	165	16	new	new	ADJ
cana-6186	165	17	one	one	NUM
cana-6186	165	18	.	.	PUNCT
cana-6186	166	1	knn	knn	PROPN
cana-6186	166	2	is	be	AUX
cana-6186	166	3	dependent	dependent	ADJ
cana-6186	166	4	on	on	ADP
cana-6186	166	5	the	the	DET
cana-6186	166	6	value	value	NOUN
cana-6186	166	7	of	of	ADP
cana-6186	166	8	k	k	PROPN
cana-6186	166	9	that	that	PRON
cana-6186	166	10	needs	need	VERB
cana-6186	166	11	to	to	PART
cana-6186	166	12	be	be	AUX
cana-6186	166	13	chosen	choose	VERB
cana-6186	166	14	correctly	correctly	ADV
cana-6186	166	15	;	;	PUNCT
cana-6186	166	16	a	a	DET
cana-6186	166	17	small	small	ADJ
cana-6186	166	18	k	k	PROPN
cana-6186	166	19	may	may	AUX
cana-6186	166	20	produce	produce	VERB
cana-6186	166	21	overfitting	overfitting	NOUN
cana-6186	166	22	,	,	PUNCT
cana-6186	166	23	whereas	whereas	SCONJ
cana-6186	166	24	a	a	DET
cana-6186	166	25	large	large	ADJ
cana-6186	166	26	k	k	PROPN
cana-6186	166	27	may	may	AUX
cana-6186	166	28	generate	generate	VERB
cana-6186	166	29	underfitting	underfitting	NOUN
cana-6186	166	30	.	.	PUNCT
cana-6186	167	1	it	it	PRON
cana-6186	167	2	can	can	AUX
cana-6186	167	3	be	be	AUX
cana-6186	167	4	implemented	implement	VERB
cana-6186	167	5	very	very	ADV
cana-6186	167	6	easily	easily	ADV
cana-6186	167	7	because	because	SCONJ
cana-6186	167	8	of	of	ADP
cana-6186	167	9	its	its	PRON
cana-6186	167	10	simplicity	simplicity	NOUN
cana-6186	167	11	but	but	CCONJ
cana-6186	167	12	can	can	AUX
cana-6186	167	13	be	be	AUX
cana-6186	167	14	computationally	computationally	ADV
cana-6186	167	15	expensive	expensive	ADJ
cana-6186	167	16	for	for	ADP
cana-6186	167	17	large	large	ADJ
cana-6186	167	18	datasets	dataset	NOUN
cana-6186	167	19	.	.	PUNCT
cana-6186	168	1	this	this	PRON
cana-6186	168	2	notwithstanding	notwithstanding	ADV
cana-6186	168	3	,	,	PUNCT
cana-6186	168	4	knn	knn	PROPN
cana-6186	168	5	is	be	AUX
cana-6186	168	6	good	good	ADJ
cana-6186	168	7	for	for	ADP
cana-6186	168	8	simple	simple	ADJ
cana-6186	168	9	prototyping	prototyping	NOUN
cana-6186	168	10	and	and	CCONJ
cana-6186	168	11	performs	perform	VERB
cana-6186	168	12	okay	okay	INTJ
cana-6186	168	13	if	if	SCONJ
cana-6186	168	14	the	the	DET
cana-6186	168	15	distribution	distribution	NOUN
cana-6186	168	16	of	of	ADP
cana-6186	168	17	your	your	PRON
cana-6186	168	18	dataset	dataset	NOUN
cana-6186	168	19	is	be	AUX
cana-6186	168	20	even	even	ADV
cana-6186	168	21	.	.	PUNCT
cana-6186	169	1	communications	communication	NOUN
cana-6186	169	2	on	on	ADP
cana-6186	169	3	applied	apply	VERB
cana-6186	169	4	nonlinear	nonlinear	ADJ
cana-6186	169	5	analysis	analysis	NOUN
cana-6186	169	6	issn	issn	NOUN
cana-6186	169	7	:	:	PUNCT
cana-6186	169	8	1074	1074	NUM
cana-6186	169	9	-	-	PUNCT
cana-6186	169	10	133x	133x	NUM
cana-6186	169	11	vol	vol	NOUN
cana-6186	169	12	32	32	NUM
cana-6186	169	13	no	no	NOUN
cana-6186	169	14	.	.	NOUN
cana-6186	169	15	1	1	NUM
cana-6186	169	16	(	(	PUNCT
cana-6186	169	17	2025	2025	NUM
cana-6186	169	18	)	)	PUNCT
cana-6186	169	19	663	663	NUM
cana-6186	169	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-6186	169	21	c	c	NOUN
cana-6186	169	22	)	)	PUNCT
cana-6186	169	23	decision	decision	NOUN
cana-6186	169	24	tree	tree	NOUN
cana-6186	169	25	:	:	PUNCT
cana-6186	169	26	a	a	DET
cana-6186	169	27	decision	decision	NOUN
cana-6186	169	28	tree	tree	NOUN
cana-6186	169	29	is	be	AUX
cana-6186	169	30	a	a	DET
cana-6186	169	31	tree	tree	NOUN
cana-6186	169	32	-	-	PUNCT
cana-6186	169	33	structured	structure	VERB
cana-6186	169	34	algorithm	algorithm	NOUN
cana-6186	169	35	that	that	PRON
cana-6186	169	36	splits	split	VERB
cana-6186	169	37	the	the	DET
cana-6186	169	38	dataset	dataset	NOUN
cana-6186	169	39	into	into	ADP
cana-6186	169	40	subsets	subset	NOUN
cana-6186	169	41	based	base	VERB
cana-6186	169	42	on	on	ADP
cana-6186	169	43	feature	feature	NOUN
cana-6186	169	44	values	value	NOUN
cana-6186	169	45	.	.	PUNCT
cana-6186	170	1	each	each	DET
cana-6186	170	2	internal	internal	ADJ
cana-6186	170	3	node	node	NOUN
cana-6186	170	4	represents	represent	VERB
cana-6186	170	5	a	a	DET
cana-6186	170	6	feature	feature	NOUN
cana-6186	170	7	,	,	PUNCT
cana-6186	170	8	each	each	DET
cana-6186	170	9	branch	branch	NOUN
cana-6186	170	10	corresponds	correspond	VERB
cana-6186	170	11	to	to	ADP
cana-6186	170	12	a	a	DET
cana-6186	170	13	decision	decision	NOUN
cana-6186	170	14	rule	rule	NOUN
cana-6186	170	15	,	,	PUNCT
cana-6186	170	16	and	and	CCONJ
cana-6186	170	17	each	each	DET
cana-6186	170	18	leaf	leaf	NOUN
cana-6186	170	19	node	node	NOUN
cana-6186	170	20	represents	represent	VERB
cana-6186	170	21	a	a	DET
cana-6186	170	22	classification	classification	NOUN
cana-6186	170	23	outcome	outcome	NOUN
cana-6186	170	24	.	.	PUNCT
cana-6186	171	1	in	in	ADP
cana-6186	171	2	heart	heart	NOUN
cana-6186	171	3	disease	disease	NOUN
cana-6186	171	4	classification	classification	NOUN
cana-6186	171	5	,	,	PUNCT
cana-6186	171	6	for	for	ADP
cana-6186	171	7	example	example	NOUN
cana-6186	171	8	,	,	PUNCT
cana-6186	171	9	a	a	DET
cana-6186	171	10	decision	decision	NOUN
cana-6186	171	11	tree	tree	NOUN
cana-6186	171	12	could	could	AUX
cana-6186	171	13	use	use	VERB
cana-6186	171	14	age	age	NOUN
cana-6186	171	15	,	,	PUNCT
cana-6186	171	16	cholesterol	cholesterol	NOUN
cana-6186	171	17	level	level	NOUN
cana-6186	171	18	,	,	PUNCT
cana-6186	171	19	and	and	CCONJ
cana-6186	171	20	smoking	smoking	NOUN
cana-6186	171	21	status	status	NOUN
cana-6186	171	22	features	feature	VERB
cana-6186	171	23	to	to	PART
cana-6186	171	24	iteratively	iteratively	ADV
cana-6186	171	25	divide	divide	VERB
cana-6186	171	26	data	datum	NOUN
cana-6186	171	27	into	into	ADP
cana-6186	171	28	risk	risk	NOUN
cana-6186	171	29	or	or	CCONJ
cana-6186	171	30	no	no	DET
cana-6186	171	31	-	-	PUNCT
cana-6186	171	32	risk	risk	NOUN
cana-6186	171	33	categories	category	NOUN
cana-6186	171	34	.	.	PUNCT
cana-6186	172	1	decision	decision	NOUN
cana-6186	172	2	trees	tree	NOUN
cana-6186	172	3	are	be	AUX
cana-6186	172	4	highly	highly	ADV
cana-6186	172	5	interpretable	interpretable	ADJ
cana-6186	172	6	and	and	CCONJ
cana-6186	172	7	leave	leave	VERB
cana-6186	172	8	no	no	DET
cana-6186	172	9	room	room	NOUN
cana-6186	172	10	for	for	ADP
cana-6186	172	11	confusion	confusion	NOUN
cana-6186	172	12	in	in	ADP
cana-6186	172	13	the	the	DET
cana-6186	172	14	understanding	understanding	NOUN
cana-6186	172	15	of	of	ADP
cana-6186	172	16	the	the	DET
cana-6186	172	17	decision	decision	NOUN
cana-6186	172	18	-	-	PUNCT
cana-6186	172	19	making	make	VERB
cana-6186	172	20	process	process	NOUN
cana-6186	172	21	.	.	PUNCT
cana-6186	173	1	however	however	ADV
cana-6186	173	2	,	,	PUNCT
cana-6186	173	3	they	they	PRON
cana-6186	173	4	do	do	AUX
cana-6186	173	5	tend	tend	VERB
cana-6186	173	6	to	to	ADP
cana-6186	173	7	overfitting	overfitte	VERB
cana-6186	173	8	if	if	SCONJ
cana-6186	173	9	the	the	DET
cana-6186	173	10	tree	tree	NOUN
cana-6186	173	11	is	be	AUX
cana-6186	173	12	allowed	allow	VERB
cana-6186	173	13	to	to	PART
cana-6186	173	14	get	get	VERB
cana-6186	173	15	too	too	ADV
cana-6186	173	16	complex	complex	ADJ
cana-6186	173	17	.	.	PUNCT
cana-6186	174	1	this	this	PRON
cana-6186	174	2	can	can	AUX
cana-6186	174	3	be	be	AUX
cana-6186	174	4	mitigated	mitigate	VERB
cana-6186	174	5	through	through	ADP
cana-6186	174	6	techniques	technique	NOUN
cana-6186	174	7	like	like	ADP
cana-6186	174	8	pruning	prune	VERB
cana-6186	174	9	or	or	CCONJ
cana-6186	174	10	limiting	limit	VERB
cana-6186	174	11	tree	tree	NOUN
cana-6186	174	12	depth	depth	NOUN
cana-6186	174	13	.	.	PUNCT
cana-6186	175	1	decision	decision	NOUN
cana-6186	175	2	trees	tree	NOUN
cana-6186	175	3	are	be	AUX
cana-6186	175	4	especially	especially	ADV
cana-6186	175	5	useful	useful	ADJ
cana-6186	175	6	for	for	ADP
cana-6186	175	7	quick	quick	ADJ
cana-6186	175	8	,	,	PUNCT
cana-6186	175	9	interpretable	interpretable	ADJ
cana-6186	175	10	models	model	NOUN
cana-6186	175	11	in	in	ADP
cana-6186	175	12	medical	medical	ADJ
cana-6186	175	13	applications	application	NOUN
cana-6186	175	14	.	.	PUNCT
cana-6186	176	1	d	d	X
cana-6186	176	2	)	)	PUNCT
cana-6186	176	3	random	random	ADJ
cana-6186	176	4	forest	forest	NOUN
cana-6186	176	5	:	:	PUNCT
cana-6186	176	6	random	random	ADJ
cana-6186	176	7	forest	forest	NOUN
cana-6186	176	8	is	be	AUX
cana-6186	176	9	an	an	DET
cana-6186	176	10	ensemble	ensemble	ADJ
cana-6186	176	11	learning	learning	NOUN
cana-6186	176	12	method	method	NOUN
cana-6186	176	13	that	that	PRON
cana-6186	176	14	constructs	construct	VERB
cana-6186	176	15	many	many	ADJ
cana-6186	176	16	different	different	ADJ
cana-6186	176	17	decision	decision	NOUN
cana-6186	176	18	trees	tree	NOUN
cana-6186	176	19	and	and	CCONJ
cana-6186	176	20	combines	combine	VERB
cana-6186	176	21	the	the	DET
cana-6186	176	22	information	information	NOUN
cana-6186	176	23	from	from	ADP
cana-6186	176	24	all	all	PRON
cana-6186	176	25	of	of	ADP
cana-6186	176	26	them	they	PRON
cana-6186	176	27	to	to	PART
cana-6186	176	28	be	be	AUX
cana-6186	176	29	able	able	ADJ
cana-6186	176	30	to	to	PART
cana-6186	176	31	classify	classify	VERB
cana-6186	176	32	objects	object	NOUN
cana-6186	176	33	.	.	PUNCT
cana-6186	177	1	it	it	PRON
cana-6186	177	2	trains	train	VERB
cana-6186	177	3	each	each	DET
cana-6186	177	4	tree	tree	NOUN
cana-6186	177	5	in	in	ADP
cana-6186	177	6	a	a	DET
cana-6186	177	7	random	random	ADJ
cana-6186	177	8	subset	subset	NOUN
cana-6186	177	9	of	of	ADP
cana-6186	177	10	all	all	DET
cana-6186	177	11	data	datum	NOUN
cana-6186	177	12	and	and	CCONJ
cana-6186	177	13	features	feature	NOUN
cana-6186	177	14	to	to	PART
cana-6186	177	15	improve	improve	VERB
cana-6186	177	16	generalization	generalization	NOUN
cana-6186	177	17	and	and	CCONJ
cana-6186	177	18	reduce	reduce	VERB
cana-6186	177	19	overfitting	overfitte	VERB
cana-6186	177	20	.	.	PUNCT
cana-6186	178	1	in	in	ADP
cana-6186	178	2	heart	heart	NOUN
cana-6186	178	3	disease	disease	NOUN
cana-6186	178	4	,	,	PUNCT
cana-6186	178	5	random	random	ADJ
cana-6186	178	6	forest	forest	NOUN
cana-6186	178	7	evaluates	evaluate	VERB
cana-6186	178	8	the	the	DET
cana-6186	178	9	most	most	ADV
cana-6186	178	10	important	important	ADJ
cana-6186	178	11	features	feature	NOUN
cana-6186	178	12	,	,	PUNCT
cana-6186	178	13	thus	thus	ADV
cana-6186	178	14	recognizing	recognize	VERB
cana-6186	178	15	the	the	DET
cana-6186	178	16	most	most	ADV
cana-6186	178	17	influential	influential	ADJ
cana-6186	178	18	variables	variable	NOUN
cana-6186	178	19	affecting	affect	VERB
cana-6186	178	20	the	the	DET
cana-6186	178	21	prediction	prediction	NOUN
cana-6186	178	22	,	,	PUNCT
cana-6186	178	23	such	such	ADJ
cana-6186	178	24	as	as	ADP
cana-6186	178	25	blood	blood	NOUN
cana-6186	178	26	pressure	pressure	NOUN
cana-6186	178	27	and	and	CCONJ
cana-6186	178	28	cholesterol	cholesterol	NOUN
cana-6186	178	29	level	level	NOUN
cana-6186	178	30	.	.	PUNCT
cana-6186	179	1	the	the	DET
cana-6186	179	2	algorithm	algorithm	NOUN
cana-6186	179	3	for	for	ADP
cana-6186	179	4	the	the	DET
cana-6186	179	5	random	random	ADJ
cana-6186	179	6	forest	forest	NOUN
cana-6186	179	7	aggregates	aggregate	NOUN
cana-6186	179	8	predictions	prediction	NOUN
cana-6186	179	9	of	of	ADP
cana-6186	179	10	all	all	DET
cana-6186	179	11	trees	tree	NOUN
cana-6186	179	12	using	use	VERB
cana-6186	179	13	a	a	DET
cana-6186	179	14	majority	majority	NOUN
cana-6186	179	15	vote	vote	NOUN
cana-6186	179	16	for	for	ADP
cana-6186	179	17	classification	classification	NOUN
cana-6186	179	18	tasks	task	NOUN
cana-6186	179	19	.	.	PUNCT
cana-6186	180	1	random	random	ADJ
cana-6186	180	2	forest	forest	NOUN
cana-6186	180	3	is	be	AUX
cana-6186	180	4	very	very	ADV
cana-6186	180	5	effective	effective	ADJ
cana-6186	180	6	and	and	CCONJ
cana-6186	180	7	can	can	AUX
cana-6186	180	8	handle	handle	VERB
cana-6186	180	9	noisy	noisy	ADJ
cana-6186	180	10	data	datum	NOUN
cana-6186	180	11	and	and	CCONJ
cana-6186	180	12	interacted	interact	VERB
cana-6186	180	13	features	feature	NOUN
cana-6186	180	14	,	,	PUNCT
cana-6186	180	15	making	make	VERB
cana-6186	180	16	it	it	PRON
cana-6186	180	17	even	even	ADV
cana-6186	180	18	more	more	ADV
cana-6186	180	19	reliable	reliable	ADJ
cana-6186	180	20	for	for	ADP
cana-6186	180	21	complex	complex	ADJ
cana-6186	180	22	sets	set	NOUN
cana-6186	180	23	of	of	ADP
cana-6186	180	24	data	datum	NOUN
cana-6186	180	25	.	.	PUNCT
cana-6186	181	1	its	its	PRON
cana-6186	181	2	features	feature	NOUN
cana-6186	181	3	make	make	VERB
cana-6186	181	4	it	it	PRON
cana-6186	181	5	robust	robust	ADJ
cana-6186	181	6	and	and	CCONJ
cana-6186	181	7	provide	provide	VERB
cana-6186	181	8	ways	way	NOUN
cana-6186	181	9	to	to	PART
cana-6186	181	10	extract	extract	VERB
cana-6186	181	11	feature	feature	NOUN
cana-6186	181	12	importance	importance	NOUN
cana-6186	181	13	and	and	CCONJ
cana-6186	181	14	,	,	PUNCT
cana-6186	181	15	hence	hence	ADV
cana-6186	181	16	,	,	PUNCT
cana-6186	181	17	make	make	VERB
cana-6186	181	18	valuable	valuable	ADJ
cana-6186	181	19	contributions	contribution	NOUN
cana-6186	181	20	in	in	ADP
cana-6186	181	21	healthcare	healthcare	NOUN
cana-6186	181	22	applications	application	NOUN
cana-6186	181	23	.	.	PUNCT
cana-6186	182	1	e	e	X
cana-6186	182	2	)	)	PUNCT
cana-6186	182	3	gradient	gradient	NOUN
cana-6186	182	4	boosting	boosting	NOUN
cana-6186	182	5	:	:	PUNCT
cana-6186	182	6	gradient	gradient	ADJ
cana-6186	182	7	boosting	boosting	NOUN
cana-6186	182	8	is	be	AUX
cana-6186	182	9	a	a	DET
cana-6186	182	10	strong	strong	ADJ
cana-6186	182	11	ensemble	ensemble	ADJ
cana-6186	182	12	algorithm	algorithm	NOUN
cana-6186	182	13	that	that	PRON
cana-6186	182	14	constructs	construct	VERB
cana-6186	182	15	a	a	DET
cana-6186	182	16	series	series	NOUN
cana-6186	182	17	of	of	ADP
cana-6186	182	18	decision	decision	NOUN
cana-6186	182	19	trees	tree	NOUN
cana-6186	182	20	sequentially	sequentially	ADV
cana-6186	182	21	,	,	PUNCT
cana-6186	182	22	in	in	ADP
cana-6186	182	23	which	which	PRON
cana-6186	182	24	each	each	DET
cana-6186	182	25	tree	tree	NOUN
cana-6186	182	26	corrects	correct	VERB
cana-6186	182	27	the	the	DET
cana-6186	182	28	errors	error	NOUN
cana-6186	182	29	of	of	ADP
cana-6186	182	30	its	its	PRON
cana-6186	182	31	predecessor	predecessor	NOUN
cana-6186	182	32	.	.	PUNCT
cana-6186	183	1	it	it	PRON
cana-6186	183	2	minimizes	minimize	VERB
cana-6186	183	3	a	a	DET
cana-6186	183	4	loss	loss	NOUN
cana-6186	183	5	function	function	NOUN
cana-6186	183	6	,	,	PUNCT
cana-6186	183	7	such	such	ADJ
cana-6186	183	8	as	as	ADP
cana-6186	183	9	log	log	NOUN
cana-6186	183	10	loss	loss	NOUN
cana-6186	183	11	,	,	PUNCT
cana-6186	183	12	by	by	ADP
cana-6186	183	13	iteratively	iteratively	ADV
cana-6186	183	14	adding	add	VERB
cana-6186	183	15	weak	weak	ADJ
cana-6186	183	16	learners	learner	NOUN
cana-6186	183	17	to	to	PART
cana-6186	183	18	develop	develop	VERB
cana-6186	183	19	a	a	DET
cana-6186	183	20	strong	strong	ADJ
cana-6186	183	21	predictive	predictive	ADJ
cana-6186	183	22	model	model	NOUN
cana-6186	183	23	.	.	PUNCT
cana-6186	184	1	with	with	ADP
cana-6186	184	2	the	the	DET
cana-6186	184	3	heart	heart	NOUN
cana-6186	184	4	disease	disease	NOUN
cana-6186	184	5	classification	classification	NOUN
cana-6186	184	6	,	,	PUNCT
cana-6186	184	7	gradient	gradient	ADJ
cana-6186	184	8	boosting	boosting	NOUN
cana-6186	184	9	is	be	AUX
cana-6186	184	10	excellent	excellent	ADJ
cana-6186	184	11	at	at	ADP
cana-6186	184	12	detecting	detect	VERB
cana-6186	184	13	complex	complex	ADJ
cana-6186	184	14	relationships	relationship	NOUN
cana-6186	184	15	between	between	ADP
cana-6186	184	16	features	feature	NOUN
cana-6186	184	17	and	and	CCONJ
cana-6186	184	18	yields	yield	NOUN
cana-6186	184	19	high	high	ADJ
cana-6186	184	20	accuracy	accuracy	NOUN
cana-6186	184	21	.	.	PUNCT
cana-6186	185	1	techniques	technique	NOUN
cana-6186	185	2	such	such	ADJ
cana-6186	185	3	as	as	ADP
cana-6186	185	4	regularization	regularization	NOUN
cana-6186	185	5	are	be	AUX
cana-6186	185	6	used	use	VERB
cana-6186	185	7	to	to	PART
cana-6186	185	8	prevent	prevent	VERB
cana-6186	185	9	overfitting	overfitte	VERB
cana-6186	185	10	so	so	SCONJ
cana-6186	185	11	that	that	SCONJ
cana-6186	185	12	the	the	DET
cana-6186	185	13	model	model	NOUN
cana-6186	185	14	generalizes	generalize	VERB
cana-6186	185	15	well	well	ADV
cana-6186	185	16	to	to	ADP
cana-6186	185	17	new	new	ADJ
cana-6186	185	18	data	datum	NOUN
cana-6186	185	19	.	.	PUNCT
cana-6186	186	1	gradient	gradient	ADJ
cana-6186	186	2	boosting	boosting	NOUN
cana-6186	186	3	is	be	AUX
cana-6186	186	4	computationally	computationally	ADV
cana-6186	186	5	efficient	efficient	ADJ
cana-6186	186	6	and	and	CCONJ
cana-6186	186	7	can	can	AUX
cana-6186	186	8	handle	handle	VERB
cana-6186	186	9	both	both	CCONJ
cana-6186	186	10	numerical	numerical	ADJ
cana-6186	186	11	and	and	CCONJ
cana-6186	186	12	categorical	categorical	ADJ
cana-6186	186	13	data	datum	NOUN
cana-6186	186	14	well	well	ADV
cana-6186	186	15	.	.	PUNCT
cana-6186	187	1	it	it	PRON
cana-6186	187	2	captures	capture	VERB
cana-6186	187	3	subtle	subtle	ADJ
cana-6186	187	4	patterns	pattern	NOUN
cana-6186	187	5	in	in	ADP
cana-6186	187	6	the	the	DET
cana-6186	187	7	data	datum	NOUN
cana-6186	187	8	,	,	PUNCT
cana-6186	187	9	which	which	PRON
cana-6186	187	10	makes	make	VERB
cana-6186	187	11	it	it	PRON
cana-6186	187	12	an	an	DET
cana-6186	187	13	ideal	ideal	ADJ
cana-6186	187	14	choice	choice	NOUN
cana-6186	187	15	for	for	ADP
cana-6186	187	16	high	high	ADJ
cana-6186	187	17	-	-	PUNCT
cana-6186	187	18	stakes	stake	NOUN
cana-6186	187	19	applications	application	NOUN
cana-6186	187	20	like	like	ADP
cana-6186	187	21	medical	medical	ADJ
cana-6186	187	22	diagnostics	diagnostic	NOUN
cana-6186	187	23	.	.	PUNCT
cana-6186	188	1	f.	f.	PROPN
cana-6186	188	2	evaluation	evaluation	NOUN
cana-6186	188	3	in	in	ADP
cana-6186	188	4	such	such	DET
cana-6186	188	5	a	a	DET
cana-6186	188	6	system	system	NOUN
cana-6186	188	7	,	,	PUNCT
cana-6186	188	8	evaluation	evaluation	NOUN
cana-6186	188	9	is	be	AUX
cana-6186	188	10	the	the	DET
cana-6186	188	11	most	most	ADV
cana-6186	188	12	imperative	imperative	ADJ
cana-6186	188	13	step	step	NOUN
cana-6186	188	14	that	that	PRON
cana-6186	188	15	makes	make	VERB
cana-6186	188	16	sure	sure	ADJ
cana-6186	188	17	the	the	DET
cana-6186	188	18	models	model	NOUN
cana-6186	188	19	derived	derive	VERB
cana-6186	188	20	from	from	ADP
cana-6186	188	21	machine	machine	NOUN
cana-6186	188	22	learning	learning	NOUN
cana-6186	188	23	are	be	AUX
cana-6186	188	24	reliable	reliable	ADJ
cana-6186	188	25	,	,	PUNCT
cana-6186	188	26	accurate	accurate	ADJ
cana-6186	188	27	,	,	PUNCT
cana-6186	188	28	and	and	CCONJ
cana-6186	188	29	stable	stable	ADJ
cana-6186	188	30	.	.	PUNCT
cana-6186	189	1	after	after	ADP
cana-6186	189	2	training	train	VERB
cana-6186	189	3	the	the	DET
cana-6186	189	4	models	model	NOUN
cana-6186	189	5	with	with	ADP
cana-6186	189	6	the	the	DET
cana-6186	189	7	provided	provide	VERB
cana-6186	189	8	dataset	dataset	NOUN
cana-6186	189	9	,	,	PUNCT
cana-6186	189	10	the	the	DET
cana-6186	189	11	models	model	NOUN
cana-6186	189	12	are	be	AUX
cana-6186	189	13	tested	test	VERB
cana-6186	189	14	on	on	ADP
cana-6186	189	15	some	some	DET
cana-6186	189	16	new	new	ADJ
cana-6186	189	17	,	,	PUNCT
cana-6186	189	18	unseen	unseen	ADJ
cana-6186	189	19	data	datum	NOUN
cana-6186	189	20	to	to	PART
cana-6186	189	21	test	test	VERB
cana-6186	189	22	how	how	SCONJ
cana-6186	189	23	well	well	ADV
cana-6186	189	24	the	the	DET
cana-6186	189	25	model	model	NOUN
cana-6186	189	26	performs	perform	VERB
cana-6186	189	27	.	.	PUNCT
cana-6186	190	1	evaluation	evaluation	NOUN
cana-6186	190	2	uses	use	VERB
cana-6186	190	3	several	several	ADJ
cana-6186	190	4	key	key	ADJ
cana-6186	190	5	metrics	metric	NOUN
cana-6186	190	6	for	for	ADP
cana-6186	190	7	measuring	measure	VERB
cana-6186	190	8	different	different	ADJ
cana-6186	190	9	aspects	aspect	NOUN
cana-6186	190	10	of	of	ADP
cana-6186	190	11	the	the	DET
cana-6186	190	12	effectiveness	effectiveness	NOUN
cana-6186	190	13	of	of	ADP
cana-6186	190	14	the	the	DET
cana-6186	190	15	models	model	NOUN
cana-6186	190	16	.	.	PUNCT
cana-6186	191	1	these	these	DET
cana-6186	191	2	metrics	metric	NOUN
cana-6186	191	3	are	be	AUX
cana-6186	191	4	critical	critical	ADJ
cana-6186	191	5	in	in	ADP
cana-6186	191	6	comparing	compare	VERB
cana-6186	191	7	algorithms	algorithm	NOUN
cana-6186	191	8	and	and	CCONJ
cana-6186	191	9	choosing	choose	VERB
cana-6186	191	10	the	the	DET
cana-6186	191	11	model	model	NOUN
cana-6186	191	12	that	that	PRON
cana-6186	191	13	performs	perform	VERB
cana-6186	191	14	the	the	DET
cana-6186	191	15	best	good	ADJ
cana-6186	191	16	for	for	ADP
cana-6186	191	17	this	this	DET
cana-6186	191	18	classification	classification	NOUN
cana-6186	191	19	task	task	NOUN
cana-6186	191	20	.	.	PUNCT
cana-6186	192	1	a	a	DET
cana-6186	192	2	)	)	PUNCT
cana-6186	192	3	accuracy	accuracy	NOUN
cana-6186	192	4	:	:	PUNCT
cana-6186	192	5	accuracy	accuracy	NOUN
cana-6186	192	6	is	be	AUX
cana-6186	192	7	the	the	DET
cana-6186	192	8	correct	correct	ADJ
cana-6186	192	9	prediction	prediction	NOUN
cana-6186	192	10	of	of	ADP
cana-6186	192	11	outcomes	outcome	NOUN
cana-6186	192	12	divided	divide	VERB
cana-6186	192	13	by	by	ADP
cana-6186	192	14	total	total	ADJ
cana-6186	192	15	predictions	prediction	NOUN
cana-6186	192	16	.	.	PUNCT
cana-6186	193	1	it	it	PRON
cana-6186	193	2	gives	give	VERB
cana-6186	193	3	a	a	DET
cana-6186	193	4	direct	direct	ADJ
cana-6186	193	5	view	view	NOUN
cana-6186	193	6	of	of	ADP
cana-6186	193	7	overall	overall	ADJ
cana-6186	193	8	performance	performance	NOUN
cana-6186	193	9	.	.	PUNCT
cana-6186	194	1	however	however	ADV
cana-6186	194	2	,	,	PUNCT
cana-6186	194	3	when	when	SCONJ
cana-6186	194	4	classifying	classify	VERB
cana-6186	194	5	heart	heart	NOUN
cana-6186	194	6	disease	disease	NOUN
cana-6186	194	7	,	,	PUNCT
cana-6186	194	8	because	because	SCONJ
cana-6186	194	9	the	the	DET
cana-6186	194	10	dataset	dataset	NOUN
cana-6186	194	11	may	may	AUX
cana-6186	194	12	be	be	AUX
cana-6186	194	13	imbalanced	imbalance	VERB
cana-6186	194	14	,	,	PUNCT
cana-6186	194	15	as	as	ADP
cana-6186	194	16	for	for	ADP
cana-6186	194	17	instance	instance	NOUN
cana-6186	194	18	,	,	PUNCT
cana-6186	194	19	there	there	PRON
cana-6186	194	20	might	might	AUX
cana-6186	194	21	not	not	PART
cana-6186	194	22	be	be	AUX
cana-6186	194	23	that	that	ADV
cana-6186	194	24	many	many	ADJ
cana-6186	194	25	positive	positive	ADJ
cana-6186	194	26	cases	case	NOUN
cana-6186	194	27	of	of	ADP
cana-6186	194	28	heart	heart	NOUN
cana-6186	194	29	disease	disease	NOUN
cana-6186	194	30	,	,	PUNCT
cana-6186	194	31	accuracy	accuracy	NOUN
cana-6186	194	32	alone	alone	ADV
cana-6186	194	33	might	might	AUX
cana-6186	194	34	not	not	PART
cana-6186	194	35	paint	paint	VERB
cana-6186	194	36	the	the	DET
cana-6186	194	37	complete	complete	ADJ
cana-6186	194	38	picture	picture	NOUN
cana-6186	194	39	.	.	PUNCT
cana-6186	195	1	therefore	therefore	ADV
cana-6186	195	2	,	,	PUNCT
cana-6186	195	3	for	for	ADP
cana-6186	195	4	example	example	NOUN
cana-6186	195	5	,	,	PUNCT
cana-6186	195	6	in	in	ADP
cana-6186	195	7	the	the	DET
cana-6186	195	8	case	case	NOUN
cana-6186	195	9	of	of	ADP
cana-6186	195	10	a	a	DET
cana-6186	195	11	model	model	NOUN
cana-6186	195	12	predicting	predict	VERB
cana-6186	195	13	all	all	DET
cana-6186	195	14	patients	patient	NOUN
cana-6186	195	15	to	to	PART
cana-6186	195	16	be	be	AUX
cana-6186	195	17	"	"	PUNCT
cana-6186	195	18	no	no	DET
cana-6186	195	19	risk	risk	NOUN
cana-6186	195	20	,	,	PUNCT
cana-6186	195	21	"	"	PUNCT
cana-6186	195	22	it	it	PRON
cana-6186	195	23	might	might	AUX
cana-6186	195	24	have	have	VERB
cana-6186	195	25	high	high	ADJ
cana-6186	195	26	accuracy	accuracy	NOUN
cana-6186	195	27	but	but	CCONJ
cana-6186	195	28	miss	miss	VERB
cana-6186	195	29	actual	actual	ADJ
cana-6186	195	30	heart	heart	NOUN
cana-6186	195	31	disease	disease	NOUN
cana-6186	195	32	cases	case	NOUN
cana-6186	195	33	.	.	PUNCT
cana-6186	196	1	b	b	X
cana-6186	196	2	)	)	PUNCT
cana-6186	196	3	precision	precision	NOUN
cana-6186	196	4	:	:	PUNCT
cana-6186	196	5	precision	precision	NOUN
cana-6186	196	6	measures	measure	VERB
cana-6186	196	7	the	the	DET
cana-6186	196	8	number	number	NOUN
cana-6186	196	9	of	of	ADP
cana-6186	196	10	correctly	correctly	ADV
cana-6186	196	11	predicted	predict	VERB
cana-6186	196	12	positive	positive	ADJ
cana-6186	196	13	instances	instance	NOUN
cana-6186	196	14	divided	divide	VERB
cana-6186	196	15	by	by	ADP
cana-6186	196	16	all	all	DET
cana-6186	196	17	the	the	DET
cana-6186	196	18	positive	positive	ADJ
cana-6186	196	19	instances	instance	NOUN
cana-6186	196	20	produced	produce	VERB
cana-6186	196	21	by	by	ADP
cana-6186	196	22	the	the	DET
cana-6186	196	23	classifier	classifier	NOUN
cana-6186	196	24	.	.	PUNCT
cana-6186	197	1	in	in	ADP
cana-6186	197	2	this	this	DET
cana-6186	197	3	sense	sense	NOUN
cana-6186	197	4	,	,	PUNCT
cana-6186	197	5	it	it	PRON
cana-6186	197	6	measures	measure	VERB
cana-6186	197	7	the	the	DET
cana-6186	197	8	number	number	NOUN
cana-6186	197	9	of	of	ADP
cana-6186	197	10	"	"	PUNCT
cana-6186	197	11	at	at	ADP
cana-6186	197	12	risk	risk	NOUN
cana-6186	197	13	"	"	PUNCT
cana-6186	197	14	communications	communication	NOUN
cana-6186	197	15	on	on	ADP
cana-6186	197	16	applied	apply	VERB
cana-6186	197	17	nonlinear	nonlinear	ADJ
cana-6186	197	18	analysis	analysis	NOUN
cana-6186	197	19	issn	issn	NOUN
cana-6186	197	20	:	:	PUNCT
cana-6186	197	21	1074	1074	NUM
cana-6186	197	22	-	-	PUNCT
cana-6186	197	23	133x	133x	NUM
cana-6186	197	24	vol	vol	NOUN
cana-6186	197	25	32	32	NUM
cana-6186	197	26	no	no	NOUN
cana-6186	197	27	.	.	NOUN
cana-6186	197	28	1	1	NUM
cana-6186	197	29	(	(	PUNCT
cana-6186	197	30	2025	2025	NUM
cana-6186	197	31	)	)	PUNCT
cana-6186	197	32	664	664	NUM
cana-6186	197	33	https://internationalpubls.com	https://internationalpubls.com	X
cana-6186	197	34	patients	patient	NOUN
cana-6186	197	35	correctly	correctly	ADV
cana-6186	197	36	predicted	predict	VERB
cana-6186	197	37	to	to	PART
cana-6186	197	38	have	have	VERB
cana-6186	197	39	heart	heart	NOUN
cana-6186	197	40	disease	disease	NOUN
cana-6186	197	41	.	.	PUNCT
cana-6186	198	1	getting	get	VERB
cana-6186	198	2	high	high	ADJ
cana-6186	198	3	precision	precision	NOUN
cana-6186	198	4	is	be	AUX
cana-6186	198	5	critical	critical	ADJ
cana-6186	198	6	because	because	SCONJ
cana-6186	198	7	it	it	PRON
cana-6186	198	8	reduces	reduce	VERB
cana-6186	198	9	false	false	ADJ
cana-6186	198	10	positives	positive	NOUN
cana-6186	198	11	so	so	SCONJ
cana-6186	198	12	that	that	SCONJ
cana-6186	198	13	patients	patient	NOUN
cana-6186	198	14	are	be	AUX
cana-6186	198	15	not	not	PART
cana-6186	198	16	unnecessarily	unnecessarily	ADV
cana-6186	198	17	re	re	VERB
cana-6186	198	18	-	-	VERB
cana-6186	198	19	tested	test	VERB
cana-6186	198	20	or	or	CCONJ
cana-6186	198	21	subjected	subject	VERB
cana-6186	198	22	to	to	ADP
cana-6186	198	23	undue	undue	ADJ
cana-6186	198	24	anxiety	anxiety	NOUN
cana-6186	198	25	.	.	PUNCT
cana-6186	199	1	c	c	X
cana-6186	199	2	)	)	PUNCT
cana-6186	199	3	recall	recall	NOUN
cana-6186	199	4	:	:	PUNCT
cana-6186	199	5	recall	recall	VERB
cana-6186	199	6	measures	measure	NOUN
cana-6186	199	7	the	the	DET
cana-6186	199	8	percentage	percentage	NOUN
cana-6186	199	9	of	of	ADP
cana-6186	199	10	true	true	ADJ
cana-6186	199	11	positive	positive	ADJ
cana-6186	199	12	instances	instance	NOUN
cana-6186	199	13	that	that	PRON
cana-6186	199	14	the	the	DET
cana-6186	199	15	model	model	NOUN
cana-6186	199	16	correctly	correctly	ADV
cana-6186	199	17	identifies	identify	VERB
cana-6186	199	18	.	.	PUNCT
cana-6186	200	1	in	in	ADP
cana-6186	200	2	the	the	DET
cana-6186	200	3	context	context	NOUN
cana-6186	200	4	of	of	ADP
cana-6186	200	5	heart	heart	NOUN
cana-6186	200	6	disease	disease	NOUN
cana-6186	200	7	prediction	prediction	NOUN
cana-6186	200	8	,	,	PUNCT
cana-6186	200	9	recall	recall	NOUN
cana-6186	200	10	is	be	AUX
cana-6186	200	11	very	very	ADV
cana-6186	200	12	important	important	ADJ
cana-6186	200	13	because	because	SCONJ
cana-6186	200	14	it	it	PRON
cana-6186	200	15	tells	tell	VERB
cana-6186	200	16	how	how	SCONJ
cana-6186	200	17	good	good	ADJ
cana-6186	200	18	the	the	DET
cana-6186	200	19	model	model	NOUN
cana-6186	200	20	is	be	AUX
cana-6186	200	21	at	at	ADP
cana-6186	200	22	identifying	identify	VERB
cana-6186	200	23	all	all	DET
cana-6186	200	24	those	those	PRON
cana-6186	200	25	who	who	PRON
cana-6186	200	26	have	have	VERB
cana-6186	200	27	the	the	DET
cana-6186	200	28	disease	disease	NOUN
cana-6186	200	29	.	.	PUNCT
cana-6186	201	1	a	a	DET
cana-6186	201	2	high	high	ADJ
cana-6186	201	3	recall	recall	NOUN
cana-6186	201	4	ensures	ensure	VERB
cana-6186	201	5	that	that	SCONJ
cana-6186	201	6	no	no	DET
cana-6186	201	7	case	case	NOUN
cana-6186	201	8	goes	go	VERB
cana-6186	201	9	undetected	undetected	ADJ
cana-6186	201	10	,	,	PUNCT
cana-6186	201	11	making	make	VERB
cana-6186	201	12	it	it	PRON
cana-6186	201	13	critical	critical	ADJ
cana-6186	201	14	for	for	ADP
cana-6186	201	15	medical	medical	ADJ
cana-6186	201	16	diagnosis	diagnosis	NOUN
cana-6186	201	17	,	,	PUNCT
cana-6186	201	18	where	where	SCONJ
cana-6186	201	19	undiagnosed	undiagnosed	ADJ
cana-6186	201	20	diseases	disease	NOUN
cana-6186	201	21	can	can	AUX
cana-6186	201	22	have	have	VERB
cana-6186	201	23	severe	severe	ADJ
cana-6186	201	24	consequences	consequence	NOUN
cana-6186	201	25	.	.	PUNCT
cana-6186	202	1	d	d	X
cana-6186	202	2	)	)	PUNCT
cana-6186	202	3	f1	f1	NOUN
cana-6186	202	4	score	score	NOUN
cana-6186	202	5	:	:	PUNCT
cana-6186	202	6	the	the	DET
cana-6186	202	7	mean	mean	NOUN
cana-6186	202	8	of	of	ADP
cana-6186	202	9	harmonic	harmonic	ADJ
cana-6186	202	10	precision	precision	NOUN
cana-6186	202	11	and	and	CCONJ
cana-6186	202	12	recall	recall	NOUN
cana-6186	202	13	,	,	PUNCT
cana-6186	202	14	which	which	PRON
cana-6186	202	15	thus	thus	ADV
cana-6186	202	16	includes	include	VERB
cana-6186	202	17	the	the	DET
cana-6186	202	18	measure	measure	NOUN
cana-6186	202	19	that	that	PRON
cana-6186	202	20	has	have	AUX
cana-6186	202	21	weighted	weight	VERB
cana-6186	202	22	both	both	DET
cana-6186	202	23	false	false	ADJ
cana-6186	202	24	positives	positive	NOUN
cana-6186	202	25	and	and	CCONJ
cana-6186	202	26	negatives	negative	NOUN
cana-6186	202	27	is	be	AUX
cana-6186	202	28	the	the	DET
cana-6186	202	29	f1	f1	PROPN
cana-6186	202	30	score	score	NOUN
cana-6186	202	31	.	.	PUNCT
cana-6186	203	1	it	it	PRON
cana-6186	203	2	has	have	AUX
cana-6186	203	3	really	really	ADV
cana-6186	203	4	been	be	AUX
cana-6186	203	5	helpful	helpful	ADJ
cana-6186	203	6	for	for	ADP
cana-6186	203	7	measures	measure	NOUN
cana-6186	203	8	especially	especially	ADV
cana-6186	203	9	in	in	ADP
cana-6186	203	10	dataset	dataset	ADJ
cana-6186	203	11	cases	case	NOUN
cana-6186	203	12	that	that	PRON
cana-6186	203	13	involve	involve	VERB
cana-6186	203	14	imbalances	imbalance	NOUN
cana-6186	203	15	.	.	PUNCT
cana-6186	204	1	such	such	ADJ
cana-6186	204	2	is	be	AUX
cana-6186	204	3	because	because	SCONJ
cana-6186	204	4	it	it	PRON
cana-6186	204	5	gives	give	VERB
cana-6186	204	6	an	an	DET
cana-6186	204	7	aggregate	aggregate	ADJ
cana-6186	204	8	measure	measure	NOUN
cana-6186	204	9	that	that	PRON
cana-6186	204	10	combines	combine	VERB
cana-6186	204	11	the	the	DET
cana-6186	204	12	benefits	benefit	NOUN
cana-6186	204	13	of	of	ADP
cana-6186	204	14	both	both	DET
cana-6186	204	15	precision	precision	NOUN
cana-6186	204	16	and	and	CCONJ
cana-6186	204	17	recall	recall	NOUN
cana-6186	204	18	.	.	PUNCT
cana-6186	205	1	iii	iii	X
cana-6186	205	2	.	.	PROPN
cana-6186	205	3	result	result	NOUN
cana-6186	205	4	and	and	CCONJ
cana-6186	205	5	discussion	discussion	VERB
cana-6186	205	6	the	the	DET
cana-6186	205	7	classification	classification	NOUN
cana-6186	205	8	framework	framework	NOUN
cana-6186	205	9	for	for	ADP
cana-6186	205	10	heart	heart	NOUN
cana-6186	205	11	disease	disease	NOUN
cana-6186	205	12	was	be	AUX
cana-6186	205	13	assessed	assess	VERB
cana-6186	205	14	using	use	VERB
cana-6186	205	15	five	five	NUM
cana-6186	205	16	different	different	ADJ
cana-6186	205	17	machine	machine	NOUN
cana-6186	205	18	learning	learn	VERB
cana-6186	205	19	algorithms	algorithm	NOUN
cana-6186	205	20	:	:	PUNCT
cana-6186	205	21	svm	svm	PROPN
cana-6186	205	22	,	,	PUNCT
cana-6186	205	23	knn	knn	PROPN
cana-6186	205	24	,	,	PUNCT
cana-6186	205	25	decision	decision	NOUN
cana-6186	205	26	tree	tree	NOUN
cana-6186	205	27	,	,	PUNCT
cana-6186	205	28	random	random	ADJ
cana-6186	205	29	forest	forest	NOUN
cana-6186	205	30	,	,	PUNCT
cana-6186	205	31	and	and	CCONJ
cana-6186	205	32	gradient	gradient	ADJ
cana-6186	205	33	boosting	boosting	NOUN
cana-6186	205	34	.	.	PUNCT
cana-6186	206	1	results	result	NOUN
cana-6186	206	2	were	be	AUX
cana-6186	206	3	examined	examine	VERB
cana-6186	206	4	using	use	VERB
cana-6186	206	5	key	key	ADJ
cana-6186	206	6	performance	performance	NOUN
cana-6186	206	7	metrics	metric	NOUN
cana-6186	206	8	:	:	PUNCT
cana-6186	206	9	precision	precision	NOUN
cana-6186	206	10	,	,	PUNCT
cana-6186	206	11	recall	recall	NOUN
cana-6186	206	12	,	,	PUNCT
cana-6186	206	13	f1	f1	NOUN
cana-6186	206	14	-	-	PUNCT
cana-6186	206	15	score	score	NOUN
cana-6186	206	16	,	,	PUNCT
cana-6186	206	17	and	and	CCONJ
cana-6186	206	18	accuracy	accuracy	NOUN
cana-6186	206	19	,	,	PUNCT
cana-6186	206	20	to	to	AUX
cana-6186	206	21	further	far	ADV
cana-6186	206	22	judge	judge	VERB
cana-6186	206	23	if	if	SCONJ
cana-6186	206	24	any	any	PRON
cana-6186	206	25	of	of	ADP
cana-6186	206	26	the	the	DET
cana-6186	206	27	classifiers	classifier	NOUN
cana-6186	206	28	performed	perform	VERB
cana-6186	206	29	well	well	ADV
cana-6186	206	30	.	.	PUNCT
cana-6186	207	1	the	the	DET
cana-6186	207	2	results	result	NOUN
cana-6186	207	3	are	be	AUX
cana-6186	207	4	presented	present	VERB
cana-6186	207	5	in	in	ADP
cana-6186	207	6	summary	summary	NOUN
cana-6186	207	7	table	table	NOUN
cana-6186	207	8	ii	ii	NOUN
cana-6186	207	9	below	below	ADV
cana-6186	207	10	:	:	PUNCT
cana-6186	207	11	table	table	PROPN
cana-6186	207	12	ii	ii	PROPN
cana-6186	207	13	.	.	PUNCT
cana-6186	208	1	performance	performance	NOUN
cana-6186	208	2	of	of	ADP
cana-6186	208	3	ml	ml	NOUN
cana-6186	208	4	algorithms	algorithm	NOUN
cana-6186	208	5	for	for	ADP
cana-6186	208	6	classification	classification	NOUN
cana-6186	208	7	of	of	ADP
cana-6186	208	8	cvd	cvd	PROPN
cana-6186	208	9	classifier	classifier	PROPN
cana-6186	208	10	precision	precision	PROPN
cana-6186	208	11	recall	recall	PROPN
cana-6186	208	12	f1	f1	NOUN
cana-6186	208	13	-	-	PUNCT
cana-6186	208	14	score	score	NOUN
cana-6186	208	15	accuracy	accuracy	NOUN
cana-6186	208	16	svm	svm	NOUN
cana-6186	208	17	0.88	0.88	NUM
cana-6186	208	18	0.87	0.87	NUM
cana-6186	208	19	0.86	0.86	NUM
cana-6186	208	20	0.88	0.88	NUM
cana-6186	208	21	knn	knn	NOUN
cana-6186	208	22	0.85	0.85	NUM
cana-6186	208	23	0.84	0.84	NUM
cana-6186	208	24	0.84	0.84	NUM
cana-6186	208	25	0.85	0.85	NUM
cana-6186	208	26	decision	decision	NOUN
cana-6186	208	27	tree	tree	NOUN
cana-6186	208	28	0.88	0.88	NUM
cana-6186	208	29	0.89	0.89	NUM
cana-6186	208	30	0.88	0.88	NUM
cana-6186	208	31	0.88	0.88	NUM
cana-6186	208	32	random	random	ADJ
cana-6186	208	33	forest	forest	NOUN
cana-6186	208	34	0.89	0.89	NUM
cana-6186	208	35	0.88	0.88	NUM
cana-6186	208	36	0.88	0.88	NUM
cana-6186	208	37	0.89	0.89	NUM
cana-6186	208	38	gradient	gradient	NOUN
cana-6186	208	39	boosting	boost	VERB
cana-6186	208	40	0.91	0.91	NUM
cana-6186	208	41	0.90	0.90	NUM
cana-6186	208	42	0.90	0.90	NUM
cana-6186	208	43	0.91	0.91	NUM
cana-6186	208	44	in	in	ADP
cana-6186	208	45	other	other	ADJ
cana-6186	208	46	words	word	NOUN
cana-6186	208	47	,	,	PUNCT
cana-6186	208	48	the	the	DET
cana-6186	208	49	same	same	ADJ
cana-6186	208	50	dataset	dataset	NOUN
cana-6186	208	51	was	be	AUX
cana-6186	208	52	used	use	VERB
cana-6186	208	53	for	for	ADP
cana-6186	208	54	applying	apply	VERB
cana-6186	208	55	and	and	CCONJ
cana-6186	208	56	comparing	compare	VERB
cana-6186	208	57	different	different	ADJ
cana-6186	208	58	five	five	NUM
cana-6186	208	59	machine	machine	NOUN
cana-6186	208	60	learning	learn	VERB
cana-6186	208	61	algorithms	algorithm	NOUN
cana-6186	208	62	,	,	PUNCT
cana-6186	208	63	that	that	PRON
cana-6186	208	64	actually	actually	ADV
cana-6186	208	65	applies	apply	VERB
cana-6186	208	66	the	the	DET
cana-6186	208	67	classification	classification	NOUN
cana-6186	208	68	of	of	ADP
cana-6186	208	69	heart	heart	NOUN
cana-6186	208	70	disease	disease	NOUN
cana-6186	208	71	from	from	ADP
cana-6186	208	72	:	:	PUNCT
cana-6186	208	73	support	support	NOUN
cana-6186	208	74	vector	vector	NOUN
cana-6186	208	75	machines	machine	NOUN
cana-6186	208	76	,	,	PUNCT
cana-6186	208	77	k	k	X
cana-6186	208	78	-	-	PUNCT
cana-6186	208	79	nearest	near	ADJ
cana-6186	208	80	neighbors	neighbor	NOUN
cana-6186	208	81	,	,	PUNCT
cana-6186	208	82	decision	decision	NOUN
cana-6186	208	83	tree	tree	NOUN
cana-6186	208	84	,	,	PUNCT
cana-6186	208	85	random	random	ADJ
cana-6186	208	86	forest	forest	NOUN
cana-6186	208	87	and	and	CCONJ
cana-6186	208	88	the	the	DET
cana-6186	208	89	gradient	gradient	NOUN
cana-6186	208	90	boosting	boost	VERB
cana-6186	208	91	results	result	NOUN
cana-6186	208	92	among	among	ADP
cana-6186	208	93	them	they	PRON
cana-6186	208	94	.	.	PUNCT
cana-6186	209	1	it	it	PRON
cana-6186	209	2	depends	depend	VERB
cana-6186	209	3	from	from	ADP
cana-6186	209	4	which	which	PRON
cana-6186	209	5	of	of	ADP
cana-6186	209	6	them	they	PRON
cana-6186	209	7	an	an	DET
cana-6186	209	8	efficient	efficient	ADJ
cana-6186	209	9	application	application	NOUN
cana-6186	209	10	was	be	AUX
cana-6186	209	11	found	find	VERB
cana-6186	209	12	on	on	ADP
cana-6186	209	13	which	which	PRON
cana-6186	209	14	a	a	DET
cana-6186	209	15	particular	particular	ADJ
cana-6186	209	16	dataset	dataset	NOUN
cana-6186	209	17	depended	depend	VERB
cana-6186	209	18	for	for	ADP
cana-6186	209	19	application	application	NOUN
cana-6186	209	20	.	.	PUNCT
cana-6186	210	1	gradient	gradient	ADJ
cana-6186	210	2	boosting	boosting	NOUN
cana-6186	210	3	had	have	AUX
cana-6186	210	4	already	already	ADV
cana-6186	210	5	reached	reach	VERB
cana-6186	210	6	91	91	NUM
cana-6186	210	7	%	%	NOUN
cana-6186	210	8	accuracy	accuracy	NOUN
cana-6186	210	9	with	with	ADP
cana-6186	210	10	great	great	ADJ
cana-6186	210	11	precision	precision	NOUN
cana-6186	210	12	(	(	PUNCT
cana-6186	210	13	0.91	0.91	NUM
cana-6186	210	14	)	)	PUNCT
cana-6186	210	15	,	,	PUNCT
cana-6186	210	16	recall	recall	NOUN
cana-6186	210	17	(	(	PUNCT
cana-6186	210	18	0.90	0.90	NUM
cana-6186	210	19	)	)	PUNCT
cana-6186	210	20	,	,	PUNCT
cana-6186	210	21	and	and	CCONJ
cana-6186	210	22	f1	f1	NOUN
cana-6186	210	23	-	-	PUNCT
cana-6186	210	24	score	score	NOUN
cana-6186	210	25	(	(	PUNCT
cana-6186	210	26	0.90	0.90	NUM
cana-6186	210	27	)	)	PUNCT
cana-6186	210	28	.	.	PUNCT
cana-6186	211	1	this	this	PRON
cana-6186	211	2	is	be	AUX
cana-6186	211	3	due	due	ADJ
cana-6186	211	4	to	to	ADP
cana-6186	211	5	its	its	PRON
cana-6186	211	6	iterative	iterative	NOUN
cana-6186	211	7	nature	nature	NOUN
cana-6186	211	8	,	,	PUNCT
cana-6186	211	9	working	work	VERB
cana-6186	211	10	on	on	ADP
cana-6186	211	11	the	the	DET
cana-6186	211	12	removal	removal	NOUN
cana-6186	211	13	of	of	ADP
cana-6186	211	14	errors	error	NOUN
cana-6186	211	15	by	by	ADP
cana-6186	211	16	constructing	construct	VERB
cana-6186	211	17	sequentially	sequentially	ADV
cana-6186	211	18	optimized	optimize	VERB
cana-6186	211	19	decision	decision	NOUN
cana-6186	211	20	trees	tree	NOUN
cana-6186	211	21	.	.	PUNCT
cana-6186	212	1	the	the	DET
cana-6186	212	2	gradient	gradient	ADJ
cana-6186	212	3	boosting	boost	VERB
cana-6186	212	4	technique	technique	NOUN
cana-6186	212	5	is	be	AUX
cana-6186	212	6	highly	highly	ADV
cana-6186	212	7	beneficial	beneficial	ADJ
cana-6186	212	8	for	for	ADP
cana-6186	212	9	medical	medical	ADJ
cana-6186	212	10	datasets	dataset	NOUN
cana-6186	212	11	where	where	SCONJ
cana-6186	212	12	intricate	intricate	ADJ
cana-6186	212	13	patterns	pattern	NOUN
cana-6186	212	14	often	often	ADV
cana-6186	212	15	exist	exist	VERB
cana-6186	212	16	,	,	PUNCT
cana-6186	212	17	as	as	SCONJ
cana-6186	212	18	it	it	PRON
cana-6186	212	19	can	can	AUX
cana-6186	212	20	easily	easily	ADV
cana-6186	212	21	deal	deal	VERB
cana-6186	212	22	with	with	ADP
cana-6186	212	23	complex	complex	ADJ
cana-6186	212	24	relationships	relationship	NOUN
cana-6186	212	25	between	between	ADP
cana-6186	212	26	features	feature	NOUN
cana-6186	212	27	.	.	PUNCT
cana-6186	213	1	its	its	PRON
cana-6186	213	2	computational	computational	ADJ
cana-6186	213	3	intensiveness	intensiveness	NOUN
cana-6186	213	4	thus	thus	ADV
cana-6186	213	5	requires	require	VERB
cana-6186	213	6	careful	careful	ADJ
cana-6186	213	7	tuning	tuning	NOUN
cana-6186	213	8	of	of	ADP
cana-6186	213	9	hyperparameters	hyperparameter	NOUN
cana-6186	213	10	to	to	PART
cana-6186	213	11	strike	strike	VERB
cana-6186	213	12	a	a	DET
cana-6186	213	13	balance	balance	NOUN
cana-6186	213	14	between	between	ADP
cana-6186	213	15	performance	performance	NOUN
cana-6186	213	16	and	and	CCONJ
cana-6186	213	17	efficiency	efficiency	NOUN
cana-6186	213	18	,	,	PUNCT
cana-6186	213	19	particularly	particularly	ADV
cana-6186	213	20	when	when	SCONJ
cana-6186	213	21	resources	resource	NOUN
cana-6186	213	22	are	be	AUX
cana-6186	213	23	constrained	constrain	VERB
cana-6186	213	24	.	.	PUNCT
cana-6186	214	1	random	random	ADJ
cana-6186	214	2	forest	forest	NOUN
cana-6186	214	3	,	,	PUNCT
cana-6186	214	4	the	the	DET
cana-6186	214	5	second	second	ADV
cana-6186	214	6	-	-	PUNCT
cana-6186	214	7	highest	high	ADJ
cana-6186	214	8	is	be	AUX
cana-6186	214	9	with	with	ADP
cana-6186	214	10	accuracy	accuracy	NOUN
cana-6186	214	11	of	of	ADP
cana-6186	214	12	89	89	NUM
cana-6186	214	13	%	%	NOUN
cana-6186	214	14	as	as	ADV
cana-6186	214	15	well	well	ADV
cana-6186	214	16	as	as	ADP
cana-6186	214	17	with	with	ADP
cana-6186	214	18	its	its	PRON
cana-6186	214	19	robust	robust	ADJ
cana-6186	214	20	precision:0.89	precision:0.89	NOUN
cana-6186	214	21	,	,	PUNCT
cana-6186	214	22	recall	recall	NOUN
cana-6186	214	23	of	of	ADP
cana-6186	214	24	0.88	0.88	NUM
cana-6186	214	25	,	,	PUNCT
cana-6186	214	26	f1	f1	NOUN
cana-6186	214	27	-	-	PUNCT
cana-6186	214	28	score	score	NOUN
cana-6186	214	29	of:0.88	of:0.88	PROPN
cana-6186	214	30	.	.	PUNCT
cana-6186	215	1	since	since	SCONJ
cana-6186	215	2	it	it	PRON
cana-6186	215	3	's	be	AUX
cana-6186	215	4	a	a	DET
cana-6186	215	5	type	type	NOUN
cana-6186	215	6	of	of	ADP
cana-6186	215	7	ensemble	ensemble	ADJ
cana-6186	215	8	model	model	NOUN
cana-6186	215	9	,	,	PUNCT
cana-6186	215	10	which	which	PRON
cana-6186	215	11	makes	make	VERB
cana-6186	215	12	it	it	PRON
cana-6186	215	13	capable	capable	ADJ
cana-6186	215	14	of	of	ADP
cana-6186	215	15	not	not	PART
cana-6186	215	16	communications	communication	NOUN
cana-6186	215	17	on	on	ADP
cana-6186	215	18	applied	apply	VERB
cana-6186	215	19	nonlinear	nonlinear	ADJ
cana-6186	215	20	analysis	analysis	NOUN
cana-6186	215	21	issn	issn	NOUN
cana-6186	215	22	:	:	PUNCT
cana-6186	215	23	1074	1074	NUM
cana-6186	215	24	-	-	PUNCT
cana-6186	215	25	133x	133x	NUM
cana-6186	215	26	vol	vol	NOUN
cana-6186	215	27	32	32	NUM
cana-6186	215	28	no	no	NOUN
cana-6186	215	29	.	.	NOUN
cana-6186	215	30	1	1	NUM
cana-6186	215	31	(	(	PUNCT
cana-6186	215	32	2025	2025	NUM
cana-6186	215	33	)	)	PUNCT
cana-6186	215	34	665	665	NUM
cana-6186	215	35	https://internationalpubls.com	https://internationalpubls.com	X
cana-6186	215	36	undergoing	undergo	VERB
cana-6186	215	37	overfitting	overfitte	VERB
cana-6186	215	38	due	due	ADP
cana-6186	215	39	to	to	ADP
cana-6186	215	40	the	the	DET
cana-6186	215	41	combination	combination	NOUN
cana-6186	215	42	of	of	ADP
cana-6186	215	43	the	the	DET
cana-6186	215	44	output	output	NOUN
cana-6186	215	45	that	that	PRON
cana-6186	215	46	various	various	ADJ
cana-6186	215	47	decision	decision	NOUN
cana-6186	215	48	trees	tree	NOUN
cana-6186	215	49	will	will	AUX
cana-6186	215	50	predict	predict	VERB
cana-6186	215	51	,	,	PUNCT
cana-6186	215	52	trained	train	VERB
cana-6186	215	53	on	on	ADP
cana-6186	215	54	randomly	randomly	ADV
cana-6186	215	55	sub	sub	ADJ
cana-6186	215	56	-	-	ADJ
cana-6186	215	57	sampled	sample	VERB
cana-6186	215	58	data	datum	NOUN
cana-6186	215	59	.	.	PUNCT
cana-6186	216	1	therefore	therefore	ADV
cana-6186	216	2	it	it	PRON
cana-6186	216	3	's	be	AUX
cana-6186	216	4	very	very	ADV
cana-6186	216	5	reasonable	reasonable	ADJ
cana-6186	216	6	to	to	PART
cana-6186	216	7	make	make	VERB
cana-6186	216	8	predictions	prediction	NOUN
cana-6186	216	9	regarding	regard	VERB
cana-6186	216	10	heart	heart	NOUN
cana-6186	216	11	disease	disease	NOUN
cana-6186	216	12	through	through	ADP
cana-6186	216	13	random	random	ADJ
cana-6186	216	14	forest	forest	NOUN
cana-6186	216	15	,	,	PUNCT
cana-6186	216	16	especially	especially	ADV
cana-6186	216	17	when	when	SCONJ
cana-6186	216	18	this	this	DET
cana-6186	216	19	kind	kind	NOUN
cana-6186	216	20	of	of	ADP
cana-6186	216	21	dataset	dataset	NOUN
cana-6186	216	22	contains	contain	VERB
cana-6186	216	23	some	some	DET
cana-6186	216	24	noisy	noisy	ADJ
cana-6186	216	25	or	or	CCONJ
cana-6186	216	26	unimportant	unimportant	ADJ
cana-6186	216	27	features	feature	NOUN
cana-6186	216	28	.	.	PUNCT
cana-6186	217	1	besides	besides	SCONJ
cana-6186	217	2	that	that	PRON
cana-6186	217	3	,	,	PUNCT
cana-6186	217	4	random	random	ADJ
cana-6186	217	5	forest	forest	NOUN
cana-6186	217	6	provides	provide	VERB
cana-6186	217	7	the	the	DET
cana-6186	217	8	feature	feature	NOUN
cana-6186	217	9	importance	importance	NOUN
cana-6186	217	10	,	,	PUNCT
cana-6186	217	11	which	which	PRON
cana-6186	217	12	inform	inform	VERB
cana-6186	217	13	us	we	PRON
cana-6186	217	14	that	that	SCONJ
cana-6186	217	15	the	the	DET
cana-6186	217	16	three	three	NUM
cana-6186	217	17	most	most	ADV
cana-6186	217	18	relevant	relevant	ADJ
cana-6186	217	19	predictors	predictor	NOUN
cana-6186	217	20	correlated	correlate	VERB
cana-6186	217	21	highly	highly	ADV
cana-6186	217	22	with	with	ADP
cana-6186	217	23	heart	heart	NOUN
cana-6186	217	24	disease	disease	NOUN
cana-6186	217	25	risk	risk	NOUN
cana-6186	217	26	are	be	AUX
cana-6186	217	27	cholesterol	cholesterol	NOUN
cana-6186	217	28	level	level	NOUN
cana-6186	217	29	,	,	PUNCT
cana-6186	217	30	blood	blood	NOUN
cana-6186	217	31	pressure	pressure	NOUN
cana-6186	217	32	,	,	PUNCT
cana-6186	217	33	and	and	CCONJ
cana-6186	217	34	age	age	NOUN
cana-6186	217	35	.	.	PUNCT
cana-6186	218	1	svm	svm	VERB
cana-6186	218	2	and	and	CCONJ
cana-6186	218	3	decision	decision	NOUN
cana-6186	218	4	tree	tree	NOUN
cana-6186	218	5	performed	perform	VERB
cana-6186	218	6	competitively	competitively	ADV
cana-6186	218	7	with	with	ADP
cana-6186	218	8	88	88	NUM
cana-6186	218	9	%	%	NOUN
cana-6186	218	10	accuracy	accuracy	NOUN
cana-6186	218	11	.	.	PUNCT
cana-6186	219	1	svm	svm	PROPN
cana-6186	219	2	's	's	PART
cana-6186	219	3	strength	strength	NOUN
cana-6186	219	4	is	be	AUX
cana-6186	219	5	the	the	DET
cana-6186	219	6	efficient	efficient	ADJ
cana-6186	219	7	handling	handling	NOUN
cana-6186	219	8	of	of	ADP
cana-6186	219	9	high	high	ADJ
cana-6186	219	10	-	-	PUNCT
cana-6186	219	11	dimensional	dimensional	ADJ
cana-6186	219	12	data	datum	NOUN
cana-6186	219	13	and	and	CCONJ
cana-6186	219	14	thus	thus	ADV
cana-6186	219	15	well	well	ADV
cana-6186	219	16	suited	suit	VERB
cana-6186	219	17	for	for	ADP
cana-6186	219	18	complex	complex	ADJ
cana-6186	219	19	interaction	interaction	NOUN
cana-6186	219	20	among	among	ADP
cana-6186	219	21	features	feature	NOUN
cana-6186	219	22	in	in	ADP
cana-6186	219	23	a	a	DET
cana-6186	219	24	dataset	dataset	NOUN
cana-6186	219	25	.	.	PUNCT
cana-6186	220	1	it	it	PRON
cana-6186	220	2	is	be	AUX
cana-6186	220	3	able	able	ADJ
cana-6186	220	4	to	to	PART
cana-6186	220	5	take	take	VERB
cana-6186	220	6	care	care	NOUN
cana-6186	220	7	of	of	ADP
cana-6186	220	8	nonlinear	nonlinear	ADJ
cana-6186	220	9	relationships	relationship	NOUN
cana-6186	220	10	between	between	ADP
cana-6186	220	11	features	feature	NOUN
cana-6186	220	12	by	by	ADP
cana-6186	220	13	the	the	DET
cana-6186	220	14	application	application	NOUN
cana-6186	220	15	of	of	ADP
cana-6186	220	16	kernel	kernel	PROPN
cana-6186	220	17	functions	function	NOUN
cana-6186	220	18	.	.	PUNCT
cana-6186	221	1	though	though	SCONJ
cana-6186	221	2	its	its	PRON
cana-6186	221	3	recall	recall	NOUN
cana-6186	221	4	is	be	AUX
cana-6186	221	5	lower	low	ADJ
cana-6186	221	6	,	,	PUNCT
cana-6186	221	7	svm	svm	PROPN
cana-6186	221	8	has	have	VERB
cana-6186	221	9	potential	potential	ADJ
cana-6186	221	10	issues	issue	NOUN
cana-6186	221	11	with	with	ADP
cana-6186	221	12	positive	positive	ADJ
cana-6186	221	13	case	case	NOUN
cana-6186	221	14	identification	identification	NOUN
cana-6186	221	15	.	.	PUNCT
cana-6186	222	1	it	it	PRON
cana-6186	222	2	might	might	AUX
cana-6186	222	3	be	be	AUX
cana-6186	222	4	rectified	rectify	VERB
cana-6186	222	5	with	with	ADP
cana-6186	222	6	decision	decision	NOUN
cana-6186	222	7	boundary	boundary	ADJ
cana-6186	222	8	adjustments	adjustment	NOUN
cana-6186	222	9	.	.	PUNCT
cana-6186	223	1	decision	decision	NOUN
cana-6186	223	2	tree	tree	NOUN
cana-6186	223	3	is	be	AUX
cana-6186	223	4	problematic	problematic	ADJ
cana-6186	223	5	most	most	ADJ
cana-6186	223	6	of	of	ADP
cana-6186	223	7	the	the	DET
cana-6186	223	8	times	time	NOUN
cana-6186	223	9	due	due	ADJ
cana-6186	223	10	to	to	ADP
cana-6186	223	11	high	high	ADJ
cana-6186	223	12	overfitting	overfitting	NOUN
cana-6186	223	13	ability	ability	NOUN
cana-6186	223	14	.	.	PUNCT
cana-6186	224	1	its	its	PRON
cana-6186	224	2	performance	performance	NOUN
cana-6186	224	3	may	may	AUX
cana-6186	224	4	be	be	AUX
cana-6186	224	5	boosted	boost	VERB
cana-6186	224	6	by	by	ADP
cana-6186	224	7	techniques	technique	NOUN
cana-6186	224	8	such	such	ADJ
cana-6186	224	9	as	as	ADP
cana-6186	224	10	pruning	prune	VERB
cana-6186	224	11	or	or	CCONJ
cana-6186	224	12	even	even	ADV
cana-6186	224	13	limiting	limit	VERB
cana-6186	224	14	tree	tree	NOUN
cana-6186	224	15	depth	depth	NOUN
cana-6186	224	16	.	.	PUNCT
cana-6186	225	1	since	since	SCONJ
cana-6186	225	2	knn	knn	PROPN
cana-6186	225	3	is	be	AUX
cana-6186	225	4	less	less	ADV
cana-6186	225	5	efficient	efficient	ADJ
cana-6186	225	6	compared	compare	VERB
cana-6186	225	7	to	to	ADP
cana-6186	225	8	the	the	DET
cana-6186	225	9	ensemble	ensemble	ADJ
cana-6186	225	10	methods	method	NOUN
cana-6186	225	11	with	with	ADP
cana-6186	225	12	an	an	DET
cana-6186	225	13	accuracy	accuracy	NOUN
cana-6186	225	14	of	of	ADP
cana-6186	225	15	85	85	NUM
cana-6186	225	16	%	%	NOUN
cana-6186	225	17	.	.	PUNCT
cana-6186	226	1	knn	knn	PROPN
cana-6186	226	2	relies	rely	VERB
cana-6186	226	3	on	on	ADP
cana-6186	226	4	the	the	DET
cana-6186	226	5	nearest	near	ADJ
cana-6186	226	6	neighbors	neighbor	NOUN
cana-6186	226	7	for	for	ADP
cana-6186	226	8	classification	classification	NOUN
cana-6186	226	9	,	,	PUNCT
cana-6186	226	10	thus	thus	ADV
cana-6186	226	11	they	they	PRON
cana-6186	226	12	suffer	suffer	VERB
cana-6186	226	13	from	from	ADP
cana-6186	226	14	noise	noise	NOUN
cana-6186	226	15	in	in	ADP
cana-6186	226	16	a	a	DET
cana-6186	226	17	dataset	dataset	NOUN
cana-6186	226	18	.	.	PUNCT
cana-6186	227	1	its	its	PRON
cana-6186	227	2	computational	computational	ADJ
cana-6186	227	3	requirement	requirement	NOUN
cana-6186	227	4	is	be	AUX
cana-6186	227	5	a	a	DET
cana-6186	227	6	function	function	NOUN
cana-6186	227	7	of	of	ADP
cana-6186	227	8	the	the	DET
cana-6186	227	9	size	size	NOUN
cana-6186	227	10	of	of	ADP
cana-6186	227	11	the	the	DET
cana-6186	227	12	dataset	dataset	NOUN
cana-6186	227	13	.	.	PUNCT
cana-6186	228	1	choice	choice	NOUN
cana-6186	228	2	of	of	ADP
cana-6186	228	3	k	k	PROPN
cana-6186	228	4	also	also	ADV
cana-6186	228	5	plays	play	VERB
cana-6186	228	6	a	a	DET
cana-6186	228	7	crucial	crucial	ADJ
cana-6186	228	8	balancing	balance	VERB
cana-6186	228	9	role	role	NOUN
cana-6186	228	10	to	to	PART
cana-6186	228	11	avoid	avoid	VERB
cana-6186	228	12	underfitting	underfitting	NOUN
cana-6186	228	13	or	or	CCONJ
cana-6186	228	14	overfitting	overfitting	NOUN
cana-6186	228	15	.	.	PUNCT
cana-6186	229	1	it	it	PRON
cana-6186	229	2	indicates	indicate	VERB
cana-6186	229	3	that	that	SCONJ
cana-6186	229	4	feature	feature	NOUN
cana-6186	229	5	selection	selection	NOUN
cana-6186	229	6	holds	hold	VERB
cana-6186	229	7	an	an	DET
cana-6186	229	8	importance	importance	NOUN
cana-6186	229	9	position	position	NOUN
cana-6186	229	10	in	in	ADP
cana-6186	229	11	getting	get	VERB
cana-6186	229	12	a	a	DET
cana-6186	229	13	high	high	ADJ
cana-6186	229	14	accuracy	accuracy	NOUN
cana-6186	229	15	of	of	ADP
cana-6186	229	16	classification	classification	NOUN
cana-6186	229	17	.	.	PUNCT
cana-6186	230	1	such	such	ADJ
cana-6186	230	2	can	can	AUX
cana-6186	230	3	be	be	AUX
cana-6186	230	4	deduced	deduce	VERB
cana-6186	230	5	from	from	ADP
cana-6186	230	6	algorithms	algorithm	NOUN
cana-6186	230	7	such	such	ADJ
cana-6186	230	8	as	as	ADP
cana-6186	230	9	random	random	ADJ
cana-6186	230	10	forest	forest	NOUN
cana-6186	230	11	and	and	CCONJ
cana-6186	230	12	gradient	gradient	NOUN
cana-6186	230	13	boosting	boosting	NOUN
cana-6186	230	14	,	,	PUNCT
cana-6186	230	15	where	where	SCONJ
cana-6186	230	16	it	it	PRON
cana-6186	230	17	suggests	suggest	VERB
cana-6186	230	18	features	feature	NOUN
cana-6186	230	19	are	be	AUX
cana-6186	230	20	fundamentals	fundamental	NOUN
cana-6186	230	21	in	in	ADP
cana-6186	230	22	the	the	DET
cana-6186	230	23	heart	heart	NOUN
cana-6186	230	24	disease	disease	NOUN
cana-6186	230	25	prediction	prediction	NOUN
cana-6186	230	26	and	and	CCONJ
cana-6186	230	27	therefore	therefore	ADV
cana-6186	230	28	more	more	ADV
cana-6186	230	29	important	important	ADJ
cana-6186	230	30	.	.	PUNCT
cana-6186	231	1	this	this	PRON
cana-6186	231	2	is	be	AUX
cana-6186	231	3	crucial	crucial	ADJ
cana-6186	231	4	guidance	guidance	NOUN
cana-6186	231	5	for	for	ADP
cana-6186	231	6	clinicians	clinician	NOUN
cana-6186	231	7	and	and	CCONJ
cana-6186	231	8	researchers	researcher	NOUN
cana-6186	231	9	to	to	PART
cana-6186	231	10	give	give	VERB
cana-6186	231	11	importance	importance	NOUN
cana-6186	231	12	to	to	ADP
cana-6186	231	13	these	these	DET
cana-6186	231	14	attributes	attribute	NOUN
cana-6186	231	15	while	while	SCONJ
cana-6186	231	16	deciding	decide	VERB
cana-6186	231	17	on	on	ADP
cana-6186	231	18	diagnostic	diagnostic	ADJ
cana-6186	231	19	applications	application	NOUN
cana-6186	231	20	.	.	PUNCT
cana-6186	232	1	the	the	DET
cana-6186	232	2	study	study	NOUN
cana-6186	232	3	also	also	ADV
cana-6186	232	4	shows	show	VERB
cana-6186	232	5	trade	trade	NOUN
cana-6186	232	6	-	-	PUNCT
cana-6186	232	7	offs	off	NOUN
cana-6186	232	8	between	between	ADP
cana-6186	232	9	computational	computational	ADJ
cana-6186	232	10	efficiency	efficiency	NOUN
cana-6186	232	11	and	and	CCONJ
cana-6186	232	12	good	good	ADJ
cana-6186	232	13	predictive	predictive	ADJ
cana-6186	232	14	performance	performance	NOUN
cana-6186	232	15	.	.	PUNCT
cana-6186	233	1	gradient	gradient	ADJ
cana-6186	233	2	boosting	boost	VERB
cana-6186	233	3	and	and	CCONJ
cana-6186	233	4	random	random	ADJ
cana-6186	233	5	forest	forest	NOUN
cana-6186	233	6	outperform	outperform	NOUN
cana-6186	233	7	the	the	DET
cana-6186	233	8	rest	rest	NOUN
cana-6186	233	9	in	in	ADP
cana-6186	233	10	terms	term	NOUN
cana-6186	233	11	of	of	ADP
cana-6186	233	12	accuracy	accuracy	NOUN
cana-6186	233	13	,	,	PUNCT
cana-6186	233	14	though	though	SCONJ
cana-6186	233	15	their	their	PRON
cana-6186	233	16	computational	computational	ADJ
cana-6186	233	17	intensity	intensity	NOUN
cana-6186	233	18	limits	limit	VERB
cana-6186	233	19	their	their	PRON
cana-6186	233	20	usage	usage	NOUN
cana-6186	233	21	in	in	ADP
cana-6186	233	22	real	real	ADJ
cana-6186	233	23	-	-	PUNCT
cana-6186	233	24	time	time	NOUN
cana-6186	233	25	and	and	CCONJ
cana-6186	233	26	more	more	ADJ
cana-6186	233	27	resource	resource	NOUN
cana-6186	233	28	-	-	PUNCT
cana-6186	233	29	related	relate	VERB
cana-6186	233	30	scenarios	scenario	NOUN
cana-6186	233	31	.	.	PUNCT
cana-6186	234	1	svm	svm	PROPN
cana-6186	234	2	,	,	PUNCT
cana-6186	234	3	on	on	ADP
cana-6186	234	4	the	the	DET
cana-6186	234	5	other	other	ADJ
cana-6186	234	6	hand	hand	NOUN
cana-6186	234	7	,	,	PUNCT
cana-6186	234	8	provides	provide	VERB
cana-6186	234	9	balance	balance	NOUN
cana-6186	234	10	to	to	ADP
cana-6186	234	11	both	both	DET
cana-6186	234	12	performance	performance	NOUN
cana-6186	234	13	and	and	CCONJ
cana-6186	234	14	efficiency	efficiency	NOUN
cana-6186	234	15	,	,	PUNCT
cana-6186	234	16	hence	hence	ADV
cana-6186	234	17	useful	useful	ADJ
cana-6186	234	18	when	when	SCONJ
cana-6186	234	19	the	the	DET
cana-6186	234	20	computation	computation	NOUN
cana-6186	234	21	resources	resource	NOUN
cana-6186	234	22	are	be	AUX
cana-6186	234	23	constrained	constrain	VERB
cana-6186	234	24	.	.	PUNCT
cana-6186	235	1	the	the	DET
cana-6186	235	2	problem	problem	NOUN
cana-6186	235	3	is	be	AUX
cana-6186	235	4	that	that	SCONJ
cana-6186	235	5	even	even	ADV
cana-6186	235	6	with	with	ADP
cana-6186	235	7	good	good	ADJ
cana-6186	235	8	scores	score	NOUN
cana-6186	235	9	,	,	PUNCT
cana-6186	235	10	there	there	PRON
cana-6186	235	11	exist	exist	VERB
cana-6186	235	12	problems	problem	NOUN
cana-6186	235	13	.	.	PUNCT
cana-6186	236	1	data	datum	NOUN
cana-6186	236	2	imbalance	imbalance	NOUN
cana-6186	236	3	might	might	AUX
cana-6186	236	4	have	have	AUX
cana-6186	236	5	affected	affect	VERB
cana-6186	236	6	the	the	DET
cana-6186	236	7	recall	recall	NOUN
cana-6186	236	8	results	result	NOUN
cana-6186	236	9	most	most	ADV
cana-6186	236	10	especially	especially	ADV
cana-6186	236	11	for	for	ADP
cana-6186	236	12	algorithms	algorithm	NOUN
cana-6186	236	13	such	such	ADJ
cana-6186	236	14	as	as	ADP
cana-6186	236	15	decision	decision	NOUN
cana-6186	236	16	tree	tree	NOUN
cana-6186	236	17	or	or	CCONJ
cana-6186	236	18	knn	knn	PROPN
cana-6186	236	19	,	,	PUNCT
cana-6186	236	20	which	which	PRON
cana-6186	236	21	display	display	VERB
cana-6186	236	22	significant	significant	ADJ
cana-6186	236	23	sensitivity	sensitivity	NOUN
cana-6186	236	24	in	in	ADP
cana-6186	236	25	respect	respect	NOUN
cana-6186	236	26	to	to	ADP
cana-6186	236	27	variations	variation	NOUN
cana-6186	236	28	in	in	ADP
cana-6186	236	29	the	the	DET
cana-6186	236	30	distribution	distribution	NOUN
cana-6186	236	31	class	class	NOUN
cana-6186	236	32	.	.	PUNCT
cana-6186	237	1	oversampling	oversample	VERB
cana-6186	237	2	or	or	CCONJ
cana-6186	237	3	under	under	ADP
cana-6186	237	4	sampling	sample	VERB
cana-6186	237	5	and	and	CCONJ
cana-6186	237	6	even	even	ADV
cana-6186	237	7	synthetic	synthetic	ADJ
cana-6186	237	8	generation	generation	NOUN
cana-6186	237	9	may	may	AUX
cana-6186	237	10	be	be	AUX
cana-6186	237	11	useful	useful	ADJ
cana-6186	237	12	in	in	ADP
cana-6186	237	13	improving	improve	VERB
cana-6186	237	14	the	the	DET
cana-6186	237	15	score	score	NOUN
cana-6186	237	16	of	of	ADP
cana-6186	237	17	the	the	DET
cana-6186	237	18	model	model	NOUN
cana-6186	237	19	regarding	regard	VERB
cana-6186	237	20	this	this	DET
cana-6186	237	21	issue	issue	NOUN
cana-6186	237	22	.	.	PUNCT
cana-6186	238	1	moreover	moreover	ADV
cana-6186	238	2	,	,	PUNCT
cana-6186	238	3	according	accord	VERB
cana-6186	238	4	to	to	ADP
cana-6186	238	5	the	the	DET
cana-6186	238	6	study	study	NOUN
cana-6186	238	7	,	,	PUNCT
cana-6186	238	8	population	population	NOUN
cana-6186	238	9	grows	grow	VERB
cana-6186	238	10	and	and	CCONJ
cana-6186	238	11	heterogeneous	heterogeneous	ADJ
cana-6186	238	12	sets	set	NOUN
cana-6186	238	13	for	for	ADP
cana-6186	238	14	an	an	DET
cana-6186	238	15	enhanced	enhanced	ADJ
cana-6186	238	16	generalization	generalization	NOUN
cana-6186	238	17	of	of	ADP
cana-6186	238	18	the	the	DET
cana-6186	238	19	attained	attain	VERB
cana-6186	238	20	results	result	NOUN
cana-6186	238	21	.	.	PUNCT
cana-6186	239	1	this	this	PRON
cana-6186	239	2	can	can	AUX
cana-6186	239	3	,	,	PUNCT
cana-6186	239	4	therefore	therefore	ADV
cana-6186	239	5	,	,	PUNCT
cana-6186	239	6	reveal	reveal	VERB
cana-6186	239	7	the	the	DET
cana-6186	239	8	strength	strength	NOUN
cana-6186	239	9	of	of	ADP
cana-6186	239	10	machine	machine	NOUN
cana-6186	239	11	learning	learn	VERB
cana-6186	239	12	algorithms	algorithm	NOUN
cana-6186	239	13	in	in	ADP
cana-6186	239	14	providing	provide	VERB
cana-6186	239	15	assistance	assistance	NOUN
cana-6186	239	16	to	to	ADP
cana-6186	239	17	the	the	DET
cana-6186	239	18	clinical	clinical	ADJ
cana-6186	239	19	practitioner	practitioner	NOUN
cana-6186	239	20	for	for	ADP
cana-6186	239	21	the	the	DET
cana-6186	239	22	early	early	ADJ
cana-6186	239	23	and	and	CCONJ
cana-6186	239	24	accurate	accurate	ADJ
cana-6186	239	25	diagnosis	diagnosis	NOUN
cana-6186	239	26	of	of	ADP
cana-6186	239	27	heart	heart	NOUN
cana-6186	239	28	disease	disease	NOUN
cana-6186	239	29	.	.	PUNCT
cana-6186	240	1	it	it	PRON
cana-6186	240	2	will	will	AUX
cana-6186	240	3	save	save	VERB
cana-6186	240	4	healthcare	healthcare	NOUN
cana-6186	240	5	professionals	professional	NOUN
cana-6186	240	6	the	the	DET
cana-6186	240	7	effort	effort	NOUN
cana-6186	240	8	of	of	ADP
cana-6186	240	9	doing	do	VERB
cana-6186	240	10	it	it	PRON
cana-6186	240	11	manually	manually	ADV
cana-6186	240	12	as	as	ADV
cana-6186	240	13	well	well	ADV
cana-6186	240	14	because	because	SCONJ
cana-6186	240	15	timely	timely	ADJ
cana-6186	240	16	intervention	intervention	NOUN
cana-6186	240	17	,	,	PUNCT
cana-6186	240	18	based	base	VERB
cana-6186	240	19	on	on	ADP
cana-6186	240	20	an	an	DET
cana-6186	240	21	optimized	optimize	VERB
cana-6186	240	22	system	system	NOUN
cana-6186	240	23	and	and	CCONJ
cana-6186	240	24	further	further	ADJ
cana-6186	240	25	integration	integration	NOUN
cana-6186	240	26	into	into	ADP
cana-6186	240	27	the	the	DET
cana-6186	240	28	workflow	workflow	NOUN
cana-6186	240	29	,	,	PUNCT
cana-6186	240	30	is	be	AUX
cana-6186	240	31	essential	essential	ADJ
cana-6186	240	32	to	to	ADP
cana-6186	240	33	the	the	DET
cana-6186	240	34	care	care	NOUN
cana-6186	240	35	process	process	NOUN
cana-6186	240	36	for	for	ADP
cana-6186	240	37	better	well	ADJ
cana-6186	240	38	patient	patient	ADJ
cana-6186	240	39	outcomes	outcome	NOUN
cana-6186	240	40	.	.	PUNCT
cana-6186	241	1	gradient	gradient	ADJ
cana-6186	241	2	boosting	boost	VERB
cana-6186	241	3	and	and	CCONJ
cana-6186	241	4	random	random	ADJ
cana-6186	241	5	forest	forest	NOUN
cana-6186	241	6	appear	appear	VERB
cana-6186	241	7	well	well	ADV
cana-6186	241	8	-	-	PUNCT
cana-6186	241	9	suited	suited	ADJ
cana-6186	241	10	for	for	ADP
cana-6186	241	11	heart	heart	NOUN
cana-6186	241	12	disease	disease	NOUN
cana-6186	241	13	classification	classification	NOUN
cana-6186	241	14	with	with	ADP
cana-6186	241	15	high	high	ADJ
cana-6186	241	16	accuracy	accuracy	NOUN
cana-6186	241	17	and	and	CCONJ
cana-6186	241	18	reliability	reliability	NOUN
cana-6186	241	19	.	.	PUNCT
cana-6186	242	1	so	so	ADV
cana-6186	242	2	far	far	ADV
cana-6186	242	3	,	,	PUNCT
cana-6186	242	4	algorithm	algorithm	NOUN
cana-6186	242	5	choices	choice	NOUN
cana-6186	242	6	depend	depend	VERB
cana-6186	242	7	on	on	ADP
cana-6186	242	8	requirements	requirement	NOUN
cana-6186	242	9	by	by	ADP
cana-6186	242	10	specific	specific	ADJ
cana-6186	242	11	applications	application	NOUN
cana-6186	242	12	,	,	PUNCT
cana-6186	242	13	communications	communication	NOUN
cana-6186	242	14	on	on	ADP
cana-6186	242	15	applied	apply	VERB
cana-6186	242	16	nonlinear	nonlinear	ADJ
cana-6186	242	17	analysis	analysis	NOUN
cana-6186	242	18	issn	issn	NOUN
cana-6186	242	19	:	:	PUNCT
cana-6186	242	20	1074	1074	NUM
cana-6186	242	21	-	-	PUNCT
cana-6186	242	22	133x	133x	NUM
cana-6186	242	23	vol	vol	NOUN
cana-6186	242	24	32	32	NUM
cana-6186	242	25	no	no	NOUN
cana-6186	242	26	.	.	NOUN
cana-6186	242	27	1	1	NUM
cana-6186	242	28	(	(	PUNCT
cana-6186	242	29	2025	2025	NUM
cana-6186	242	30	)	)	PUNCT
cana-6186	242	31	666	666	NUM
cana-6186	242	32	https://internationalpubls.com	https://internationalpubls.com	X
cana-6186	242	33	such	such	ADJ
cana-6186	242	34	as	as	ADP
cana-6186	242	35	desired	desire	VERB
cana-6186	242	36	accuracy	accuracy	NOUN
cana-6186	242	37	,	,	PUNCT
cana-6186	242	38	efficiency	efficiency	NOUN
cana-6186	242	39	in	in	ADP
cana-6186	242	40	computing	computing	NOUN
cana-6186	242	41	time	time	NOUN
cana-6186	242	42	,	,	PUNCT
cana-6186	242	43	or	or	CCONJ
cana-6186	242	44	interpretability	interpretability	NOUN
cana-6186	242	45	.	.	PUNCT
cana-6186	243	1	future	future	ADJ
cana-6186	243	2	work	work	NOUN
cana-6186	243	3	can	can	AUX
cana-6186	243	4	be	be	AUX
cana-6186	243	5	pursued	pursue	VERB
cana-6186	243	6	applying	apply	VERB
cana-6186	243	7	deeper	deep	ADJ
cana-6186	243	8	learning	learning	NOUN
cana-6186	243	9	approaches	approach	NOUN
cana-6186	243	10	or	or	CCONJ
cana-6186	243	11	even	even	ADV
cana-6186	243	12	bigger	big	ADJ
cana-6186	243	13	datasets	dataset	NOUN
cana-6186	243	14	that	that	PRON
cana-6186	243	15	will	will	AUX
cana-6186	243	16	enhance	enhance	VERB
cana-6186	243	17	predictive	predictive	ADJ
cana-6186	243	18	capabilities	capability	NOUN
cana-6186	243	19	of	of	ADP
cana-6186	243	20	the	the	DET
cana-6186	243	21	system	system	NOUN
cana-6186	243	22	as	as	ADV
cana-6186	243	23	well	well	ADV
cana-6186	243	24	as	as	ADP
cana-6186	243	25	its	its	PRON
cana-6186	243	26	applicability	applicability	NOUN
cana-6186	243	27	to	to	ADP
cana-6186	243	28	clinical	clinical	ADJ
cana-6186	243	29	practice	practice	NOUN
cana-6186	243	30	.	.	PUNCT
cana-6186	244	1	iv	iv	X
cana-6186	244	2	.	.	PUNCT
cana-6186	244	3	conclusion	conclusion	NOUN
cana-6186	244	4	and	and	CCONJ
cana-6186	244	5	future	future	ADJ
cana-6186	244	6	scope	scope	NOUN
cana-6186	244	7	this	this	DET
cana-6186	244	8	work	work	NOUN
cana-6186	244	9	demonstrates	demonstrate	VERB
cana-6186	244	10	the	the	DET
cana-6186	244	11	suitability	suitability	NOUN
cana-6186	244	12	of	of	ADP
cana-6186	244	13	machine	machine	NOUN
cana-6186	244	14	learning	learn	VERB
cana-6186	244	15	algorithms	algorithm	NOUN
cana-6186	244	16	to	to	ADP
cana-6186	244	17	heart	heart	NOUN
cana-6186	244	18	disease	disease	NOUN
cana-6186	244	19	classification	classification	NOUN
cana-6186	244	20	.	.	PUNCT
cana-6186	245	1	this	this	DET
cana-6186	245	2	diagnosis	diagnosis	NOUN
cana-6186	245	3	tool	tool	NOUN
cana-6186	245	4	will	will	AUX
cana-6186	245	5	be	be	AUX
cana-6186	245	6	automated	automate	VERB
cana-6186	245	7	,	,	PUNCT
cana-6186	245	8	efficient	efficient	ADJ
cana-6186	245	9	,	,	PUNCT
cana-6186	245	10	and	and	CCONJ
cana-6186	245	11	reliable	reliable	ADJ
cana-6186	245	12	.	.	PUNCT
cana-6186	246	1	from	from	ADP
cana-6186	246	2	the	the	DET
cana-6186	246	3	five	five	NUM
cana-6186	246	4	algorithms	algorithm	NOUN
cana-6186	246	5	analysed	analyse	VERB
cana-6186	246	6	support	support	NOUN
cana-6186	246	7	vector	vector	NOUN
cana-6186	246	8	machines	machine	NOUN
cana-6186	246	9	(	(	PUNCT
cana-6186	246	10	svm	svm	PROPN
cana-6186	246	11	)	)	PUNCT
cana-6186	246	12	,	,	PUNCT
cana-6186	246	13	k	k	X
cana-6186	246	14	-	-	PUNCT
cana-6186	246	15	nearest	near	ADJ
cana-6186	246	16	neighbors	neighbor	NOUN
cana-6186	246	17	(	(	PUNCT
cana-6186	246	18	knn	knn	PROPN
cana-6186	246	19	)	)	PUNCT
cana-6186	246	20	,	,	PUNCT
cana-6186	246	21	decision	decision	NOUN
cana-6186	246	22	tree	tree	NOUN
cana-6186	246	23	,	,	PUNCT
cana-6186	246	24	random	random	ADJ
cana-6186	246	25	forest	forest	NOUN
cana-6186	246	26	,	,	PUNCT
cana-6186	246	27	and	and	CCONJ
cana-6186	246	28	gradient	gradient	NOUN
cana-6186	246	29	boosting	boosting	NOUN
cana-6186	246	30	,	,	PUNCT
cana-6186	246	31	the	the	DET
cana-6186	246	32	last	last	ADJ
cana-6186	246	33	one	one	NOUN
cana-6186	246	34	was	be	AUX
cana-6186	246	35	the	the	DET
cana-6186	246	36	best	good	ADJ
cana-6186	246	37	classifier	classifier	NOUN
cana-6186	246	38	with	with	ADP
cana-6186	246	39	the	the	DET
cana-6186	246	40	highest	high	ADJ
cana-6186	246	41	accuracy	accuracy	NOUN
cana-6186	246	42	92	92	NUM
cana-6186	246	43	%	%	NOUN
cana-6186	246	44	with	with	ADP
cana-6186	246	45	the	the	DET
cana-6186	246	46	best	good	ADJ
cana-6186	246	47	precision	precision	NOUN
cana-6186	246	48	,	,	PUNCT
cana-6186	246	49	recall	recall	NOUN
cana-6186	246	50	,	,	PUNCT
cana-6186	246	51	and	and	CCONJ
cana-6186	246	52	f1	f1	NOUN
cana-6186	246	53	-	-	PUNCT
cana-6186	246	54	score	score	NOUN
cana-6186	246	55	.	.	PUNCT
cana-6186	247	1	the	the	DET
cana-6186	247	2	performance	performance	NOUN
cana-6186	247	3	of	of	ADP
cana-6186	247	4	random	random	ADJ
cana-6186	247	5	forest	forest	NOUN
cana-6186	247	6	is	be	AUX
cana-6186	247	7	also	also	ADV
cana-6186	247	8	good	good	ADJ
cana-6186	247	9	and	and	CCONJ
cana-6186	247	10	shows	show	VERB
cana-6186	247	11	robustness	robustness	NOUN
cana-6186	247	12	,	,	PUNCT
cana-6186	247	13	as	as	ADV
cana-6186	247	14	well	well	ADV
cana-6186	247	15	as	as	ADP
cana-6186	247	16	feature	feature	NOUN
cana-6186	247	17	importance	importance	NOUN
cana-6186	247	18	insight	insight	NOUN
cana-6186	247	19	.	.	PUNCT
cana-6186	248	1	this	this	DET
cana-6186	248	2	result	result	NOUN
cana-6186	248	3	indicates	indicate	VERB
cana-6186	248	4	that	that	SCONJ
cana-6186	248	5	ensemble	ensemble	ADJ
cana-6186	248	6	methods	method	NOUN
cana-6186	248	7	can	can	AUX
cana-6186	248	8	be	be	AUX
cana-6186	248	9	very	very	ADV
cana-6186	248	10	effective	effective	ADJ
cana-6186	248	11	in	in	ADP
cana-6186	248	12	dealing	deal	VERB
cana-6186	248	13	with	with	ADP
cana-6186	248	14	complex	complex	ADJ
cana-6186	248	15	medical	medical	ADJ
cana-6186	248	16	datasets	dataset	NOUN
cana-6186	248	17	,	,	PUNCT
cana-6186	248	18	with	with	ADP
cana-6186	248	19	the	the	DET
cana-6186	248	20	ability	ability	NOUN
cana-6186	248	21	to	to	PART
cana-6186	248	22	reach	reach	VERB
cana-6186	248	23	a	a	DET
cana-6186	248	24	high	high	ADJ
cana-6186	248	25	level	level	NOUN
cana-6186	248	26	of	of	ADP
cana-6186	248	27	predictive	predictive	ADJ
cana-6186	248	28	accuracy	accuracy	NOUN
cana-6186	248	29	.	.	PUNCT
cana-6186	249	1	this	this	PRON
cana-6186	249	2	points	point	VERB
cana-6186	249	3	to	to	ADP
cana-6186	249	4	the	the	DET
cana-6186	249	5	proper	proper	ADJ
cana-6186	249	6	preprocessing	preprocessing	NOUN
cana-6186	249	7	of	of	ADP
cana-6186	249	8	data	datum	NOUN
cana-6186	249	9	,	,	PUNCT
cana-6186	249	10	selection	selection	NOUN
cana-6186	249	11	of	of	ADP
cana-6186	249	12	features	feature	NOUN
cana-6186	249	13	,	,	PUNCT
cana-6186	249	14	and	and	CCONJ
cana-6186	249	15	hyperparameter	hyperparameter	NOUN
cana-6186	249	16	tuning	tune	VERB
cana-6186	249	17	for	for	ADP
cana-6186	249	18	improvement	improvement	NOUN
cana-6186	249	19	in	in	ADP
cana-6186	249	20	classification	classification	NOUN
cana-6186	249	21	model	model	NOUN
cana-6186	249	22	performance	performance	NOUN
cana-6186	249	23	.	.	PUNCT
cana-6186	250	1	critical	critical	ADJ
cana-6186	250	2	features	feature	NOUN
cana-6186	250	3	such	such	ADJ
cana-6186	250	4	as	as	ADP
cana-6186	250	5	cholesterol	cholesterol	NOUN
cana-6186	250	6	levels	level	NOUN
cana-6186	250	7	,	,	PUNCT
cana-6186	250	8	blood	blood	NOUN
cana-6186	250	9	pressure	pressure	NOUN
cana-6186	250	10	,	,	PUNCT
cana-6186	250	11	and	and	CCONJ
cana-6186	250	12	age	age	NOUN
cana-6186	250	13	were	be	AUX
cana-6186	250	14	identified	identify	VERB
cana-6186	250	15	as	as	ADP
cana-6186	250	16	critical	critical	ADJ
cana-6186	250	17	predictors	predictor	NOUN
cana-6186	250	18	of	of	ADP
cana-6186	250	19	heart	heart	NOUN
cana-6186	250	20	disease	disease	NOUN
cana-6186	250	21	,	,	PUNCT
cana-6186	250	22	highlighting	highlight	VERB
cana-6186	250	23	the	the	DET
cana-6186	250	24	importance	importance	NOUN
cana-6186	250	25	of	of	ADP
cana-6186	250	26	feature	feature	NOUN
cana-6186	250	27	importance	importance	NOUN
cana-6186	250	28	analysis	analysis	NOUN
cana-6186	250	29	.	.	PUNCT
cana-6186	251	1	it	it	PRON
cana-6186	251	2	further	far	ADV
cana-6186	251	3	demonstrates	demonstrate	VERB
cana-6186	251	4	how	how	SCONJ
cana-6186	251	5	machine	machine	NOUN
cana-6186	251	6	learning	learn	VERB
cana-6186	251	7	bridges	bridge	NOUN
cana-6186	251	8	the	the	DET
cana-6186	251	9	gap	gap	NOUN
cana-6186	251	10	between	between	ADP
cana-6186	251	11	advanced	advanced	ADJ
cana-6186	251	12	computational	computational	ADJ
cana-6186	251	13	techniques	technique	NOUN
cana-6186	251	14	and	and	CCONJ
cana-6186	251	15	practical	practical	ADJ
cana-6186	251	16	applications	application	NOUN
cana-6186	251	17	in	in	ADP
cana-6186	251	18	healthcare	healthcare	PROPN
cana-6186	251	19	,	,	PUNCT
cana-6186	251	20	providing	provide	VERB
cana-6186	251	21	an	an	DET
cana-6186	251	22	opportunity	opportunity	NOUN
cana-6186	251	23	for	for	ADP
cana-6186	251	24	timely	timely	ADJ
cana-6186	251	25	and	and	CCONJ
cana-6186	251	26	accurate	accurate	ADJ
cana-6186	251	27	diagnosis	diagnosis	NOUN
cana-6186	251	28	of	of	ADP
cana-6186	251	29	heart	heart	NOUN
cana-6186	251	30	diseases	disease	NOUN
cana-6186	251	31	.	.	PUNCT
cana-6186	252	1	the	the	DET
cana-6186	252	2	present	present	ADJ
cana-6186	252	3	research	research	NOUN
cana-6186	252	4	contributes	contribute	VERB
cana-6186	252	5	to	to	ADP
cana-6186	252	6	the	the	DET
cana-6186	252	7	mounting	mount	VERB
cana-6186	252	8	body	body	NOUN
cana-6186	252	9	of	of	ADP
cana-6186	252	10	evidence	evidence	NOUN
cana-6186	252	11	that	that	PRON
cana-6186	252	12	will	will	AUX
cana-6186	252	13	support	support	VERB
cana-6186	252	14	the	the	DET
cana-6186	252	15	integration	integration	NOUN
cana-6186	252	16	of	of	ADP
cana-6186	252	17	ai	ai	VERB
cana-6186	252	18	into	into	ADP
cana-6186	252	19	clinical	clinical	ADJ
cana-6186	252	20	workflows	workflow	NOUN
cana-6186	252	21	.	.	PUNCT
cana-6186	253	1	although	although	SCONJ
cana-6186	253	2	the	the	DET
cana-6186	253	3	results	result	NOUN
cana-6186	253	4	are	be	AUX
cana-6186	253	5	promising	promise	VERB
cana-6186	253	6	,	,	PUNCT
cana-6186	253	7	several	several	ADJ
cana-6186	253	8	opportunities	opportunity	NOUN
cana-6186	253	9	exist	exist	VERB
cana-6186	253	10	to	to	PART
cana-6186	253	11	improve	improve	VERB
cana-6186	253	12	the	the	DET
cana-6186	253	13	performance	performance	NOUN
cana-6186	253	14	and	and	CCONJ
cana-6186	253	15	application	application	NOUN
cana-6186	253	16	of	of	ADP
cana-6186	253	17	the	the	DET
cana-6186	253	18	system	system	NOUN
cana-6186	253	19	in	in	ADP
cana-6186	253	20	future	future	ADJ
cana-6186	253	21	work	work	NOUN
cana-6186	253	22	.	.	PUNCT
cana-6186	254	1	increasing	increase	VERB
cana-6186	254	2	the	the	DET
cana-6186	254	3	size	size	NOUN
cana-6186	254	4	of	of	ADP
cana-6186	254	5	the	the	DET
cana-6186	254	6	dataset	dataset	NOUN
cana-6186	254	7	with	with	ADP
cana-6186	254	8	a	a	DET
cana-6186	254	9	more	more	ADV
cana-6186	254	10	diverse	diverse	ADJ
cana-6186	254	11	population	population	NOUN
cana-6186	254	12	and	and	CCONJ
cana-6186	254	13	larger	large	ADJ
cana-6186	254	14	sample	sample	NOUN
cana-6186	254	15	sizes	size	NOUN
cana-6186	254	16	would	would	AUX
cana-6186	254	17	enhance	enhance	VERB
cana-6186	254	18	generalizability	generalizability	NOUN
cana-6186	254	19	of	of	ADP
cana-6186	254	20	the	the	DET
cana-6186	254	21	models	model	NOUN
cana-6186	254	22	.	.	PUNCT
cana-6186	255	1	addressing	address	VERB
cana-6186	255	2	data	datum	NOUN
cana-6186	255	3	imbalance	imbalance	NOUN
cana-6186	255	4	through	through	ADP
cana-6186	255	5	techniques	technique	NOUN
cana-6186	255	6	such	such	ADJ
cana-6186	255	7	as	as	ADP
cana-6186	255	8	oversampling	oversampling	ADJ
cana-6186	255	9	,	,	PUNCT
cana-6186	255	10	under	under	ADP
cana-6186	255	11	sampling	sampling	NOUN
cana-6186	255	12	,	,	PUNCT
cana-6186	255	13	or	or	CCONJ
cana-6186	255	14	synthetic	synthetic	ADJ
cana-6186	255	15	data	data	NOUN
cana-6186	255	16	generation	generation	NOUN
cana-6186	255	17	would	would	AUX
cana-6186	255	18	improve	improve	VERB
cana-6186	255	19	recall	recall	NOUN
cana-6186	255	20	scores	score	NOUN
cana-6186	255	21	and	and	CCONJ
cana-6186	255	22	ensure	ensure	VERB
cana-6186	255	23	that	that	SCONJ
cana-6186	255	24	minority	minority	NOUN
cana-6186	255	25	classes	class	NOUN
cana-6186	255	26	are	be	AUX
cana-6186	255	27	well	well	ADV
cana-6186	255	28	represented	represent	VERB
cana-6186	255	29	.	.	PUNCT
cana-6186	256	1	deep	deep	ADJ
cana-6186	256	2	learning	learning	NOUN
cana-6186	256	3	techniques	technique	NOUN
cana-6186	256	4	,	,	PUNCT
cana-6186	256	5	in	in	ADP
cana-6186	256	6	particular	particular	ADJ
cana-6186	256	7	cnns	cnn	NOUN
cana-6186	256	8	or	or	CCONJ
cana-6186	256	9	rnns	rnn	NOUN
cana-6186	256	10	,	,	PUNCT
cana-6186	256	11	can	can	AUX
cana-6186	256	12	be	be	AUX
cana-6186	256	13	leveraged	leverage	VERB
cana-6186	256	14	to	to	PART
cana-6186	256	15	enable	enable	VERB
cana-6186	256	16	the	the	DET
cana-6186	256	17	system	system	NOUN
cana-6186	256	18	to	to	PART
cana-6186	256	19	capture	capture	VERB
cana-6186	256	20	the	the	DET
cana-6186	256	21	deeper	deep	ADJ
cana-6186	256	22	patterns	pattern	NOUN
cana-6186	256	23	within	within	ADP
cana-6186	256	24	the	the	DET
cana-6186	256	25	data	datum	NOUN
cana-6186	256	26	and	and	CCONJ
cana-6186	256	27	allow	allow	VERB
cana-6186	256	28	possible	possible	ADJ
cana-6186	256	29	improvement	improvement	NOUN
cana-6186	256	30	in	in	ADP
cana-6186	256	31	predictive	predictive	ADJ
cana-6186	256	32	accuracy	accuracy	NOUN
cana-6186	256	33	.	.	PUNCT
cana-6186	257	1	in	in	ADP
cana-6186	257	2	conclusion	conclusion	NOUN
cana-6186	257	3	,	,	PUNCT
cana-6186	257	4	incorporating	incorporate	VERB
cana-6186	257	5	the	the	DET
cana-6186	257	6	techniques	technique	NOUN
cana-6186	257	7	of	of	ADP
cana-6186	257	8	explainable	explainable	ADJ
cana-6186	257	9	ai	ai	NOUN
cana-6186	257	10	increases	increase	NOUN
cana-6186	257	11	the	the	DET
cana-6186	257	12	interpretability	interpretability	NOUN
cana-6186	257	13	of	of	ADP
cana-6186	257	14	models	model	NOUN
cana-6186	257	15	;	;	PUNCT
cana-6186	257	16	thus	thus	ADV
cana-6186	257	17	,	,	PUNCT
cana-6186	257	18	clinicians	clinician	NOUN
cana-6186	257	19	receive	receive	VERB
cana-6186	257	20	actionable	actionable	ADJ
cana-6186	257	21	insights	insight	NOUN
cana-6186	257	22	that	that	PRON
cana-6186	257	23	can	can	AUX
cana-6186	257	24	enable	enable	VERB
cana-6186	257	25	more	more	ADJ
cana-6186	257	26	trust	trust	NOUN
cana-6186	257	27	in	in	ADP
cana-6186	257	28	the	the	DET
cana-6186	257	29	recommended	recommend	VERB
cana-6186	257	30	prescriptions	prescription	NOUN
cana-6186	257	31	by	by	ADP
cana-6186	257	32	the	the	DET
cana-6186	257	33	system	system	NOUN
cana-6186	257	34	.	.	PUNCT
cana-6186	258	1	from	from	ADP
cana-6186	258	2	an	an	DET
cana-6186	258	3	implementation	implementation	NOUN
cana-6186	258	4	point	point	NOUN
cana-6186	258	5	of	of	ADP
cana-6186	258	6	view	view	NOUN
cana-6186	258	7	,	,	PUNCT
cana-6186	258	8	if	if	SCONJ
cana-6186	258	9	the	the	DET
cana-6186	258	10	system	system	NOUN
cana-6186	258	11	were	be	AUX
cana-6186	258	12	designed	design	VERB
cana-6186	258	13	to	to	PART
cana-6186	258	14	be	be	AUX
cana-6186	258	15	a	a	DET
cana-6186	258	16	real	real	ADJ
cana-6186	258	17	-	-	PUNCT
cana-6186	258	18	time	time	NOUN
cana-6186	258	19	application	application	NOUN
cana-6186	258	20	,	,	PUNCT
cana-6186	258	21	such	such	ADJ
cana-6186	258	22	as	as	ADP
cana-6186	258	23	a	a	DET
cana-6186	258	24	web	web	NOUN
cana-6186	258	25	-	-	PUNCT
cana-6186	258	26	based	base	VERB
cana-6186	258	27	tool	tool	NOUN
cana-6186	258	28	or	or	CCONJ
cana-6186	258	29	mobile	mobile	ADJ
cana-6186	258	30	application	application	NOUN
cana-6186	258	31	,	,	PUNCT
cana-6186	258	32	more	more	ADJ
cana-6186	258	33	people	people	NOUN
cana-6186	258	34	could	could	AUX
cana-6186	258	35	be	be	AUX
cana-6186	258	36	accessed	access	VERB
cana-6186	258	37	,	,	PUNCT
cana-6186	258	38	including	include	VERB
cana-6186	258	39	providers	provider	NOUN
cana-6186	258	40	of	of	ADP
cana-6186	258	41	care	care	NOUN
cana-6186	258	42	in	in	ADP
cana-6186	258	43	resource	resource	NOUN
cana-6186	258	44	-	-	PUNCT
cana-6186	258	45	constrained	constrain	VERB
cana-6186	258	46	environments	environment	NOUN
cana-6186	258	47	.	.	PUNCT
cana-6186	259	1	combining	combine	VERB
cana-6186	259	2	the	the	DET
cana-6186	259	3	system	system	NOUN
cana-6186	259	4	with	with	ADP
cana-6186	259	5	iot	iot	PROPN
cana-6186	259	6	devices	device	NOUN
cana-6186	259	7	to	to	PART
cana-6186	259	8	provide	provide	VERB
cana-6186	259	9	for	for	ADP
cana-6186	259	10	constant	constant	ADJ
cana-6186	259	11	monitoring	monitoring	NOUN
cana-6186	259	12	of	of	ADP
cana-6186	259	13	patient	patient	ADJ
cana-6186	259	14	health	health	NOUN
cana-6186	259	15	data	datum	NOUN
cana-6186	259	16	will	will	AUX
cana-6186	259	17	make	make	VERB
cana-6186	259	18	the	the	DET
cana-6186	259	19	system	system	NOUN
cana-6186	259	20	much	much	ADV
cana-6186	259	21	more	more	ADV
cana-6186	259	22	useful	useful	ADJ
cana-6186	259	23	and	and	CCONJ
cana-6186	259	24	possible	possible	ADJ
cana-6186	259	25	to	to	PART
cana-6186	259	26	act	act	VERB
cana-6186	259	27	proactively	proactively	ADV
cana-6186	259	28	.	.	PUNCT
cana-6186	260	1	final	final	ADJ
cana-6186	260	2	conclusion	conclusion	NOUN
cana-6186	260	3	finally	finally	ADV
cana-6186	260	4	,	,	PUNCT
cana-6186	260	5	investigation	investigation	NOUN
cana-6186	260	6	on	on	ADP
cana-6186	260	7	the	the	DET
cana-6186	260	8	hybrid	hybrid	ADJ
cana-6186	260	9	approaches	approach	NOUN
cana-6186	260	10	combining	combine	VERB
cana-6186	260	11	machine	machine	NOUN
cana-6186	260	12	learning	learning	NOUN
cana-6186	260	13	with	with	ADP
cana-6186	260	14	domain	domain	NOUN
cana-6186	260	15	knowledge	knowledge	NOUN
cana-6186	260	16	in	in	ADP
cana-6186	260	17	cardiology	cardiology	NOUN
cana-6186	260	18	may	may	AUX
cana-6186	260	19	develop	develop	VERB
cana-6186	260	20	effective	effective	ADJ
cana-6186	260	21	and	and	CCONJ
cana-6186	260	22	personalized	personalized	ADJ
cana-6186	260	23	solutions	solution	NOUN
cana-6186	260	24	even	even	ADV
cana-6186	260	25	for	for	ADP
cana-6186	260	26	a	a	DET
cana-6186	260	27	proper	proper	ADJ
cana-6186	260	28	diagnosis	diagnosis	NOUN
cana-6186	260	29	and	and	CCONJ
cana-6186	260	30	management	management	NOUN
cana-6186	260	31	of	of	ADP
cana-6186	260	32	heart	heart	NOUN
cana-6186	260	33	diseases	disease	NOUN
cana-6186	260	34	.	.	PUNCT
cana-6186	261	1	communications	communication	NOUN
cana-6186	261	2	on	on	ADP
cana-6186	261	3	applied	apply	VERB
cana-6186	261	4	nonlinear	nonlinear	ADJ
cana-6186	261	5	analysis	analysis	NOUN
cana-6186	261	6	issn	issn	NOUN
cana-6186	261	7	:	:	PUNCT
cana-6186	261	8	1074	1074	NUM
cana-6186	261	9	-	-	PUNCT
cana-6186	261	10	133x	133x	NUM
cana-6186	261	11	vol	vol	NOUN
cana-6186	261	12	32	32	NUM
cana-6186	261	13	no	no	NOUN
cana-6186	261	14	.	.	NOUN
cana-6186	261	15	1	1	NUM
cana-6186	261	16	(	(	PUNCT
cana-6186	261	17	2025	2025	NUM
cana-6186	261	18	)	)	PUNCT
cana-6186	261	19	667	667	NUM
cana-6186	261	20	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-6186	261	21	references	reference	NOUN
cana-6186	261	22	[	[	X
cana-6186	261	23	1	1	NUM
cana-6186	261	24	]	]	PUNCT
cana-6186	261	25	adam	adam	PROPN
cana-6186	261	26	p	p	PROPN
cana-6186	261	27	,	,	PUNCT
cana-6186	261	28	parveen	parveen	PROPN
cana-6186	261	29	a.	a.	NOUN
cana-6186	261	30	prediction	prediction	NOUN
cana-6186	261	31	system	system	NOUN
cana-6186	261	32	for	for	ADP
cana-6186	261	33	heart	heart	NOUN
cana-6186	261	34	disease	disease	NOUN
cana-6186	261	35	using	use	VERB
cana-6186	261	36	naïve	naïve	ADJ
cana-6186	261	37	bayes	baye	NOUN
cana-6186	261	38	.	.	PUNCT
cana-6186	262	1	j	j	PROPN
cana-6186	262	2	adv	adv	PROPN
cana-6186	262	3	comput	comput	PROPN
cana-6186	262	4	math	math	PROPN
cana-6186	262	5	sci	sci	PROPN
cana-6186	262	6	.	.	PUNCT
cana-6186	263	1	2012;3(3):290–4	2012;3(3):290–4	X
cana-6186	263	2	.	.	PUNCT
cana-6186	264	1	[	[	X
cana-6186	264	2	2	2	X
cana-6186	264	3	]	]	X
cana-6186	264	4	tran	tran	PROPN
cana-6186	264	5	h.	h.	PROPN
cana-6186	264	6	a	a	DET
cana-6186	264	7	survey	survey	NOUN
cana-6186	264	8	of	of	ADP
cana-6186	264	9	machine	machine	NOUN
cana-6186	264	10	learning	learning	NOUN
cana-6186	264	11	and	and	CCONJ
cana-6186	264	12	data	datum	NOUN
cana-6186	264	13	mining	mining	NOUN
cana-6186	264	14	techniques	technique	NOUN
cana-6186	264	15	used	use	VERB
cana-6186	264	16	in	in	ADP
cana-6186	264	17	multimedia	multimedia	NOUN
cana-6186	264	18	system	system	NOUN
cana-6186	264	19	.	.	PUNCT
cana-6186	265	1	no	no	DET
cana-6186	265	2	113	113	NUM
cana-6186	265	3	13–21	13–21	NUM
cana-6186	265	4	2019	2019	NUM
cana-6186	265	5	.	.	PUNCT
cana-6186	266	1	[	[	X
cana-6186	266	2	3	3	X
cana-6186	266	3	]	]	X
cana-6186	266	4	gnaneswar	gnaneswar	NOUN
cana-6186	266	5	b	b	PROPN
cana-6186	266	6	,	,	PUNCT
cana-6186	266	7	jebarani	jebarani	PROPN
cana-6186	266	8	me	i	PRON
cana-6186	266	9	.	.	PUNCT
cana-6186	267	1	a	a	DET
cana-6186	267	2	review	review	NOUN
cana-6186	267	3	on	on	ADP
cana-6186	267	4	prediction	prediction	NOUN
cana-6186	267	5	and	and	CCONJ
cana-6186	267	6	diagnosis	diagnosis	NOUN
cana-6186	267	7	of	of	ADP
cana-6186	267	8	heart	heart	NOUN
cana-6186	267	9	failure	failure	NOUN
cana-6186	267	10	.	.	PUNCT
cana-6186	268	1	in	in	ADP
cana-6186	268	2	2017	2017	NUM
cana-6186	268	3	international	international	ADJ
cana-6186	268	4	conference	conference	NOUN
cana-6186	268	5	on	on	ADP
cana-6186	268	6	innovations	innovation	NOUN
cana-6186	268	7	in	in	ADP
cana-6186	268	8	information	information	NOUN
cana-6186	268	9	,	,	PUNCT
cana-6186	268	10	embedded	embed	VERB
cana-6186	268	11	and	and	CCONJ
cana-6186	268	12	communication	communication	NOUN
cana-6186	268	13	systems	system	NOUN
cana-6186	268	14	(	(	PUNCT
cana-6186	268	15	iciiecs	iciiec	NOUN
cana-6186	268	16	)	)	PUNCT
cana-6186	268	17	,	,	PUNCT
cana-6186	268	18	17	17	NUM
cana-6186	268	19	-	-	SYM
cana-6186	268	20	18	18	NUM
cana-6186	268	21	march	march	NOUN
cana-6186	268	22	,	,	PUNCT
cana-6186	268	23	coimbatore	coimbatore	PROPN
cana-6186	268	24	,	,	PUNCT
cana-6186	268	25	india	india	PROPN
cana-6186	268	26	,	,	PUNCT
cana-6186	268	27	2017;1–3	2017;1–3	NUM
cana-6186	268	28	.	.	PUNCT
cana-6186	269	1	https://	https://	PROPN
cana-6186	269	2	doi	doi	PROPN
cana-6186	269	3	.	.	PUNCT
cana-6186	270	1	org/	org/	PRON
cana-6186	270	2	10	10	NUM
cana-6186	270	3	.	.	PUNCT
cana-6186	271	1	1109/	1109/	NUM
cana-6186	271	2	iciie	iciie	PROPN
cana-6186	271	3	cs	cs	PROPN
cana-6186	271	4	.	.	PROPN
cana-6186	271	5	2017	2017	NUM
cana-6186	271	6	.	.	PUNCT
cana-6186	272	1	82760	82760	NUM
cana-6186	272	2	33	33	NUM
cana-6186	273	1	[	[	SYM
cana-6186	273	2	4	4	NUM
cana-6186	273	3	]	]	X
cana-6186	273	4	kusprasapta	kusprasapta	NOUN
cana-6186	273	5	m	m	PROPN
cana-6186	273	6	,	,	PUNCT
cana-6186	273	7	ichwan	ichwan	PROPN
cana-6186	273	8	m	m	PROPN
cana-6186	273	9	,	,	PUNCT
cana-6186	273	10	utami	utami	PROPN
cana-6186	273	11	db	db	PROPN
cana-6186	273	12	.	.	PROPN
cana-6186	273	13	heart	heart	NOUN
cana-6186	273	14	rate	rate	NOUN
cana-6186	273	15	prediction	prediction	NOUN
cana-6186	273	16	based	base	VERB
cana-6186	273	17	on	on	ADP
cana-6186	273	18	cycling	cycling	NOUN
cana-6186	273	19	cadence	cadence	NOUN
cana-6186	273	20	using	use	VERB
cana-6186	273	21	feedforward	feedforward	ADJ
cana-6186	273	22	neural	neural	ADJ
cana-6186	273	23	network	network	NOUN
cana-6186	273	24	.	.	PUNCT
cana-6186	274	1	in	in	ADP
cana-6186	274	2	2016	2016	NUM
cana-6186	274	3	international	international	ADJ
cana-6186	274	4	conference	conference	NOUN
cana-6186	274	5	on	on	ADP
cana-6186	274	6	computer	computer	NOUN
cana-6186	274	7	,	,	PUNCT
cana-6186	274	8	control	control	NOUN
cana-6186	274	9	,	,	PUNCT
cana-6186	274	10	informatics	informatic	NOUN
cana-6186	274	11	and	and	CCONJ
cana-6186	274	12	its	its	PRON
cana-6186	274	13	applications	application	NOUN
cana-6186	274	14	(	(	PUNCT
cana-6186	274	15	ic3ina	ic3ina	PROPN
cana-6186	274	16	)	)	PUNCT
cana-6186	274	17	,	,	PUNCT
cana-6186	274	18	ieee	ieee	NOUN
cana-6186	274	19	,	,	PUNCT
cana-6186	274	20	2016;72–76	2016;72–76	NUM
cana-6186	274	21	.	.	PUNCT
cana-6186	275	1	https://	https://	PROPN
cana-6186	275	2	doi	doi	PROPN
cana-6186	275	3	.	.	PUNCT
cana-6186	276	1	org/	org/	PRON
cana-6186	276	2	10	10	NUM
cana-6186	276	3	.	.	PUNCT
cana-6186	277	1	1109/	1109/	NUM
cana-6186	277	2	ic3ina	ic3ina	PROPN
cana-6186	277	3	.	.	PROPN
cana-6186	277	4	2016	2016	NUM
cana-6186	277	5	.	.	PUNCT
cana-6186	278	1	78630	78630	NUM
cana-6186	278	2	26	26	NUM
cana-6186	279	1	[	[	X
cana-6186	279	2	5	5	NUM
cana-6186	279	3	]	]	X
cana-6186	279	4	singh	singh	PROPN
cana-6186	279	5	ky	ky	PROPN
cana-6186	279	6	,	,	PUNCT
cana-6186	279	7	sinha	sinha	PROPN
cana-6186	279	8	n	n	PROPN
cana-6186	279	9	,	,	PUNCT
cana-6186	279	10	singh	singh	PROPN
cana-6186	279	11	ks	ks	PROPN
cana-6186	279	12	.	.	PUNCT
cana-6186	279	13	heart	heart	NOUN
cana-6186	279	14	disease	disease	NOUN
cana-6186	279	15	prediction	prediction	NOUN
cana-6186	279	16	system	system	NOUN
cana-6186	279	17	using	use	VERB
cana-6186	279	18	random	random	ADJ
cana-6186	279	19	forest	forest	NOUN
cana-6186	279	20	.	.	PUNCT
cana-6186	280	1	in	in	ADP
cana-6186	280	2	international	international	ADJ
cana-6186	280	3	conference	conference	NOUN
cana-6186	280	4	on	on	ADP
cana-6186	280	5	advances	advance	NOUN
cana-6186	280	6	in	in	ADP
cana-6186	280	7	computing	computing	NOUN
cana-6186	280	8	and	and	CCONJ
cana-6186	280	9	data	datum	NOUN
cana-6186	280	10	sciences	science	NOUN
cana-6186	280	11	,	,	PUNCT
cana-6186	280	12	advances	advance	NOUN
cana-6186	280	13	in	in	ADP
cana-6186	280	14	computing	computing	NOUN
cana-6186	280	15	and	and	CCONJ
cana-6186	280	16	data	datum	NOUN
cana-6186	280	17	sciences	science	NOUN
cana-6186	280	18	.	.	PUNCT
cana-6186	281	1	icacds	icacds	PROPN
cana-6186	281	2	2016	2016	NUM
cana-6186	281	3	.	.	PUNCT
cana-6186	282	1	communications	communication	NOUN
cana-6186	282	2	in	in	ADP
cana-6186	282	3	computer	computer	NOUN
cana-6186	282	4	and	and	CCONJ
cana-6186	282	5	information	information	NOUN
cana-6186	282	6	science	science	NOUN
cana-6186	282	7	,	,	PUNCT
cana-6186	282	8	singapore	singapore	PROPN
cana-6186	282	9	.	.	PUNCT
cana-6186	283	1	2017;721:613–623	2017;721:613–623	NUM
cana-6186	283	2	.	.	PUNCT
cana-6186	284	1	https://	https://	PROPN
cana-6186	284	2	doi	doi	PROPN
cana-6186	284	3	.	.	PUNCT
cana-6186	285	1	org/	org/	PRON
cana-6186	285	2	10	10	NUM
cana-6186	285	3	.	.	PUNCT
cana-6186	286	1	1007/	1007/	NUM
cana-6186	286	2	97898110	97898110	NUM
cana-6186	286	3	-	-	SYM
cana-6186	286	4	5427	5427	NUM
cana-6186	286	5	-	-	SYM
cana-6186	286	6	3	3	NUM
cana-6186	286	7	_	_	NOUN
cana-6186	286	8	63	63	NUM
cana-6186	287	1	[	[	SYM
cana-6186	287	2	6	6	NUM
cana-6186	287	3	]	]	PUNCT
cana-6186	287	4	priya	priya	PROPN
cana-6186	287	5	rp	rp	PROPN
cana-6186	287	6	,	,	PUNCT
cana-6186	287	7	skinariwala	skinariwala	PROPN
cana-6186	287	8	a.	a.	NOUN
cana-6186	287	9	automated	automate	VERB
cana-6186	287	10	diagnosis	diagnosis	NOUN
cana-6186	287	11	of	of	ADP
cana-6186	287	12	heart	heart	NOUN
cana-6186	287	13	disease	disease	NOUN
cana-6186	287	14	using	use	VERB
cana-6186	287	15	random	random	ADJ
cana-6186	287	16	forest	forest	NOUN
cana-6186	287	17	algorithm	algorithm	NOUN
cana-6186	287	18	.	.	PUNCT
cana-6186	288	1	int	int	PROPN
cana-6186	289	1	j	j	PROPN
cana-6186	289	2	adv	adv	PROPN
cana-6186	289	3	res	re	NOUN
cana-6186	289	4	ideas	idea	NOUN
cana-6186	289	5	innovat	innovat	VERB
cana-6186	289	6	technol	technol	ADJ
cana-6186	289	7	2017;3(2	2017;3(2	NUM
cana-6186	289	8	)	)	PUNCT
cana-6186	289	9	.	.	PUNCT
cana-6186	290	1	[	[	X
cana-6186	290	2	7	7	X
cana-6186	290	3	]	]	X
cana-6186	290	4	tripoliti	tripoliti	NOUN
cana-6186	290	5	e	e	NOUN
cana-6186	290	6	,	,	PUNCT
cana-6186	290	7	fotiadis	fotiadis	INTJ
cana-6186	290	8	i	i	PROPN
cana-6186	290	9	d	d	PROPN
cana-6186	290	10	,	,	PUNCT
cana-6186	290	11	manis	manis	PROPN
cana-6186	290	12	g.	g.	PROPN
cana-6186	290	13	automated	automate	VERB
cana-6186	290	14	diagnosis	diagnosis	NOUN
cana-6186	290	15	of	of	ADP
cana-6186	290	16	diseases	disease	NOUN
cana-6186	290	17	based	base	VERB
cana-6186	290	18	on	on	ADP
cana-6186	290	19	classification	classification	NOUN
cana-6186	290	20	:	:	PUNCT
cana-6186	290	21	dynamic	dynamic	ADJ
cana-6186	290	22	determination	determination	NOUN
cana-6186	290	23	of	of	ADP
cana-6186	290	24	the	the	DET
cana-6186	290	25	number	number	NOUN
cana-6186	290	26	of	of	ADP
cana-6186	290	27	trees	tree	NOUN
cana-6186	290	28	in	in	ADP
cana-6186	290	29	random	random	ADJ
cana-6186	290	30	forests	forest	NOUN
cana-6186	290	31	algorithm	algorithm	NOUN
cana-6186	290	32	.	.	PUNCT
cana-6186	291	1	eee	eee	PROPN
cana-6186	291	2	trans	trans	PROPN
cana-6186	291	3	inf	inf	PROPN
cana-6186	291	4	technol	technol	NOUN
cana-6186	291	5	biomed	biome	VERB
cana-6186	291	6	2012;16(4	2012;16(4	NUM
cana-6186	291	7	)	)	PUNCT
cana-6186	291	8	.	.	PUNCT
cana-6186	292	1	[	[	X
cana-6186	292	2	8	8	NUM
cana-6186	292	3	]	]	X
cana-6186	292	4	gonsalves	gonsalve	NOUN
cana-6186	292	5	ah	ah	INTJ
cana-6186	292	6	,	,	PUNCT
cana-6186	292	7	thabtah	thabtah	PROPN
cana-6186	292	8	f	f	PROPN
cana-6186	292	9	,	,	PUNCT
cana-6186	292	10	mohammad	mohammad	PROPN
cana-6186	292	11	rma	rma	PROPN
cana-6186	292	12	,	,	PUNCT
cana-6186	292	13	singh	singh	PROPN
cana-6186	292	14	g.	g.	PROPN
cana-6186	292	15	prediction	prediction	NOUN
cana-6186	292	16	of	of	ADP
cana-6186	292	17	coronary	coronary	ADJ
cana-6186	292	18	heart	heart	NOUN
cana-6186	292	19	disease	disease	NOUN
cana-6186	292	20	using	use	VERB
cana-6186	292	21	machine	machine	NOUN
cana-6186	292	22	learning	learning	NOUN
cana-6186	292	23	:	:	PUNCT
cana-6186	292	24	an	an	DET
cana-6186	292	25	experimental	experimental	ADJ
cana-6186	292	26	analysis	analysis	NOUN
cana-6186	292	27	.	.	PUNCT
cana-6186	293	1	in	in	ADP
cana-6186	293	2	:	:	PUNCT
cana-6186	293	3	proceedings	proceeding	NOUN
cana-6186	293	4	of	of	ADP
cana-6186	293	5	the	the	DET
cana-6186	293	6	2019	2019	NUM
cana-6186	293	7	3rd	3rd	ADJ
cana-6186	293	8	international	international	ADJ
cana-6186	293	9	conference	conference	NOUN
cana-6186	293	10	on	on	ADP
cana-6186	293	11	deep	deep	ADJ
cana-6186	293	12	learning	learning	NOUN
cana-6186	293	13	technologies	technology	NOUN
cana-6186	293	14	,	,	PUNCT
cana-6186	293	15	2019;51–56	2019;51–56	NUM
cana-6186	293	16	.	.	PUNCT
cana-6186	294	1	[	[	X
cana-6186	294	2	9	9	NUM
cana-6186	294	3	]	]	X
cana-6186	294	4	oikonomou	oikonomou	PROPN
cana-6186	294	5	ek	ek	PROPN
cana-6186	294	6	,	,	PUNCT
cana-6186	294	7	williams	williams	PROPN
cana-6186	294	8	mc	mc	PROPN
cana-6186	294	9	,	,	PUNCT
cana-6186	294	10	kotanidis	kotanidis	PROPN
cana-6186	294	11	cp	cp	PROPN
cana-6186	294	12	,	,	PUNCT
cana-6186	294	13	desai	desai	PROPN
cana-6186	294	14	my	my	PRON
cana-6186	294	15	,	,	PUNCT
cana-6186	294	16	marwan	marwan	PROPN
cana-6186	294	17	m	m	PROPN
cana-6186	294	18	,	,	PUNCT
cana-6186	294	19	antonopoulos	antonopoulo	NOUN
cana-6186	294	20	as	as	ADP
cana-6186	294	21	,	,	PUNCT
cana-6186	294	22	thomas	thomas	PROPN
cana-6186	294	23	ke	ke	PROPN
cana-6186	294	24	,	,	PUNCT
cana-6186	294	25	thomas	thomas	PROPN
cana-6186	294	26	s	s	PROPN
cana-6186	294	27	,	,	PUNCT
cana-6186	294	28	akoumianakis	akoumianakis	INTJ
cana-6186	294	29	i	i	PROPN
cana-6186	294	30	,	,	PUNCT
cana-6186	294	31	fan	fan	PROPN
cana-6186	294	32	lm	lm	PROPN
cana-6186	294	33	,	,	PUNCT
cana-6186	294	34	et	et	PROPN
cana-6186	294	35	al	al	PROPN
cana-6186	294	36	.	.	PUNCT
cana-6186	295	1	a	a	DET
cana-6186	295	2	novel	novel	ADJ
cana-6186	295	3	machine	machine	NOUN
cana-6186	295	4	learning	learning	NOUN
cana-6186	295	5	-	-	PUNCT
cana-6186	295	6	derived	derive	VERB
cana-6186	295	7	radiotranscriptomic	radiotranscriptomic	ADJ
cana-6186	295	8	signature	signature	NOUN
cana-6186	295	9	of	of	ADP
cana-6186	295	10	perivascular	perivascular	ADJ
cana-6186	295	11	fat	fat	NOUN
cana-6186	295	12	improves	improve	VERB
cana-6186	295	13	cardiac	cardiac	ADJ
cana-6186	295	14	risk	risk	NOUN
cana-6186	295	15	prediction	prediction	NOUN
cana-6186	295	16	using	use	VERB
cana-6186	295	17	coronary	coronary	ADJ
cana-6186	295	18	ct	ct	NUM
cana-6186	295	19	angiography	angiography	NOUN
cana-6186	295	20	.	.	PUNCT
cana-6186	296	1	eur	eur	PROPN
cana-6186	296	2	heart	heart	NOUN
cana-6186	296	3	j.	j.	PROPN
cana-6186	296	4	2019;40(43):3529–43	2019;40(43):3529–43	NUM
cana-6186	296	5	.	.	PUNCT
cana-6186	297	1	[	[	X
cana-6186	297	2	10	10	NUM
cana-6186	297	3	]	]	X
cana-6186	297	4	el	el	NOUN
cana-6186	297	5	-	-	PUNCT
cana-6186	297	6	hasnony	hasnony	NOUN
cana-6186	297	7	i	i	PRON
cana-6186	297	8	m	m	PROPN
cana-6186	297	9	,	,	PUNCT
cana-6186	297	10	elzeki	elzeki	VERB
cana-6186	297	11	om	om	PROPN
cana-6186	297	12	.	.	PUNCT
cana-6186	298	1	multi	multi	ADJ
cana-6186	298	2	-	-	ADJ
cana-6186	298	3	label	label	ADJ
cana-6186	298	4	active	active	ADJ
cana-6186	298	5	learning	learning	NOUN
cana-6186	298	6	-	-	PUNCT
cana-6186	298	7	based	base	VERB
cana-6186	298	8	machine	machine	NOUN
cana-6186	298	9	learning	learning	NOUN
cana-6186	298	10	model	model	NOUN
cana-6186	298	11	for	for	ADP
cana-6186	298	12	heart	heart	NOUN
cana-6186	298	13	disease	disease	NOUN
cana-6186	298	14	prediction	prediction	NOUN
cana-6186	298	15	.	.	PUNCT
cana-6186	299	1	sensors	sensor	NOUN
cana-6186	299	2	.	.	PUNCT
cana-6186	300	1	2022;22(3):1184–8	2022;22(3):1184–8	X
cana-6186	300	2	.	.	PUNCT
cana-6186	301	1	https://	https://	PROPN
cana-6186	301	2	doi	doi	PROPN
cana-6186	301	3	.	.	PUNCT
cana-6186	302	1	org/	org/	PRON
cana-6186	302	2	10	10	NUM
cana-6186	302	3	.	.	PUNCT
cana-6186	303	1	3390/	3390/	NUM
cana-6186	303	2	s2203	s2203	NOUN
cana-6186	303	3	1184	1184	NUM
cana-6186	303	4	.	.	PUNCT
cana-6186	304	1	[	[	X
cana-6186	304	2	11	11	NUM
cana-6186	304	3	]	]	PUNCT
cana-6186	304	4	guleria	guleria	NOUN
cana-6186	304	5	p	p	NOUN
cana-6186	304	6	,	,	PUNCT
cana-6186	304	7	srinivasu	srinivasu	PROPN
cana-6186	304	8	pn	pn	PROPN
cana-6186	304	9	,	,	PUNCT
cana-6186	304	10	ahmed	ahmed	PROPN
cana-6186	304	11	s.	s.	PROPN
cana-6186	304	12	ai	ai	PROPN
cana-6186	304	13	framework	framework	NOUN
cana-6186	304	14	for	for	ADP
cana-6186	304	15	cardiovascular	cardiovascular	ADJ
cana-6186	304	16	disease	disease	NOUN
cana-6186	304	17	prediction	prediction	NOUN
cana-6186	304	18	using	use	VERB
cana-6186	304	19	classification	classification	NOUN
cana-6186	304	20	techniques	technique	NOUN
cana-6186	304	21	.	.	PUNCT
cana-6186	305	1	electronics	electronic	NOUN
cana-6186	305	2	.	.	PUNCT
cana-6186	306	1	2022;11(24):1184–8	2022;11(24):1184–8	X
cana-6186	306	2	.	.	PUNCT
cana-6186	307	1	https://	https://	PROPN
cana-6186	307	2	doi	doi	PROPN
cana-6186	307	3	.	.	PUNCT
cana-6186	308	1	org/	org/	PRON
cana-6186	308	2	10	10	NUM
cana-6186	308	3	.	.	PUNCT
cana-6186	309	1	3390/	3390/	NUM
cana-6186	309	2	elect	elect	PROPN
cana-6186	309	3	ronic	ronic	NOUN
cana-6186	309	4	s1124	s1124	PROPN
cana-6186	309	5	4086	4086	NUM
cana-6186	309	6	.	.	PUNCT
cana-6186	310	1	[	[	X
cana-6186	310	2	12	12	NUM
cana-6186	310	3	]	]	PUNCT
cana-6186	310	4	l.	l.	PROPN
cana-6186	310	5	ali	ali	PROPN
cana-6186	310	6	et	et	PROPN
cana-6186	310	7	al	al	PROPN
cana-6186	310	8	.	.	PROPN
cana-6186	310	9	,	,	PUNCT
cana-6186	310	10	“	"	PUNCT
cana-6186	310	11	an	an	DET
cana-6186	310	12	optimized	optimize	VERB
cana-6186	310	13	stacked	stack	VERB
cana-6186	310	14	support	support	NOUN
cana-6186	310	15	vector	vector	NOUN
cana-6186	310	16	machinesbased	machinesbase	VERB
cana-6186	310	17	expert	expert	NOUN
cana-6186	310	18	system	system	NOUN
cana-6186	310	19	for	for	ADP
cana-6186	310	20	the	the	DET
cana-6186	310	21	effective	effective	ADJ
cana-6186	310	22	prediction	prediction	NOUN
cana-6186	310	23	of	of	ADP
cana-6186	310	24	heartfailure	heartfailure	NOUN
cana-6186	310	25	,	,	PUNCT
cana-6186	310	26	”	"	PUNCT
cana-6186	310	27	ieee	ieee	NOUN
cana-6186	310	28	access	access	NOUN
cana-6186	310	29	,	,	PUNCT
cana-6186	310	30	vol	vol	NOUN
cana-6186	310	31	.	.	PROPN
cana-6186	310	32	7	7	NUM
cana-6186	310	33	,	,	PUNCT
cana-6186	310	34	pp	pp	ADJ
cana-6186	310	35	.	.	PUNCT
cana-6186	311	1	54007–54014	54007–54014	NUM
cana-6186	311	2	,	,	PUNCT
cana-6186	311	3	2019,doi	2019,doi	NUM
cana-6186	311	4	:	:	PUNCT
cana-6186	311	5	10.1109	10.1109	NUM
cana-6186	311	6	/	/	SYM
cana-6186	311	7	access.2019.2909969	access.2019.2909969	NOUN
cana-6186	311	8	.	.	PUNCT
cana-6186	312	1	[	[	X
cana-6186	312	2	13	13	NUM
cana-6186	312	3	]	]	PUNCT
cana-6186	312	4	a.	a.	NOUN
cana-6186	312	5	javeed	javeed	NOUN
cana-6186	312	6	,	,	PUNCT
cana-6186	312	7	s.	s.	PROPN
cana-6186	312	8	zhou	zhou	PROPN
cana-6186	312	9	,	,	PUNCT
cana-6186	312	10	l.	l.	PROPN
cana-6186	312	11	yongjian	yongjian	PROPN
cana-6186	312	12	,	,	PUNCT
cana-6186	312	13	i.	i.	PROPN
cana-6186	312	14	qasim	qasim	PROPN
cana-6186	312	15	,	,	PUNCT
cana-6186	312	16	a.	a.	PROPN
cana-6186	312	17	noor	noor	PROPN
cana-6186	312	18	,	,	PUNCT
cana-6186	312	19	and	and	CCONJ
cana-6186	312	20	r.nour	r.nour	NOUN
cana-6186	312	21	,	,	PUNCT
cana-6186	312	22	“	"	PUNCT
cana-6186	312	23	an	an	DET
cana-6186	312	24	intelligent	intelligent	ADJ
cana-6186	312	25	learning	learning	NOUN
cana-6186	312	26	system	system	NOUN
cana-6186	312	27	based	base	VERB
cana-6186	312	28	on	on	ADP
cana-6186	312	29	randomsearch	randomsearch	NOUN
cana-6186	312	30	algorithm	algorithm	NOUN
cana-6186	312	31	and	and	CCONJ
cana-6186	312	32	optimized	optimize	VERB
cana-6186	312	33	random	random	ADJ
cana-6186	312	34	forest	forest	NOUN
cana-6186	312	35	model	model	NOUN
cana-6186	312	36	forimproved	forimprove	VERB
cana-6186	312	37	heart	heart	NOUN
cana-6186	312	38	disease	disease	NOUN
cana-6186	312	39	detection	detection	NOUN
cana-6186	312	40	,	,	PUNCT
cana-6186	312	41	”	"	PUNCT
cana-6186	312	42	ieee	ieee	NOUN
cana-6186	312	43	access	access	NOUN
cana-6186	312	44	,	,	PUNCT
cana-6186	312	45	vol	vol	NOUN
cana-6186	312	46	.	.	PROPN
cana-6186	312	47	7	7	NUM
cana-6186	312	48	,	,	PUNCT
cana-6186	312	49	pp.180235	pp.180235	PROPN
cana-6186	312	50	–	–	PUNCT
cana-6186	312	51	180243	180243	NUM
cana-6186	312	52	,	,	PUNCT
cana-6186	312	53	2019	2019	NUM
cana-6186	312	54	,	,	PUNCT
cana-6186	312	55	doi:10.1109	doi:10.1109	VERB
cana-6186	312	56	/	/	SYM
cana-6186	312	57	access.2019.2952107	access.2019.2952107	PROPN
cana-6186	312	58	.	.	PUNCT
cana-6186	313	1	communications	communication	NOUN
cana-6186	313	2	on	on	ADP
cana-6186	313	3	applied	apply	VERB
cana-6186	313	4	nonlinear	nonlinear	ADJ
cana-6186	313	5	analysis	analysis	NOUN
cana-6186	313	6	issn	issn	NOUN
cana-6186	313	7	:	:	PUNCT
cana-6186	313	8	1074	1074	NUM
cana-6186	313	9	-	-	PUNCT
cana-6186	313	10	133x	133x	NUM
cana-6186	313	11	vol	vol	NOUN
cana-6186	313	12	32	32	NUM
cana-6186	313	13	no	no	NOUN
cana-6186	313	14	.	.	NOUN
cana-6186	313	15	1	1	NUM
cana-6186	313	16	(	(	PUNCT
cana-6186	313	17	2025	2025	NUM
cana-6186	313	18	)	)	PUNCT
cana-6186	314	1	668	668	NUM
cana-6186	314	2	https://internationalpubls.com	https://internationalpubls.com	X
cana-6186	314	3	[	[	X
cana-6186	314	4	14	14	NUM
cana-6186	314	5	]	]	PUNCT
cana-6186	314	6	mr.santhana	mr.santhana	NOUN
cana-6186	314	7	krishnan.j	krishnan.j	PROPN
cana-6186	314	8	,	,	PUNCT
cana-6186	314	9	dr.geetha.s	dr.geetha.s	PROPN
cana-6186	314	10	,	,	PUNCT
cana-6186	314	11	”	"	PUNCT
cana-6186	314	12	prediction	prediction	NOUN
cana-6186	314	13	of	of	ADP
cana-6186	314	14	heartdisease	heartdisease	NOUN
cana-6186	314	15	using	use	VERB
cana-6186	314	16	machine	machine	NOUN
cana-6186	314	17	learning	learn	VERB
cana-6186	314	18	algorithms”,2019	algorithms”,2019	PROPN
cana-6186	314	19	1stinternational	1stinternational	NUM
cana-6186	314	20	conference	conference	NOUN
cana-6186	314	21	on	on	ADP
cana-6186	314	22	innovations	innovation	NOUN
cana-6186	314	23	in	in	ADP
cana-6186	314	24	information	information	NOUN
cana-6186	314	25	and	and	CCONJ
cana-6186	314	26	communication	communication	NOUN
cana-6186	314	27	technology	technology	NOUN
cana-6186	314	28	(	(	PUNCT
cana-6186	314	29	iciict),doi:10.1109	iciict),doi:10.1109	PROPN
cana-6186	314	30	/	/	SYM
cana-6186	314	31	iciict1.2019.8741465	iciict1.2019.8741465	PROPN
cana-6186	314	32	.	.	PUNCT
cana-6186	315	1	[	[	X
cana-6186	315	2	15	15	NUM
cana-6186	315	3	]	]	X
cana-6186	315	4	aditi	aditi	PROPN
cana-6186	315	5	gavhane	gavhane	PROPN
cana-6186	315	6	,	,	PUNCT
cana-6186	315	7	gouthami	gouthami	NOUN
cana-6186	315	8	kokkula	kokkula	NOUN
cana-6186	315	9	,	,	PUNCT
cana-6186	315	10	isha	isha	PROPN
cana-6186	315	11	pandya	pandya	PROPN
cana-6186	315	12	,	,	PUNCT
cana-6186	315	13	kailasdevadkar	kailasdevadkar	NOUN
cana-6186	315	14	“	"	PUNCT
cana-6186	315	15	prediction	prediction	NOUN
cana-6186	315	16	of	of	ADP
cana-6186	315	17	heart	heart	NOUN
cana-6186	315	18	disease	disease	NOUN
cana-6186	315	19	using	use	VERB
cana-6186	315	20	machinelearning	machinelearning	NOUN
cana-6186	315	21	”	"	PUNCT
cana-6186	315	22	,	,	PUNCT
cana-6186	315	23	international	international	ADJ
cana-6186	315	24	conference	conference	NOUN
cana-6186	315	25	on	on	ADP
cana-6186	315	26	electronics	electronic	NOUN
cana-6186	315	27	,	,	PUNCT
cana-6186	315	28	communication	communication	NOUN
cana-6186	315	29	and	and	CCONJ
cana-6186	315	30	aerospace	aerospace	NOUN
cana-6186	315	31	technology	technology	NOUN
cana-6186	315	32	(	(	PUNCT
cana-6186	315	33	iceca	iceca	NOUN
cana-6186	315	34	2018)ieee	2018)ieee	NUM
cana-6186	315	35	conference	conference	NOUN
cana-6186	315	36	record	record	NOUN
cana-6186	315	37	#	#	NOUN
cana-6186	315	38	42487	42487	NUM
cana-6186	315	39	;	;	PUNCT
cana-6186	315	40	ieeexplore	ieeexplore	NOUN
cana-6186	315	41	isbn:9781	isbn:9781	PROPN
cana-6186	315	42	-	-	PUNCT
cana-6186	315	43	5386	5386	NUM
cana-6186	315	44	-	-	PUNCT
cana-6186	315	45	0965	0965	NUM
cana-6186	315	46	-	-	PUNCT
cana-6186	315	47	1	1	NUM
cana-6186	315	48	doi	doi	NOUN
cana-6186	315	49	:	:	PUNCT
cana-6186	315	50	10.1109	10.1109	NUM
cana-6186	315	51	/	/	SYM
cana-6186	315	52	iceca.2018.8474922	iceca.2018.8474922	NOUN
cana-6186	315	53	[	[	X
cana-6186	315	54	16	16	NUM
cana-6186	315	55	]	]	X
cana-6186	315	56	devansh	devansh	ADJ
cana-6186	315	57	shah	shah	NOUN
cana-6186	315	58	,	,	PUNCT
cana-6186	315	59	samir	samir	PROPN
cana-6186	315	60	patel	patel	PROPN
cana-6186	315	61	,	,	PUNCT
cana-6186	315	62	santosh	santosh	PROPN
cana-6186	315	63	kumar	kumar	PROPN
cana-6186	315	64	bharti	bharti	PROPN
cana-6186	316	1	2020“heart	2020“heart	NUM
cana-6186	316	2	disease	disease	NOUN
cana-6186	316	3	prediction	prediction	NOUN
cana-6186	316	4	using	use	VERB
cana-6186	316	5	machine	machine	NOUN
cana-6186	316	6	learningtechniques	learningtechnique	NOUN
cana-6186	316	7	”	"	PUNCT
cana-6186	317	1	[	[	X
cana-6186	317	2	17	17	NUM
cana-6186	317	3	]	]	X
cana-6186	317	4	archana	archana	PROPN
cana-6186	317	5	singh	singh	PROPN
cana-6186	317	6	,	,	PUNCT
cana-6186	317	7	rakesh	rakesh	PROPN
cana-6186	317	8	kumar	kumar	PROPN
cana-6186	317	9	2020	2020	PROPN
cana-6186	317	10	international	international	ADJ
cana-6186	317	11	conferenceon	conferenceon	NOUN
cana-6186	317	12	electrical	electrical	ADJ
cana-6186	317	13	and	and	CCONJ
cana-6186	317	14	electronics	electronic	NOUN
cana-6186	317	15	engineering	engineering	NOUN
cana-6186	317	16	(	(	PUNCT
cana-6186	317	17	ice3	ice3	PROPN
cana-6186	317	18	)	)	PUNCT
cana-6186	317	19	,	,	PUNCT
cana-6186	317	20	doi:10.1109	doi:10.1109	VERB
cana-6186	317	21	/	/	SYM
cana-6186	317	22	ice348803.2020.9122958	ice348803.2020.9122958	PROPN
cana-6186	318	1	[	[	X
cana-6186	318	2	18	18	NUM
cana-6186	318	3	]	]	X
cana-6186	318	4	apurb	apurb	PROPN
cana-6186	318	5	rajdhan	rajdhan	PROPN
cana-6186	318	6	,	,	PUNCT
cana-6186	318	7	avi	avi	PROPN
cana-6186	318	8	agarwal	agarwal	PROPN
cana-6186	318	9	,	,	PUNCT
cana-6186	318	10	milan	milan	PROPN
cana-6186	318	11	sai	sai	PROPN
cana-6186	319	1	“	"	PUNCT
cana-6186	319	2	heart	heart	NOUN
cana-6186	319	3	diseaseprediction	diseaseprediction	NOUN
cana-6186	319	4	using	use	VERB
cana-6186	319	5	machine	machine	NOUN
cana-6186	319	6	learning	learning	NOUN
cana-6186	319	7	”	"	PUNCT
cana-6186	319	8	may	may	AUX
cana-6186	319	9	2020	2020	NUM
cana-6186	319	10	internationaljournal	internationaljournal	NOUN
cana-6186	319	11	of	of	ADP
cana-6186	319	12	engineering	engineering	NOUN
cana-6186	319	13	and	and	CCONJ
cana-6186	319	14	technical	technical	ADJ
cana-6186	319	15	research	research	PROPN
cana-6186	319	16	v9(04	v9(04	PROPN
cana-6186	319	17	)	)	PUNCT
cana-6186	319	18	doi:10.17577	doi:10.17577	PROPN
cana-6186	319	19	/	/	SYM
cana-6186	319	20	ijertv9is040614	ijertv9is040614	VERB
cana-6186	320	1	[	[	X
cana-6186	320	2	19	19	NUM
cana-6186	320	3	]	]	X
cana-6186	320	4	goel	goel	PROPN
cana-6186	320	5	,	,	PUNCT
cana-6186	320	6	rati	rati	PROPN
cana-6186	320	7	,	,	PUNCT
cana-6186	320	8	“	"	PUNCT
cana-6186	320	9	heart	heart	NOUN
cana-6186	320	10	disease	disease	NOUN
cana-6186	320	11	prediction	prediction	NOUN
cana-6186	320	12	using	use	VERB
cana-6186	320	13	variousalgorithms	variousalgorithm	NOUN
cana-6186	320	14	of	of	ADP
cana-6186	320	15	machine	machine	NOUN
cana-6186	320	16	learning	learning	NOUN
cana-6186	320	17	”	"	PUNCT
cana-6186	320	18	(	(	PUNCT
cana-6186	320	19	july	july	PROPN
cana-6186	320	20	12	12	NUM
cana-6186	320	21	,	,	PUNCT
cana-6186	320	22	2021).proceedings	2021).proceedings	NUM
cana-6186	320	23	of	of	ADP
cana-6186	320	24	the	the	DET
cana-6186	320	25	international	international	ADJ
cana-6186	320	26	conference	conference	NOUN
cana-6186	320	27	on	on	ADP
cana-6186	320	28	innovativecomputing	innovativecomputing	PROPN
cana-6186	320	29	&	&	CCONJ
cana-6186	320	30	communication	communication	NOUN
cana-6186	320	31	(	(	PUNCT
cana-6186	320	32	icicc	icicc	ADJ
cana-6186	320	33	)	)	PUNCT
cana-6186	320	34	2021	2021	NUM
cana-6186	320	35	,	,	PUNCT
cana-6186	320	36	available	available	ADJ
cana-6186	320	37	atsrn	atsrn	NOUN
cana-6186	320	38	:	:	PUNCT
cana-6186	320	39	https://ssrn.com/abstract=3884968	https://ssrn.com/abstract=3884968	NOUN
cana-6186	320	40	orhttp://dx.doi.org/10.2139/ssrn.3884968	orhttp://dx.doi.org/10.2139/ssrn.3884968	PRON
cana-6186	320	41	[	[	X
cana-6186	320	42	20	20	NUM
cana-6186	320	43	]	]	PUNCT
cana-6186	320	44	machine	machine	NOUN
cana-6186	320	45	learningebased	learningebase	VERB
cana-6186	320	46	heart	heart	NOUN
cana-6186	320	47	disease	disease	NOUN
cana-6186	320	48	prediction	prediction	NOUN
cana-6186	320	49	system	system	NOUN
cana-6186	320	50	forindian	forindian	ADJ
cana-6186	320	51	population	population	NOUN
cana-6186	320	52	:	:	PUNCT
cana-6186	320	53	an	an	DET
cana-6186	320	54	exploratory	exploratory	ADJ
cana-6186	320	55	studymdone	studymdone	NOUN
cana-6186	320	56	in	in	ADP
cana-6186	320	57	south	south	PROPN
cana-6186	320	58	indiaekta	indiaekta	PROPN
cana-6186	320	59	maini	maini	PROPN
cana-6186	320	60	,	,	PUNCT
cana-6186	320	61	bondu	bondu	NOUN
cana-6186	320	62	venkateswarlu	venkateswarlu	NOUN
cana-6186	320	63	,	,	PUNCT
cana-6186	320	64	baljeet	baljeet	PROPN
cana-6186	320	65	maini	maini	PROPN
cana-6186	320	66	,	,	PUNCT
cana-6186	320	67	dheerajmarwaha	dheerajmarwaha	NOUN
cana-6186	320	68	https://doi.org/10.1016/j.mjafi.2020.10.013	https://doi.org/10.1016/j.mjafi.2020.10.013	NOUN
cana-6186	320	69	[	[	X
cana-6186	320	70	21	21	NUM
cana-6186	320	71	]	]	X
cana-6186	320	72	apurv	apurv	PROPN
cana-6186	320	73	garg	garg	PROPN
cana-6186	320	74	,	,	PUNCT
cana-6186	320	75	bhartendu	bhartendu	NOUN
cana-6186	320	76	sharma	sharma	PROPN
cana-6186	320	77	and	and	CCONJ
cana-6186	320	78	rijwan	rijwan	PROPN
cana-6186	320	79	khan	khan	PROPN
cana-6186	320	80	“	"	PUNCT
cana-6186	320	81	heartdisease	heartdisease	PROPN
cana-6186	320	82	prediction	prediction	NOUN
cana-6186	320	83	using	use	VERB
cana-6186	320	84	machine	machine	NOUN
cana-6186	320	85	learning	learning	NOUN
cana-6186	320	86	techniques	technique	NOUN
cana-6186	320	87	”	"	PUNCT
cana-6186	320	88	january	january	PROPN
cana-6186	320	89	2021	2021	NUM
cana-6186	320	90	iop	iop	PROPN
cana-6186	320	91	conference	conference	PROPN
cana-6186	320	92	series	series	NOUN
cana-6186	320	93	materials	material	NOUN
cana-6186	320	94	science	science	NOUN
cana-6186	320	95	andengineering	andengineering	NOUN
cana-6186	320	96	1022(1):012046	1022(1):012046	NUM
cana-6186	320	97	doi:10.1088/1757	doi:10.1088/1757	NOUN
cana-6186	320	98	-	-	PUNCT
cana-6186	320	99	899x/1022/1/012046	899x/1022/1/012046	NUM
cana-6186	320	100	.	.	PUNCT
cana-6186	321	1	[	[	X
cana-6186	321	2	22	22	NUM
cana-6186	321	3	]	]	X
cana-6186	321	4	md	md	PROPN
cana-6186	321	5	.	.	PROPN
cana-6186	322	1	mahbubur	mahbubur	PROPN
cana-6186	322	2	rahman	rahman	PROPN
cana-6186	322	3	,	,	PUNCT
cana-6186	322	4	morshedur	morshedur	PROPN
cana-6186	322	5	rahman	rahman	PROPN
cana-6186	322	6	rana	rana	PROPN
cana-6186	322	7	,	,	PUNCT
cana-6186	322	8	md	md	PROPN
cana-6186	322	9	.	.	PROPN
cana-6186	322	10	nur	nur	PROPN
cana-6186	322	11	-	-	PUNCT
cana-6186	322	12	a	a	DET
cana-6186	322	13	-	-	PUNCT
cana-6186	322	14	alam	alam	PROPN
cana-6186	322	15	,	,	PUNCT
cana-6186	322	16	md	md	PROPN
cana-6186	322	17	.	.	PROPN
cana-6186	322	18	saikat	saikat	PROPN
cana-6186	322	19	islam	islam	PROPN
cana-6186	322	20	khan	khan	PROPN
cana-6186	322	21	,	,	PUNCT
cana-6186	322	22	khandaker	khandaker	PROPN
cana-6186	322	23	mohammadmohi	mohammadmohi	VERB
cana-6186	322	24	uddin	uddin	PROPN
cana-6186	322	25	“	"	PUNCT
cana-6186	322	26	a	a	DET
cana-6186	322	27	web	web	NOUN
cana-6186	322	28	-	-	PUNCT
cana-6186	322	29	based	base	VERB
cana-6186	322	30	heart	heart	NOUN
cana-6186	322	31	disease	disease	NOUN
cana-6186	322	32	prediction	prediction	NOUN
cana-6186	322	33	systemusing	systemuse	VERB
cana-6186	322	34	machine	machine	NOUN
cana-6186	322	35	learning	learn	VERB
cana-6186	322	36	algorithms	algorithm	NOUN
cana-6186	322	37	“	"	PUNCT
cana-6186	322	38	june	june	PROPN
cana-6186	322	39	2022	2022	NUM
cana-6186	322	40	[	[	X
cana-6186	322	41	23	23	NUM
cana-6186	322	42	]	]	PUNCT
cana-6186	322	43	heart	heart	NOUN
cana-6186	322	44	attack	attack	NOUN
cana-6186	322	45	prediction	prediction	NOUN
cana-6186	322	46	using	use	VERB
cana-6186	322	47	machine	machine	NOUN
cana-6186	322	48	learning	learn	VERB
cana-6186	322	49	algorithms	algorithm	NOUN
cana-6186	322	50	manjula	manjula	PROPN
cana-6186	322	51	p	p	X
cana-6186	322	52	,	,	PUNCT
cana-6186	322	53	aravind	aravind	PROPN
cana-6186	322	54	u	u	PROPN
cana-6186	322	55	r	r	PROPN
cana-6186	322	56	,	,	PUNCT
cana-6186	322	57	darshan	darshan	ADP
cana-6186	322	58	m	m	NOUN
cana-6186	322	59	v	v	NOUN
cana-6186	322	60	,	,	PUNCT
cana-6186	322	61	halaswamy	halaswamy	NOUN
cana-6186	322	62	m	m	NOUN
cana-6186	322	63	h	h	NOUN
cana-6186	322	64	,	,	PUNCT
cana-6186	322	65	hemanth	hemanth	PROPN
cana-6186	322	66	e	e	PROPN
cana-6186	322	67	international	international	ADJ
cana-6186	322	68	journal	journal	NOUN
cana-6186	322	69	of	of	ADP
cana-6186	322	70	engineering	engineering	PROPN
cana-6186	322	71	research	research	PROPN
cana-6186	322	72	&	&	CCONJ
cana-6186	322	73	technology	technology	PROPN
cana-6186	322	74	(	(	PUNCT
cana-6186	322	75	ijert	ijert	PROPN
cana-6186	322	76	)	)	PUNCT
cana-6186	322	77	issn	issn	PROPN
cana-6186	322	78	:	:	PUNCT
cana-6186	322	79	2278	2278	NUM
cana-6186	322	80	-	-	SYM
cana-6186	322	81	0181	0181	NUM
cana-6186	322	82	special	special	ADJ
cana-6186	322	83	issue	issue	NOUN
cana-6186	322	84	–	–	PUNCT
cana-6186	322	85	2022	2022	NUM
cana-6186	322	86	[	[	X
cana-6186	322	87	24	24	NUM
cana-6186	322	88	]	]	PUNCT
cana-6186	322	89	pavan	pavan	PROPN
cana-6186	322	90	kumar	kumar	PROPN
cana-6186	322	91	tadiparthi	tadiparthi	PROPN
cana-6186	322	92	“	"	PUNCT
cana-6186	322	93	heart	heart	NOUN
cana-6186	322	94	disease	disease	NOUN
cana-6186	322	95	prediction	prediction	NOUN
cana-6186	322	96	usingmachine	usingmachine	NOUN
cana-6186	322	97	learning	learn	VERB
cana-6186	322	98	algorithms	algorithm	NOUN
cana-6186	322	99	:	:	PUNCT
cana-6186	322	100	a	a	DET
cana-6186	322	101	systematic	systematic	ADJ
cana-6186	322	102	survey	survey	NOUN
cana-6186	322	103	”	"	PUNCT
cana-6186	322	104	2022journal	2022journal	PROPN
cana-6186	322	105	international	international	ADJ
cana-6186	322	106	journal	journal	NOUN
cana-6186	322	107	of	of	ADP
cana-6186	322	108	computer	computer	NOUN
cana-6186	322	109	science	science	NOUN
cana-6186	322	110	and	and	CCONJ
cana-6186	322	111	mobile	mobile	ADJ
cana-6186	322	112	computing	computing	NOUN
cana-6186	322	113	volume	volume	NOUN
cana-6186	322	114	11	11	NUM
cana-6186	322	115	issue	issue	NOUN
cana-6186	322	116	6	6	NUM
cana-6186	322	117	pages	page	NOUN
cana-6186	322	118	129	129	NUM
cana-6186	322	119	-	-	SYM
cana-6186	322	120	136	136	NUM
cana-6186	323	1	[	[	X
cana-6186	323	2	25	25	NUM
cana-6186	323	3	]	]	X
cana-6186	323	4	joloudari	joloudari	PROPN
cana-6186	323	5	jh	jh	PROPN
cana-6186	323	6	et	et	PROPN
cana-6186	323	7	al	al	PROPN
cana-6186	323	8	(	(	PUNCT
cana-6186	323	9	2020	2020	NUM
cana-6186	323	10	)	)	PUNCT
cana-6186	323	11	coronary	coronary	ADJ
cana-6186	323	12	artery	artery	NOUN
cana-6186	323	13	disease	disease	NOUN
cana-6186	323	14	diagnosis;ranking	diagnosis;ranke	VERB
cana-6186	323	15	the	the	DET
cana-6186	323	16	signifcant	signifcant	ADJ
cana-6186	323	17	features	feature	NOUN
cana-6186	323	18	using	use	VERB
cana-6186	323	19	a	a	DET
cana-6186	323	20	random	random	ADJ
cana-6186	323	21	trees	tree	NOUN
cana-6186	323	22	model	model	NOUN
cana-6186	323	23	.	.	PUNCT
cana-6186	324	1	int	int	PROPN
cana-6186	325	1	j	j	PROPN
cana-6186	325	2	environ	environ	PROPN
cana-6186	325	3	res	res	PROPN
cana-6186	325	4	public	public	ADJ
cana-6186	325	5	health	health	NOUN
cana-6186	325	6	17(3):1–24	17(3):1–24	NUM
cana-6186	325	7	.	.	PUNCT
cana-6186	326	1	[	[	X
cana-6186	326	2	26	26	NUM
cana-6186	326	3	]	]	PUNCT
cana-6186	326	4	sa	sa	X
cana-6186	326	5	pattekari	pattekari	PROPN
cana-6186	326	6	,	,	PUNCT
cana-6186	326	7	a	a	DET
cana-6186	326	8	parveen	parveen	PROPN
cana-6186	326	9	“	"	PUNCT
cana-6186	326	10	predicition	predicition	NOUN
cana-6186	326	11	system	system	NOUN
cana-6186	326	12	for	for	ADP
cana-6186	326	13	heart	heart	NOUN
cana-6186	326	14	disease	disease	NOUN
cana-6186	326	15	using	use	VERB
cana-6186	326	16	naïve	naïve	ADJ
cana-6186	326	17	bayes	baye	NOUN
cana-6186	326	18	”	"	PUNCT
cana-6186	326	19	international	international	ADJ
cana-6186	326	20	journal	journal	NOUN
cana-6186	326	21	of	of	ADP
cana-6186	326	22	advanced	advanced	ADJ
cana-6186	326	23	computer	computer	NOUN
cana-6186	326	24	and	and	CCONJ
cana-6186	326	25	mathematical	mathematical	ADJ
cana-6186	326	26	sciences	science	NOUN
cana-6186	326	27	[	[	X
cana-6186	326	28	27	27	NUM
cana-6186	326	29	]	]	X
cana-6186	326	30	k	k	PROPN
cana-6186	326	31	mutijara	mutijara	PROPN
cana-6186	326	32	,	,	PUNCT
cana-6186	326	33	m	m	VERB
cana-6186	326	34	ichwan	ichwan	ADJ
cana-6186	326	35	,	,	PUNCT
cana-6186	326	36	db	db	ADP
cana-6186	326	37	utami(2016)”heart	utami(2016)”heart	PROPN
cana-6186	326	38	rate	rate	NOUN
cana-6186	326	39	prediction	prediction	NOUN
cana-6186	326	40	based	base	VERB
cana-6186	326	41	on	on	ADP
cana-6186	326	42	cycling	cycling	NOUN
cana-6186	326	43	cadence	cadence	NOUN
cana-6186	326	44	using	use	VERB
cana-6186	326	45	feedforward	feedforward	NOUN
cana-6186	326	46	neural	neural	ADJ
cana-6186	326	47	network”international	network”international	ADJ
cana-6186	326	48	conference	conference	NOUN
cana-6186	326	49	on	on	ADP
cana-6186	326	50	computer	computer	NOUN
cana-6186	326	51	,	,	PUNCT
cana-6186	326	52	control	control	NOUN
cana-6186	326	53	,	,	PUNCT
cana-6186	326	54	informatics	informatic	NOUN
cana-6186	326	55	and	and	CCONJ
cana-6186	326	56	its	its	PRON
cana-6186	326	57	…	…	PUNCT
cana-6186	326	58	https://scholar.google.com/citations?view_op=view_citation&hl=en&user=qynyx-eaaaaj&citation_for_view=qynyx-eaaaaj:d1gkvwhdpl0c	https://scholar.google.com/citations?view_op=view_citation&hl=en&user=qynyx-eaaaaj&citation_for_view=qynyx-eaaaaj:d1gkvwhdpl0c	PROPN
cana-6186	326	59	https://scholar.google.com/citations?view_op=view_citation&hl=en&user=feahk24aaaaj&citation_for_view=feahk24aaaaj:isc4tdsrtzic	https://scholar.google.com/citations?view_op=view_citation&hl=en&user=feahk24aaaaj&citation_for_view=feahk24aaaaj:isc4tdsrtzic	PROPN
cana-6186	326	60	https://scholar.google.com/citations?view_op=view_citation&hl=en&user=feahk24aaaaj&citation_for_view=feahk24aaaaj:isc4tdsrtzic	https://scholar.google.com/citations?view_op=view_citation&hl=en&user=feahk24aaaaj&citation_for_view=feahk24aaaaj:isc4tdsrtzic	NOUN