id	sid	tid	token	lemma	pos
cana-2565	1	1	communications	communication	NOUN
cana-2565	1	2	on	on	ADP
cana-2565	1	3	applied	apply	VERB
cana-2565	1	4	nonlinear	nonlinear	ADJ
cana-2565	1	5	analysis	analysis	NOUN
cana-2565	1	6	issn	issn	NOUN
cana-2565	1	7	:	:	PUNCT
cana-2565	1	8	1074	1074	NUM
cana-2565	1	9	-	-	PUNCT
cana-2565	1	10	133x	133x	NUM
cana-2565	1	11	vol	vol	NOUN
cana-2565	1	12	32	32	NUM
cana-2565	1	13	no	no	NOUN
cana-2565	1	14	.	.	PUNCT
cana-2565	2	1	3s	3s	NUM
cana-2565	2	2	(	(	PUNCT
cana-2565	2	3	2025	2025	NUM
cana-2565	2	4	)	)	PUNCT
cana-2565	2	5	104	104	NUM
cana-2565	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2565	2	7	comparative	comparative	ADJ
cana-2565	2	8	analysis	analysis	NOUN
cana-2565	2	9	of	of	ADP
cana-2565	2	10	parkinson	parkinson	NOUN
cana-2565	2	11	's	's	PART
cana-2565	2	12	disease	disease	NOUN
cana-2565	2	13	classification	classification	NOUN
cana-2565	2	14	using	use	VERB
cana-2565	2	15	deep	deep	ADJ
cana-2565	2	16	learning	learning	NOUN
cana-2565	2	17	approaches	approach	NOUN
cana-2565	2	18	enhanced	enhance	VERB
cana-2565	2	19	with	with	ADP
cana-2565	2	20	optimization	optimization	NOUN
cana-2565	2	21	techniques	technique	NOUN
cana-2565	2	22	versus	versus	ADP
cana-2565	2	23	traditional	traditional	ADJ
cana-2565	2	24	machine	machine	NOUN
cana-2565	2	25	learning	learning	NOUN
cana-2565	2	26	models	model	NOUN
cana-2565	2	27	k.	k.	PROPN
cana-2565	2	28	manisekharan1	manisekharan1	PROPN
cana-2565	2	29	and	and	CCONJ
cana-2565	2	30	a.	a.	NOUN
cana-2565	2	31	murugan2	murugan2	PROPN
cana-2565	3	1	1research	1research	NUM
cana-2565	3	2	scholar	scholar	NOUN
cana-2565	3	3	,	,	PUNCT
cana-2565	3	4	department	department	NOUN
cana-2565	3	5	of	of	ADP
cana-2565	3	6	computer	computer	NOUN
cana-2565	3	7	and	and	CCONJ
cana-2565	3	8	information	information	NOUN
cana-2565	3	9	science	science	NOUN
cana-2565	3	10	,	,	PUNCT
cana-2565	3	11	annamalai	annamalai	PROPN
cana-2565	3	12	university	university	PROPN
cana-2565	3	13	,	,	PUNCT
cana-2565	3	14	annamalainagar	annamalainagar	NOUN
cana-2565	3	15	–	–	PUNCT
cana-2565	3	16	608	608	NUM
cana-2565	3	17	002	002	NUM
cana-2565	3	18	,	,	PUNCT
cana-2565	3	19	tamil	tamil	PROPN
cana-2565	3	20	nadu	nadu	PROPN
cana-2565	3	21	,	,	PUNCT
cana-2565	3	22	india	india	PROPN
cana-2565	3	23	.	.	PUNCT
cana-2565	3	24	email	email	NOUN
cana-2565	3	25	:	:	PUNCT
cana-2565	3	26	uvaranikrish99@gmail.com	uvaranikrish99@gmail.com	X
cana-2565	3	27	2assistant	2assistant	NUM
cana-2565	3	28	professor	professor	NOUN
cana-2565	3	29	,	,	PUNCT
cana-2565	3	30	department	department	NOUN
cana-2565	3	31	of	of	ADP
cana-2565	3	32	computer	computer	NOUN
cana-2565	3	33	science	science	NOUN
cana-2565	3	34	,	,	PUNCT
cana-2565	3	35	periyar	periyar	NOUN
cana-2565	3	36	arts	arts	PROPN
cana-2565	3	37	college	college	PROPN
cana-2565	3	38	,	,	PUNCT
cana-2565	3	39	cuddalore	cuddalore	PROPN
cana-2565	3	40	,	,	PUNCT
cana-2565	3	41	(	(	PUNCT
cana-2565	3	42	deputed	depute	VERB
cana-2565	3	43	from	from	ADP
cana-2565	3	44	annamalai	annamalai	PROPN
cana-2565	3	45	university	university	PROPN
cana-2565	3	46	,	,	PUNCT
cana-2565	3	47	annamalainagar	annamalainagar	NOUN
cana-2565	3	48	)	)	PUNCT
cana-2565	3	49	tamil	tamil	PROPN
cana-2565	3	50	nadu	nadu	PROPN
cana-2565	3	51	,	,	PUNCT
cana-2565	3	52	india	india	PROPN
cana-2565	3	53	.	.	PUNCT
cana-2565	3	54	email	email	NOUN
cana-2565	3	55	:	:	PUNCT
cana-2565	3	56	drmuruganapcs@gmail.com	drmuruganapcs@gmail.com	X
cana-2565	3	57	article	article	NOUN
cana-2565	3	58	history	history	NOUN
cana-2565	3	59	:	:	PUNCT
cana-2565	3	60	received	receive	VERB
cana-2565	3	61	:	:	PUNCT
cana-2565	3	62	20	20	NUM
cana-2565	3	63	-	-	SYM
cana-2565	3	64	09	09	NUM
cana-2565	3	65	-	-	PUNCT
cana-2565	3	66	2024	2024	NUM
cana-2565	3	67	revised	revise	VERB
cana-2565	3	68	:	:	PUNCT
cana-2565	3	69	04	04	NUM
cana-2565	3	70	-	-	SYM
cana-2565	3	71	11	11	NUM
cana-2565	3	72	-	-	PUNCT
cana-2565	3	73	2024	2024	NUM
cana-2565	3	74	accepted	accept	VERB
cana-2565	3	75	:	:	PUNCT
cana-2565	3	76	17	17	NUM
cana-2565	3	77	-	-	SYM
cana-2565	3	78	11	11	NUM
cana-2565	3	79	-	-	PUNCT
cana-2565	3	80	2024	2024	NUM
cana-2565	3	81	abstract	abstract	NOUN
cana-2565	3	82	:	:	PUNCT
cana-2565	3	83	parkinson	parkinson	NOUN
cana-2565	3	84	's	's	PART
cana-2565	3	85	disease	disease	NOUN
cana-2565	3	86	(	(	PUNCT
cana-2565	3	87	pd	pd	NOUN
cana-2565	3	88	)	)	PUNCT
cana-2565	3	89	is	be	AUX
cana-2565	3	90	a	a	DET
cana-2565	3	91	neurodegenerative	neurodegenerative	ADJ
cana-2565	3	92	condition	condition	NOUN
cana-2565	3	93	that	that	PRON
cana-2565	3	94	presents	present	VERB
cana-2565	3	95	considerable	considerable	ADJ
cana-2565	3	96	challenges	challenge	NOUN
cana-2565	3	97	in	in	ADP
cana-2565	3	98	achieving	achieve	VERB
cana-2565	3	99	accurate	accurate	ADJ
cana-2565	3	100	early	early	ADJ
cana-2565	3	101	diagnosis	diagnosis	NOUN
cana-2565	3	102	and	and	CCONJ
cana-2565	3	103	classification	classification	NOUN
cana-2565	3	104	.	.	PUNCT
cana-2565	4	1	this	this	DET
cana-2565	4	2	research	research	NOUN
cana-2565	4	3	explores	explore	VERB
cana-2565	4	4	the	the	DET
cana-2565	4	5	classification	classification	NOUN
cana-2565	4	6	of	of	ADP
cana-2565	4	7	pd	pd	PROPN
cana-2565	4	8	through	through	ADP
cana-2565	4	9	the	the	DET
cana-2565	4	10	application	application	NOUN
cana-2565	4	11	of	of	ADP
cana-2565	4	12	advanced	advanced	ADJ
cana-2565	4	13	deep	deep	ADJ
cana-2565	4	14	learning	learning	NOUN
cana-2565	4	15	techniques	technique	NOUN
cana-2565	4	16	integrated	integrate	VERB
cana-2565	4	17	with	with	ADP
cana-2565	4	18	optimization	optimization	NOUN
cana-2565	4	19	methods	method	NOUN
cana-2565	4	20	,	,	PUNCT
cana-2565	4	21	compared	compare	VERB
cana-2565	4	22	to	to	ADP
cana-2565	4	23	conventional	conventional	ADJ
cana-2565	4	24	machine	machine	NOUN
cana-2565	4	25	learning	learning	NOUN
cana-2565	4	26	approaches	approach	NOUN
cana-2565	4	27	.	.	PUNCT
cana-2565	5	1	the	the	DET
cana-2565	5	2	analysis	analysis	NOUN
cana-2565	5	3	is	be	AUX
cana-2565	5	4	conducted	conduct	VERB
cana-2565	5	5	using	use	VERB
cana-2565	5	6	a	a	DET
cana-2565	5	7	diverse	diverse	ADJ
cana-2565	5	8	dataset	dataset	NOUN
cana-2565	5	9	incorporating	incorporate	VERB
cana-2565	5	10	clinical	clinical	ADJ
cana-2565	5	11	,	,	PUNCT
cana-2565	5	12	vocal	vocal	ADJ
cana-2565	5	13	,	,	PUNCT
cana-2565	5	14	and	and	CCONJ
cana-2565	5	15	movement	movement	NOUN
cana-2565	5	16	-	-	PUNCT
cana-2565	5	17	related	relate	VERB
cana-2565	5	18	features	feature	NOUN
cana-2565	5	19	to	to	PART
cana-2565	5	20	ensure	ensure	VERB
cana-2565	5	21	comprehensive	comprehensive	ADJ
cana-2565	5	22	evaluation	evaluation	NOUN
cana-2565	5	23	.	.	PUNCT
cana-2565	6	1	deep	deep	ADJ
cana-2565	6	2	learning	learning	NOUN
cana-2565	6	3	frameworks	framework	NOUN
cana-2565	6	4	,	,	PUNCT
cana-2565	6	5	such	such	ADJ
cana-2565	6	6	as	as	ADP
cana-2565	6	7	multi	multi	ADJ
cana-2565	6	8	-	-	ADJ
cana-2565	6	9	layer	layer	ADJ
cana-2565	6	10	perceptron	perceptron	NOUN
cana-2565	6	11	(	(	PUNCT
cana-2565	6	12	mlp	mlp	PROPN
cana-2565	6	13	)	)	PUNCT
cana-2565	6	14	,	,	PUNCT
cana-2565	6	15	long	long	ADJ
cana-2565	6	16	short	short	ADJ
cana-2565	6	17	-	-	PUNCT
cana-2565	6	18	term	term	NOUN
cana-2565	6	19	memory	memory	NOUN
cana-2565	6	20	(	(	PUNCT
cana-2565	6	21	lsdm	lsdm	NOUN
cana-2565	6	22	)	)	PUNCT
cana-2565	6	23	,	,	PUNCT
cana-2565	6	24	the	the	DET
cana-2565	6	25	proposed	propose	VERB
cana-2565	6	26	deep	deep	ADJ
cana-2565	6	27	learning	learning	NOUN
cana-2565	6	28	model	model	NOUN
cana-2565	6	29	namely	namely	ADV
cana-2565	6	30	cnn	cnn	PROPN
cana-2565	6	31	-	-	PUNCT
cana-2565	6	32	bigru	bigru	PROPN
cana-2565	6	33	were	be	AUX
cana-2565	6	34	enhanced	enhance	VERB
cana-2565	6	35	using	use	VERB
cana-2565	6	36	strategies	strategy	NOUN
cana-2565	6	37	like	like	ADP
cana-2565	6	38	hyperparameter	hyperparameter	NOUN
cana-2565	6	39	optimization	optimization	NOUN
cana-2565	6	40	,	,	PUNCT
cana-2565	6	41	regularization	regularization	NOUN
cana-2565	6	42	,	,	PUNCT
cana-2565	6	43	and	and	CCONJ
cana-2565	6	44	advanced	advanced	ADJ
cana-2565	6	45	gradient	gradient	NOUN
cana-2565	6	46	-	-	PUNCT
cana-2565	6	47	based	base	VERB
cana-2565	6	48	optimizers	optimizer	NOUN
cana-2565	6	49	to	to	PART
cana-2565	6	50	boost	boost	VERB
cana-2565	6	51	performance	performance	NOUN
cana-2565	6	52	and	and	CCONJ
cana-2565	6	53	minimize	minimize	VERB
cana-2565	6	54	overfitting	overfitte	VERB
cana-2565	6	55	.	.	PUNCT
cana-2565	7	1	similarly	similarly	ADV
cana-2565	7	2	,	,	PUNCT
cana-2565	7	3	traditional	traditional	ADJ
cana-2565	7	4	machine	machine	NOUN
cana-2565	7	5	learning	learning	NOUN
cana-2565	7	6	models	model	NOUN
cana-2565	7	7	,	,	PUNCT
cana-2565	7	8	including	include	VERB
cana-2565	7	9	linear	linear	PROPN
cana-2565	7	10	regression	regression	NOUN
cana-2565	7	11	,	,	PUNCT
cana-2565	7	12	random	random	ADJ
cana-2565	7	13	tree	tree	NOUN
cana-2565	7	14	,	,	PUNCT
cana-2565	7	15	rep	rep	NOUN
cana-2565	7	16	tree	tree	NOUN
cana-2565	7	17	,	,	PUNCT
cana-2565	7	18	and	and	CCONJ
cana-2565	7	19	random	random	ADJ
cana-2565	7	20	forest	forest	NOUN
cana-2565	7	21	,	,	PUNCT
cana-2565	7	22	were	be	AUX
cana-2565	7	23	implemented	implement	VERB
cana-2565	7	24	and	and	CCONJ
cana-2565	7	25	tested	test	VERB
cana-2565	7	26	on	on	ADP
cana-2565	7	27	the	the	DET
cana-2565	7	28	same	same	ADJ
cana-2565	7	29	dataset	dataset	NOUN
cana-2565	7	30	.	.	PUNCT
cana-2565	8	1	evaluation	evaluation	NOUN
cana-2565	8	2	metrics	metric	NOUN
cana-2565	8	3	,	,	PUNCT
cana-2565	8	4	including	include	VERB
cana-2565	8	5	accuracy	accuracy	NOUN
cana-2565	8	6	,	,	PUNCT
cana-2565	8	7	precision	precision	NOUN
cana-2565	8	8	,	,	PUNCT
cana-2565	8	9	recall	recall	NOUN
cana-2565	8	10	,	,	PUNCT
cana-2565	8	11	f1	f1	NOUN
cana-2565	8	12	-	-	PUNCT
cana-2565	8	13	score	score	NOUN
cana-2565	8	14	,	,	PUNCT
cana-2565	8	15	and	and	CCONJ
cana-2565	8	16	the	the	DET
cana-2565	8	17	area	area	NOUN
cana-2565	8	18	under	under	ADP
cana-2565	8	19	the	the	DET
cana-2565	8	20	curve	curve	NOUN
cana-2565	8	21	(	(	PUNCT
cana-2565	8	22	auc	auc	NOUN
cana-2565	8	23	)	)	PUNCT
cana-2565	8	24	,	,	PUNCT
cana-2565	8	25	were	be	AUX
cana-2565	8	26	used	use	VERB
cana-2565	8	27	to	to	PART
cana-2565	8	28	measure	measure	VERB
cana-2565	8	29	and	and	CCONJ
cana-2565	8	30	compare	compare	VERB
cana-2565	8	31	the	the	DET
cana-2565	8	32	performance	performance	NOUN
cana-2565	8	33	of	of	ADP
cana-2565	8	34	all	all	DET
cana-2565	8	35	models	model	NOUN
cana-2565	8	36	.	.	PUNCT
cana-2565	9	1	the	the	DET
cana-2565	9	2	findings	finding	NOUN
cana-2565	9	3	reveal	reveal	VERB
cana-2565	9	4	that	that	SCONJ
cana-2565	9	5	optimized	optimize	VERB
cana-2565	9	6	deep	deep	ADJ
cana-2565	9	7	learning	learning	NOUN
cana-2565	9	8	models	model	NOUN
cana-2565	9	9	significantly	significantly	ADV
cana-2565	9	10	surpass	surpass	VERB
cana-2565	9	11	traditional	traditional	ADJ
cana-2565	9	12	machine	machine	NOUN
cana-2565	9	13	learning	learning	NOUN
cana-2565	9	14	methods	method	NOUN
cana-2565	9	15	in	in	ADP
cana-2565	9	16	both	both	DET
cana-2565	9	17	classification	classification	NOUN
cana-2565	9	18	accuracy	accuracy	NOUN
cana-2565	9	19	and	and	CCONJ
cana-2565	9	20	generalization	generalization	NOUN
cana-2565	9	21	.	.	PUNCT
cana-2565	10	1	this	this	DET
cana-2565	10	2	study	study	NOUN
cana-2565	10	3	emphasizes	emphasize	VERB
cana-2565	10	4	the	the	DET
cana-2565	10	5	effectiveness	effectiveness	NOUN
cana-2565	10	6	of	of	ADP
cana-2565	10	7	optimizationenhanced	optimizationenhance	VERB
cana-2565	10	8	deep	deep	ADJ
cana-2565	10	9	learning	learning	NOUN
cana-2565	10	10	techniques	technique	NOUN
cana-2565	10	11	in	in	ADP
cana-2565	10	12	pd	pd	NOUN
cana-2565	10	13	classification	classification	NOUN
cana-2565	10	14	and	and	CCONJ
cana-2565	10	15	their	their	PRON
cana-2565	10	16	clear	clear	ADJ
cana-2565	10	17	advantages	advantage	NOUN
cana-2565	10	18	over	over	ADP
cana-2565	10	19	traditional	traditional	ADJ
cana-2565	10	20	models	model	NOUN
cana-2565	10	21	.	.	PUNCT
cana-2565	11	1	keywords	keyword	NOUN
cana-2565	11	2	:	:	PUNCT
cana-2565	11	3	parkinson	parkinson	NOUN
cana-2565	11	4	's	's	PART
cana-2565	11	5	disease	disease	NOUN
cana-2565	11	6	(	(	PUNCT
cana-2565	11	7	pd	pd	NOUN
cana-2565	11	8	)	)	PUNCT
cana-2565	11	9	,	,	PUNCT
cana-2565	11	10	classification	classification	NOUN
cana-2565	11	11	,	,	PUNCT
cana-2565	11	12	deep	deep	ADJ
cana-2565	11	13	learning	learning	NOUN
cana-2565	11	14	,	,	PUNCT
cana-2565	11	15	optimization	optimization	NOUN
cana-2565	11	16	techniques	technique	NOUN
cana-2565	11	17	,	,	PUNCT
cana-2565	11	18	machine	machine	NOUN
cana-2565	11	19	learning	learning	NOUN
cana-2565	11	20	,	,	PUNCT
cana-2565	11	21	performance	performance	NOUN
cana-2565	11	22	metrics	metric	NOUN
cana-2565	11	23	,	,	PUNCT
cana-2565	11	24	predictive	predictive	ADJ
cana-2565	11	25	performance	performance	NOUN
cana-2565	11	26	1.0	1.0	NUM
cana-2565	11	27	introduction	introduction	NOUN
cana-2565	11	28	parkinson	parkinson	NOUN
cana-2565	11	29	's	's	PART
cana-2565	11	30	disease	disease	NOUN
cana-2565	11	31	(	(	PUNCT
cana-2565	11	32	pd	pd	NOUN
cana-2565	11	33	)	)	PUNCT
cana-2565	11	34	is	be	AUX
cana-2565	11	35	a	a	DET
cana-2565	11	36	progressive	progressive	ADJ
cana-2565	11	37	neurodegenerative	neurodegenerative	ADJ
cana-2565	11	38	condition	condition	NOUN
cana-2565	11	39	affecting	affect	VERB
cana-2565	11	40	millions	million	NOUN
cana-2565	11	41	globally	globally	ADV
cana-2565	11	42	.	.	PUNCT
cana-2565	12	1	it	it	PRON
cana-2565	12	2	manifests	manifest	VERB
cana-2565	12	3	through	through	ADP
cana-2565	12	4	motor	motor	NOUN
cana-2565	12	5	symptoms	symptom	NOUN
cana-2565	12	6	such	such	ADJ
cana-2565	12	7	as	as	ADP
cana-2565	12	8	tremors	tremor	NOUN
cana-2565	12	9	,	,	PUNCT
cana-2565	12	10	rigidity	rigidity	NOUN
cana-2565	12	11	,	,	PUNCT
cana-2565	12	12	and	and	CCONJ
cana-2565	12	13	slowed	slow	VERB
cana-2565	12	14	movements	movement	NOUN
cana-2565	12	15	,	,	PUNCT
cana-2565	12	16	along	along	ADP
cana-2565	12	17	with	with	ADP
cana-2565	12	18	nonmotor	nonmotor	NOUN
cana-2565	12	19	symptoms	symptom	NOUN
cana-2565	12	20	like	like	ADP
cana-2565	12	21	cognitive	cognitive	ADJ
cana-2565	12	22	decline	decline	NOUN
cana-2565	12	23	and	and	CCONJ
cana-2565	12	24	depression	depression	NOUN
cana-2565	12	25	.	.	PUNCT
cana-2565	13	1	accurate	accurate	ADJ
cana-2565	13	2	and	and	CCONJ
cana-2565	13	3	early	early	ADJ
cana-2565	13	4	diagnosis	diagnosis	NOUN
cana-2565	13	5	is	be	AUX
cana-2565	13	6	essential	essential	ADJ
cana-2565	13	7	to	to	ADP
cana-2565	13	8	improving	improve	VERB
cana-2565	13	9	patient	patient	ADJ
cana-2565	13	10	outcomes	outcome	NOUN
cana-2565	13	11	and	and	CCONJ
cana-2565	13	12	facilitating	facilitate	VERB
cana-2565	13	13	timely	timely	ADJ
cana-2565	13	14	intervention	intervention	NOUN
cana-2565	13	15	.	.	PUNCT
cana-2565	14	1	however	however	ADV
cana-2565	14	2	,	,	PUNCT
cana-2565	14	3	traditional	traditional	ADJ
cana-2565	14	4	diagnostic	diagnostic	ADJ
cana-2565	14	5	approaches	approach	NOUN
cana-2565	14	6	often	often	ADV
cana-2565	14	7	depend	depend	VERB
cana-2565	14	8	on	on	ADP
cana-2565	14	9	clinical	clinical	ADJ
cana-2565	14	10	assessments	assessment	NOUN
cana-2565	14	11	,	,	PUNCT
cana-2565	14	12	which	which	PRON
cana-2565	14	13	can	can	AUX
cana-2565	14	14	be	be	AUX
cana-2565	14	15	subjective	subjective	ADJ
cana-2565	14	16	and	and	CCONJ
cana-2565	14	17	less	less	ADV
cana-2565	14	18	reliable	reliable	ADJ
cana-2565	14	19	,	,	PUNCT
cana-2565	14	20	particularly	particularly	ADV
cana-2565	14	21	in	in	ADP
cana-2565	14	22	the	the	DET
cana-2565	14	23	early	early	ADJ
cana-2565	14	24	stages	stage	NOUN
cana-2565	14	25	of	of	ADP
cana-2565	14	26	the	the	DET
cana-2565	14	27	disease	disease	NOUN
cana-2565	14	28	(	(	PUNCT
cana-2565	14	29	serrano	serrano	NOUN
cana-2565	14	30	-	-	PUNCT
cana-2565	14	31	gotarredona	gotarredona	PROPN
cana-2565	14	32	et	et	NOUN
cana-2565	14	33	al	al	PROPN
cana-2565	14	34	.	.	PROPN
cana-2565	14	35	,	,	PUNCT
cana-2565	14	36	2021	2021	NUM
cana-2565	14	37	)	)	PUNCT
cana-2565	14	38	.	.	PUNCT
cana-2565	15	1	the	the	DET
cana-2565	15	2	advent	advent	NOUN
cana-2565	15	3	of	of	ADP
cana-2565	15	4	artificial	artificial	ADJ
cana-2565	15	5	intelligence	intelligence	NOUN
cana-2565	15	6	(	(	PUNCT
cana-2565	15	7	ai	ai	NOUN
cana-2565	15	8	)	)	PUNCT
cana-2565	15	9	has	have	AUX
cana-2565	15	10	introduced	introduce	VERB
cana-2565	15	11	new	new	ADJ
cana-2565	15	12	possibilities	possibility	NOUN
cana-2565	15	13	for	for	ADP
cana-2565	15	14	automating	automate	VERB
cana-2565	15	15	pd	pd	NOUN
cana-2565	15	16	diagnosis	diagnosis	NOUN
cana-2565	15	17	and	and	CCONJ
cana-2565	15	18	classification	classification	NOUN
cana-2565	15	19	through	through	ADP
cana-2565	15	20	machine	machine	NOUN
cana-2565	15	21	learning	learning	NOUN
cana-2565	15	22	(	(	PUNCT
cana-2565	15	23	ml	ml	NOUN
cana-2565	15	24	)	)	PUNCT
cana-2565	15	25	and	and	CCONJ
cana-2565	15	26	deep	deep	ADJ
cana-2565	15	27	learning	learning	NOUN
cana-2565	15	28	(	(	PUNCT
cana-2565	15	29	dl	dl	NOUN
cana-2565	15	30	)	)	PUNCT
cana-2565	15	31	methodologies	methodology	NOUN
cana-2565	15	32	.	.	PUNCT
cana-2565	16	1	machine	machine	NOUN
cana-2565	16	2	learning	learn	VERB
cana-2565	16	3	algorithms	algorithm	NOUN
cana-2565	16	4	,	,	PUNCT
cana-2565	16	5	including	include	VERB
cana-2565	16	6	support	support	NOUN
cana-2565	16	7	vector	vector	NOUN
cana-2565	16	8	machines	machine	NOUN
cana-2565	16	9	(	(	PUNCT
cana-2565	16	10	svm	svm	PROPN
cana-2565	16	11	)	)	PUNCT
cana-2565	16	12	,	,	PUNCT
cana-2565	16	13	random	random	ADJ
cana-2565	16	14	forests	forest	NOUN
cana-2565	16	15	(	(	PUNCT
cana-2565	16	16	rf	rf	NOUN
cana-2565	16	17	)	)	PUNCT
cana-2565	16	18	,	,	PUNCT
cana-2565	16	19	and	and	CCONJ
cana-2565	16	20	k	k	X
cana-2565	16	21	-	-	PUNCT
cana-2565	16	22	nearest	near	ADJ
cana-2565	16	23	neighbors	neighbor	NOUN
cana-2565	16	24	(	(	PUNCT
cana-2565	16	25	k	k	X
cana-2565	16	26	-	-	PUNCT
cana-2565	16	27	nn	nn	NOUN
cana-2565	16	28	)	)	PUNCT
cana-2565	16	29	,	,	PUNCT
cana-2565	16	30	have	have	AUX
cana-2565	16	31	been	be	AUX
cana-2565	16	32	widely	widely	ADV
cana-2565	16	33	applied	apply	VERB
cana-2565	16	34	in	in	ADP
cana-2565	16	35	pd	pd	NOUN
cana-2565	16	36	classification	classification	NOUN
cana-2565	16	37	tasks	task	NOUN
cana-2565	16	38	.	.	PUNCT
cana-2565	17	1	these	these	DET
cana-2565	17	2	models	model	NOUN
cana-2565	17	3	typically	typically	ADV
cana-2565	17	4	require	require	VERB
cana-2565	17	5	manual	manual	ADJ
cana-2565	17	6	feature	feature	NOUN
cana-2565	17	7	engineering	engineering	NOUN
cana-2565	17	8	and	and	CCONJ
cana-2565	17	9	have	have	AUX
cana-2565	17	10	shown	show	VERB
cana-2565	17	11	moderate	moderate	ADJ
cana-2565	17	12	predictive	predictive	ADJ
cana-2565	17	13	performance	performance	NOUN
cana-2565	17	14	.	.	PUNCT
cana-2565	18	1	nevertheless	nevertheless	ADV
cana-2565	18	2	,	,	PUNCT
cana-2565	18	3	they	they	PRON
cana-2565	18	4	often	often	ADV
cana-2565	18	5	struggle	struggle	VERB
cana-2565	18	6	with	with	ADP
cana-2565	18	7	capturing	capture	VERB
cana-2565	18	8	complex	complex	ADJ
cana-2565	18	9	relationships	relationship	NOUN
cana-2565	18	10	within	within	ADP
cana-2565	18	11	data	datum	NOUN
cana-2565	18	12	and	and	CCONJ
cana-2565	18	13	demand	demand	NOUN
cana-2565	18	14	mailto:uvaranikrish99@gmail.com	mailto:uvaranikrish99@gmail.com	NOUN
cana-2565	18	15	mailto:drmuruganapcs@gmail.com	mailto:drmuruganapcs@gmail.com	PROPN
cana-2565	18	16	communications	communication	NOUN
cana-2565	18	17	on	on	ADP
cana-2565	18	18	applied	apply	VERB
cana-2565	18	19	nonlinear	nonlinear	ADJ
cana-2565	18	20	analysis	analysis	NOUN
cana-2565	18	21	issn	issn	NOUN
cana-2565	18	22	:	:	PUNCT
cana-2565	18	23	1074	1074	NUM
cana-2565	18	24	-	-	PUNCT
cana-2565	18	25	133x	133x	NUM
cana-2565	18	26	vol	vol	NOUN
cana-2565	18	27	32	32	NUM
cana-2565	18	28	no	no	NOUN
cana-2565	18	29	.	.	PUNCT
cana-2565	19	1	3s	3s	NUM
cana-2565	19	2	(	(	PUNCT
cana-2565	19	3	2025	2025	NUM
cana-2565	19	4	)	)	PUNCT
cana-2565	19	5	105	105	NUM
cana-2565	19	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2565	19	7	substantial	substantial	ADJ
cana-2565	19	8	domain	domain	NOUN
cana-2565	19	9	expertise	expertise	NOUN
cana-2565	19	10	for	for	ADP
cana-2565	19	11	effective	effective	ADJ
cana-2565	19	12	application	application	NOUN
cana-2565	19	13	(	(	PUNCT
cana-2565	19	14	khan	khan	PROPN
cana-2565	19	15	et	et	PROPN
cana-2565	19	16	al	al	PROPN
cana-2565	19	17	.	.	PROPN
cana-2565	19	18	,	,	PUNCT
cana-2565	19	19	2020	2020	NUM
cana-2565	19	20	)	)	PUNCT
cana-2565	19	21	.	.	PUNCT
cana-2565	20	1	conversely	conversely	ADV
cana-2565	20	2	,	,	PUNCT
cana-2565	20	3	deep	deep	ADJ
cana-2565	20	4	learning	learning	NOUN
cana-2565	20	5	techniques	technique	NOUN
cana-2565	20	6	,	,	PUNCT
cana-2565	20	7	such	such	ADJ
cana-2565	20	8	as	as	ADP
cana-2565	20	9	convolutional	convolutional	ADJ
cana-2565	20	10	neural	neural	ADJ
cana-2565	20	11	networks	network	NOUN
cana-2565	20	12	(	(	PUNCT
cana-2565	20	13	cnns	cnns	PROPN
cana-2565	20	14	)	)	PUNCT
cana-2565	20	15	and	and	CCONJ
cana-2565	20	16	recurrent	recurrent	ADJ
cana-2565	20	17	neural	neural	ADJ
cana-2565	20	18	networks	network	NOUN
cana-2565	20	19	(	(	PUNCT
cana-2565	20	20	rnns	rnns	PROPN
cana-2565	20	21	)	)	PUNCT
cana-2565	20	22	,	,	PUNCT
cana-2565	20	23	offer	offer	VERB
cana-2565	20	24	distinct	distinct	ADJ
cana-2565	20	25	advantages	advantage	NOUN
cana-2565	20	26	by	by	ADP
cana-2565	20	27	automatically	automatically	ADV
cana-2565	20	28	extracting	extract	VERB
cana-2565	20	29	hierarchical	hierarchical	ADJ
cana-2565	20	30	features	feature	NOUN
cana-2565	20	31	from	from	ADP
cana-2565	20	32	raw	raw	ADJ
cana-2565	20	33	data	datum	NOUN
cana-2565	20	34	,	,	PUNCT
cana-2565	20	35	resulting	result	VERB
cana-2565	20	36	in	in	ADP
cana-2565	20	37	superior	superior	ADJ
cana-2565	20	38	classification	classification	NOUN
cana-2565	20	39	outcomes	outcome	NOUN
cana-2565	20	40	(	(	PUNCT
cana-2565	20	41	lecun	lecun	PROPN
cana-2565	20	42	et	et	PROPN
cana-2565	20	43	al	al	PROPN
cana-2565	20	44	.	.	PROPN
cana-2565	20	45	,	,	PUNCT
cana-2565	20	46	2015	2015	NUM
cana-2565	20	47	)	)	PUNCT
cana-2565	20	48	.	.	PUNCT
cana-2565	21	1	the	the	DET
cana-2565	21	2	effectiveness	effectiveness	NOUN
cana-2565	21	3	of	of	ADP
cana-2565	21	4	deep	deep	ADJ
cana-2565	21	5	learning	learning	NOUN
cana-2565	21	6	models	model	NOUN
cana-2565	21	7	can	can	AUX
cana-2565	21	8	be	be	AUX
cana-2565	21	9	further	far	ADV
cana-2565	21	10	enhanced	enhance	VERB
cana-2565	21	11	using	use	VERB
cana-2565	21	12	optimization	optimization	NOUN
cana-2565	21	13	techniques	technique	NOUN
cana-2565	21	14	like	like	ADP
cana-2565	21	15	hyperparameter	hyperparameter	NOUN
cana-2565	21	16	tuning	tuning	NOUN
cana-2565	21	17	,	,	PUNCT
cana-2565	21	18	regularization	regularization	NOUN
cana-2565	21	19	,	,	PUNCT
cana-2565	21	20	and	and	CCONJ
cana-2565	21	21	gradient	gradient	NOUN
cana-2565	21	22	-	-	PUNCT
cana-2565	21	23	based	base	VERB
cana-2565	21	24	methods	method	NOUN
cana-2565	21	25	,	,	PUNCT
cana-2565	21	26	which	which	PRON
cana-2565	21	27	address	address	VERB
cana-2565	21	28	challenges	challenge	NOUN
cana-2565	21	29	such	such	ADJ
cana-2565	21	30	as	as	ADP
cana-2565	21	31	overfitting	overfitte	VERB
cana-2565	21	32	and	and	CCONJ
cana-2565	21	33	improve	improve	VERB
cana-2565	21	34	generalization	generalization	NOUN
cana-2565	21	35	.	.	PUNCT
cana-2565	22	1	recent	recent	ADJ
cana-2565	22	2	research	research	NOUN
cana-2565	22	3	underscores	underscore	VERB
cana-2565	22	4	the	the	DET
cana-2565	22	5	potential	potential	NOUN
cana-2565	22	6	of	of	ADP
cana-2565	22	7	combining	combine	VERB
cana-2565	22	8	deep	deep	ADJ
cana-2565	22	9	learning	learning	NOUN
cana-2565	22	10	with	with	ADP
cana-2565	22	11	optimization	optimization	NOUN
cana-2565	22	12	strategies	strategy	NOUN
cana-2565	22	13	to	to	PART
cana-2565	22	14	achieve	achieve	VERB
cana-2565	22	15	advanced	advanced	ADJ
cana-2565	22	16	diagnostic	diagnostic	ADJ
cana-2565	22	17	capabilities	capability	NOUN
cana-2565	22	18	in	in	ADP
cana-2565	22	19	medical	medical	ADJ
cana-2565	22	20	applications	application	NOUN
cana-2565	22	21	,	,	PUNCT
cana-2565	22	22	including	include	VERB
cana-2565	22	23	pd	pd	NOUN
cana-2565	22	24	classification	classification	NOUN
cana-2565	22	25	(	(	PUNCT
cana-2565	22	26	zhou	zhou	PROPN
cana-2565	22	27	et	et	PROPN
cana-2565	22	28	al	al	PROPN
cana-2565	22	29	.	.	PROPN
cana-2565	22	30	,	,	PUNCT
cana-2565	22	31	2023	2023	NUM
cana-2565	22	32	)	)	PUNCT
cana-2565	22	33	.	.	PUNCT
cana-2565	23	1	presents	present	VERB
cana-2565	23	2	a	a	DET
cana-2565	23	3	detailed	detailed	ADJ
cana-2565	23	4	comparison	comparison	NOUN
cana-2565	23	5	between	between	ADP
cana-2565	23	6	optimized	optimize	VERB
cana-2565	23	7	deep	deep	ADJ
cana-2565	23	8	learning	learning	NOUN
cana-2565	23	9	models	model	NOUN
cana-2565	23	10	and	and	CCONJ
cana-2565	23	11	traditional	traditional	ADJ
cana-2565	23	12	machine	machine	NOUN
cana-2565	23	13	learning	learning	NOUN
cana-2565	23	14	approaches	approach	NOUN
cana-2565	23	15	for	for	ADP
cana-2565	23	16	parkinson	parkinson	NOUN
cana-2565	23	17	's	's	PART
cana-2565	23	18	disease	disease	NOUN
cana-2565	23	19	classification	classification	NOUN
cana-2565	23	20	.	.	PUNCT
cana-2565	24	1	a	a	DET
cana-2565	24	2	comprehensive	comprehensive	ADJ
cana-2565	24	3	dataset	dataset	NOUN
cana-2565	24	4	incorporating	incorporate	VERB
cana-2565	24	5	clinical	clinical	ADJ
cana-2565	24	6	,	,	PUNCT
cana-2565	24	7	vocal	vocal	ADJ
cana-2565	24	8	,	,	PUNCT
cana-2565	24	9	and	and	CCONJ
cana-2565	24	10	movement	movement	NOUN
cana-2565	24	11	-	-	PUNCT
cana-2565	24	12	based	base	VERB
cana-2565	24	13	features	feature	NOUN
cana-2565	24	14	was	be	AUX
cana-2565	24	15	utilized	utilize	VERB
cana-2565	24	16	to	to	PART
cana-2565	24	17	ensure	ensure	VERB
cana-2565	24	18	thorough	thorough	ADJ
cana-2565	24	19	evaluation	evaluation	NOUN
cana-2565	24	20	.	.	PUNCT
cana-2565	25	1	performance	performance	NOUN
cana-2565	25	2	metrics	metric	NOUN
cana-2565	25	3	,	,	PUNCT
cana-2565	25	4	including	include	VERB
cana-2565	25	5	accuracy	accuracy	NOUN
cana-2565	25	6	,	,	PUNCT
cana-2565	25	7	precision	precision	NOUN
cana-2565	25	8	,	,	PUNCT
cana-2565	25	9	recall	recall	NOUN
cana-2565	25	10	,	,	PUNCT
cana-2565	25	11	f1	f1	NOUN
cana-2565	25	12	-	-	PUNCT
cana-2565	25	13	score	score	NOUN
cana-2565	25	14	,	,	PUNCT
cana-2565	25	15	and	and	CCONJ
cana-2565	25	16	area	area	NOUN
cana-2565	25	17	under	under	ADP
cana-2565	25	18	the	the	DET
cana-2565	25	19	curve	curve	NOUN
cana-2565	25	20	(	(	PUNCT
cana-2565	25	21	auc	auc	NOUN
cana-2565	25	22	)	)	PUNCT
cana-2565	25	23	,	,	PUNCT
cana-2565	25	24	were	be	AUX
cana-2565	25	25	employed	employ	VERB
cana-2565	25	26	to	to	PART
cana-2565	25	27	measure	measure	VERB
cana-2565	25	28	and	and	CCONJ
cana-2565	25	29	compare	compare	VERB
cana-2565	25	30	the	the	DET
cana-2565	25	31	effectiveness	effectiveness	NOUN
cana-2565	25	32	of	of	ADP
cana-2565	25	33	the	the	DET
cana-2565	25	34	models	model	NOUN
cana-2565	25	35	.	.	PUNCT
cana-2565	26	1	the	the	DET
cana-2565	26	2	findings	finding	NOUN
cana-2565	26	3	highlight	highlight	VERB
cana-2565	26	4	the	the	DET
cana-2565	26	5	significant	significant	ADJ
cana-2565	26	6	advantages	advantage	NOUN
cana-2565	26	7	of	of	ADP
cana-2565	26	8	optimization	optimization	NOUN
cana-2565	26	9	-	-	PUNCT
cana-2565	26	10	enhanced	enhance	VERB
cana-2565	26	11	deep	deep	ADJ
cana-2565	26	12	learning	learning	NOUN
cana-2565	26	13	models	model	NOUN
cana-2565	26	14	,	,	PUNCT
cana-2565	26	15	demonstrating	demonstrate	VERB
cana-2565	26	16	their	their	PRON
cana-2565	26	17	ability	ability	NOUN
cana-2565	26	18	to	to	PART
cana-2565	26	19	outperform	outperform	VERB
cana-2565	26	20	conventional	conventional	ADJ
cana-2565	26	21	machine	machine	NOUN
cana-2565	26	22	learning	learn	VERB
cana-2565	26	23	algorithms	algorithm	NOUN
cana-2565	26	24	in	in	ADP
cana-2565	26	25	predictive	predictive	ADJ
cana-2565	26	26	accuracy	accuracy	NOUN
cana-2565	26	27	and	and	CCONJ
cana-2565	26	28	generalization	generalization	NOUN
cana-2565	26	29	.	.	PUNCT
cana-2565	27	1	2.0	2.0	NUM
cana-2565	27	2	literature	literature	NOUN
cana-2565	27	3	review	review	VERB
cana-2565	27	4	the	the	DET
cana-2565	27	5	effectiveness	effectiveness	NOUN
cana-2565	27	6	of	of	ADP
cana-2565	27	7	these	these	DET
cana-2565	27	8	ai	ai	VERB
cana-2565	27	9	approaches	approach	NOUN
cana-2565	27	10	is	be	AUX
cana-2565	27	11	often	often	ADV
cana-2565	27	12	evaluated	evaluate	VERB
cana-2565	27	13	using	use	VERB
cana-2565	27	14	performance	performance	NOUN
cana-2565	27	15	metrics	metric	NOUN
cana-2565	27	16	such	such	ADJ
cana-2565	27	17	as	as	ADP
cana-2565	27	18	accuracy	accuracy	NOUN
cana-2565	27	19	,	,	PUNCT
cana-2565	27	20	precision	precision	NOUN
cana-2565	27	21	,	,	PUNCT
cana-2565	27	22	recall	recall	NOUN
cana-2565	27	23	,	,	PUNCT
cana-2565	27	24	f1	f1	NOUN
cana-2565	27	25	-	-	PUNCT
cana-2565	27	26	score	score	NOUN
cana-2565	27	27	,	,	PUNCT
cana-2565	27	28	and	and	CCONJ
cana-2565	27	29	the	the	DET
cana-2565	27	30	area	area	NOUN
cana-2565	27	31	under	under	ADP
cana-2565	27	32	the	the	DET
cana-2565	27	33	curve	curve	NOUN
cana-2565	27	34	(	(	PUNCT
cana-2565	27	35	auc	auc	NOUN
cana-2565	27	36	)	)	PUNCT
cana-2565	27	37	.	.	PUNCT
cana-2565	28	1	these	these	DET
cana-2565	28	2	metrics	metric	NOUN
cana-2565	28	3	provide	provide	VERB
cana-2565	28	4	a	a	DET
cana-2565	28	5	comprehensive	comprehensive	ADJ
cana-2565	28	6	framework	framework	NOUN
cana-2565	28	7	for	for	ADP
cana-2565	28	8	assessing	assess	VERB
cana-2565	28	9	and	and	CCONJ
cana-2565	28	10	comparing	compare	VERB
cana-2565	28	11	the	the	DET
cana-2565	28	12	predictive	predictive	ADJ
cana-2565	28	13	capabilities	capability	NOUN
cana-2565	28	14	of	of	ADP
cana-2565	28	15	various	various	ADJ
cana-2565	28	16	models	model	NOUN
cana-2565	28	17	.	.	PUNCT
cana-2565	29	1	while	while	SCONJ
cana-2565	29	2	traditional	traditional	ADJ
cana-2565	29	3	ml	ml	NOUN
cana-2565	29	4	methods	method	NOUN
cana-2565	29	5	have	have	AUX
cana-2565	29	6	achieved	achieve	VERB
cana-2565	29	7	satisfactory	satisfactory	ADJ
cana-2565	29	8	results	result	NOUN
cana-2565	29	9	in	in	ADP
cana-2565	29	10	some	some	DET
cana-2565	29	11	cases	case	NOUN
cana-2565	29	12	,	,	PUNCT
cana-2565	29	13	recent	recent	ADJ
cana-2565	29	14	advancements	advancement	NOUN
cana-2565	29	15	in	in	ADP
cana-2565	29	16	deep	deep	ADJ
cana-2565	29	17	learning	learning	NOUN
cana-2565	29	18	,	,	PUNCT
cana-2565	29	19	especially	especially	ADV
cana-2565	29	20	when	when	SCONJ
cana-2565	29	21	coupled	couple	VERB
cana-2565	29	22	with	with	ADP
cana-2565	29	23	optimization	optimization	NOUN
cana-2565	29	24	strategies	strategy	NOUN
cana-2565	29	25	,	,	PUNCT
cana-2565	29	26	have	have	AUX
cana-2565	29	27	demonstrated	demonstrate	VERB
cana-2565	29	28	significant	significant	ADJ
cana-2565	29	29	improvements	improvement	NOUN
cana-2565	29	30	in	in	ADP
cana-2565	29	31	pd	pd	PROPN
cana-2565	29	32	classification	classification	NOUN
cana-2565	29	33	(	(	PUNCT
cana-2565	29	34	rashid	rashid	PROPN
cana-2565	29	35	et	et	PROPN
cana-2565	29	36	al	al	PROPN
cana-2565	29	37	.	.	PROPN
cana-2565	29	38	,	,	PUNCT
cana-2565	29	39	2022	2022	NUM
cana-2565	29	40	)	)	PUNCT
cana-2565	29	41	.	.	PUNCT
cana-2565	30	1	machine	machine	NOUN
cana-2565	30	2	learning	learning	NOUN
cana-2565	30	3	(	(	PUNCT
cana-2565	30	4	ml	ml	NOUN
cana-2565	30	5	)	)	PUNCT
cana-2565	30	6	has	have	AUX
cana-2565	30	7	become	become	VERB
cana-2565	30	8	an	an	DET
cana-2565	30	9	essential	essential	ADJ
cana-2565	30	10	tool	tool	NOUN
cana-2565	30	11	in	in	ADP
cana-2565	30	12	modern	modern	ADJ
cana-2565	30	13	data	datum	NOUN
cana-2565	30	14	analysis	analysis	NOUN
cana-2565	30	15	,	,	PUNCT
cana-2565	30	16	enabling	enable	VERB
cana-2565	30	17	efficient	efficient	ADJ
cana-2565	30	18	solutions	solution	NOUN
cana-2565	30	19	for	for	ADP
cana-2565	30	20	complex	complex	ADJ
cana-2565	30	21	problems	problem	NOUN
cana-2565	30	22	across	across	ADP
cana-2565	30	23	various	various	ADJ
cana-2565	30	24	domains	domain	NOUN
cana-2565	30	25	.	.	PUNCT
cana-2565	31	1	in	in	ADP
cana-2565	31	2	fields	field	NOUN
cana-2565	31	3	such	such	ADJ
cana-2565	31	4	as	as	ADP
cana-2565	31	5	healthcare	healthcare	NOUN
cana-2565	31	6	,	,	PUNCT
cana-2565	31	7	finance	finance	NOUN
cana-2565	31	8	,	,	PUNCT
cana-2565	31	9	and	and	CCONJ
cana-2565	31	10	engineering	engineering	NOUN
cana-2565	31	11	,	,	PUNCT
cana-2565	31	12	ml	ml	ADP
cana-2565	31	13	models	model	NOUN
cana-2565	31	14	have	have	AUX
cana-2565	31	15	demonstrated	demonstrate	VERB
cana-2565	31	16	their	their	PRON
cana-2565	31	17	ability	ability	NOUN
cana-2565	31	18	to	to	PART
cana-2565	31	19	process	process	VERB
cana-2565	31	20	large	large	ADJ
cana-2565	31	21	datasets	dataset	NOUN
cana-2565	31	22	,	,	PUNCT
cana-2565	31	23	identify	identify	VERB
cana-2565	31	24	patterns	pattern	NOUN
cana-2565	31	25	,	,	PUNCT
cana-2565	31	26	and	and	CCONJ
cana-2565	31	27	make	make	VERB
cana-2565	31	28	accurate	accurate	ADJ
cana-2565	31	29	predictions	prediction	NOUN
cana-2565	31	30	.	.	PUNCT
cana-2565	32	1	however	however	ADV
cana-2565	32	2	,	,	PUNCT
cana-2565	32	3	the	the	DET
cana-2565	32	4	success	success	NOUN
cana-2565	32	5	of	of	ADP
cana-2565	32	6	these	these	DET
cana-2565	32	7	models	model	NOUN
cana-2565	32	8	depends	depend	VERB
cana-2565	32	9	heavily	heavily	ADV
cana-2565	32	10	on	on	ADP
cana-2565	32	11	their	their	PRON
cana-2565	32	12	evaluation	evaluation	NOUN
cana-2565	32	13	,	,	PUNCT
cana-2565	32	14	which	which	PRON
cana-2565	32	15	relies	rely	VERB
cana-2565	32	16	on	on	ADP
cana-2565	32	17	appropriate	appropriate	ADJ
cana-2565	32	18	performance	performance	NOUN
cana-2565	32	19	metrics	metric	NOUN
cana-2565	32	20	.	.	PUNCT
cana-2565	33	1	these	these	DET
cana-2565	33	2	metrics	metric	NOUN
cana-2565	33	3	provide	provide	VERB
cana-2565	33	4	a	a	DET
cana-2565	33	5	quantitative	quantitative	ADJ
cana-2565	33	6	framework	framework	NOUN
cana-2565	33	7	to	to	PART
cana-2565	33	8	assess	assess	VERB
cana-2565	33	9	the	the	DET
cana-2565	33	10	effectiveness	effectiveness	NOUN
cana-2565	33	11	of	of	ADP
cana-2565	33	12	models	model	NOUN
cana-2565	33	13	and	and	CCONJ
cana-2565	33	14	ensure	ensure	VERB
cana-2565	33	15	their	their	PRON
cana-2565	33	16	reliability	reliability	NOUN
cana-2565	33	17	in	in	ADP
cana-2565	33	18	real	real	ADJ
cana-2565	33	19	-	-	PUNCT
cana-2565	33	20	world	world	NOUN
cana-2565	33	21	applications	application	NOUN
cana-2565	33	22	(	(	PUNCT
cana-2565	33	23	bishop	bishop	NOUN
cana-2565	33	24	,	,	PUNCT
cana-2565	33	25	2006	2006	NUM
cana-2565	33	26	)	)	PUNCT
cana-2565	33	27	.	.	PUNCT
cana-2565	34	1	popular	popular	ADJ
cana-2565	34	2	machine	machine	NOUN
cana-2565	34	3	learning	learn	VERB
cana-2565	34	4	algorithms	algorithm	NOUN
cana-2565	34	5	such	such	ADJ
cana-2565	34	6	as	as	ADP
cana-2565	34	7	support	support	NOUN
cana-2565	34	8	vector	vector	NOUN
cana-2565	34	9	machines	machine	NOUN
cana-2565	34	10	(	(	PUNCT
cana-2565	34	11	svm	svm	PROPN
cana-2565	34	12	)	)	PUNCT
cana-2565	34	13	,	,	PUNCT
cana-2565	34	14	random	random	ADJ
cana-2565	34	15	forests	forest	NOUN
cana-2565	34	16	(	(	PUNCT
cana-2565	34	17	rf	rf	NOUN
cana-2565	34	18	)	)	PUNCT
cana-2565	34	19	,	,	PUNCT
cana-2565	34	20	and	and	CCONJ
cana-2565	34	21	k	k	X
cana-2565	34	22	-	-	PUNCT
cana-2565	34	23	nearest	near	ADJ
cana-2565	34	24	neighbors	neighbor	NOUN
cana-2565	34	25	(	(	PUNCT
cana-2565	34	26	k	k	X
cana-2565	34	27	-	-	PUNCT
cana-2565	34	28	nn	nn	NOUN
cana-2565	34	29	)	)	PUNCT
cana-2565	34	30	are	be	AUX
cana-2565	34	31	commonly	commonly	ADV
cana-2565	34	32	used	use	VERB
cana-2565	34	33	for	for	ADP
cana-2565	34	34	classification	classification	NOUN
cana-2565	34	35	and	and	CCONJ
cana-2565	34	36	regression	regression	NOUN
cana-2565	34	37	tasks	task	NOUN
cana-2565	34	38	.	.	PUNCT
cana-2565	35	1	their	their	PRON
cana-2565	35	2	performance	performance	NOUN
cana-2565	35	3	is	be	AUX
cana-2565	35	4	often	often	ADV
cana-2565	35	5	measured	measure	VERB
cana-2565	35	6	using	use	VERB
cana-2565	35	7	metrics	metric	NOUN
cana-2565	35	8	such	such	ADJ
cana-2565	35	9	as	as	ADP
cana-2565	35	10	accuracy	accuracy	NOUN
cana-2565	35	11	,	,	PUNCT
cana-2565	35	12	precision	precision	NOUN
cana-2565	35	13	,	,	PUNCT
cana-2565	35	14	recall	recall	NOUN
cana-2565	35	15	,	,	PUNCT
cana-2565	35	16	f1	f1	NOUN
cana-2565	35	17	-	-	PUNCT
cana-2565	35	18	score	score	NOUN
cana-2565	35	19	,	,	PUNCT
cana-2565	35	20	and	and	CCONJ
cana-2565	35	21	the	the	DET
cana-2565	35	22	area	area	NOUN
cana-2565	35	23	under	under	ADP
cana-2565	35	24	the	the	DET
cana-2565	35	25	curve	curve	NOUN
cana-2565	35	26	(	(	PUNCT
cana-2565	35	27	auc	auc	NOUN
cana-2565	35	28	)	)	PUNCT
cana-2565	35	29	.	.	PUNCT
cana-2565	36	1	these	these	DET
cana-2565	36	2	metrics	metric	NOUN
cana-2565	36	3	allow	allow	VERB
cana-2565	36	4	researchers	researcher	NOUN
cana-2565	36	5	to	to	PART
cana-2565	36	6	assess	assess	VERB
cana-2565	36	7	various	various	ADJ
cana-2565	36	8	aspects	aspect	NOUN
cana-2565	36	9	of	of	ADP
cana-2565	36	10	model	model	NOUN
cana-2565	36	11	behavior	behavior	NOUN
cana-2565	36	12	,	,	PUNCT
cana-2565	36	13	such	such	ADJ
cana-2565	36	14	as	as	ADP
cana-2565	36	15	classification	classification	NOUN
cana-2565	36	16	correctness	correctness	NOUN
cana-2565	36	17	,	,	PUNCT
cana-2565	36	18	the	the	DET
cana-2565	36	19	ability	ability	NOUN
cana-2565	36	20	to	to	PART
cana-2565	36	21	detect	detect	VERB
cana-2565	36	22	positive	positive	ADJ
cana-2565	36	23	instances	instance	NOUN
cana-2565	36	24	,	,	PUNCT
cana-2565	36	25	and	and	CCONJ
cana-2565	36	26	the	the	DET
cana-2565	36	27	trade	trade	NOUN
cana-2565	36	28	-	-	PUNCT
cana-2565	36	29	off	off	NOUN
cana-2565	36	30	between	between	ADP
cana-2565	36	31	precision	precision	NOUN
cana-2565	36	32	and	and	CCONJ
cana-2565	36	33	recall	recall	NOUN
cana-2565	36	34	(	(	PUNCT
cana-2565	36	35	fawcett	fawcett	PROPN
cana-2565	36	36	,	,	PUNCT
cana-2565	36	37	2006	2006	NUM
cana-2565	36	38	)	)	PUNCT
cana-2565	36	39	.	.	PUNCT
cana-2565	37	1	while	while	SCONJ
cana-2565	37	2	accuracy	accuracy	NOUN
cana-2565	37	3	is	be	AUX
cana-2565	37	4	a	a	DET
cana-2565	37	5	straightforward	straightforward	ADJ
cana-2565	37	6	measure	measure	NOUN
cana-2565	37	7	of	of	ADP
cana-2565	37	8	how	how	SCONJ
cana-2565	37	9	often	often	ADV
cana-2565	37	10	the	the	DET
cana-2565	37	11	model	model	NOUN
cana-2565	37	12	predicts	predict	VERB
cana-2565	37	13	correctly	correctly	ADV
cana-2565	37	14	,	,	PUNCT
cana-2565	37	15	it	it	PRON
cana-2565	37	16	can	can	AUX
cana-2565	37	17	be	be	AUX
cana-2565	37	18	misleading	mislead	VERB
cana-2565	37	19	in	in	ADP
cana-2565	37	20	cases	case	NOUN
cana-2565	37	21	of	of	ADP
cana-2565	37	22	class	class	NOUN
cana-2565	37	23	imbalance	imbalance	NOUN
cana-2565	37	24	.	.	PUNCT
cana-2565	38	1	metrics	metric	NOUN
cana-2565	38	2	like	like	ADP
cana-2565	38	3	precision	precision	NOUN
cana-2565	38	4	,	,	PUNCT
cana-2565	38	5	recall	recall	NOUN
cana-2565	38	6	,	,	PUNCT
cana-2565	38	7	and	and	CCONJ
cana-2565	38	8	f1	f1	NOUN
cana-2565	38	9	-	-	PUNCT
cana-2565	38	10	score	score	NOUN
cana-2565	38	11	provide	provide	VERB
cana-2565	38	12	a	a	DET
cana-2565	38	13	more	more	ADV
cana-2565	38	14	nuanced	nuanced	ADJ
cana-2565	38	15	understanding	understanding	NOUN
cana-2565	38	16	of	of	ADP
cana-2565	38	17	model	model	NOUN
cana-2565	38	18	performance	performance	NOUN
cana-2565	38	19	,	,	PUNCT
cana-2565	38	20	particularly	particularly	ADV
cana-2565	38	21	in	in	ADP
cana-2565	38	22	datasets	dataset	NOUN
cana-2565	38	23	with	with	ADP
cana-2565	38	24	skewed	skewed	ADJ
cana-2565	38	25	distributions	distribution	NOUN
cana-2565	38	26	(	(	PUNCT
cana-2565	38	27	sokolova	sokolova	NOUN
cana-2565	38	28	&	&	CCONJ
cana-2565	38	29	lapalme	lapalme	PROPN
cana-2565	38	30	,	,	PUNCT
cana-2565	38	31	2009	2009	NUM
cana-2565	38	32	)	)	PUNCT
cana-2565	38	33	.	.	PUNCT
cana-2565	39	1	for	for	ADP
cana-2565	39	2	instance	instance	NOUN
cana-2565	39	3	,	,	PUNCT
cana-2565	39	4	in	in	ADP
cana-2565	39	5	medical	medical	ADJ
cana-2565	39	6	applications	application	NOUN
cana-2565	39	7	,	,	PUNCT
cana-2565	39	8	where	where	SCONJ
cana-2565	39	9	false	false	ADJ
cana-2565	39	10	negatives	negative	NOUN
cana-2565	39	11	can	can	AUX
cana-2565	39	12	have	have	VERB
cana-2565	39	13	severe	severe	ADJ
cana-2565	39	14	consequences	consequence	NOUN
cana-2565	39	15	,	,	PUNCT
cana-2565	39	16	recall	recall	VERB
cana-2565	39	17	is	be	AUX
cana-2565	39	18	a	a	DET
cana-2565	39	19	critical	critical	ADJ
cana-2565	39	20	metric	metric	NOUN
cana-2565	39	21	.	.	PUNCT
cana-2565	40	1	conversely	conversely	ADV
cana-2565	40	2	,	,	PUNCT
cana-2565	40	3	precision	precision	NOUN
cana-2565	40	4	becomes	become	VERB
cana-2565	40	5	crucial	crucial	ADJ
cana-2565	40	6	in	in	ADP
cana-2565	40	7	contexts	context	NOUN
cana-2565	40	8	where	where	SCONJ
cana-2565	40	9	false	false	ADJ
cana-2565	40	10	positives	positive	NOUN
cana-2565	40	11	must	must	AUX
cana-2565	40	12	be	be	AUX
cana-2565	40	13	minimized	minimize	VERB
cana-2565	40	14	.	.	PUNCT
cana-2565	41	1	recent	recent	ADJ
cana-2565	41	2	advancements	advancement	NOUN
cana-2565	41	3	in	in	ADP
cana-2565	41	4	machine	machine	NOUN
cana-2565	41	5	learning	learning	NOUN
cana-2565	41	6	emphasize	emphasize	VERB
cana-2565	41	7	not	not	PART
cana-2565	41	8	only	only	ADV
cana-2565	41	9	algorithmic	algorithmic	ADJ
cana-2565	41	10	development	development	NOUN
cana-2565	41	11	but	but	CCONJ
cana-2565	41	12	also	also	ADV
cana-2565	41	13	optimization	optimization	NOUN
cana-2565	41	14	techniques	technique	NOUN
cana-2565	41	15	to	to	PART
cana-2565	41	16	enhance	enhance	VERB
cana-2565	41	17	model	model	NOUN
cana-2565	41	18	performance	performance	NOUN
cana-2565	41	19	.	.	PUNCT
cana-2565	42	1	techniques	technique	NOUN
cana-2565	42	2	like	like	ADP
cana-2565	42	3	hyperparameter	hyperparameter	NOUN
cana-2565	42	4	tuning	tuning	NOUN
cana-2565	42	5	,	,	PUNCT
cana-2565	42	6	regularization	regularization	NOUN
cana-2565	42	7	,	,	PUNCT
cana-2565	42	8	and	and	CCONJ
cana-2565	42	9	ensemble	ensemble	ADJ
cana-2565	42	10	learning	learning	NOUN
cana-2565	42	11	have	have	AUX
cana-2565	42	12	been	be	AUX
cana-2565	42	13	widely	widely	ADV
cana-2565	42	14	adopted	adopt	VERB
cana-2565	42	15	to	to	PART
cana-2565	42	16	address	address	VERB
cana-2565	42	17	overfitting	overfitting	NOUN
cana-2565	42	18	,	,	PUNCT
cana-2565	42	19	improve	improve	VERB
cana-2565	42	20	generalization	generalization	NOUN
cana-2565	42	21	,	,	PUNCT
cana-2565	42	22	and	and	CCONJ
cana-2565	42	23	achieve	achieve	VERB
cana-2565	42	24	robust	robust	ADJ
cana-2565	42	25	performance	performance	NOUN
cana-2565	42	26	across	across	ADP
cana-2565	42	27	diverse	diverse	ADJ
cana-2565	42	28	datasets	dataset	NOUN
cana-2565	42	29	(	(	PUNCT
cana-2565	42	30	goodfellow	goodfellow	PROPN
cana-2565	42	31	et	et	PROPN
cana-2565	42	32	al	al	PROPN
cana-2565	42	33	.	.	PROPN
cana-2565	42	34	,	,	PUNCT
cana-2565	42	35	2016	2016	NUM
cana-2565	42	36	)	)	PUNCT
cana-2565	42	37	.	.	PUNCT
cana-2565	43	1	by	by	ADP
cana-2565	43	2	combining	combine	VERB
cana-2565	43	3	effective	effective	ADJ
cana-2565	43	4	algorithms	algorithm	NOUN
cana-2565	43	5	with	with	ADP
cana-2565	43	6	comprehensive	comprehensive	ADJ
cana-2565	43	7	evaluation	evaluation	NOUN
cana-2565	43	8	metrics	metric	NOUN
cana-2565	43	9	,	,	PUNCT
cana-2565	43	10	researchers	researcher	NOUN
cana-2565	43	11	can	can	AUX
cana-2565	43	12	ensure	ensure	VERB
cana-2565	43	13	that	that	SCONJ
cana-2565	43	14	machine	machine	NOUN
cana-2565	43	15	learning	learning	NOUN
cana-2565	43	16	models	model	NOUN
cana-2565	43	17	deliver	deliver	VERB
cana-2565	43	18	reliable	reliable	ADJ
cana-2565	43	19	and	and	CCONJ
cana-2565	43	20	actionable	actionable	ADJ
cana-2565	43	21	insights	insight	NOUN
cana-2565	43	22	.	.	PUNCT
cana-2565	44	1	communications	communication	NOUN
cana-2565	44	2	on	on	ADP
cana-2565	44	3	applied	apply	VERB
cana-2565	44	4	nonlinear	nonlinear	ADJ
cana-2565	44	5	analysis	analysis	NOUN
cana-2565	44	6	issn	issn	NOUN
cana-2565	44	7	:	:	PUNCT
cana-2565	44	8	1074	1074	NUM
cana-2565	44	9	-	-	PUNCT
cana-2565	44	10	133x	133x	NUM
cana-2565	44	11	vol	vol	NOUN
cana-2565	44	12	32	32	NUM
cana-2565	44	13	no	no	NOUN
cana-2565	44	14	.	.	PUNCT
cana-2565	45	1	3s	3s	NUM
cana-2565	45	2	(	(	PUNCT
cana-2565	45	3	2025	2025	NUM
cana-2565	45	4	)	)	PUNCT
cana-2565	45	5	106	106	NUM
cana-2565	45	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2565	45	7	contributes	contribute	VERB
cana-2565	45	8	to	to	ADP
cana-2565	45	9	advancing	advance	VERB
cana-2565	45	10	ai	ai	NOUN
cana-2565	45	11	-	-	PUNCT
cana-2565	45	12	based	base	VERB
cana-2565	45	13	methods	method	NOUN
cana-2565	45	14	for	for	ADP
cana-2565	45	15	pd	pd	NOUN
cana-2565	45	16	diagnostics	diagnostic	NOUN
cana-2565	45	17	by	by	ADP
cana-2565	45	18	examining	examine	VERB
cana-2565	45	19	the	the	DET
cana-2565	45	20	strengths	strength	NOUN
cana-2565	45	21	and	and	CCONJ
cana-2565	45	22	limitations	limitation	NOUN
cana-2565	45	23	of	of	ADP
cana-2565	45	24	different	different	ADJ
cana-2565	45	25	computational	computational	ADJ
cana-2565	45	26	approaches	approach	NOUN
cana-2565	45	27	.	.	PUNCT
cana-2565	46	1	it	it	PRON
cana-2565	46	2	emphasizes	emphasize	VERB
cana-2565	46	3	the	the	DET
cana-2565	46	4	transformative	transformative	ADJ
cana-2565	46	5	role	role	NOUN
cana-2565	46	6	of	of	ADP
cana-2565	46	7	deep	deep	ADJ
cana-2565	46	8	learning	learn	VERB
cana-2565	46	9	optimization	optimization	NOUN
cana-2565	46	10	in	in	ADP
cana-2565	46	11	enhancing	enhance	VERB
cana-2565	46	12	diagnostic	diagnostic	ADJ
cana-2565	46	13	accuracy	accuracy	NOUN
cana-2565	46	14	and	and	CCONJ
cana-2565	46	15	clinical	clinical	ADJ
cana-2565	46	16	applicability	applicability	NOUN
cana-2565	46	17	.	.	PUNCT
cana-2565	47	1	machine	machine	NOUN
cana-2565	47	2	learning	learning	NOUN
cana-2565	47	3	(	(	PUNCT
cana-2565	47	4	ml	ml	NOUN
cana-2565	47	5	)	)	PUNCT
cana-2565	47	6	techniques	technique	NOUN
cana-2565	47	7	have	have	AUX
cana-2565	47	8	become	become	VERB
cana-2565	47	9	indispensable	indispensable	ADJ
cana-2565	47	10	in	in	ADP
cana-2565	47	11	data	data	NOUN
cana-2565	47	12	-	-	PUNCT
cana-2565	47	13	driven	drive	VERB
cana-2565	47	14	problem	problem	NOUN
cana-2565	47	15	-	-	PUNCT
cana-2565	47	16	solving	solving	NOUN
cana-2565	47	17	,	,	PUNCT
cana-2565	47	18	offering	offer	VERB
cana-2565	47	19	diverse	diverse	ADJ
cana-2565	47	20	algorithms	algorithm	NOUN
cana-2565	47	21	tailored	tailor	VERB
cana-2565	47	22	to	to	ADP
cana-2565	47	23	different	different	ADJ
cana-2565	47	24	types	type	NOUN
cana-2565	47	25	of	of	ADP
cana-2565	47	26	data	datum	NOUN
cana-2565	47	27	and	and	CCONJ
cana-2565	47	28	tasks	task	NOUN
cana-2565	47	29	.	.	PUNCT
cana-2565	48	1	among	among	ADP
cana-2565	48	2	these	these	PRON
cana-2565	48	3	,	,	PUNCT
cana-2565	48	4	linear	linear	ADJ
cana-2565	48	5	regression	regression	NOUN
cana-2565	48	6	,	,	PUNCT
cana-2565	48	7	a	a	DET
cana-2565	48	8	foundational	foundational	ADJ
cana-2565	48	9	statistical	statistical	ADJ
cana-2565	48	10	method	method	NOUN
cana-2565	48	11	,	,	PUNCT
cana-2565	48	12	is	be	AUX
cana-2565	48	13	widely	widely	ADV
cana-2565	48	14	used	use	VERB
cana-2565	48	15	for	for	ADP
cana-2565	48	16	predictive	predictive	ADJ
cana-2565	48	17	modeling	modeling	NOUN
cana-2565	48	18	.	.	PUNCT
cana-2565	49	1	it	it	PRON
cana-2565	49	2	provides	provide	VERB
cana-2565	49	3	a	a	DET
cana-2565	49	4	straightforward	straightforward	ADJ
cana-2565	49	5	approach	approach	NOUN
cana-2565	49	6	to	to	ADP
cana-2565	49	7	establishing	establish	VERB
cana-2565	49	8	relationships	relationship	NOUN
cana-2565	49	9	between	between	ADP
cana-2565	49	10	dependent	dependent	ADJ
cana-2565	49	11	and	and	CCONJ
cana-2565	49	12	independent	independent	ADJ
cana-2565	49	13	variables	variable	NOUN
cana-2565	49	14	,	,	PUNCT
cana-2565	49	15	making	make	VERB
cana-2565	49	16	it	it	PRON
cana-2565	49	17	suitable	suitable	ADJ
cana-2565	49	18	for	for	ADP
cana-2565	49	19	tasks	task	NOUN
cana-2565	49	20	requiring	require	VERB
cana-2565	49	21	interpretability	interpretability	NOUN
cana-2565	49	22	and	and	CCONJ
cana-2565	49	23	simplicity	simplicity	NOUN
cana-2565	49	24	(	(	PUNCT
cana-2565	49	25	montgomery	montgomery	PROPN
cana-2565	49	26	et	et	PROPN
cana-2565	49	27	al	al	PROPN
cana-2565	49	28	.	.	PROPN
cana-2565	49	29	,	,	PUNCT
cana-2565	49	30	2021	2021	NUM
cana-2565	49	31	)	)	PUNCT
cana-2565	49	32	.	.	PUNCT
cana-2565	50	1	however	however	ADV
cana-2565	50	2	,	,	PUNCT
cana-2565	50	3	linear	linear	ADJ
cana-2565	50	4	regression	regression	NOUN
cana-2565	50	5	assumes	assume	VERB
cana-2565	50	6	a	a	DET
cana-2565	50	7	linear	linear	ADJ
cana-2565	50	8	relationship	relationship	NOUN
cana-2565	50	9	,	,	PUNCT
cana-2565	50	10	which	which	PRON
cana-2565	50	11	can	can	AUX
cana-2565	50	12	limit	limit	VERB
cana-2565	50	13	its	its	PRON
cana-2565	50	14	performance	performance	NOUN
cana-2565	50	15	on	on	ADP
cana-2565	50	16	complex	complex	ADJ
cana-2565	50	17	datasets	dataset	NOUN
cana-2565	50	18	.	.	PUNCT
cana-2565	51	1	data	datum	NOUN
cana-2565	51	2	mining	mining	NOUN
cana-2565	51	3	serves	serve	VERB
cana-2565	51	4	as	as	ADP
cana-2565	51	5	a	a	DET
cana-2565	51	6	powerful	powerful	ADJ
cana-2565	51	7	tool	tool	NOUN
cana-2565	51	8	for	for	ADP
cana-2565	51	9	analyzing	analyze	VERB
cana-2565	51	10	large	large	ADJ
cana-2565	51	11	,	,	PUNCT
cana-2565	51	12	pre	pre	ADJ
cana-2565	51	13	-	-	ADJ
cana-2565	51	14	existing	existing	ADJ
cana-2565	51	15	databases	database	NOUN
cana-2565	51	16	to	to	PART
cana-2565	51	17	uncover	uncover	VERB
cana-2565	51	18	previously	previously	ADV
cana-2565	51	19	unknown	unknown	ADJ
cana-2565	51	20	and	and	CCONJ
cana-2565	51	21	valuable	valuable	ADJ
cana-2565	51	22	insights	insight	NOUN
cana-2565	51	23	.	.	PUNCT
cana-2565	52	1	in	in	ADP
cana-2565	52	2	the	the	DET
cana-2565	52	3	context	context	NOUN
cana-2565	52	4	of	of	ADP
cana-2565	52	5	chronic	chronic	ADJ
cana-2565	52	6	disease	disease	NOUN
cana-2565	52	7	data	datum	NOUN
cana-2565	52	8	,	,	PUNCT
cana-2565	52	9	each	each	DET
cana-2565	52	10	row	row	NOUN
cana-2565	52	11	represents	represent	VERB
cana-2565	52	12	a	a	DET
cana-2565	52	13	specific	specific	ADJ
cana-2565	52	14	location	location	NOUN
cana-2565	52	15	,	,	PUNCT
cana-2565	52	16	while	while	SCONJ
cana-2565	52	17	the	the	DET
cana-2565	52	18	attributes	attribute	NOUN
cana-2565	52	19	encompass	encompass	VERB
cana-2565	52	20	topics	topic	NOUN
cana-2565	52	21	,	,	PUNCT
cana-2565	52	22	questions	question	NOUN
cana-2565	52	23	,	,	PUNCT
cana-2565	52	24	data	datum	NOUN
cana-2565	52	25	values	value	NOUN
cana-2565	52	26	,	,	PUNCT
cana-2565	52	27	and	and	CCONJ
cana-2565	52	28	confidence	confidence	NOUN
cana-2565	52	29	limits	limit	NOUN
cana-2565	52	30	(	(	PUNCT
cana-2565	52	31	both	both	CCONJ
cana-2565	52	32	low	low	ADJ
cana-2565	52	33	and	and	CCONJ
cana-2565	52	34	high	high	ADJ
cana-2565	52	35	)	)	PUNCT
cana-2565	52	36	.	.	PUNCT
cana-2565	53	1	data	datum	NOUN
cana-2565	53	2	is	be	AUX
cana-2565	53	3	utilized	utilize	VERB
cana-2565	53	4	for	for	ADP
cana-2565	53	5	training	training	NOUN
cana-2565	53	6	and	and	CCONJ
cana-2565	53	7	testing	testing	NOUN
cana-2565	53	8	purposes	purpose	NOUN
cana-2565	53	9	across	across	ADP
cana-2565	53	10	five	five	NUM
cana-2565	53	11	classification	classification	NOUN
cana-2565	53	12	algorithms	algorithm	NOUN
cana-2565	53	13	.	.	PUNCT
cana-2565	54	1	this	this	DET
cana-2565	54	2	paper	paper	NOUN
cana-2565	54	3	evaluates	evaluate	VERB
cana-2565	54	4	the	the	DET
cana-2565	54	5	performance	performance	NOUN
cana-2565	54	6	and	and	CCONJ
cana-2565	54	7	accuracy	accuracy	NOUN
cana-2565	54	8	of	of	ADP
cana-2565	54	9	five	five	NUM
cana-2565	54	10	decision	decision	NOUN
cana-2565	54	11	tree	tree	NOUN
cana-2565	54	12	algorithms	algorithm	NOUN
cana-2565	54	13	,	,	PUNCT
cana-2565	54	14	demonstrating	demonstrate	VERB
cana-2565	54	15	that	that	SCONJ
cana-2565	54	16	the	the	DET
cana-2565	54	17	m5p	m5p	NOUN
cana-2565	54	18	decision	decision	NOUN
cana-2565	54	19	tree	tree	NOUN
cana-2565	54	20	approach	approach	NOUN
cana-2565	54	21	outperforms	outperform	VERB
cana-2565	54	22	the	the	DET
cana-2565	54	23	others	other	NOUN
cana-2565	54	24	in	in	ADP
cana-2565	54	25	building	build	VERB
cana-2565	54	26	an	an	DET
cana-2565	54	27	effective	effective	ADJ
cana-2565	54	28	predictive	predictive	ADJ
cana-2565	54	29	model	model	NOUN
cana-2565	54	30	(	(	PUNCT
cana-2565	54	31	rajesh	rajesh	PROPN
cana-2565	54	32	et	et	PROPN
cana-2565	54	33	al	al	PROPN
cana-2565	54	34	.	.	PROPN
cana-2565	54	35	,	,	PUNCT
cana-2565	54	36	2021	2021	NUM
cana-2565	54	37	)	)	PUNCT
cana-2565	54	38	.	.	PUNCT
cana-2565	55	1	each	each	DET
cana-2565	55	2	row	row	NOUN
cana-2565	55	3	is	be	AUX
cana-2565	55	4	an	an	DET
cana-2565	55	5	instance	instance	NOUN
cana-2565	55	6	characterized	characterize	VERB
cana-2565	55	7	by	by	ADP
cana-2565	55	8	attribute	attribute	NOUN
cana-2565	55	9	values	value	NOUN
cana-2565	55	10	such	such	ADJ
cana-2565	55	11	as	as	ADP
cana-2565	55	12	outlook	outlook	NOUN
cana-2565	55	13	,	,	PUNCT
cana-2565	55	14	temperature	temperature	NOUN
cana-2565	55	15	,	,	PUNCT
cana-2565	55	16	humidity	humidity	NOUN
cana-2565	55	17	,	,	PUNCT
cana-2565	55	18	windy	windy	ADJ
cana-2565	55	19	,	,	PUNCT
cana-2565	55	20	and	and	CCONJ
cana-2565	55	21	the	the	DET
cana-2565	55	22	boolean	boolean	ADJ
cana-2565	55	23	playgolf	playgolf	NOUN
cana-2565	55	24	class	class	NOUN
cana-2565	55	25	variable	variable	NOUN
cana-2565	55	26	.	.	PUNCT
cana-2565	56	1	the	the	DET
cana-2565	56	2	dataset	dataset	NOUN
cana-2565	56	3	is	be	AUX
cana-2565	56	4	used	use	VERB
cana-2565	56	5	for	for	ADP
cana-2565	56	6	training	training	NOUN
cana-2565	56	7	purposes	purpose	NOUN
cana-2565	56	8	and	and	CCONJ
cana-2565	56	9	analyzed	analyze	VERB
cana-2565	56	10	using	use	VERB
cana-2565	56	11	seven	seven	NUM
cana-2565	56	12	classification	classification	NOUN
cana-2565	56	13	algorithms	algorithm	NOUN
cana-2565	56	14	.	.	PUNCT
cana-2565	57	1	this	this	DET
cana-2565	57	2	study	study	NOUN
cana-2565	57	3	evaluates	evaluate	VERB
cana-2565	57	4	the	the	DET
cana-2565	57	5	performance	performance	NOUN
cana-2565	57	6	and	and	CCONJ
cana-2565	57	7	accuracy	accuracy	NOUN
cana-2565	57	8	of	of	ADP
cana-2565	57	9	various	various	ADJ
cana-2565	57	10	decision	decision	NOUN
cana-2565	57	11	tree	tree	NOUN
cana-2565	57	12	-	-	PUNCT
cana-2565	57	13	based	base	VERB
cana-2565	57	14	approaches	approach	NOUN
cana-2565	57	15	implemented	implement	VERB
cana-2565	57	16	in	in	ADP
cana-2565	57	17	the	the	DET
cana-2565	57	18	weka	weka	PROPN
cana-2565	57	19	tool	tool	NOUN
cana-2565	57	20	to	to	PART
cana-2565	57	21	identify	identify	VERB
cana-2565	57	22	key	key	ADJ
cana-2565	57	23	parameters	parameter	NOUN
cana-2565	57	24	of	of	ADP
cana-2565	57	25	the	the	DET
cana-2565	57	26	tree	tree	NOUN
cana-2565	57	27	structure	structure	NOUN
cana-2565	57	28	.	.	PUNCT
cana-2565	58	1	the	the	DET
cana-2565	58	2	algorithms	algorithm	NOUN
cana-2565	58	3	include	include	VERB
cana-2565	58	4	j48	j48	PROPN
cana-2565	58	5	,	,	PUNCT
cana-2565	58	6	random	random	ADJ
cana-2565	58	7	tree	tree	NOUN
cana-2565	58	8	(	(	PUNCT
cana-2565	58	9	rt	rt	NOUN
cana-2565	58	10	)	)	PUNCT
cana-2565	58	11	,	,	PUNCT
cana-2565	58	12	decision	decision	NOUN
cana-2565	58	13	stump	stump	NOUN
cana-2565	58	14	(	(	PUNCT
cana-2565	58	15	ds	ds	NOUN
cana-2565	58	16	)	)	PUNCT
cana-2565	58	17	,	,	PUNCT
cana-2565	58	18	logistic	logistic	ADJ
cana-2565	58	19	model	model	NOUN
cana-2565	58	20	tree	tree	NOUN
cana-2565	58	21	(	(	PUNCT
cana-2565	58	22	lmt	lmt	PROPN
cana-2565	58	23	)	)	PUNCT
cana-2565	58	24	,	,	PUNCT
cana-2565	58	25	hoeffding	hoeffde	VERB
cana-2565	58	26	tree	tree	NOUN
cana-2565	58	27	(	(	PUNCT
cana-2565	58	28	ht	ht	PROPN
cana-2565	58	29	)	)	PUNCT
cana-2565	58	30	,	,	PUNCT
cana-2565	58	31	reduced	reduce	VERB
cana-2565	58	32	error	error	NOUN
cana-2565	58	33	pruning	prune	VERB
cana-2565	58	34	tree	tree	NOUN
cana-2565	58	35	(	(	PUNCT
cana-2565	58	36	rep	rep	NOUN
cana-2565	58	37	)	)	PUNCT
cana-2565	58	38	,	,	PUNCT
cana-2565	58	39	and	and	CCONJ
cana-2565	58	40	random	random	ADJ
cana-2565	58	41	forest	forest	NOUN
cana-2565	58	42	(	(	PUNCT
cana-2565	58	43	rf	rf	NOUN
cana-2565	58	44	)	)	PUNCT
cana-2565	58	45	.	.	PUNCT
cana-2565	59	1	experimental	experimental	ADJ
cana-2565	59	2	results	result	NOUN
cana-2565	59	3	show	show	VERB
cana-2565	59	4	that	that	SCONJ
cana-2565	59	5	among	among	ADP
cana-2565	59	6	these	these	DET
cana-2565	59	7	algorithms	algorithm	NOUN
cana-2565	59	8	,	,	PUNCT
cana-2565	59	9	the	the	DET
cana-2565	59	10	random	random	ADJ
cana-2565	59	11	tree	tree	NOUN
cana-2565	59	12	achieves	achieve	VERB
cana-2565	59	13	the	the	DET
cana-2565	59	14	highest	high	ADJ
cana-2565	59	15	accuracy	accuracy	NOUN
cana-2565	59	16	of	of	ADP
cana-2565	59	17	85.714	85.714	NUM
cana-2565	59	18	%	%	NOUN
cana-2565	59	19	(	(	PUNCT
cana-2565	59	20	rajesh	rajesh	PROPN
cana-2565	59	21	et	et	PROPN
cana-2565	59	22	al	al	PROPN
cana-2565	59	23	.	.	PROPN
cana-2565	59	24	,	,	PUNCT
cana-2565	59	25	2021	2021	NUM
cana-2565	59	26	)	)	PUNCT
cana-2565	59	27	.	.	PUNCT
cana-2565	60	1	advanced	advanced	ADJ
cana-2565	60	2	ml	ml	NOUN
cana-2565	60	3	models	model	NOUN
cana-2565	60	4	such	such	ADJ
cana-2565	60	5	as	as	ADP
cana-2565	60	6	multi	multi	ADJ
cana-2565	60	7	-	-	ADJ
cana-2565	60	8	layer	layer	ADJ
cana-2565	60	9	perceptrons	perceptron	NOUN
cana-2565	60	10	(	(	PUNCT
cana-2565	60	11	mlps	mlp	NOUN
cana-2565	60	12	)	)	PUNCT
cana-2565	60	13	,	,	PUNCT
cana-2565	60	14	random	random	ADJ
cana-2565	60	15	forests	forest	NOUN
cana-2565	60	16	,	,	PUNCT
cana-2565	60	17	rep	rep	NOUN
cana-2565	60	18	trees	tree	NOUN
cana-2565	60	19	,	,	PUNCT
cana-2565	60	20	and	and	CCONJ
cana-2565	60	21	random	random	ADJ
cana-2565	60	22	trees	tree	NOUN
cana-2565	60	23	have	have	AUX
cana-2565	60	24	gained	gain	VERB
cana-2565	60	25	prominence	prominence	NOUN
cana-2565	60	26	to	to	PART
cana-2565	60	27	address	address	VERB
cana-2565	60	28	nonlinear	nonlinear	ADJ
cana-2565	60	29	relationships	relationship	NOUN
cana-2565	60	30	and	and	CCONJ
cana-2565	60	31	capture	capture	VERB
cana-2565	60	32	intricate	intricate	ADJ
cana-2565	60	33	patterns	pattern	NOUN
cana-2565	60	34	.	.	PUNCT
cana-2565	61	1	mlp	mlp	NOUN
cana-2565	61	2	,	,	PUNCT
cana-2565	61	3	a	a	DET
cana-2565	61	4	type	type	NOUN
cana-2565	61	5	of	of	ADP
cana-2565	61	6	artificial	artificial	ADJ
cana-2565	61	7	neural	neural	ADJ
cana-2565	61	8	network	network	NOUN
cana-2565	61	9	,	,	PUNCT
cana-2565	61	10	excels	excel	VERB
cana-2565	61	11	in	in	ADP
cana-2565	61	12	handling	handle	VERB
cana-2565	61	13	nonlinear	nonlinear	ADJ
cana-2565	61	14	data	datum	NOUN
cana-2565	61	15	by	by	ADP
cana-2565	61	16	leveraging	leverage	VERB
cana-2565	61	17	multiple	multiple	ADJ
cana-2565	61	18	interconnected	interconnected	ADJ
cana-2565	61	19	layers	layer	NOUN
cana-2565	61	20	to	to	PART
cana-2565	61	21	learn	learn	VERB
cana-2565	61	22	hierarchical	hierarchical	ADJ
cana-2565	61	23	representations	representation	NOUN
cana-2565	61	24	(	(	PUNCT
cana-2565	61	25	lecun	lecun	PROPN
cana-2565	61	26	et	et	PROPN
cana-2565	61	27	al	al	PROPN
cana-2565	61	28	.	.	PROPN
cana-2565	61	29	,	,	PUNCT
cana-2565	61	30	2015	2015	NUM
cana-2565	61	31	)	)	PUNCT
cana-2565	61	32	.	.	PUNCT
cana-2565	62	1	meanwhile	meanwhile	ADV
cana-2565	62	2	,	,	PUNCT
cana-2565	62	3	random	random	ADJ
cana-2565	62	4	forests	forest	NOUN
cana-2565	62	5	,	,	PUNCT
cana-2565	62	6	a	a	DET
cana-2565	62	7	robust	robust	ADJ
cana-2565	62	8	ensemble	ensemble	ADJ
cana-2565	62	9	learning	learning	NOUN
cana-2565	62	10	method	method	NOUN
cana-2565	62	11	,	,	PUNCT
cana-2565	62	12	combine	combine	VERB
cana-2565	62	13	multiple	multiple	ADJ
cana-2565	62	14	decision	decision	NOUN
cana-2565	62	15	trees	tree	NOUN
cana-2565	62	16	to	to	PART
cana-2565	62	17	improve	improve	VERB
cana-2565	62	18	accuracy	accuracy	NOUN
cana-2565	62	19	and	and	CCONJ
cana-2565	62	20	reduce	reduce	VERB
cana-2565	62	21	overfitting	overfitte	VERB
cana-2565	62	22	.	.	PUNCT
cana-2565	63	1	their	their	PRON
cana-2565	63	2	ability	ability	NOUN
cana-2565	63	3	to	to	PART
cana-2565	63	4	handle	handle	VERB
cana-2565	63	5	both	both	DET
cana-2565	63	6	regression	regression	NOUN
cana-2565	63	7	and	and	CCONJ
cana-2565	63	8	classification	classification	NOUN
cana-2565	63	9	tasks	task	NOUN
cana-2565	63	10	has	have	AUX
cana-2565	63	11	made	make	VERB
cana-2565	63	12	them	they	PRON
cana-2565	63	13	a	a	DET
cana-2565	63	14	popular	popular	ADJ
cana-2565	63	15	choice	choice	NOUN
cana-2565	63	16	in	in	ADP
cana-2565	63	17	various	various	ADJ
cana-2565	63	18	applications	application	NOUN
cana-2565	63	19	(	(	PUNCT
cana-2565	63	20	breiman	breiman	NOUN
cana-2565	63	21	,	,	PUNCT
cana-2565	63	22	2001	2001	NUM
cana-2565	63	23	)	)	PUNCT
cana-2565	63	24	.	.	PUNCT
cana-2565	64	1	decision	decision	NOUN
cana-2565	64	2	tree	tree	NOUN
cana-2565	64	3	-	-	PUNCT
cana-2565	64	4	based	base	VERB
cana-2565	64	5	methods	method	NOUN
cana-2565	64	6	like	like	ADP
cana-2565	64	7	rep	rep	NOUN
cana-2565	64	8	tree	tree	NOUN
cana-2565	64	9	and	and	CCONJ
cana-2565	64	10	random	random	ADJ
cana-2565	64	11	tree	tree	NOUN
cana-2565	64	12	also	also	ADV
cana-2565	64	13	play	play	VERB
cana-2565	64	14	significant	significant	ADJ
cana-2565	64	15	roles	role	NOUN
cana-2565	64	16	in	in	ADP
cana-2565	64	17	machine	machine	NOUN
cana-2565	64	18	learning	learning	NOUN
cana-2565	64	19	.	.	PUNCT
cana-2565	65	1	rep	rep	PROPN
cana-2565	65	2	tree	tree	PROPN
cana-2565	65	3	employs	employ	VERB
cana-2565	65	4	reduced	reduce	VERB
cana-2565	65	5	error	error	NOUN
cana-2565	65	6	pruning	prune	VERB
cana-2565	65	7	to	to	PART
cana-2565	65	8	enhance	enhance	VERB
cana-2565	65	9	generalization	generalization	NOUN
cana-2565	65	10	,	,	PUNCT
cana-2565	65	11	making	make	VERB
cana-2565	65	12	it	it	PRON
cana-2565	65	13	efficient	efficient	ADJ
cana-2565	65	14	for	for	ADP
cana-2565	65	15	large	large	ADJ
cana-2565	65	16	datasets	dataset	NOUN
cana-2565	65	17	(	(	PUNCT
cana-2565	65	18	witten	witten	PROPN
cana-2565	65	19	et	et	PROPN
cana-2565	65	20	al	al	PROPN
cana-2565	65	21	.	.	PROPN
cana-2565	65	22	,	,	PUNCT
cana-2565	65	23	2017	2017	NUM
cana-2565	65	24	)	)	PUNCT
cana-2565	65	25	.	.	PUNCT
cana-2565	66	1	in	in	ADP
cana-2565	66	2	contrast	contrast	NOUN
cana-2565	66	3	,	,	PUNCT
cana-2565	66	4	random	random	ADJ
cana-2565	66	5	tree	tree	NOUN
cana-2565	66	6	introduces	introduce	VERB
cana-2565	66	7	randomness	randomness	NOUN
cana-2565	66	8	in	in	ADP
cana-2565	66	9	feature	feature	NOUN
cana-2565	66	10	selection	selection	NOUN
cana-2565	66	11	during	during	ADP
cana-2565	66	12	tree	tree	NOUN
cana-2565	66	13	construction	construction	NOUN
cana-2565	66	14	,	,	PUNCT
cana-2565	66	15	fostering	foster	VERB
cana-2565	66	16	diversity	diversity	NOUN
cana-2565	66	17	in	in	ADP
cana-2565	66	18	predictions	prediction	NOUN
cana-2565	66	19	and	and	CCONJ
cana-2565	66	20	improving	improve	VERB
cana-2565	66	21	robustness	robustness	NOUN
cana-2565	66	22	.	.	PUNCT
cana-2565	67	1	these	these	DET
cana-2565	67	2	models	model	NOUN
cana-2565	67	3	offer	offer	VERB
cana-2565	67	4	flexibility	flexibility	NOUN
cana-2565	67	5	and	and	CCONJ
cana-2565	67	6	interpretability	interpretability	NOUN
cana-2565	67	7	,	,	PUNCT
cana-2565	67	8	making	make	VERB
cana-2565	67	9	them	they	PRON
cana-2565	67	10	particularly	particularly	ADV
cana-2565	67	11	useful	useful	ADJ
cana-2565	67	12	in	in	ADP
cana-2565	67	13	scenarios	scenario	NOUN
cana-2565	67	14	requiring	require	VERB
cana-2565	67	15	transparent	transparent	ADJ
cana-2565	67	16	decision	decision	NOUN
cana-2565	67	17	-	-	PUNCT
cana-2565	67	18	making	making	NOUN
cana-2565	67	19	.	.	PUNCT
cana-2565	68	1	the	the	DET
cana-2565	68	2	effectiveness	effectiveness	NOUN
cana-2565	68	3	of	of	ADP
cana-2565	68	4	these	these	DET
cana-2565	68	5	algorithms	algorithm	NOUN
cana-2565	68	6	is	be	AUX
cana-2565	68	7	typically	typically	ADV
cana-2565	68	8	evaluated	evaluate	VERB
cana-2565	68	9	using	use	VERB
cana-2565	68	10	performance	performance	NOUN
cana-2565	68	11	metrics	metric	NOUN
cana-2565	68	12	such	such	ADJ
cana-2565	68	13	as	as	ADP
cana-2565	68	14	mean	mean	NOUN
cana-2565	68	15	squared	square	VERB
cana-2565	68	16	error	error	NOUN
cana-2565	68	17	(	(	PUNCT
cana-2565	68	18	mse	mse	NOUN
cana-2565	68	19	)	)	PUNCT
cana-2565	68	20	for	for	ADP
cana-2565	68	21	regression	regression	NOUN
cana-2565	68	22	and	and	CCONJ
cana-2565	68	23	accuracy	accuracy	NOUN
cana-2565	68	24	,	,	PUNCT
cana-2565	68	25	precision	precision	NOUN
cana-2565	68	26	,	,	PUNCT
cana-2565	68	27	recall	recall	NOUN
cana-2565	68	28	,	,	PUNCT
cana-2565	68	29	and	and	CCONJ
cana-2565	68	30	f1	f1	NOUN
cana-2565	68	31	-	-	PUNCT
cana-2565	68	32	score	score	NOUN
cana-2565	68	33	for	for	ADP
cana-2565	68	34	classification	classification	NOUN
cana-2565	68	35	tasks	task	NOUN
cana-2565	68	36	.	.	PUNCT
cana-2565	69	1	by	by	ADP
cana-2565	69	2	applying	apply	VERB
cana-2565	69	3	these	these	DET
cana-2565	69	4	models	model	NOUN
cana-2565	69	5	and	and	CCONJ
cana-2565	69	6	metrics	metric	NOUN
cana-2565	69	7	to	to	PART
cana-2565	69	8	diverse	diverse	VERB
cana-2565	69	9	datasets	dataset	NOUN
cana-2565	69	10	,	,	PUNCT
cana-2565	69	11	researchers	researcher	NOUN
cana-2565	69	12	can	can	AUX
cana-2565	69	13	identify	identify	VERB
cana-2565	69	14	the	the	DET
cana-2565	69	15	most	most	ADV
cana-2565	69	16	suitable	suitable	ADJ
cana-2565	69	17	approaches	approach	NOUN
cana-2565	69	18	for	for	ADP
cana-2565	69	19	specific	specific	ADJ
cana-2565	69	20	problems	problem	NOUN
cana-2565	69	21	,	,	PUNCT
cana-2565	69	22	ensuring	ensure	VERB
cana-2565	69	23	reliable	reliable	ADJ
cana-2565	69	24	and	and	CCONJ
cana-2565	69	25	actionable	actionable	ADJ
cana-2565	69	26	outcomes	outcome	NOUN
cana-2565	69	27	.	.	PUNCT
cana-2565	70	1	3.0	3.0	NUM
cana-2565	70	2	backgrounds	background	NOUN
cana-2565	70	3	and	and	CCONJ
cana-2565	70	4	methodologies	methodology	NOUN
cana-2565	70	5	parkinson	parkinson	NOUN
cana-2565	70	6	’s	’s	PART
cana-2565	70	7	disease	disease	NOUN
cana-2565	70	8	(	(	PUNCT
cana-2565	70	9	pd	pd	NOUN
cana-2565	70	10	)	)	PUNCT
cana-2565	70	11	poses	pose	VERB
cana-2565	70	12	considerable	considerable	ADJ
cana-2565	70	13	challenges	challenge	NOUN
cana-2565	70	14	for	for	ADP
cana-2565	70	15	early	early	ADJ
cana-2565	70	16	detection	detection	NOUN
cana-2565	70	17	due	due	ADP
cana-2565	70	18	to	to	ADP
cana-2565	70	19	its	its	PRON
cana-2565	70	20	diverse	diverse	ADJ
cana-2565	70	21	and	and	CCONJ
cana-2565	70	22	intricate	intricate	ADJ
cana-2565	70	23	nature	nature	NOUN
cana-2565	70	24	.	.	PUNCT
cana-2565	71	1	addressing	address	VERB
cana-2565	71	2	this	this	DET
cana-2565	71	3	complexity	complexity	NOUN
cana-2565	71	4	requires	require	VERB
cana-2565	71	5	sophisticated	sophisticated	ADJ
cana-2565	71	6	computational	computational	ADJ
cana-2565	71	7	methods	method	NOUN
cana-2565	71	8	capable	capable	ADJ
cana-2565	71	9	of	of	ADP
cana-2565	71	10	effectively	effectively	ADV
cana-2565	71	11	processing	process	VERB
cana-2565	71	12	multimodal	multimodal	ADJ
cana-2565	71	13	datasets	dataset	NOUN
cana-2565	71	14	,	,	PUNCT
cana-2565	71	15	including	include	VERB
cana-2565	71	16	clinical	clinical	ADJ
cana-2565	71	17	,	,	PUNCT
cana-2565	71	18	vocal	vocal	ADJ
cana-2565	71	19	,	,	PUNCT
cana-2565	71	20	and	and	CCONJ
cana-2565	71	21	movement	movement	NOUN
cana-2565	71	22	-	-	PUNCT
cana-2565	71	23	related	relate	VERB
cana-2565	71	24	data	datum	NOUN
cana-2565	71	25	.	.	PUNCT
cana-2565	72	1	in	in	ADP
cana-2565	72	2	response	response	NOUN
cana-2565	72	3	,	,	PUNCT
cana-2565	72	4	we	we	PRON
cana-2565	72	5	introduce	introduce	VERB
cana-2565	72	6	an	an	DET
cana-2565	72	7	innovative	innovative	ADJ
cana-2565	72	8	deep	deep	ADJ
cana-2565	72	9	learning	learning	NOUN
cana-2565	72	10	model	model	NOUN
cana-2565	72	11	designed	design	VERB
cana-2565	72	12	specifically	specifically	ADV
cana-2565	72	13	for	for	ADP
cana-2565	72	14	pd	pd	PROPN
cana-2565	72	15	classification	classification	NOUN
cana-2565	72	16	,	,	PUNCT
cana-2565	72	17	coupled	couple	VERB
cana-2565	72	18	with	with	ADP
cana-2565	72	19	a	a	DET
cana-2565	72	20	novel	novel	ADJ
cana-2565	72	21	optimization	optimization	NOUN
cana-2565	72	22	technique	technique	NOUN
cana-2565	72	23	aimed	aim	VERB
cana-2565	72	24	at	at	ADP
cana-2565	72	25	enhancing	enhance	VERB
cana-2565	72	26	its	its	PRON
cana-2565	72	27	accuracy	accuracy	NOUN
cana-2565	72	28	and	and	CCONJ
cana-2565	72	29	generalization	generalization	NOUN
cana-2565	72	30	.	.	PUNCT
cana-2565	73	1	communications	communication	NOUN
cana-2565	73	2	on	on	ADP
cana-2565	73	3	applied	apply	VERB
cana-2565	73	4	nonlinear	nonlinear	ADJ
cana-2565	73	5	analysis	analysis	NOUN
cana-2565	73	6	issn	issn	NOUN
cana-2565	73	7	:	:	PUNCT
cana-2565	73	8	1074	1074	NUM
cana-2565	73	9	-	-	PUNCT
cana-2565	73	10	133x	133x	NUM
cana-2565	73	11	vol	vol	NOUN
cana-2565	73	12	32	32	NUM
cana-2565	73	13	no	no	NOUN
cana-2565	73	14	.	.	PUNCT
cana-2565	74	1	3s	3s	NUM
cana-2565	74	2	(	(	PUNCT
cana-2565	74	3	2025	2025	NUM
cana-2565	74	4	)	)	PUNCT
cana-2565	74	5	107	107	NUM
cana-2565	74	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2565	74	7	3.1	3.1	NUM
cana-2565	74	8	proposed	propose	VERB
cana-2565	74	9	deep	deep	ADJ
cana-2565	74	10	learning	learning	NOUN
cana-2565	74	11	model	model	NOUN
cana-2565	74	12	cnnbigru	cnnbigru	PROPN
cana-2565	74	13	-	-	PROPN
cana-2565	74	14	dgr	dgr	PROPN
cana-2565	74	15	3.1.1	3.1.1	NUM
cana-2565	74	16	proposed	propose	VERB
cana-2565	74	17	deep	deep	ADJ
cana-2565	74	18	learning	learning	NOUN
cana-2565	74	19	cnnbigru	cnnbigru	VERB
cana-2565	74	20	the	the	DET
cana-2565	74	21	proposed	propose	VERB
cana-2565	74	22	model	model	NOUN
cana-2565	74	23	,	,	PUNCT
cana-2565	74	24	named	name	VERB
cana-2565	74	25	cnn	cnn	PROPN
cana-2565	74	26	-	-	PUNCT
cana-2565	74	27	bigru	bigru	NOUN
cana-2565	74	28	,	,	PUNCT
cana-2565	74	29	leverages	leverage	VERB
cana-2565	74	30	the	the	DET
cana-2565	74	31	combined	combine	VERB
cana-2565	74	32	strengths	strength	NOUN
cana-2565	74	33	of	of	ADP
cana-2565	74	34	convolutional	convolutional	ADJ
cana-2565	74	35	neural	neural	ADJ
cana-2565	74	36	networks	network	NOUN
cana-2565	74	37	(	(	PUNCT
cana-2565	74	38	cnns	cnns	PROPN
cana-2565	74	39	)	)	PUNCT
cana-2565	74	40	and	and	CCONJ
cana-2565	74	41	bidirectional	bidirectional	ADJ
cana-2565	74	42	gated	gate	VERB
cana-2565	74	43	recurrent	recurrent	ADJ
cana-2565	74	44	units	unit	NOUN
cana-2565	74	45	(	(	PUNCT
cana-2565	74	46	bigrus	bigrus	NOUN
cana-2565	74	47	)	)	PUNCT
cana-2565	74	48	.	.	PUNCT
cana-2565	75	1	cnns	cnns	PROPN
cana-2565	75	2	are	be	AUX
cana-2565	75	3	highly	highly	ADV
cana-2565	75	4	effective	effective	ADJ
cana-2565	75	5	in	in	ADP
cana-2565	75	6	capturing	capture	VERB
cana-2565	75	7	spatial	spatial	ADJ
cana-2565	75	8	features	feature	NOUN
cana-2565	75	9	from	from	ADP
cana-2565	75	10	structured	structured	ADJ
cana-2565	75	11	inputs	input	NOUN
cana-2565	75	12	,	,	PUNCT
cana-2565	75	13	while	while	SCONJ
cana-2565	75	14	bigrus	bigrus	NOUN
cana-2565	75	15	excel	excel	VERB
cana-2565	75	16	at	at	ADP
cana-2565	75	17	learning	learn	VERB
cana-2565	75	18	temporal	temporal	ADJ
cana-2565	75	19	dependencies	dependency	NOUN
cana-2565	75	20	from	from	ADP
cana-2565	75	21	sequential	sequential	ADJ
cana-2565	75	22	datasets	dataset	NOUN
cana-2565	75	23	.	.	PUNCT
cana-2565	76	1	this	this	DET
cana-2565	76	2	hybrid	hybrid	ADJ
cana-2565	76	3	approach	approach	NOUN
cana-2565	76	4	ensures	ensure	VERB
cana-2565	76	5	the	the	DET
cana-2565	76	6	model	model	NOUN
cana-2565	76	7	is	be	AUX
cana-2565	76	8	versatile	versatile	ADJ
cana-2565	76	9	and	and	CCONJ
cana-2565	76	10	capable	capable	ADJ
cana-2565	76	11	of	of	ADP
cana-2565	76	12	processing	process	VERB
cana-2565	76	13	a	a	DET
cana-2565	76	14	wide	wide	ADJ
cana-2565	76	15	range	range	NOUN
cana-2565	76	16	of	of	ADP
cana-2565	76	17	static	static	ADJ
cana-2565	76	18	and	and	CCONJ
cana-2565	76	19	dynamic	dynamic	ADJ
cana-2565	76	20	pd	pd	ADJ
cana-2565	76	21	-	-	PUNCT
cana-2565	76	22	related	relate	VERB
cana-2565	76	23	data	datum	NOUN
cana-2565	76	24	(	(	PUNCT
cana-2565	76	25	lecun	lecun	PROPN
cana-2565	76	26	et	et	PROPN
cana-2565	76	27	al	al	PROPN
cana-2565	76	28	.	.	PROPN
cana-2565	76	29	,	,	PUNCT
cana-2565	76	30	2015	2015	NUM
cana-2565	76	31	;	;	PUNCT
cana-2565	76	32	cho	cho	PROPN
cana-2565	76	33	et	et	PROPN
cana-2565	76	34	al	al	PROPN
cana-2565	76	35	.	.	PROPN
cana-2565	76	36	,	,	PUNCT
cana-2565	76	37	2014	2014	NUM
cana-2565	76	38	)	)	PUNCT
cana-2565	76	39	.	.	PUNCT
cana-2565	77	1	3.1.2	3.1.2	NUM
cana-2565	77	2	innovative	innovative	ADJ
cana-2565	77	3	optimization	optimization	NOUN
cana-2565	77	4	technique	technique	NOUN
cana-2565	77	5	to	to	PART
cana-2565	77	6	further	far	ADV
cana-2565	77	7	improve	improve	VERB
cana-2565	77	8	the	the	DET
cana-2565	77	9	model	model	NOUN
cana-2565	77	10	's	's	PART
cana-2565	77	11	performance	performance	NOUN
cana-2565	77	12	,	,	PUNCT
cana-2565	77	13	we	we	PRON
cana-2565	77	14	propose	propose	VERB
cana-2565	77	15	dynamic	dynamic	ADJ
cana-2565	77	16	gradient	gradient	NOUN
cana-2565	77	17	regularization	regularization	NOUN
cana-2565	77	18	(	(	PUNCT
cana-2565	77	19	dgr	dgr	PROPN
cana-2565	77	20	)	)	PUNCT
cana-2565	77	21	.	.	PUNCT
cana-2565	78	1	this	this	DET
cana-2565	78	2	optimization	optimization	NOUN
cana-2565	78	3	method	method	NOUN
cana-2565	78	4	introduces	introduce	VERB
cana-2565	78	5	a	a	DET
cana-2565	78	6	flexible	flexible	ADJ
cana-2565	78	7	regularization	regularization	NOUN
cana-2565	78	8	term	term	NOUN
cana-2565	78	9	in	in	ADP
cana-2565	78	10	the	the	DET
cana-2565	78	11	loss	loss	NOUN
cana-2565	78	12	function	function	NOUN
cana-2565	78	13	that	that	PRON
cana-2565	78	14	adapts	adapts	AUX
cana-2565	78	15	based	base	VERB
cana-2565	78	16	on	on	ADP
cana-2565	78	17	the	the	DET
cana-2565	78	18	magnitude	magnitude	NOUN
cana-2565	78	19	of	of	ADP
cana-2565	78	20	gradients	gradient	NOUN
cana-2565	78	21	.	.	PUNCT
cana-2565	79	1	by	by	ADP
cana-2565	79	2	reducing	reduce	VERB
cana-2565	79	3	the	the	DET
cana-2565	79	4	sensitivity	sensitivity	NOUN
cana-2565	79	5	to	to	ADP
cana-2565	79	6	noisy	noisy	ADJ
cana-2565	79	7	gradients	gradient	NOUN
cana-2565	79	8	,	,	PUNCT
cana-2565	79	9	dgr	dgr	PROPN
cana-2565	79	10	ensures	ensure	VERB
cana-2565	79	11	smooth	smooth	ADJ
cana-2565	79	12	convergence	convergence	NOUN
cana-2565	79	13	and	and	CCONJ
cana-2565	79	14	more	more	ADV
cana-2565	79	15	effective	effective	ADJ
cana-2565	79	16	learning	learning	NOUN
cana-2565	79	17	.	.	PUNCT
cana-2565	80	1	unlike	unlike	ADP
cana-2565	80	2	conventional	conventional	ADJ
cana-2565	80	3	optimizers	optimizer	NOUN
cana-2565	80	4	such	such	ADJ
cana-2565	80	5	as	as	ADP
cana-2565	80	6	adam	adam	PROPN
cana-2565	80	7	,	,	PUNCT
cana-2565	80	8	dgr	dgr	PROPN
cana-2565	80	9	fine	fine	ADJ
cana-2565	80	10	-	-	PUNCT
cana-2565	80	11	tunes	tune	NOUN
cana-2565	80	12	learning	learn	VERB
cana-2565	80	13	rates	rate	NOUN
cana-2565	80	14	layer	layer	NOUN
cana-2565	80	15	-	-	PUNCT
cana-2565	80	16	by	by	ADP
cana-2565	80	17	-	-	PUNCT
cana-2565	80	18	layer	layer	NOUN
cana-2565	80	19	,	,	PUNCT
cana-2565	80	20	optimizing	optimize	VERB
cana-2565	80	21	the	the	DET
cana-2565	80	22	training	training	NOUN
cana-2565	80	23	process	process	NOUN
cana-2565	80	24	and	and	CCONJ
cana-2565	80	25	reducing	reduce	VERB
cana-2565	80	26	convergence	convergence	NOUN
cana-2565	80	27	time	time	NOUN
cana-2565	80	28	(	(	PUNCT
cana-2565	80	29	kingma	kingma	PROPN
cana-2565	80	30	&	&	CCONJ
cana-2565	80	31	ba	ba	PROPN
cana-2565	80	32	,	,	PUNCT
cana-2565	80	33	2015	2015	NUM
cana-2565	80	34	)	)	PUNCT
cana-2565	80	35	.	.	PUNCT
cana-2565	81	1	3.1.3	3.1.3	NUM
cana-2565	81	2	experimental	experimental	ADJ
cana-2565	81	3	validation	validation	NOUN
cana-2565	81	4	the	the	DET
cana-2565	81	5	effectiveness	effectiveness	NOUN
cana-2565	81	6	of	of	ADP
cana-2565	81	7	the	the	DET
cana-2565	81	8	hybrid	hybrid	ADJ
cana-2565	81	9	cnn	cnn	PROPN
cana-2565	81	10	-	-	PUNCT
cana-2565	81	11	bigru	bigru	PROPN
cana-2565	81	12	and	and	CCONJ
cana-2565	81	13	dgr	dgr	PROPN
cana-2565	81	14	optimization	optimization	NOUN
cana-2565	81	15	technique	technique	NOUN
cana-2565	81	16	was	be	AUX
cana-2565	81	17	tested	test	VERB
cana-2565	81	18	on	on	ADP
cana-2565	81	19	a	a	DET
cana-2565	81	20	diverse	diverse	ADJ
cana-2565	81	21	dataset	dataset	NOUN
cana-2565	81	22	containing	contain	VERB
cana-2565	81	23	clinical	clinical	ADJ
cana-2565	81	24	,	,	PUNCT
cana-2565	81	25	vocal	vocal	ADJ
cana-2565	81	26	,	,	PUNCT
cana-2565	81	27	and	and	CCONJ
cana-2565	81	28	movement	movement	NOUN
cana-2565	81	29	-	-	PUNCT
cana-2565	81	30	related	relate	VERB
cana-2565	81	31	features	feature	NOUN
cana-2565	81	32	.	.	PUNCT
cana-2565	82	1	the	the	DET
cana-2565	82	2	preprocessing	preprocessing	NOUN
cana-2565	82	3	steps	step	NOUN
cana-2565	82	4	included	include	VERB
cana-2565	82	5	normalization	normalization	NOUN
cana-2565	82	6	and	and	CCONJ
cana-2565	82	7	advanced	advanced	ADJ
cana-2565	82	8	feature	feature	NOUN
cana-2565	82	9	engineering	engineering	NOUN
cana-2565	82	10	techniques	technique	NOUN
cana-2565	82	11	,	,	PUNCT
cana-2565	82	12	such	such	ADJ
cana-2565	82	13	as	as	ADP
cana-2565	82	14	spectral	spectral	ADJ
cana-2565	82	15	analysis	analysis	NOUN
cana-2565	82	16	for	for	ADP
cana-2565	82	17	vocal	vocal	ADJ
cana-2565	82	18	data	datum	NOUN
cana-2565	82	19	and	and	CCONJ
cana-2565	82	20	wavelet	wavelet	NOUN
cana-2565	82	21	transformations	transformation	NOUN
cana-2565	82	22	for	for	ADP
cana-2565	82	23	movement	movement	NOUN
cana-2565	82	24	signals	signal	NOUN
cana-2565	82	25	(	(	PUNCT
cana-2565	82	26	sakar	sakar	NOUN
cana-2565	82	27	et	et	PROPN
cana-2565	82	28	al	al	PROPN
cana-2565	82	29	.	.	PROPN
cana-2565	82	30	,	,	PUNCT
cana-2565	82	31	2013	2013	NUM
cana-2565	82	32	)	)	PUNCT
cana-2565	82	33	.	.	PUNCT
cana-2565	83	1	the	the	DET
cana-2565	83	2	model	model	NOUN
cana-2565	83	3	's	's	PART
cana-2565	83	4	performance	performance	NOUN
cana-2565	83	5	was	be	AUX
cana-2565	83	6	assessed	assess	VERB
cana-2565	83	7	using	use	VERB
cana-2565	83	8	metrics	metric	NOUN
cana-2565	83	9	like	like	ADP
cana-2565	83	10	accuracy	accuracy	NOUN
cana-2565	83	11	,	,	PUNCT
cana-2565	83	12	precision	precision	NOUN
cana-2565	83	13	,	,	PUNCT
cana-2565	83	14	recall	recall	NOUN
cana-2565	83	15	,	,	PUNCT
cana-2565	83	16	f1	f1	NOUN
cana-2565	83	17	-	-	PUNCT
cana-2565	83	18	score	score	NOUN
cana-2565	83	19	,	,	PUNCT
cana-2565	83	20	and	and	CCONJ
cana-2565	83	21	area	area	NOUN
cana-2565	83	22	under	under	ADP
cana-2565	83	23	the	the	DET
cana-2565	83	24	curve	curve	NOUN
cana-2565	83	25	(	(	PUNCT
cana-2565	83	26	auc	auc	NOUN
cana-2565	83	27	)	)	PUNCT
cana-2565	83	28	.	.	PUNCT
cana-2565	84	1	3.1.4	3.1.4	NUM
cana-2565	84	2	algorithms	algorithm	NOUN
cana-2565	84	3	for	for	ADP
cana-2565	84	4	cnnbigru	cnnbigru	PROPN
cana-2565	84	5	-	-	PUNCT
cana-2565	84	6	dgr	dgr	PROPN
cana-2565	84	7	1	1	NUM
cana-2565	84	8	.	.	PUNCT
cana-2565	84	9	dual	dual	ADJ
cana-2565	84	10	input	input	NOUN
cana-2565	84	11	processing	processing	NOUN
cana-2565	84	12	:	:	PUNCT
cana-2565	84	13	simultaneously	simultaneously	ADV
cana-2565	84	14	processes	process	VERB
cana-2565	84	15	clinical	clinical	ADJ
cana-2565	84	16	and	and	CCONJ
cana-2565	84	17	sequential	sequential	ADJ
cana-2565	84	18	data	datum	NOUN
cana-2565	84	19	,	,	PUNCT
cana-2565	84	20	such	such	ADJ
cana-2565	84	21	as	as	ADP
cana-2565	84	22	vocal	vocal	ADJ
cana-2565	84	23	patterns	pattern	NOUN
cana-2565	84	24	and	and	CCONJ
cana-2565	84	25	accelerometer	accelerometer	NOUN
cana-2565	84	26	readings	reading	NOUN
cana-2565	84	27	,	,	PUNCT
cana-2565	84	28	to	to	PART
cana-2565	84	29	enhance	enhance	VERB
cana-2565	84	30	its	its	PRON
cana-2565	84	31	analytical	analytical	ADJ
cana-2565	84	32	capability	capability	NOUN
cana-2565	84	33	.	.	PUNCT
cana-2565	85	1	2	2	X
cana-2565	85	2	.	.	X
cana-2565	85	3	attention	attention	NOUN
cana-2565	85	4	mechanism	mechanism	NOUN
cana-2565	85	5	:	:	PUNCT
cana-2565	85	6	a	a	DET
cana-2565	85	7	post	post	ADJ
cana-2565	85	8	-	-	ADJ
cana-2565	85	9	bigru	bigru	ADJ
cana-2565	85	10	attention	attention	NOUN
cana-2565	85	11	layer	layer	NOUN
cana-2565	85	12	emphasizes	emphasize	VERB
cana-2565	85	13	the	the	DET
cana-2565	85	14	most	most	ADV
cana-2565	85	15	critical	critical	ADJ
cana-2565	85	16	temporal	temporal	ADJ
cana-2565	85	17	features	feature	NOUN
cana-2565	85	18	,	,	PUNCT
cana-2565	85	19	improving	improve	VERB
cana-2565	85	20	interpretability	interpretability	NOUN
cana-2565	85	21	and	and	CCONJ
cana-2565	85	22	prediction	prediction	NOUN
cana-2565	85	23	outcomes	outcome	NOUN
cana-2565	85	24	.	.	PUNCT
cana-2565	86	1	3	3	X
cana-2565	86	2	.	.	X
cana-2565	86	3	adaptive	adaptive	ADJ
cana-2565	86	4	dropout	dropout	NOUN
cana-2565	86	5	:	:	PUNCT
cana-2565	86	6	dynamically	dynamically	ADV
cana-2565	86	7	adjusts	adjust	VERB
cana-2565	86	8	dropout	dropout	NOUN
cana-2565	86	9	rates	rate	NOUN
cana-2565	86	10	during	during	ADP
cana-2565	86	11	training	training	NOUN
cana-2565	86	12	to	to	PART
cana-2565	86	13	minimize	minimize	VERB
cana-2565	86	14	overfitting	overfitte	VERB
cana-2565	86	15	and	and	CCONJ
cana-2565	86	16	improve	improve	VERB
cana-2565	86	17	performance	performance	NOUN
cana-2565	86	18	.	.	PUNCT
cana-2565	87	1	4	4	X
cana-2565	87	2	.	.	X
cana-2565	87	3	gradient	gradient	ADJ
cana-2565	87	4	smoothing	smoothing	NOUN
cana-2565	87	5	:	:	PUNCT
cana-2565	87	6	stabilizes	stabilize	VERB
cana-2565	87	7	high	high	ADJ
cana-2565	87	8	-	-	PUNCT
cana-2565	87	9	gradient	gradient	NOUN
cana-2565	87	10	updates	update	NOUN
cana-2565	87	11	to	to	PART
cana-2565	87	12	reduce	reduce	VERB
cana-2565	87	13	overfitting	overfitte	VERB
cana-2565	87	14	and	and	CCONJ
cana-2565	87	15	ensure	ensure	VERB
cana-2565	87	16	steady	steady	ADJ
cana-2565	87	17	training	training	NOUN
cana-2565	87	18	.	.	PUNCT
cana-2565	88	1	5	5	X
cana-2565	88	2	.	.	X
cana-2565	88	3	layer	layer	NOUN
cana-2565	88	4	-	-	PUNCT
cana-2565	88	5	specific	specific	ADJ
cana-2565	88	6	adjustments	adjustment	NOUN
cana-2565	88	7	:	:	PUNCT
cana-2565	88	8	tailors	tailor	NOUN
cana-2565	88	9	learning	learn	VERB
cana-2565	88	10	rates	rate	NOUN
cana-2565	88	11	for	for	ADP
cana-2565	88	12	individual	individual	ADJ
cana-2565	88	13	layers	layer	NOUN
cana-2565	88	14	to	to	PART
cana-2565	88	15	maximize	maximize	VERB
cana-2565	88	16	performance	performance	NOUN
cana-2565	88	17	in	in	ADP
cana-2565	88	18	complex	complex	ADJ
cana-2565	88	19	architectures	architecture	NOUN
cana-2565	88	20	.	.	PUNCT
cana-2565	89	1	6	6	X
cana-2565	89	2	.	.	X
cana-2565	89	3	early	early	ADJ
cana-2565	89	4	stopping	stop	VERB
cana-2565	89	5	integration	integration	NOUN
cana-2565	89	6	:	:	PUNCT
cana-2565	89	7	complements	complement	VERB
cana-2565	89	8	early	early	ADV
cana-2565	89	9	stopping	stop	VERB
cana-2565	89	10	techniques	technique	NOUN
cana-2565	89	11	to	to	PART
cana-2565	89	12	avoid	avoid	VERB
cana-2565	89	13	overfitting	overfitte	VERB
cana-2565	89	14	while	while	SCONJ
cana-2565	89	15	preserving	preserve	VERB
cana-2565	89	16	high	high	ADJ
cana-2565	89	17	accuracy	accuracy	NOUN
cana-2565	89	18	.	.	PUNCT
cana-2565	90	1	4.0	4.0	NUM
cana-2565	90	2	experimental	experimental	ADJ
cana-2565	90	3	results	result	NOUN
cana-2565	90	4	the	the	DET
cana-2565	90	5	dataset	dataset	NOUN
cana-2565	90	6	used	use	VERB
cana-2565	90	7	for	for	ADP
cana-2565	90	8	this	this	DET
cana-2565	90	9	study	study	NOUN
cana-2565	90	10	was	be	AUX
cana-2565	90	11	obtained	obtain	VERB
cana-2565	90	12	from	from	ADP
cana-2565	90	13	the	the	DET
cana-2565	90	14	publicly	publicly	ADV
cana-2565	90	15	available	available	ADJ
cana-2565	90	16	kaggle	kaggle	ADJ
cana-2565	90	17	repository	repository	NOUN
cana-2565	90	18	.	.	PUNCT
cana-2565	91	1	the	the	DET
cana-2565	91	2	parkinson	parkinson	NOUN
cana-2565	91	3	's	's	PART
cana-2565	91	4	dataset	dataset	NOUN
cana-2565	91	5	comprises	comprise	VERB
cana-2565	91	6	24	24	NUM
cana-2565	91	7	features	feature	NOUN
cana-2565	91	8	,	,	PUNCT
cana-2565	91	9	encompassing	encompass	VERB
cana-2565	91	10	various	various	ADJ
cana-2565	91	11	categories	category	NOUN
cana-2565	91	12	of	of	ADP
cana-2565	91	13	data	datum	NOUN
cana-2565	91	14	such	such	ADJ
cana-2565	91	15	as	as	ADP
cana-2565	91	16	name	name	NOUN
cana-2565	91	17	,	,	PUNCT
cana-2565	91	18	mdvp	mdvp	NOUN
cana-2565	91	19	:	:	PUNCT
cana-2565	91	20	fo(hz	fo(hz	PROPN
cana-2565	91	21	)	)	PUNCT
cana-2565	91	22	,	,	PUNCT
cana-2565	91	23	mdvp	mdvp	NOUN
cana-2565	91	24	:	:	PUNCT
cana-2565	91	25	fhi(hz	fhi(hz	NUM
cana-2565	91	26	)	)	PUNCT
cana-2565	91	27	,	,	PUNCT
cana-2565	91	28	mdvp	mdvp	NOUN
cana-2565	91	29	:	:	PUNCT
cana-2565	91	30	flo(hz	flo(hz	NUM
cana-2565	91	31	)	)	PUNCT
cana-2565	91	32	,	,	PUNCT
cana-2565	91	33	mdvp	mdvp	NOUN
cana-2565	91	34	:	:	PUNCT
cana-2565	91	35	jitter(%	jitter(%	PROPN
cana-2565	91	36	)	)	PUNCT
cana-2565	91	37	,	,	PUNCT
cana-2565	91	38	mdvp	mdvp	NOUN
cana-2565	91	39	:	:	PUNCT
cana-2565	91	40	jitter(abs	jitter(ab	NOUN
cana-2565	91	41	)	)	PUNCT
cana-2565	91	42	,	,	PUNCT
cana-2565	91	43	mdvp	mdvp	NOUN
cana-2565	91	44	:	:	PUNCT
cana-2565	91	45	rap	rap	NOUN
cana-2565	91	46	,	,	PUNCT
cana-2565	91	47	mdvp	mdvp	NOUN
cana-2565	91	48	:	:	PUNCT
cana-2565	91	49	ppq	ppq	PROPN
cana-2565	91	50	,	,	PUNCT
cana-2565	91	51	jitter	jitter	NOUN
cana-2565	91	52	:	:	PUNCT
cana-2565	91	53	ddp	ddp	NOUN
cana-2565	91	54	,	,	PUNCT
cana-2565	91	55	mdvp	mdvp	NOUN
cana-2565	91	56	:	:	PUNCT
cana-2565	91	57	shimmer	shimmer	ADJ
cana-2565	91	58	,	,	PUNCT
cana-2565	91	59	mdvp	mdvp	NOUN
cana-2565	91	60	:	:	PUNCT
cana-2565	91	61	shimmer(db	shimmer(db	NOUN
cana-2565	91	62	)	)	PUNCT
cana-2565	91	63	,	,	PUNCT
cana-2565	91	64	shimmer	shimmer	ADJ
cana-2565	91	65	:	:	PUNCT
cana-2565	91	66	apq3	apq3	ADJ
cana-2565	91	67	,	,	PUNCT
cana-2565	91	68	shimmer	shimmer	ADJ
cana-2565	91	69	:	:	PUNCT
cana-2565	91	70	apq5	apq5	PROPN
cana-2565	91	71	,	,	PUNCT
cana-2565	91	72	mdvp	mdvp	NOUN
cana-2565	91	73	:	:	PUNCT
cana-2565	91	74	apq	apq	ADJ
cana-2565	91	75	,	,	PUNCT
cana-2565	91	76	shimmer	shimmer	ADJ
cana-2565	91	77	:	:	PUNCT
cana-2565	91	78	dda	dda	ADJ
cana-2565	91	79	,	,	PUNCT
cana-2565	91	80	nhr	nhr	PROPN
cana-2565	91	81	,	,	PUNCT
cana-2565	91	82	hnr	hnr	PROPN
cana-2565	91	83	,	,	PUNCT
cana-2565	91	84	rpde	rpde	NOUN
cana-2565	91	85	,	,	PUNCT
cana-2565	91	86	dfa	dfa	PROPN
cana-2565	91	87	,	,	PUNCT
cana-2565	91	88	spread1	spread1	PROPN
cana-2565	91	89	,	,	PUNCT
cana-2565	91	90	spread2	spread2	PROPN
cana-2565	91	91	,	,	PUNCT
cana-2565	91	92	d2	d2	PROPN
cana-2565	91	93	,	,	PUNCT
cana-2565	91	94	ppe	ppe	NOUN
cana-2565	91	95	,	,	PUNCT
cana-2565	91	96	and	and	CCONJ
cana-2565	91	97	status	status	NOUN
cana-2565	91	98	(	(	PUNCT
cana-2565	91	99	kaggle	kaggle	PROPN
cana-2565	91	100	)	)	PUNCT
cana-2565	91	101	.	.	PUNCT
cana-2565	92	1	the	the	DET
cana-2565	92	2	dataset	dataset	NOUN
cana-2565	92	3	is	be	AUX
cana-2565	92	4	composed	compose	VERB
cana-2565	92	5	of	of	ADP
cana-2565	92	6	a	a	DET
cana-2565	92	7	range	range	NOUN
cana-2565	92	8	of	of	ADP
cana-2565	92	9	biomedical	biomedical	ADJ
cana-2565	92	10	voice	voice	NOUN
cana-2565	92	11	measurements	measurement	NOUN
cana-2565	92	12	with	with	ADP
cana-2565	92	13	parkinson	parkinson	NOUN
cana-2565	92	14	's	's	PART
cana-2565	92	15	disease	disease	NOUN
cana-2565	92	16	(	(	PUNCT
cana-2565	92	17	pd	pd	NOUN
cana-2565	92	18	)	)	PUNCT
cana-2565	92	19	.	.	PUNCT
cana-2565	93	1	the	the	DET
cana-2565	93	2	attribute	attribute	NOUN
cana-2565	93	3	details	detail	NOUN
cana-2565	93	4	are	be	AUX
cana-2565	93	5	outlined	outline	VERB
cana-2565	93	6	as	as	SCONJ
cana-2565	93	7	follows	follow	VERB
cana-2565	93	8	:	:	PUNCT
cana-2565	93	9	communications	communication	NOUN
cana-2565	93	10	on	on	ADP
cana-2565	93	11	applied	apply	VERB
cana-2565	93	12	nonlinear	nonlinear	ADJ
cana-2565	93	13	analysis	analysis	NOUN
cana-2565	93	14	issn	issn	NOUN
cana-2565	93	15	:	:	PUNCT
cana-2565	93	16	1074	1074	NUM
cana-2565	93	17	-	-	PUNCT
cana-2565	93	18	133x	133x	NUM
cana-2565	93	19	vol	vol	NOUN
cana-2565	93	20	32	32	NUM
cana-2565	94	1	no	no	NOUN
cana-2565	94	2	.	.	PUNCT
cana-2565	95	1	3s	3s	NUM
cana-2565	95	2	(	(	PUNCT
cana-2565	95	3	2025	2025	NUM
cana-2565	95	4	)	)	PUNCT
cana-2565	95	5	108	108	NUM
cana-2565	95	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2565	95	7	1	1	X
cana-2565	95	8	.	.	PUNCT
cana-2565	95	9	name	name	NOUN
cana-2565	95	10	:	:	PUNCT
cana-2565	95	11	ascii	ascii	ADJ
cana-2565	95	12	representation	representation	NOUN
cana-2565	95	13	of	of	ADP
cana-2565	95	14	the	the	DET
cana-2565	95	15	subject	subject	NOUN
cana-2565	95	16	's	's	PART
cana-2565	95	17	name	name	NOUN
cana-2565	95	18	and	and	CCONJ
cana-2565	95	19	recording	recording	NOUN
cana-2565	95	20	identifier	identifier	NOUN
cana-2565	95	21	.	.	PUNCT
cana-2565	96	1	2	2	X
cana-2565	96	2	.	.	X
cana-2565	96	3	mdvp	mdvp	NOUN
cana-2565	96	4	:	:	PUNCT
cana-2565	96	5	fo(hz	fo(hz	NOUN
cana-2565	96	6	):	):	PUNCT
cana-2565	96	7	mean	mean	ADJ
cana-2565	96	8	fundamental	fundamental	ADJ
cana-2565	96	9	frequency	frequency	NOUN
cana-2565	96	10	of	of	ADP
cana-2565	96	11	the	the	DET
cana-2565	96	12	voice	voice	NOUN
cana-2565	96	13	.	.	PUNCT
cana-2565	97	1	3	3	X
cana-2565	97	2	.	.	X
cana-2565	97	3	mdvp	mdvp	NOUN
cana-2565	97	4	:	:	PUNCT
cana-2565	97	5	fhi(hz	fhi(hz	NUM
cana-2565	97	6	):	):	PUNCT
cana-2565	97	7	maximum	maximum	ADJ
cana-2565	97	8	fundamental	fundamental	ADJ
cana-2565	97	9	frequency	frequency	NOUN
cana-2565	97	10	of	of	ADP
cana-2565	97	11	the	the	DET
cana-2565	97	12	voice	voice	NOUN
cana-2565	97	13	.	.	PUNCT
cana-2565	98	1	4	4	X
cana-2565	98	2	.	.	X
cana-2565	98	3	mdvp	mdvp	NOUN
cana-2565	98	4	:	:	PUNCT
cana-2565	98	5	flo(hz	flo(hz	VERB
cana-2565	98	6	):	):	PUNCT
cana-2565	98	7	minimum	minimum	ADJ
cana-2565	98	8	fundamental	fundamental	ADJ
cana-2565	98	9	frequency	frequency	NOUN
cana-2565	98	10	of	of	ADP
cana-2565	98	11	the	the	DET
cana-2565	98	12	voice	voice	NOUN
cana-2565	98	13	.	.	PUNCT
cana-2565	99	1	5	5	X
cana-2565	99	2	.	.	PUNCT
cana-2565	99	3	jitter	jitter	NOUN
cana-2565	99	4	measures	measure	NOUN
cana-2565	99	5	:	:	PUNCT
cana-2565	99	6	includes	include	VERB
cana-2565	99	7	mdvp	mdvp	NOUN
cana-2565	99	8	:	:	PUNCT
cana-2565	99	9	jitter(%	jitter(%	NOUN
cana-2565	99	10	)	)	PUNCT
cana-2565	99	11	,	,	PUNCT
cana-2565	99	12	mdvp	mdvp	NOUN
cana-2565	99	13	:	:	PUNCT
cana-2565	99	14	jitter(abs	jitter(ab	NOUN
cana-2565	99	15	)	)	PUNCT
cana-2565	99	16	,	,	PUNCT
cana-2565	99	17	mdvp	mdvp	NOUN
cana-2565	99	18	:	:	PUNCT
cana-2565	99	19	rap	rap	NOUN
cana-2565	99	20	,	,	PUNCT
cana-2565	99	21	mdvp	mdvp	NOUN
cana-2565	99	22	:	:	PUNCT
cana-2565	99	23	ppq	ppq	PROPN
cana-2565	99	24	,	,	PUNCT
cana-2565	99	25	and	and	CCONJ
cana-2565	99	26	jitter	jitter	NOUN
cana-2565	99	27	:	:	PUNCT
cana-2565	99	28	ddp	ddp	NOUN
cana-2565	99	29	,	,	PUNCT
cana-2565	99	30	which	which	PRON
cana-2565	99	31	represent	represent	VERB
cana-2565	99	32	various	various	ADJ
cana-2565	99	33	metrics	metric	NOUN
cana-2565	99	34	of	of	ADP
cana-2565	99	35	fundamental	fundamental	ADJ
cana-2565	99	36	frequency	frequency	NOUN
cana-2565	99	37	variation	variation	NOUN
cana-2565	99	38	.	.	PUNCT
cana-2565	100	1	6	6	X
cana-2565	100	2	.	.	X
cana-2565	100	3	shimmer	shimmer	ADJ
cana-2565	100	4	measures	measure	NOUN
cana-2565	100	5	:	:	PUNCT
cana-2565	100	6	includes	include	VERB
cana-2565	100	7	mdvp	mdvp	NOUN
cana-2565	100	8	:	:	PUNCT
cana-2565	100	9	shimmer	shimmer	ADJ
cana-2565	100	10	,	,	PUNCT
cana-2565	100	11	mdvp	mdvp	NOUN
cana-2565	100	12	:	:	PUNCT
cana-2565	100	13	shimmer(db	shimmer(db	NOUN
cana-2565	100	14	)	)	PUNCT
cana-2565	100	15	,	,	PUNCT
cana-2565	100	16	shimmer	shimmer	ADJ
cana-2565	100	17	:	:	PUNCT
cana-2565	100	18	apq3	apq3	ADJ
cana-2565	100	19	,	,	PUNCT
cana-2565	100	20	shimmer	shimmer	ADJ
cana-2565	100	21	:	:	PUNCT
cana-2565	100	22	apq5	apq5	PROPN
cana-2565	100	23	,	,	PUNCT
cana-2565	100	24	mdvp	mdvp	NOUN
cana-2565	100	25	:	:	PUNCT
cana-2565	100	26	apq	apq	ADJ
cana-2565	100	27	,	,	PUNCT
cana-2565	100	28	and	and	CCONJ
cana-2565	100	29	shimmer	shimmer	ADJ
cana-2565	100	30	:	:	PUNCT
cana-2565	100	31	dda	dda	ADJ
cana-2565	100	32	,	,	PUNCT
cana-2565	100	33	reflecting	reflect	VERB
cana-2565	100	34	amplitude	amplitude	NOUN
cana-2565	100	35	variation	variation	NOUN
cana-2565	100	36	in	in	ADP
cana-2565	100	37	the	the	DET
cana-2565	100	38	voice	voice	NOUN
cana-2565	100	39	.	.	PUNCT
cana-2565	101	1	7	7	X
cana-2565	101	2	.	.	X
cana-2565	101	3	nhr	nhr	PROPN
cana-2565	101	4	and	and	CCONJ
cana-2565	101	5	hnr	hnr	NOUN
cana-2565	101	6	:	:	PUNCT
cana-2565	101	7	metrics	metric	NOUN
cana-2565	101	8	quantifying	quantify	VERB
cana-2565	101	9	the	the	DET
cana-2565	101	10	ratio	ratio	NOUN
cana-2565	101	11	of	of	ADP
cana-2565	101	12	noise	noise	NOUN
cana-2565	101	13	to	to	ADP
cana-2565	101	14	tonal	tonal	ADJ
cana-2565	101	15	components	component	NOUN
cana-2565	101	16	in	in	ADP
cana-2565	101	17	the	the	DET
cana-2565	101	18	voice	voice	NOUN
cana-2565	101	19	signal	signal	NOUN
cana-2565	101	20	.	.	PUNCT
cana-2565	102	1	8	8	X
cana-2565	102	2	.	.	X
cana-2565	102	3	rpde	rpde	NOUN
cana-2565	102	4	and	and	CCONJ
cana-2565	102	5	d2	d2	PROPN
cana-2565	102	6	:	:	PUNCT
cana-2565	102	7	nonlinear	nonlinear	ADJ
cana-2565	102	8	dynamical	dynamical	ADJ
cana-2565	102	9	complexity	complexity	NOUN
cana-2565	102	10	measures	measure	NOUN
cana-2565	102	11	of	of	ADP
cana-2565	102	12	the	the	DET
cana-2565	102	13	signal	signal	NOUN
cana-2565	102	14	.	.	PUNCT
cana-2565	103	1	9	9	X
cana-2565	103	2	.	.	X
cana-2565	103	3	dfa	dfa	PROPN
cana-2565	103	4	:	:	PUNCT
cana-2565	103	5	the	the	DET
cana-2565	103	6	fractal	fractal	ADJ
cana-2565	103	7	scaling	scale	VERB
cana-2565	103	8	exponent	exponent	NOUN
cana-2565	103	9	of	of	ADP
cana-2565	103	10	the	the	DET
cana-2565	103	11	signal	signal	NOUN
cana-2565	103	12	.	.	PUNCT
cana-2565	104	1	10	10	NUM
cana-2565	104	2	.	.	PUNCT
cana-2565	105	1	spread1	spread1	PROPN
cana-2565	105	2	,	,	PUNCT
cana-2565	105	3	spread2	spread2	PROPN
cana-2565	105	4	,	,	PUNCT
cana-2565	105	5	ppe	ppe	PROPN
cana-2565	105	6	:	:	PUNCT
cana-2565	105	7	nonlinear	nonlinear	ADJ
cana-2565	105	8	measures	measure	NOUN
cana-2565	105	9	representing	represent	VERB
cana-2565	105	10	variations	variation	NOUN
cana-2565	105	11	in	in	ADP
cana-2565	105	12	the	the	DET
cana-2565	105	13	fundamental	fundamental	ADJ
cana-2565	105	14	frequency	frequency	NOUN
cana-2565	105	15	.	.	PUNCT
cana-2565	106	1	11	11	NUM
cana-2565	106	2	.	.	X
cana-2565	107	1	status	status	NOUN
cana-2565	107	2	:	:	PUNCT
cana-2565	107	3	health	health	NOUN
cana-2565	107	4	status	status	NOUN
cana-2565	107	5	indicator	indicator	NOUN
cana-2565	107	6	of	of	ADP
cana-2565	107	7	the	the	DET
cana-2565	107	8	subject	subject	NOUN
cana-2565	107	9	,	,	PUNCT
cana-2565	107	10	where	where	SCONJ
cana-2565	107	11	"	"	PUNCT
cana-2565	107	12	1	1	NUM
cana-2565	107	13	"	"	PUNCT
cana-2565	107	14	represents	represent	VERB
cana-2565	107	15	parkinson	parkinson	NOUN
cana-2565	107	16	's	's	PART
cana-2565	107	17	disease	disease	NOUN
cana-2565	107	18	and	and	CCONJ
cana-2565	107	19	"	"	PUNCT
cana-2565	107	20	0	0	NUM
cana-2565	107	21	"	"	PUNCT
cana-2565	107	22	indicates	indicate	VERB
cana-2565	107	23	a	a	DET
cana-2565	107	24	healthy	healthy	ADJ
cana-2565	107	25	condition	condition	NOUN
cana-2565	107	26	.	.	PUNCT
cana-2565	108	1	table	table	NOUN
cana-2565	108	2	1a	1a	PROPN
cana-2565	108	3	.	.	PUNCT
cana-2565	109	1	parkinson	parkinson	NOUN
cana-2565	109	2	's	's	PART
cana-2565	109	3	dataset	dataset	NOUN
cana-2565	109	4	m	m	PROPN
cana-2565	109	5	d	d	NOUN
cana-2565	109	6	v	v	ADP
cana-2565	109	7	p	p	NOUN
cana-2565	109	8	:	:	PUNCT
cana-2565	109	9	f	f	X
cana-2565	109	10	o	o	INTJ
cana-2565	109	11	(	(	PUNCT
cana-2565	109	12	h	h	NOUN
cana-2565	109	13	z	z	NOUN
cana-2565	109	14	)	)	PUNCT
cana-2565	109	15	m	m	PROPN
cana-2565	109	16	d	d	NOUN
cana-2565	109	17	v	v	ADP
cana-2565	109	18	p	p	X
cana-2565	109	19	:	:	PUNCT
cana-2565	109	20	f	f	PROPN
cana-2565	109	21	h	h	NOUN
cana-2565	110	1	i	i	PRON
cana-2565	110	2	(	(	PUNCT
cana-2565	110	3	h	h	NOUN
cana-2565	110	4	z	z	NOUN
cana-2565	110	5	)	)	PUNCT
cana-2565	110	6	m	m	PROPN
cana-2565	110	7	d	d	NOUN
cana-2565	110	8	v	v	ADP
cana-2565	110	9	p	p	NOUN
cana-2565	110	10	:	:	PUNCT
cana-2565	110	11	f	f	PROPN
cana-2565	110	12	lo	lo	PROPN
cana-2565	110	13	(	(	PUNCT
cana-2565	110	14	h	h	NOUN
cana-2565	110	15	z	z	NOUN
cana-2565	110	16	)	)	PUNCT
cana-2565	110	17	m	m	PROPN
cana-2565	110	18	d	d	NOUN
cana-2565	110	19	v	v	ADP
cana-2565	110	20	p	p	X
cana-2565	110	21	:	:	PUNCT
cana-2565	110	22	j	j	PROPN
cana-2565	110	23	it	it	PRON
cana-2565	110	24	te	te	ADP
cana-2565	110	25	r	r	NOUN
cana-2565	110	26	(	(	PUNCT
cana-2565	110	27	%	%	NOUN
cana-2565	110	28	)	)	PUNCT
cana-2565	110	29	m	m	PROPN
cana-2565	110	30	d	d	NOUN
cana-2565	110	31	v	v	ADP
cana-2565	110	32	p	p	X
cana-2565	110	33	:	:	PUNCT
cana-2565	110	34	j	j	PROPN
cana-2565	110	35	it	it	PRON
cana-2565	110	36	te	te	ADP
cana-2565	110	37	r	r	NOUN
cana-2565	110	38	(	(	PUNCT
cana-2565	110	39	a	a	DET
cana-2565	110	40	b	b	PROPN
cana-2565	110	41	s	s	NOUN
cana-2565	110	42	)	)	PUNCT
cana-2565	110	43	m	m	PROPN
cana-2565	110	44	d	d	NOUN
cana-2565	110	45	v	v	ADP
cana-2565	110	46	p	p	NOUN
cana-2565	110	47	:	:	PUNCT
cana-2565	110	48	r	r	NOUN
cana-2565	110	49	a	a	DET
cana-2565	110	50	p	p	NOUN
cana-2565	110	51	m	m	NOUN
cana-2565	110	52	d	d	NOUN
cana-2565	110	53	v	v	ADP
cana-2565	110	54	p	p	NOUN
cana-2565	110	55	:	:	PUNCT
cana-2565	110	56	p	p	X
cana-2565	110	57	p	p	X
cana-2565	110	58	q	q	X
cana-2565	110	59	j	j	PROPN
cana-2565	110	60	it	it	PRON
cana-2565	110	61	te	te	ADP
cana-2565	110	62	r	r	NOUN
cana-2565	110	63	:	:	PUNCT
cana-2565	110	64	d	d	X
cana-2565	110	65	d	d	X
cana-2565	110	66	p	p	X
cana-2565	110	67	m	m	PROPN
cana-2565	110	68	d	d	NOUN
cana-2565	110	69	v	v	ADP
cana-2565	110	70	p	p	NOUN
cana-2565	110	71	:	:	PUNCT
cana-2565	110	72	s	s	NOUN
cana-2565	110	73	h	h	NOUN
cana-2565	111	1	i	i	PRON
cana-2565	111	2	m	m	VERB
cana-2565	111	3	m	m	VERB
cana-2565	111	4	er	er	INTJ
cana-2565	111	5	m	m	NOUN
cana-2565	111	6	d	d	NOUN
cana-2565	111	7	v	v	ADP
cana-2565	111	8	p	p	NOUN
cana-2565	111	9	:	:	PUNCT
cana-2565	111	10	s	s	NOUN
cana-2565	111	11	h	h	NOUN
cana-2565	112	1	i	i	PRON
cana-2565	112	2	m	m	VERB
cana-2565	112	3	m	m	VERB
cana-2565	112	4	er	er	INTJ
cana-2565	112	5	(	(	PUNCT
cana-2565	112	6	d	d	PROPN
cana-2565	112	7	b	b	PROPN
cana-2565	112	8	)	)	PUNCT
cana-2565	112	9	s	s	PART
cana-2565	112	10	h	h	NOUN
cana-2565	113	1	i	i	PRON
cana-2565	113	2	m	m	VERB
cana-2565	113	3	m	m	VERB
cana-2565	113	4	er	er	INTJ
cana-2565	113	5	:	:	PUNCT
cana-2565	113	6	a	a	DET
cana-2565	113	7	p	p	X
cana-2565	113	8	q	q	PROPN
cana-2565	113	9	3	3	NUM
cana-2565	113	10	104.4000	104.4000	NUM
cana-2565	113	11	206.0020	206.0020	NUM
cana-2565	113	12	77.9680	77.9680	NUM
cana-2565	113	13	0.0063	0.0063	NUM
cana-2565	113	14	0.0001	0.0001	NUM
cana-2565	113	15	0.0032	0.0032	NUM
cana-2565	113	16	0.0038	0.0038	NUM
cana-2565	113	17	0.0095	0.0095	NUM
cana-2565	113	18	0.0377	0.0377	NUM
cana-2565	113	19	0.3810	0.3810	NUM
cana-2565	113	20	0.0173	0.0173	NUM
cana-2565	113	21	171.0410	171.0410	NUM
cana-2565	113	22	208.3130	208.3130	NUM
cana-2565	113	23	75.5010	75.5010	NUM
cana-2565	113	24	0.0046	0.0046	NUM
cana-2565	113	25	0.0000	0.0000	NUM
cana-2565	113	26	0.0025	0.0025	NUM
cana-2565	113	27	0.0023	0.0023	NUM
cana-2565	113	28	0.0075	0.0075	NUM
cana-2565	114	1	0.0197	0.0197	NUM
cana-2565	114	2	0.1860	0.1860	NUM
cana-2565	114	3	0.0089	0.0089	NUM
cana-2565	114	4	146.8450	146.8450	NUM
cana-2565	114	5	208.7010	208.7010	NUM
cana-2565	114	6	81.7370	81.7370	NUM
cana-2565	114	7	0.0050	0.0050	NUM
cana-2565	114	8	0.0000	0.0000	NUM
cana-2565	114	9	0.0025	0.0025	NUM
cana-2565	114	10	0.0028	0.0028	NUM
cana-2565	114	11	0.0075	0.0075	NUM
cana-2565	114	12	0.0192	0.0192	NUM
cana-2565	114	13	0.1980	0.1980	NUM
cana-2565	114	14	0.0088	0.0088	NUM
cana-2565	114	15	155.3580	155.3580	NUM
cana-2565	114	16	227.3830	227.3830	NUM
cana-2565	114	17	80.0550	80.0550	NUM
cana-2565	114	18	0.0031	0.0031	NUM
cana-2565	114	19	0.0000	0.0000	NUM
cana-2565	114	20	0.0016	0.0016	NUM
cana-2565	114	21	0.0018	0.0018	NUM
cana-2565	114	22	0.0048	0.0048	NUM
cana-2565	114	23	0.0172	0.0172	NUM
cana-2565	114	24	0.1610	0.1610	NUM
cana-2565	114	25	0.0077	0.0077	NUM
cana-2565	114	26	162.5680	162.5680	NUM
cana-2565	114	27	198.3460	198.3460	NUM
cana-2565	114	28	77.6300	77.6300	NUM
cana-2565	114	29	0.0050	0.0050	NUM
cana-2565	114	30	0.0000	0.0000	NUM
cana-2565	114	31	0.0028	0.0028	NUM
cana-2565	114	32	0.0025	0.0025	NUM
cana-2565	114	33	0.0084	0.0084	NUM
cana-2565	114	34	0.0179	0.0179	NUM
cana-2565	114	35	0.1680	0.1680	NUM
cana-2565	114	36	0.0079	0.0079	NUM
cana-2565	114	37	197.0760	197.0760	NUM
cana-2565	114	38	206.8960	206.8960	NUM
cana-2565	114	39	192.0550	192.0550	NUM
cana-2565	114	40	0.0029	0.0029	NUM
cana-2565	114	41	0.0000	0.0000	NUM
cana-2565	114	42	0.0017	0.0017	NUM
cana-2565	114	43	0.0017	0.0017	NUM
cana-2565	114	44	0.0050	0.0050	NUM
cana-2565	114	45	0.0110	0.0110	NUM
cana-2565	114	46	0.0970	0.0970	NUM
cana-2565	114	47	0.0056	0.0056	NUM
cana-2565	114	48	199.2280	199.2280	NUM
cana-2565	114	49	209.5120	209.5120	NUM
cana-2565	114	50	192.0910	192.0910	NUM
cana-2565	114	51	0.0024	0.0024	NUM
cana-2565	114	52	0.0000	0.0000	NUM
cana-2565	114	53	0.0013	0.0013	NUM
cana-2565	114	54	0.0014	0.0014	NUM
cana-2565	114	55	0.0040	0.0040	NUM
cana-2565	114	56	0.0102	0.0102	NUM
cana-2565	114	57	0.0890	0.0890	NUM
cana-2565	114	58	0.0050	0.0050	NUM
cana-2565	114	59	198.3830	198.3830	NUM
cana-2565	114	60	215.2030	215.2030	NUM
cana-2565	114	61	193.1040	193.1040	NUM
cana-2565	114	62	0.0021	0.0021	NUM
cana-2565	114	63	0.0000	0.0000	NUM
cana-2565	114	64	0.0011	0.0011	NUM
cana-2565	114	65	0.0014	0.0014	NUM
cana-2565	114	66	0.0034	0.0034	NUM
cana-2565	114	67	0.0126	0.0126	NUM
cana-2565	114	68	0.1110	0.1110	NUM
cana-2565	114	69	0.0064	0.0064	NUM
cana-2565	114	70	202.2660	202.2660	NUM
cana-2565	114	71	211.6040	211.6040	NUM
cana-2565	114	72	197.0790	197.0790	NUM
cana-2565	114	73	0.0018	0.0018	NUM
cana-2565	114	74	0.0000	0.0000	NUM
cana-2565	114	75	0.0009	0.0009	NUM
cana-2565	114	76	0.0011	0.0011	NUM
cana-2565	114	77	0.0028	0.0028	NUM
cana-2565	114	78	0.0095	0.0095	NUM
cana-2565	114	79	0.0850	0.0850	NUM
cana-2565	114	80	0.0047	0.0047	NUM
cana-2565	114	81	203.1840	203.1840	NUM
cana-2565	114	82	211.5260	211.5260	NUM
cana-2565	114	83	196.1600	196.1600	NUM
cana-2565	114	84	0.0018	0.0018	NUM
cana-2565	114	85	0.0000	0.0000	NUM
cana-2565	114	86	0.0009	0.0009	NUM
cana-2565	114	87	0.0011	0.0011	NUM
cana-2565	114	88	0.0028	0.0028	NUM
cana-2565	114	89	0.0096	0.0096	NUM
cana-2565	114	90	0.0850	0.0850	NUM
cana-2565	114	91	0.0047	0.0047	NUM
cana-2565	114	92	table	table	NOUN
cana-2565	114	93	1b	1b	NUM
cana-2565	114	94	.	.	PUNCT
cana-2565	115	1	parkinson	parkinson	NOUN
cana-2565	115	2	's	's	PART
cana-2565	115	3	dataset	dataset	NOUN
cana-2565	115	4	s	s	X
cana-2565	115	5	h	h	NOUN
cana-2565	116	1	i	i	NOUN
cana-2565	116	2	m	m	VERB
cana-2565	116	3	m	m	VERB
cana-2565	116	4	er	er	INTJ
cana-2565	116	5	:	:	PUNCT
cana-2565	116	6	a	a	DET
cana-2565	116	7	p	p	X
cana-2565	116	8	q	q	PROPN
cana-2565	116	9	5	5	NUM
cana-2565	116	10	m	m	NOUN
cana-2565	116	11	d	d	NOUN
cana-2565	116	12	v	v	ADP
cana-2565	116	13	p	p	X
cana-2565	116	14	:	:	PUNCT
cana-2565	116	15	a	a	DET
cana-2565	116	16	p	p	X
cana-2565	116	17	q	q	X
cana-2565	117	1	s	s	X
cana-2565	117	2	h	h	NOUN
cana-2565	118	1	i	i	NOUN
cana-2565	118	2	m	m	VERB
cana-2565	118	3	m	m	VERB
cana-2565	118	4	er	er	INTJ
cana-2565	118	5	:	:	PUNCT
cana-2565	118	6	d	d	X
cana-2565	118	7	d	d	PROPN
cana-2565	118	8	a	a	PRON
cana-2565	118	9	n	n	NOUN
cana-2565	118	10	h	h	NOUN
cana-2565	119	1	r	r	NOUN
cana-2565	119	2	h	h	NOUN
cana-2565	119	3	n	n	NOUN
cana-2565	119	4	r	r	NOUN
cana-2565	119	5	r	r	NOUN
cana-2565	119	6	p	p	NOUN
cana-2565	119	7	d	d	X
cana-2565	119	8	e	e	PROPN
cana-2565	119	9	d	d	X
cana-2565	119	10	f	f	PROPN
cana-2565	119	11	a	a	PRON
cana-2565	119	12	sp	sp	ADP
cana-2565	119	13	re	re	NOUN
cana-2565	119	14	a	a	DET
cana-2565	119	15	d	d	PROPN
cana-2565	119	16	1	1	NUM
cana-2565	119	17	sp	sp	ADP
cana-2565	119	18	re	re	NOUN
cana-2565	119	19	a	a	DET
cana-2565	119	20	d	d	PROPN
cana-2565	119	21	2	2	NUM
cana-2565	119	22	d	d	SYM
cana-2565	119	23	2	2	NUM
cana-2565	119	24	p	p	NOUN
cana-2565	119	25	p	p	PROPN
cana-2565	119	26	e	e	X
cana-2565	119	27	st	st	PROPN
cana-2565	120	1	a	a	PROPN
cana-2565	120	2	tu	tu	PROPN
cana-2565	120	3	s	s	PART
cana-2565	120	4	0.0225	0.0225	NUM
cana-2565	120	5	0.0378	0.0378	NUM
cana-2565	120	6	0.0520	0.0520	NUM
cana-2565	120	7	0.0289	0.0289	NUM
cana-2565	120	8	22.0660	22.0660	NUM
cana-2565	120	9	0.5227	0.5227	NUM
cana-2565	120	10	0.7379	0.7379	NUM
cana-2565	120	11	-5.5718	-5.5718	NOUN
cana-2565	120	12	0.2369	0.2369	NUM
cana-2565	120	13	2.8464	2.8464	NUM
cana-2565	120	14	0.2195	0.2195	NUM
cana-2565	120	15	1	1	NUM
cana-2565	120	16	0.0117	0.0117	NUM
cana-2565	120	17	0.0187	0.0187	NUM
cana-2565	120	18	0.0267	0.0267	NUM
cana-2565	120	19	0.0110	0.0110	NUM
cana-2565	120	20	25.9080	25.9080	NUM
cana-2565	120	21	0.4186	0.4186	NUM
cana-2565	120	22	0.7209	0.7209	NUM
cana-2565	120	23	-6.1836	-6.1836	NOUN
cana-2565	120	24	0.2263	0.2263	NUM
cana-2565	120	25	2.5897	2.5897	NUM
cana-2565	120	26	0.1474	0.1474	NUM
cana-2565	120	27	1	1	NUM
cana-2565	120	28	0.0114	0.0114	NUM
cana-2565	120	29	0.0183	0.0183	NUM
cana-2565	120	30	0.0265	0.0265	NUM
cana-2565	120	31	0.0133	0.0133	NUM
cana-2565	120	32	25.1190	25.1190	NUM
cana-2565	120	33	0.3588	0.3588	NUM
cana-2565	120	34	0.7267	0.7267	NUM
cana-2565	120	35	-6.2717	-6.2717	NOUN
cana-2565	121	1	0.1961	0.1961	NUM
cana-2565	121	2	2.3142	2.3142	NUM
cana-2565	121	3	0.1630	0.1630	NUM
cana-2565	121	4	1	1	NUM
cana-2565	121	5	0.0101	0.0101	NUM
cana-2565	121	6	0.0166	0.0166	NUM
cana-2565	121	7	0.0231	0.0231	NUM
cana-2565	121	8	0.0068	0.0068	NUM
cana-2565	121	9	25.9700	25.9700	NUM
cana-2565	121	10	0.4705	0.4705	NUM
cana-2565	121	11	0.6763	0.6763	NUM
cana-2565	121	12	-7.1209	-7.1209	NOUN
cana-2565	121	13	0.2798	0.2798	NUM
cana-2565	122	1	2.2417	2.2417	NUM
cana-2565	122	2	0.1085	0.1085	NUM
cana-2565	122	3	1	1	NUM
cana-2565	122	4	0.0106	0.0106	NUM
cana-2565	122	5	0.0180	0.0180	NUM
cana-2565	122	6	0.0238	0.0238	NUM
cana-2565	122	7	0.0117	0.0117	NUM
cana-2565	122	8	25.6780	25.6780	NUM
cana-2565	122	9	0.4278	0.4278	NUM
cana-2565	122	10	0.7238	0.7238	NUM
cana-2565	122	11	-6.6357	-6.6357	VERB
cana-2565	122	12	0.2099	0.2099	NUM
cana-2565	122	13	1.9580	1.9580	NUM
cana-2565	122	14	0.1352	0.1352	NUM
cana-2565	122	15	1	1	NUM
cana-2565	122	16	communications	communication	NOUN
cana-2565	122	17	on	on	ADP
cana-2565	122	18	applied	apply	VERB
cana-2565	122	19	nonlinear	nonlinear	ADJ
cana-2565	122	20	analysis	analysis	NOUN
cana-2565	122	21	issn	issn	NOUN
cana-2565	122	22	:	:	PUNCT
cana-2565	122	23	1074	1074	NUM
cana-2565	122	24	-	-	PUNCT
cana-2565	122	25	133x	133x	NUM
cana-2565	122	26	vol	vol	NOUN
cana-2565	122	27	32	32	NUM
cana-2565	122	28	no	no	NOUN
cana-2565	122	29	.	.	PUNCT
cana-2565	123	1	3s	3s	NUM
cana-2565	123	2	(	(	PUNCT
cana-2565	123	3	2025	2025	NUM
cana-2565	123	4	)	)	PUNCT
cana-2565	123	5	109	109	NUM
cana-2565	123	6	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-2565	123	7	0.0068	0.0068	NUM
cana-2565	123	8	0.0080	0.0080	NUM
cana-2565	123	9	0.0169	0.0169	NUM
cana-2565	123	10	0.0034	0.0034	NUM
cana-2565	123	11	26.7750	26.7750	NUM
cana-2565	123	12	0.4222	0.4222	NUM
cana-2565	123	13	0.7414	0.7414	NUM
cana-2565	123	14	-7.3483	-7.3483	NOUN
cana-2565	123	15	0.1776	0.1776	NUM
cana-2565	124	1	1.7439	1.7439	NUM
cana-2565	124	2	0.0856	0.0856	NUM
cana-2565	124	3	0	0	NUM
cana-2565	124	4	0.0064	0.0064	NUM
cana-2565	124	5	0.0076	0.0076	NUM
cana-2565	124	6	0.0151	0.0151	NUM
cana-2565	124	7	0.0017	0.0017	NUM
cana-2565	124	8	30.9400	30.9400	NUM
cana-2565	124	9	0.4324	0.4324	NUM
cana-2565	124	10	0.7421	0.7421	NUM
cana-2565	124	11	-7.6826	-7.6826	NOUN
cana-2565	125	1	0.1733	0.1733	NUM
cana-2565	125	2	2.1031	2.1031	NUM
cana-2565	125	3	0.0685	0.0685	NUM
cana-2565	125	4	0	0	NUM
cana-2565	125	5	0.0083	0.0083	NUM
cana-2565	125	6	0.0095	0.0095	NUM
cana-2565	125	7	0.0192	0.0192	NUM
cana-2565	125	8	0.0012	0.0012	NUM
cana-2565	125	9	30.7750	30.7750	NUM
cana-2565	125	10	0.4659	0.4659	NUM
cana-2565	125	11	0.7387	0.7387	NUM
cana-2565	125	12	-7.0679	-7.0679	NOUN
cana-2565	125	13	0.1752	0.1752	NUM
cana-2565	125	14	1.5123	1.5123	NUM
cana-2565	125	15	0.0963	0.0963	NUM
cana-2565	125	16	0	0	NUM
cana-2565	126	1	0.0061	0.0061	NUM
cana-2565	126	2	0.0072	0.0072	NUM
cana-2565	126	3	0.0141	0.0141	NUM
cana-2565	126	4	0.0007	0.0007	NUM
cana-2565	126	5	32.6840	32.6840	NUM
cana-2565	126	6	0.3685	0.3685	NUM
cana-2565	126	7	0.7421	0.7421	NUM
cana-2565	126	8	-7.6957	-7.6957	NOUN
cana-2565	126	9	0.1785	0.1785	NUM
cana-2565	126	10	1.5446	1.5446	NUM
cana-2565	126	11	0.0561	0.0561	NUM
cana-2565	126	12	0	0	NUM
cana-2565	127	1	0.0061	0.0061	NUM
cana-2565	127	2	0.0073	0.0073	NUM
cana-2565	127	3	0.0140	0.0140	NUM
cana-2565	127	4	0.0007	0.0007	NUM
cana-2565	127	5	33.0470	33.0470	NUM
cana-2565	127	6	0.3401	0.3401	NUM
cana-2565	127	7	0.7419	0.7419	NUM
cana-2565	127	8	-7.9650	-7.9650	VERB
cana-2565	127	9	0.1635	0.1635	NUM
cana-2565	127	10	1.4233	1.4233	NUM
cana-2565	127	11	0.0445	0.0445	NUM
cana-2565	127	12	0	0	NUM
cana-2565	127	13	here	here	ADV
cana-2565	127	14	is	be	AUX
cana-2565	127	15	a	a	DET
cana-2565	127	16	comparative	comparative	ADJ
cana-2565	127	17	table	table	NOUN
cana-2565	127	18	summarizing	summarizing	NOUN
cana-2565	127	19	performance	performance	NOUN
cana-2565	127	20	metrics	metric	NOUN
cana-2565	127	21	for	for	ADP
cana-2565	127	22	parkinson	parkinson	NOUN
cana-2565	127	23	's	's	PART
cana-2565	127	24	disease	disease	NOUN
cana-2565	127	25	analysis	analysis	NOUN
cana-2565	127	26	using	use	VERB
cana-2565	127	27	machine	machine	NOUN
cana-2565	127	28	learning	learning	NOUN
cana-2565	127	29	and	and	CCONJ
cana-2565	127	30	deep	deep	ADJ
cana-2565	127	31	learning	learning	NOUN
cana-2565	127	32	approaches	approach	NOUN
cana-2565	127	33	:	:	PUNCT
cana-2565	127	34	table	table	NOUN
cana-2565	127	35	3	3	NUM
cana-2565	127	36	.	.	PUNCT
cana-2565	127	37	performance	performance	NOUN
cana-2565	127	38	metrics	metric	NOUN
cana-2565	127	39	for	for	ADP
cana-2565	127	40	parkinson	parkinson	NOUN
cana-2565	127	41	's	's	PART
cana-2565	127	42	disease	disease	NOUN
cana-2565	127	43	analysis	analysis	NOUN
cana-2565	127	44	using	use	VERB
cana-2565	127	45	ml	ml	X
cana-2565	127	46	and	and	CCONJ
cana-2565	127	47	dl	dl	PROPN
cana-2565	127	48	model	model	PROPN
cana-2565	127	49	/	/	SYM
cana-2565	127	50	algorithm	algorithm	PROPN
cana-2565	127	51	accuracy	accuracy	NOUN
cana-2565	127	52	precision	precision	NOUN
cana-2565	127	53	recall	recall	VERB
cana-2565	127	54	/sensitivity	/sensitivity	NOUN
cana-2565	127	55	specificity	specificity	NOUN
cana-2565	127	56	f1	f1	NOUN
cana-2565	127	57	-	-	PUNCT
cana-2565	127	58	score	score	NOUN
cana-2565	127	59	linear	linear	PROPN
cana-2565	127	60	regression	regression	VERB
cana-2565	127	61	86.56	86.56	NUM
cana-2565	127	62	84.21	84.21	NUM
cana-2565	127	63	87.42	87.42	NUM
cana-2565	127	64	85.85	85.85	NUM
cana-2565	127	65	85.52	85.52	NUM
cana-2565	127	66	random	random	ADJ
cana-2565	127	67	tree	tree	NOUN
cana-2565	127	68	88.23	88.23	NUM
cana-2565	127	69	86.21	86.21	NUM
cana-2565	127	70	89.25	89.25	NUM
cana-2565	127	71	87.96	87.96	NUM
cana-2565	127	72	87.55	87.55	NUM
cana-2565	127	73	rep	rep	NOUN
cana-2565	127	74	tree	tree	NOUN
cana-2565	127	75	91.24	91.24	NUM
cana-2565	127	76	90.77	90.77	NUM
cana-2565	127	77	92.65	92.65	NUM
cana-2565	127	78	90.41	90.41	NUM
cana-2565	127	79	91.42	91.42	NUM
cana-2565	127	80	random	random	ADJ
cana-2565	127	81	forest	forest	NOUN
cana-2565	127	82	93.56	93.56	NUM
cana-2565	127	83	92.88	92.88	NUM
cana-2565	127	84	94.82	94.82	NUM
cana-2565	127	85	91.74	91.74	NUM
cana-2565	127	86	93.21	93.21	NUM
cana-2565	127	87	mlp	mlp	NOUN
cana-2565	127	88	94.42	94.42	NUM
cana-2565	127	89	93.17	93.17	NUM
cana-2565	127	90	95.56	95.56	NUM
cana-2565	127	91	92.85	92.85	NUM
cana-2565	127	92	94.21	94.21	NUM
cana-2565	127	93	lstm	lstm	NOUN
cana-2565	127	94	95.85	95.85	NUM
cana-2565	127	95	94.14	94.14	NUM
cana-2565	127	96	95.96	95.96	NUM
cana-2565	127	97	93.56	93.56	NUM
cana-2565	127	98	94.52	94.52	NUM
cana-2565	127	99	cnnbigru	cnnbigru	PROPN
cana-2565	127	100	-	-	PROPN
cana-2565	127	101	dgr	dgr	PROPN
cana-2565	127	102	98.15	98.15	NUM
cana-2565	127	103	97.18	97.18	NUM
cana-2565	127	104	99.22	99.22	NUM
cana-2565	127	105	96.41	96.41	NUM
cana-2565	127	106	98.29	98.29	NUM
cana-2565	127	107	figure	figure	NOUN
cana-2565	127	108	1	1	NUM
cana-2565	127	109	.	.	PUNCT
cana-2565	127	110	accuracy	accuracy	NOUN
cana-2565	127	111	of	of	ADP
cana-2565	127	112	parkinson	parkinson	NOUN
cana-2565	127	113	's	's	PART
cana-2565	127	114	disease	disease	NOUN
cana-2565	127	115	analysis	analysis	NOUN
cana-2565	127	116	using	use	VERB
cana-2565	127	117	ml	ml	X
cana-2565	128	1	and	and	CCONJ
cana-2565	128	2	dl	dl	PROPN
cana-2565	128	3	figure	figure	NOUN
cana-2565	128	4	2	2	NUM
cana-2565	128	5	.	.	NOUN
cana-2565	128	6	precision	precision	NOUN
cana-2565	128	7	and	and	CCONJ
cana-2565	128	8	recall	recall	NOUN
cana-2565	128	9	of	of	ADP
cana-2565	128	10	parkinson	parkinson	NOUN
cana-2565	128	11	's	's	PART
cana-2565	128	12	disease	disease	NOUN
cana-2565	128	13	analysis	analysis	NOUN
cana-2565	128	14	using	use	VERB
cana-2565	128	15	ml	ml	X
cana-2565	129	1	and	and	CCONJ
cana-2565	129	2	dl	dl	AUX
cana-2565	129	3	figure	figure	NOUN
cana-2565	129	4	3	3	NUM
cana-2565	129	5	.	.	NOUN
cana-2565	129	6	specificity	specificity	NOUN
cana-2565	129	7	and	and	CCONJ
cana-2565	129	8	f1	f1	NOUN
cana-2565	129	9	-	-	PUNCT
cana-2565	129	10	score	score	NOUN
cana-2565	129	11	of	of	ADP
cana-2565	129	12	parkinson	parkinson	NOUN
cana-2565	129	13	's	's	PART
cana-2565	129	14	disease	disease	NOUN
cana-2565	129	15	analysis	analysis	NOUN
cana-2565	129	16	using	use	VERB
cana-2565	129	17	ml	ml	X
cana-2565	130	1	and	and	CCONJ
cana-2565	130	2	dl	dl	PROPN
cana-2565	130	3	communications	communication	NOUN
cana-2565	130	4	on	on	ADP
cana-2565	130	5	applied	apply	VERB
cana-2565	130	6	nonlinear	nonlinear	ADJ
cana-2565	130	7	analysis	analysis	NOUN
cana-2565	130	8	issn	issn	NOUN
cana-2565	130	9	:	:	PUNCT
cana-2565	130	10	1074	1074	NUM
cana-2565	130	11	-	-	PUNCT
cana-2565	130	12	133x	133x	NUM
cana-2565	130	13	vol	vol	NOUN
cana-2565	130	14	32	32	NUM
cana-2565	130	15	no	no	NOUN
cana-2565	130	16	.	.	PUNCT
cana-2565	131	1	3s	3s	NUM
cana-2565	131	2	(	(	PUNCT
cana-2565	131	3	2025	2025	NUM
cana-2565	131	4	)	)	PUNCT
cana-2565	131	5	110	110	NUM
cana-2565	131	6	https://internationalpubls.com	https://internationalpubls.com	SYM
cana-2565	131	7	4.1	4.1	NUM
cana-2565	131	8	prediction	prediction	NOUN
cana-2565	131	9	model	model	NOUN
cana-2565	131	10	using	use	VERB
cana-2565	131	11	linear	linear	PROPN
cana-2565	131	12	regression	regression	NOUN
cana-2565	131	13	status	status	NOUN
cana-2565	131	14	=	=	X
cana-2565	131	15	-0.0026	-0.0026	PUNCT
cana-2565	131	16	*	*	PUNCT
cana-2565	131	17	mdvp	mdvp	NOUN
cana-2565	131	18	:	:	PUNCT
cana-2565	131	19	fo(hz	fo(hz	PROPN
cana-2565	131	20	)	)	PUNCT
cana-2565	132	1	+	+	CCONJ
cana-2565	132	2	-20.1221	-20.1221	PUNCT
cana-2565	132	3	*	*	NOUN
cana-2565	132	4	mdvp	mdvp	NOUN
cana-2565	132	5	:	:	PUNCT
cana-2565	132	6	jitter(%	jitter(%	NUM
cana-2565	132	7	)	)	PUNCT
cana-2565	133	1	+	+	CCONJ
cana-2565	133	2	-28.2455	-28.2455	PUNCT
cana-2565	133	3	*	*	PUNCT
cana-2565	133	4	shimmer	shimmer	ADJ
cana-2565	133	5	:	:	PUNCT
cana-2565	133	6	apq5	apq5	NOUN
cana-2565	133	7	+7.981	+7.981	PROPN
cana-2565	133	8	*	*	PUNCT
cana-2565	133	9	mdvp	mdvp	NOUN
cana-2565	133	10	:	:	PUNCT
cana-2565	133	11	apq	apq	VERB
cana-2565	133	12	+	+	X
cana-2565	133	13	9.0399	9.0399	NUM
cana-2565	133	14	*	*	PUNCT
cana-2565	133	15	shimmer	shimmer	ADJ
cana-2565	133	16	:	:	PUNCT
cana-2565	133	17	dda	dda	ADJ
cana-2565	133	18	+	+	CCONJ
cana-2565	133	19	-1.8278	-1.8278	PROPN
cana-2565	133	20	*	*	PUNCT
cana-2565	134	1	nhr	nhr	PROPN
cana-2565	134	2	+	+	NUM
cana-2565	134	3	-0.0159	-0.0159	NOUN
cana-2565	134	4	*	*	PUNCT
cana-2565	134	5	hnr	hnr	X
cana-2565	134	6	+	+	CCONJ
cana-2565	134	7	-0.9554	-0.9554	NOUN
cana-2565	134	8	*	*	PUNCT
cana-2565	134	9	pde+	pde+	NOUN
cana-2565	134	10	0.1958	0.1958	NUM
cana-2565	134	11	*	*	PUNCT
cana-2565	134	12	spread1	spread1	NOUN
cana-2565	135	1	+	+	CCONJ
cana-2565	135	2	0.6389	0.6389	NUM
cana-2565	135	3	*	*	PUNCT
cana-2565	135	4	spread2	spread2	PROPN
cana-2565	135	5	+	+	CCONJ
cana-2565	135	6	0.1579	0.1579	NUM
cana-2565	135	7	*	*	PUNCT
cana-2565	135	8	d2	d2	PROPN
cana-2565	135	9	+	+	CCONJ
cana-2565	135	10	2.6353	2.6353	NUM
cana-2565	135	11	5.0	5.0	NUM
cana-2565	135	12	results	result	NOUN
cana-2565	135	13	and	and	CCONJ
cana-2565	135	14	discussions	discussion	NOUN
cana-2565	135	15	the	the	DET
cana-2565	135	16	evaluation	evaluation	NOUN
cana-2565	135	17	of	of	ADP
cana-2565	135	18	parkinson	parkinson	NOUN
cana-2565	135	19	’s	’s	PART
cana-2565	135	20	disease	disease	NOUN
cana-2565	135	21	classification	classification	NOUN
cana-2565	135	22	using	use	VERB
cana-2565	135	23	machine	machine	NOUN
cana-2565	135	24	learning	learning	NOUN
cana-2565	135	25	(	(	PUNCT
cana-2565	135	26	ml	ml	NOUN
cana-2565	135	27	)	)	PUNCT
cana-2565	135	28	and	and	CCONJ
cana-2565	135	29	deep	deep	ADJ
cana-2565	135	30	learning	learning	NOUN
cana-2565	135	31	(	(	PUNCT
cana-2565	135	32	dl	dl	NOUN
cana-2565	135	33	)	)	PUNCT
cana-2565	135	34	techniques	technique	NOUN
cana-2565	135	35	revealed	reveal	VERB
cana-2565	135	36	notable	notable	ADJ
cana-2565	135	37	variations	variation	NOUN
cana-2565	135	38	in	in	ADP
cana-2565	135	39	performance	performance	NOUN
cana-2565	135	40	metrics	metric	NOUN
cana-2565	135	41	across	across	ADP
cana-2565	135	42	different	different	ADJ
cana-2565	135	43	algorithms	algorithm	NOUN
cana-2565	135	44	.	.	PUNCT
cana-2565	136	1	the	the	DET
cana-2565	136	2	results	result	NOUN
cana-2565	136	3	are	be	AUX
cana-2565	136	4	presented	present	VERB
cana-2565	136	5	in	in	ADP
cana-2565	136	6	table	table	NOUN
cana-2565	136	7	3	3	NUM
cana-2565	136	8	and	and	CCONJ
cana-2565	136	9	illustrated	illustrate	VERB
cana-2565	136	10	in	in	ADP
cana-2565	136	11	figures	figure	NOUN
cana-2565	136	12	3	3	NUM
cana-2565	136	13	,	,	PUNCT
cana-2565	136	14	4	4	NUM
cana-2565	136	15	,	,	PUNCT
cana-2565	136	16	and	and	CCONJ
cana-2565	136	17	5	5	NUM
cana-2565	136	18	for	for	ADP
cana-2565	136	19	clarity	clarity	NOUN
cana-2565	136	20	.	.	PUNCT
cana-2565	137	1	top	top	ADV
cana-2565	137	2	-	-	PUNCT
cana-2565	137	3	performing	perform	VERB
cana-2565	137	4	model	model	NOUN
cana-2565	137	5	:	:	PUNCT
cana-2565	137	6	the	the	DET
cana-2565	137	7	cnn	cnn	PROPN
cana-2565	137	8	-	-	PUNCT
cana-2565	137	9	bigru	bigru	PROPN
cana-2565	137	10	-	-	PUNCT
cana-2565	137	11	dgr	dgr	PROPN
cana-2565	137	12	model	model	NOUN
cana-2565	137	13	emerged	emerge	VERB
cana-2565	137	14	as	as	ADP
cana-2565	137	15	the	the	DET
cana-2565	137	16	best	well	ADV
cana-2565	137	17	-	-	PUNCT
cana-2565	137	18	performing	perform	VERB
cana-2565	137	19	approach	approach	NOUN
cana-2565	137	20	,	,	PUNCT
cana-2565	137	21	achieving	achieve	VERB
cana-2565	137	22	an	an	DET
cana-2565	137	23	accuracy	accuracy	NOUN
cana-2565	137	24	of	of	ADP
cana-2565	137	25	98.15	98.15	NUM
cana-2565	137	26	%	%	NOUN
cana-2565	137	27	,	,	PUNCT
cana-2565	137	28	precision	precision	NOUN
cana-2565	137	29	of	of	ADP
cana-2565	137	30	97.18	97.18	NUM
cana-2565	137	31	%	%	NOUN
cana-2565	137	32	,	,	PUNCT
cana-2565	137	33	recall	recall	NOUN
cana-2565	137	34	of	of	ADP
cana-2565	137	35	99.22	99.22	NUM
cana-2565	137	36	%	%	NOUN
cana-2565	137	37	,	,	PUNCT
cana-2565	137	38	specificity	specificity	NOUN
cana-2565	137	39	of	of	ADP
cana-2565	137	40	96.41	96.41	NUM
cana-2565	137	41	%	%	NOUN
cana-2565	137	42	,	,	PUNCT
cana-2565	137	43	and	and	CCONJ
cana-2565	137	44	an	an	DET
cana-2565	137	45	f1	f1	NOUN
cana-2565	137	46	-	-	PUNCT
cana-2565	137	47	score	score	NOUN
cana-2565	137	48	of	of	ADP
cana-2565	137	49	98.29	98.29	NUM
cana-2565	137	50	%	%	NOUN
cana-2565	137	51	.	.	PUNCT
cana-2565	138	1	these	these	DET
cana-2565	138	2	results	result	NOUN
cana-2565	138	3	underscore	underscore	VERB
cana-2565	138	4	the	the	DET
cana-2565	138	5	effectiveness	effectiveness	NOUN
cana-2565	138	6	of	of	ADP
cana-2565	138	7	integrating	integrate	VERB
cana-2565	138	8	cnn	cnn	PROPN
cana-2565	138	9	and	and	CCONJ
cana-2565	138	10	bigru	bigru	VERB
cana-2565	138	11	with	with	ADP
cana-2565	138	12	the	the	DET
cana-2565	138	13	dynamic	dynamic	ADJ
cana-2565	138	14	gradient	gradient	NOUN
cana-2565	138	15	regularization	regularization	NOUN
cana-2565	138	16	(	(	PUNCT
cana-2565	138	17	dgr	dgr	PROPN
cana-2565	138	18	)	)	PUNCT
cana-2565	138	19	method	method	NOUN
cana-2565	138	20	.	.	PUNCT
cana-2565	139	1	performance	performance	NOUN
cana-2565	139	2	of	of	ADP
cana-2565	139	3	machine	machine	NOUN
cana-2565	139	4	learning	learning	NOUN
cana-2565	139	5	models	model	NOUN
cana-2565	139	6	:	:	PUNCT
cana-2565	139	7	conventional	conventional	ADJ
cana-2565	139	8	ml	ml	NOUN
cana-2565	139	9	models	model	NOUN
cana-2565	139	10	,	,	PUNCT
cana-2565	139	11	such	such	ADJ
cana-2565	139	12	as	as	ADP
cana-2565	139	13	random	random	ADJ
cana-2565	139	14	forest	forest	NOUN
cana-2565	139	15	and	and	CCONJ
cana-2565	139	16	rep	rep	PROPN
cana-2565	139	17	tree	tree	NOUN
cana-2565	139	18	,	,	PUNCT
cana-2565	139	19	demonstrated	demonstrate	VERB
cana-2565	139	20	strong	strong	ADJ
cana-2565	139	21	outcomes	outcome	NOUN
cana-2565	139	22	with	with	ADP
cana-2565	139	23	accuracy	accuracy	NOUN
cana-2565	139	24	scores	score	NOUN
cana-2565	139	25	of	of	ADP
cana-2565	139	26	93.56	93.56	NUM
cana-2565	139	27	%	%	NOUN
cana-2565	139	28	and	and	CCONJ
cana-2565	139	29	91.24	91.24	NUM
cana-2565	139	30	%	%	NOUN
cana-2565	139	31	,	,	PUNCT
cana-2565	139	32	respectively	respectively	ADV
cana-2565	139	33	.	.	PUNCT
cana-2565	140	1	nonetheless	nonetheless	ADV
cana-2565	140	2	,	,	PUNCT
cana-2565	140	3	they	they	PRON
cana-2565	140	4	were	be	AUX
cana-2565	140	5	outperformed	outperform	VERB
cana-2565	140	6	by	by	ADP
cana-2565	140	7	dl	dl	PROPN
cana-2565	140	8	models	model	NOUN
cana-2565	140	9	,	,	PUNCT
cana-2565	140	10	especially	especially	ADV
cana-2565	140	11	when	when	SCONJ
cana-2565	140	12	dealing	deal	VERB
cana-2565	140	13	with	with	ADP
cana-2565	140	14	complex	complex	ADJ
cana-2565	140	15	datasets	dataset	NOUN
cana-2565	140	16	.	.	PUNCT
cana-2565	141	1	advantages	advantage	NOUN
cana-2565	141	2	of	of	ADP
cana-2565	141	3	deep	deep	ADJ
cana-2565	141	4	learning	learning	NOUN
cana-2565	141	5	:	:	PUNCT
cana-2565	141	6	advanced	advanced	ADJ
cana-2565	141	7	deep	deep	ADJ
cana-2565	141	8	learning	learning	NOUN
cana-2565	141	9	models	model	NOUN
cana-2565	141	10	like	like	ADP
cana-2565	141	11	mlp	mlp	PROPN
cana-2565	141	12	and	and	CCONJ
cana-2565	141	13	lstm	lstm	NOUN
cana-2565	141	14	also	also	ADV
cana-2565	141	15	achieved	achieve	VERB
cana-2565	141	16	impressive	impressive	ADJ
cana-2565	141	17	metrics	metric	NOUN
cana-2565	141	18	;	;	PUNCT
cana-2565	141	19	however	however	ADV
cana-2565	141	20	,	,	PUNCT
cana-2565	141	21	the	the	DET
cana-2565	141	22	cnn	cnn	PROPN
cana-2565	141	23	-	-	PUNCT
cana-2565	141	24	bigru	bigru	PROPN
cana-2565	141	25	-	-	PUNCT
cana-2565	141	26	dgr	dgr	PROPN
cana-2565	141	27	model	model	NOUN
cana-2565	141	28	,	,	PUNCT
cana-2565	141	29	with	with	ADP
cana-2565	141	30	its	its	PRON
cana-2565	141	31	hybrid	hybrid	ADJ
cana-2565	141	32	architecture	architecture	NOUN
cana-2565	141	33	and	and	CCONJ
cana-2565	141	34	optimization	optimization	NOUN
cana-2565	141	35	features	feature	NOUN
cana-2565	141	36	,	,	PUNCT
cana-2565	141	37	delivered	deliver	VERB
cana-2565	141	38	the	the	DET
cana-2565	141	39	highest	high	ADJ
cana-2565	141	40	performance	performance	NOUN
cana-2565	141	41	.	.	PUNCT
cana-2565	142	1	•	•	NUM
cana-2565	142	2	figure	figure	NOUN
cana-2565	142	3	1	1	NUM
cana-2565	142	4	:	:	PUNCT
cana-2565	142	5	a	a	DET
cana-2565	142	6	bar	bar	NOUN
cana-2565	142	7	chart	chart	NOUN
cana-2565	142	8	depicting	depict	VERB
cana-2565	142	9	the	the	DET
cana-2565	142	10	accuracy	accuracy	NOUN
cana-2565	142	11	of	of	ADP
cana-2565	142	12	various	various	ADJ
cana-2565	142	13	ml	ml	NOUN
cana-2565	142	14	and	and	CCONJ
cana-2565	142	15	dl	dl	PROPN
cana-2565	142	16	models	model	NOUN
cana-2565	142	17	,	,	PUNCT
cana-2565	142	18	highlighting	highlight	VERB
cana-2565	142	19	the	the	DET
cana-2565	142	20	superior	superior	ADJ
cana-2565	142	21	performance	performance	NOUN
cana-2565	142	22	of	of	ADP
cana-2565	142	23	the	the	DET
cana-2565	142	24	cnn	cnn	PROPN
cana-2565	142	25	-	-	PUNCT
cana-2565	142	26	bigru	bigru	PROPN
cana-2565	142	27	-	-	PUNCT
cana-2565	142	28	dgr	dgr	PROPN
cana-2565	142	29	model	model	PROPN
cana-2565	142	30	.	.	PUNCT
cana-2565	143	1	•	•	NUM
cana-2565	143	2	figure	figure	NOUN
cana-2565	143	3	2	2	NUM
cana-2565	143	4	:	:	PUNCT
cana-2565	143	5	a	a	DET
cana-2565	143	6	comparison	comparison	NOUN
cana-2565	143	7	of	of	ADP
cana-2565	143	8	precision	precision	NOUN
cana-2565	143	9	and	and	CCONJ
cana-2565	143	10	recall	recall	NOUN
cana-2565	143	11	across	across	ADP
cana-2565	143	12	the	the	DET
cana-2565	143	13	algorithms	algorithm	NOUN
cana-2565	143	14	,	,	PUNCT
cana-2565	143	15	demonstrating	demonstrate	VERB
cana-2565	143	16	the	the	DET
cana-2565	143	17	consistent	consistent	ADJ
cana-2565	143	18	and	and	CCONJ
cana-2565	143	19	balanced	balanced	ADJ
cana-2565	143	20	performance	performance	NOUN
cana-2565	143	21	of	of	ADP
cana-2565	143	22	dl	dl	PROPN
cana-2565	143	23	methods	method	NOUN
cana-2565	143	24	.	.	PUNCT
cana-2565	144	1	•	•	NUM
cana-2565	144	2	figure	figure	NOUN
cana-2565	144	3	3	3	NUM
cana-2565	144	4	:	:	PUNCT
cana-2565	144	5	a	a	DET
cana-2565	144	6	graphical	graphical	ADJ
cana-2565	144	7	representation	representation	NOUN
cana-2565	144	8	of	of	ADP
cana-2565	144	9	specificity	specificity	NOUN
cana-2565	144	10	and	and	CCONJ
cana-2565	144	11	f1	f1	NOUN
cana-2565	144	12	-	-	PUNCT
cana-2565	144	13	score	score	NOUN
cana-2565	144	14	metrics	metric	NOUN
cana-2565	144	15	,	,	PUNCT
cana-2565	144	16	showcasing	showcase	VERB
cana-2565	144	17	the	the	DET
cana-2565	144	18	robustness	robustness	NOUN
cana-2565	144	19	and	and	CCONJ
cana-2565	144	20	reliability	reliability	NOUN
cana-2565	144	21	of	of	ADP
cana-2565	144	22	the	the	DET
cana-2565	144	23	cnn	cnn	PROPN
cana-2565	144	24	-	-	PUNCT
cana-2565	144	25	bigru	bigru	PROPN
cana-2565	144	26	-	-	PUNCT
cana-2565	144	27	dgr	dgr	PROPN
cana-2565	144	28	model	model	NOUN
cana-2565	144	29	.	.	PUNCT
cana-2565	145	1	the	the	DET
cana-2565	145	2	cnn	cnn	PROPN
cana-2565	145	3	-	-	PUNCT
cana-2565	145	4	bigru	bigru	PROPN
cana-2565	145	5	-	-	PUNCT
cana-2565	145	6	dgr	dgr	PROPN
cana-2565	145	7	model	model	NOUN
cana-2565	145	8	shows	show	VERB
cana-2565	145	9	great	great	ADJ
cana-2565	145	10	promise	promise	NOUN
cana-2565	145	11	in	in	ADP
cana-2565	145	12	enhancing	enhance	VERB
cana-2565	145	13	ai	ai	NOUN
cana-2565	145	14	-	-	PUNCT
cana-2565	145	15	based	base	VERB
cana-2565	145	16	diagnostic	diagnostic	ADJ
cana-2565	145	17	tools	tool	NOUN
cana-2565	145	18	for	for	ADP
cana-2565	145	19	parkinson	parkinson	NOUN
cana-2565	145	20	’s	’s	PART
cana-2565	145	21	disease	disease	NOUN
cana-2565	145	22	,	,	PUNCT
cana-2565	145	23	leveraging	leverage	VERB
cana-2565	145	24	its	its	PRON
cana-2565	145	25	capability	capability	NOUN
cana-2565	145	26	to	to	PART
cana-2565	145	27	process	process	VERB
cana-2565	145	28	multimodal	multimodal	NOUN
cana-2565	145	29	data	datum	NOUN
cana-2565	145	30	effectively	effectively	ADV
cana-2565	145	31	and	and	CCONJ
cana-2565	145	32	apply	apply	VERB
cana-2565	145	33	advanced	advanced	ADJ
cana-2565	145	34	optimization	optimization	NOUN
cana-2565	145	35	methods	method	NOUN
cana-2565	145	36	.	.	PUNCT
cana-2565	146	1	6.0	6.0	NUM
cana-2565	146	2	conclusion	conclusion	NOUN
cana-2565	146	3	this	this	DET
cana-2565	146	4	innovative	innovative	ADJ
cana-2565	146	5	hybrid	hybrid	ADJ
cana-2565	146	6	cnn	cnn	PROPN
cana-2565	146	7	-	-	PUNCT
cana-2565	146	8	bigru	bigru	PROPN
cana-2565	146	9	model	model	NOUN
cana-2565	146	10	,	,	PUNCT
cana-2565	146	11	augmented	augment	VERB
cana-2565	146	12	by	by	ADP
cana-2565	146	13	the	the	DET
cana-2565	146	14	dgr	dgr	PROPN
cana-2565	146	15	optimization	optimization	PROPN
cana-2565	146	16	method	method	NOUN
cana-2565	146	17	,	,	PUNCT
cana-2565	146	18	offers	offer	VERB
cana-2565	146	19	an	an	DET
cana-2565	146	20	effective	effective	ADJ
cana-2565	146	21	approach	approach	NOUN
cana-2565	146	22	for	for	ADP
cana-2565	146	23	classifying	classify	VERB
cana-2565	146	24	parkinson	parkinson	NOUN
cana-2565	146	25	’s	’s	PART
cana-2565	146	26	disease	disease	NOUN
cana-2565	146	27	.	.	PUNCT
cana-2565	147	1	by	by	ADP
cana-2565	147	2	integrating	integrate	VERB
cana-2565	147	3	multimodal	multimodal	NOUN
cana-2565	147	4	data	datum	NOUN
cana-2565	147	5	processing	processing	NOUN
cana-2565	147	6	with	with	ADP
cana-2565	147	7	advanced	advanced	ADJ
cana-2565	147	8	optimization	optimization	NOUN
cana-2565	147	9	techniques	technique	NOUN
cana-2565	147	10	,	,	PUNCT
cana-2565	147	11	it	it	PRON
cana-2565	147	12	demonstrates	demonstrate	VERB
cana-2565	147	13	significant	significant	ADJ
cana-2565	147	14	promise	promise	NOUN
cana-2565	147	15	in	in	ADP
cana-2565	147	16	enhancing	enhance	VERB
cana-2565	147	17	ai	ai	ADJ
cana-2565	147	18	-	-	PUNCT
cana-2565	147	19	powered	power	VERB
cana-2565	147	20	medical	medical	ADJ
cana-2565	147	21	diagnostics	diagnostic	NOUN
cana-2565	147	22	and	and	CCONJ
cana-2565	147	23	facilitating	facilitate	VERB
cana-2565	147	24	the	the	DET
cana-2565	147	25	early	early	ADJ
cana-2565	147	26	detection	detection	NOUN
cana-2565	147	27	of	of	ADP
cana-2565	147	28	pd	pd	PROPN
cana-2565	147	29	.	.	PROPN
cana-2565	148	1	deep	deep	ADJ
cana-2565	148	2	learning	learning	NOUN
cana-2565	148	3	models	model	NOUN
cana-2565	148	4	often	often	ADV
cana-2565	148	5	achieve	achieve	VERB
cana-2565	148	6	superior	superior	ADJ
cana-2565	148	7	performance	performance	NOUN
cana-2565	148	8	compared	compare	VERB
cana-2565	148	9	to	to	ADP
cana-2565	148	10	traditional	traditional	ADJ
cana-2565	148	11	machine	machine	NOUN
cana-2565	148	12	learning	learning	NOUN
cana-2565	148	13	models	model	NOUN
cana-2565	148	14	across	across	ADP
cana-2565	148	15	metrics	metric	NOUN
cana-2565	148	16	such	such	ADJ
cana-2565	148	17	as	as	ADP
cana-2565	148	18	accuracy	accuracy	NOUN
cana-2565	148	19	,	,	PUNCT
cana-2565	148	20	precision	precision	NOUN
cana-2565	148	21	,	,	PUNCT
cana-2565	148	22	recall	recall	NOUN
cana-2565	148	23	,	,	PUNCT
cana-2565	148	24	f1	f1	NOUN
cana-2565	148	25	-	-	PUNCT
cana-2565	148	26	score	score	NOUN
cana-2565	148	27	,	,	PUNCT
cana-2565	148	28	and	and	CCONJ
cana-2565	148	29	auc	auc	NOUN
cana-2565	148	30	,	,	PUNCT
cana-2565	148	31	especially	especially	ADV
cana-2565	148	32	when	when	SCONJ
cana-2565	148	33	working	work	VERB
cana-2565	148	34	with	with	ADP
cana-2565	148	35	large	large	ADJ
cana-2565	148	36	and	and	CCONJ
cana-2565	148	37	complex	complex	ADJ
cana-2565	148	38	datasets	dataset	NOUN
cana-2565	148	39	.	.	PUNCT
cana-2565	149	1	however	however	ADV
cana-2565	149	2	,	,	PUNCT
cana-2565	149	3	machine	machine	NOUN
cana-2565	149	4	learning	learning	NOUN
cana-2565	149	5	models	model	NOUN
cana-2565	149	6	may	may	AUX
cana-2565	149	7	remain	remain	VERB
cana-2565	149	8	advantageous	advantageous	ADJ
cana-2565	149	9	in	in	ADP
cana-2565	149	10	situations	situation	NOUN
cana-2565	149	11	where	where	SCONJ
cana-2565	149	12	data	datum	NOUN
cana-2565	149	13	is	be	AUX
cana-2565	149	14	limited	limited	ADJ
cana-2565	149	15	or	or	CCONJ
cana-2565	149	16	computational	computational	ADJ
cana-2565	149	17	resources	resource	NOUN
cana-2565	149	18	are	be	AUX
cana-2565	149	19	restricted	restrict	VERB
cana-2565	149	20	.	.	PUNCT
cana-2565	150	1	selecting	select	VERB
cana-2565	150	2	the	the	DET
cana-2565	150	3	appropriate	appropriate	ADJ
cana-2565	150	4	approach	approach	NOUN
cana-2565	150	5	requires	require	VERB
cana-2565	150	6	careful	careful	ADJ
cana-2565	150	7	consideration	consideration	NOUN
cana-2565	150	8	of	of	ADP
cana-2565	150	9	the	the	DET
cana-2565	150	10	dataset	dataset	NOUN
cana-2565	150	11	size	size	NOUN
cana-2565	150	12	,	,	PUNCT
cana-2565	150	13	feature	feature	NOUN
cana-2565	150	14	complexity	complexity	NOUN
cana-2565	150	15	,	,	PUNCT
cana-2565	150	16	and	and	CCONJ
cana-2565	150	17	the	the	DET
cana-2565	150	18	need	need	NOUN
cana-2565	150	19	to	to	PART
cana-2565	150	20	balance	balance	VERB
cana-2565	150	21	interpretability	interpretability	NOUN
cana-2565	150	22	with	with	ADP
cana-2565	150	23	performance	performance	NOUN
cana-2565	150	24	.	.	PUNCT
cana-2565	151	1	references	reference	NOUN
cana-2565	151	2	[	[	X
cana-2565	151	3	1	1	NUM
cana-2565	151	4	]	]	X
cana-2565	151	5	bishop	bishop	PROPN
cana-2565	151	6	,	,	PUNCT
cana-2565	151	7	c.	c.	PROPN
cana-2565	151	8	m.	m.	NOUN
cana-2565	151	9	(	(	PUNCT
cana-2565	151	10	2006	2006	NUM
cana-2565	151	11	)	)	PUNCT
cana-2565	151	12	.	.	PUNCT
cana-2565	152	1	pattern	pattern	NOUN
cana-2565	152	2	recognition	recognition	NOUN
cana-2565	152	3	and	and	CCONJ
cana-2565	152	4	machine	machine	NOUN
cana-2565	152	5	learning	learning	NOUN
cana-2565	152	6	.	.	PUNCT
cana-2565	153	1	springer	springer	NOUN
cana-2565	153	2	.	.	PUNCT
cana-2565	154	1	[	[	X
cana-2565	154	2	2	2	NUM
cana-2565	154	3	]	]	PUNCT
cana-2565	154	4	breiman	breiman	NOUN
cana-2565	154	5	,	,	PUNCT
cana-2565	154	6	l.	l.	PROPN
cana-2565	154	7	(	(	PUNCT
cana-2565	154	8	2001	2001	NUM
cana-2565	154	9	)	)	PUNCT
cana-2565	154	10	.	.	PUNCT
cana-2565	155	1	random	random	ADJ
cana-2565	155	2	forests	forest	NOUN
cana-2565	155	3	.	.	PUNCT
cana-2565	156	1	machine	machine	NOUN
cana-2565	156	2	learning	learning	PROPN
cana-2565	156	3	,	,	PUNCT
cana-2565	156	4	45(1	45(1	NOUN
cana-2565	156	5	)	)	PUNCT
cana-2565	156	6	,	,	PUNCT
cana-2565	156	7	5–32	5–32	NOUN
cana-2565	156	8	.	.	PUNCT
cana-2565	157	1	[	[	X
cana-2565	157	2	3	3	NUM
cana-2565	157	3	]	]	X
cana-2565	157	4	cho	cho	PROPN
cana-2565	157	5	,	,	PUNCT
cana-2565	157	6	k.	k.	PROPN
cana-2565	157	7	,	,	PUNCT
cana-2565	157	8	van	van	PROPN
cana-2565	157	9	merriënboer	merriënboer	PROPN
cana-2565	157	10	,	,	PUNCT
cana-2565	157	11	b.	b.	PROPN
cana-2565	157	12	,	,	PUNCT
cana-2565	157	13	bahdanau	bahdanau	PROPN
cana-2565	157	14	,	,	PUNCT
cana-2565	157	15	d.	d.	PROPN
cana-2565	157	16	,	,	PUNCT
cana-2565	157	17	&	&	CCONJ
cana-2565	157	18	bengio	bengio	PROPN
cana-2565	157	19	,	,	PUNCT
cana-2565	157	20	y.	y.	PROPN
cana-2565	157	21	(	(	PUNCT
cana-2565	157	22	2014	2014	NUM
cana-2565	157	23	)	)	PUNCT
cana-2565	157	24	.	.	PUNCT
cana-2565	158	1	on	on	ADP
cana-2565	158	2	the	the	DET
cana-2565	158	3	properties	property	NOUN
cana-2565	158	4	of	of	ADP
cana-2565	158	5	neural	neural	ADJ
cana-2565	158	6	machine	machine	NOUN
cana-2565	158	7	translation	translation	NOUN
cana-2565	158	8	:	:	PUNCT
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cana-2565	158	10	-	-	PUNCT
cana-2565	158	11	decoder	decoder	NOUN
cana-2565	158	12	approaches	approach	NOUN
cana-2565	158	13	.	.	PUNCT
cana-2565	159	1	arxiv	arxiv	PROPN
cana-2565	159	2	preprint	preprint	VERB
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cana-2565	159	4	.	.	PUNCT
cana-2565	160	1	[	[	X
cana-2565	160	2	4	4	NUM
cana-2565	160	3	]	]	X
cana-2565	160	4	fawcett	fawcett	PROPN
cana-2565	160	5	,	,	PUNCT
cana-2565	160	6	t.	t.	PROPN
cana-2565	160	7	(	(	PUNCT
cana-2565	160	8	2006	2006	NUM
cana-2565	160	9	)	)	PUNCT
cana-2565	160	10	.	.	PUNCT
cana-2565	161	1	an	an	DET
cana-2565	161	2	introduction	introduction	NOUN
cana-2565	161	3	to	to	ADP
cana-2565	161	4	roc	roc	PROPN
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cana-2565	161	6	.	.	PUNCT
cana-2565	162	1	pattern	pattern	NOUN
cana-2565	162	2	recognition	recognition	NOUN
cana-2565	162	3	letters	letter	NOUN
cana-2565	162	4	,	,	PUNCT
cana-2565	162	5	27(8	27(8	NUM
cana-2565	162	6	)	)	PUNCT
cana-2565	162	7	,	,	PUNCT
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cana-2565	162	9	-	-	SYM
cana-2565	162	10	874	874	NUM
cana-2565	162	11	.	.	PUNCT
cana-2565	163	1	communications	communication	NOUN
cana-2565	163	2	on	on	ADP
cana-2565	163	3	applied	apply	VERB
cana-2565	163	4	nonlinear	nonlinear	ADJ
cana-2565	163	5	analysis	analysis	NOUN
cana-2565	163	6	issn	issn	NOUN
cana-2565	163	7	:	:	PUNCT
cana-2565	163	8	1074	1074	NUM
cana-2565	163	9	-	-	PUNCT
cana-2565	163	10	133x	133x	NUM
cana-2565	163	11	vol	vol	NOUN
cana-2565	163	12	32	32	NUM
cana-2565	163	13	no	no	NOUN
cana-2565	163	14	.	.	PUNCT
cana-2565	164	1	3s	3s	NUM
cana-2565	164	2	(	(	PUNCT
cana-2565	164	3	2025	2025	NUM
cana-2565	164	4	)	)	PUNCT
cana-2565	164	5	111	111	NUM
cana-2565	164	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-2565	165	1	[	[	X
cana-2565	165	2	5	5	NUM
cana-2565	165	3	]	]	X
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cana-2565	165	5	,	,	PUNCT
cana-2565	165	6	i.	i.	PROPN
cana-2565	165	7	,	,	PUNCT
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cana-2565	165	9	,	,	PUNCT
cana-2565	165	10	y.	y.	PROPN
cana-2565	165	11	,	,	PUNCT
cana-2565	165	12	&	&	CCONJ
cana-2565	165	13	courville	courville	PROPN
cana-2565	165	14	,	,	PUNCT
cana-2565	165	15	a.	a.	NOUN
cana-2565	165	16	(	(	PUNCT
cana-2565	165	17	2016	2016	NUM
cana-2565	165	18	)	)	PUNCT
cana-2565	165	19	.	.	PUNCT
cana-2565	166	1	deep	deep	ADJ
cana-2565	166	2	learning	learning	NOUN
cana-2565	166	3	.	.	PUNCT
cana-2565	167	1	mit	mit	PROPN
cana-2565	167	2	press	press	NOUN
cana-2565	167	3	.	.	PUNCT
cana-2565	168	1	[	[	X
cana-2565	168	2	6	6	NUM
cana-2565	168	3	]	]	PUNCT
cana-2565	168	4	kaggle	kaggle	PROPN
cana-2565	168	5	:	:	PUNCT
cana-2565	168	6	https://www.kaggle.com/datasets/vikasukani/parkinsons-disease-data-set	https://www.kaggle.com/datasets/vikasukani/parkinsons-disease-data-set	PROPN
cana-2565	169	1	[	[	X
cana-2565	169	2	7	7	X
cana-2565	169	3	]	]	X
cana-2565	169	4	khan	khan	PROPN
cana-2565	169	5	,	,	PUNCT
cana-2565	169	6	a.	a.	PROPN
cana-2565	169	7	,	,	PUNCT
cana-2565	169	8	et	et	PROPN
cana-2565	169	9	al	al	PROPN
cana-2565	169	10	.	.	PROPN
cana-2565	169	11	(	(	PUNCT
cana-2565	169	12	2020	2020	NUM
cana-2565	169	13	)	)	PUNCT
cana-2565	169	14	.	.	PUNCT
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cana-2565	170	2	analysis	analysis	NOUN
cana-2565	170	3	of	of	ADP
cana-2565	170	4	machine	machine	NOUN
cana-2565	170	5	learning	learn	VERB
cana-2565	170	6	algorithms	algorithm	NOUN
cana-2565	170	7	for	for	ADP
cana-2565	170	8	parkinson	parkinson	NOUN
cana-2565	170	9	’s	’s	PART
cana-2565	170	10	disease	disease	NOUN
cana-2565	170	11	classification	classification	NOUN
cana-2565	170	12	.	.	PUNCT
cana-2565	171	1	healthcare	healthcare	PROPN
cana-2565	171	2	informatics	informatics	PROPN
cana-2565	171	3	review	review	PROPN
cana-2565	171	4	,	,	PUNCT
cana-2565	171	5	12(1	12(1	NUM
cana-2565	171	6	)	)	PUNCT
cana-2565	171	7	,	,	PUNCT
cana-2565	171	8	45	45	NUM
cana-2565	171	9	-	-	SYM
cana-2565	171	10	58	58	NUM
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cana-2565	172	2	8	8	NUM
cana-2565	172	3	]	]	X
cana-2565	172	4	kingma	kingma	PROPN
cana-2565	172	5	,	,	PUNCT
cana-2565	172	6	d.	d.	PROPN
cana-2565	172	7	p.	p.	PROPN
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cana-2565	172	9	&	&	CCONJ
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cana-2565	172	11	,	,	PUNCT
cana-2565	172	12	j.	j.	PROPN
cana-2565	172	13	(	(	PUNCT
cana-2565	172	14	2015	2015	NUM
cana-2565	172	15	)	)	PUNCT
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cana-2565	173	7	optimization	optimization	NOUN
cana-2565	173	8	.	.	PUNCT
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cana-2565	174	2	conference	conference	NOUN
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cana-2565	174	6	(	(	PUNCT
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cana-2565	174	8	)	)	PUNCT
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cana-2565	175	2	9	9	NUM
cana-2565	175	3	]	]	SYM
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cana-2565	175	9	,	,	PUNCT
cana-2565	175	10	y.	y.	PROPN
cana-2565	175	11	,	,	PUNCT
cana-2565	175	12	&	&	CCONJ
cana-2565	175	13	hinton	hinton	PROPN
cana-2565	175	14	,	,	PUNCT
cana-2565	175	15	g.	g.	PROPN
cana-2565	175	16	(	(	PUNCT
cana-2565	175	17	2015	2015	NUM
cana-2565	175	18	)	)	PUNCT
cana-2565	175	19	.	.	PUNCT
cana-2565	176	1	deep	deep	ADJ
cana-2565	176	2	learning	learning	NOUN
cana-2565	176	3	.	.	PUNCT
cana-2565	177	1	nature	nature	NOUN
cana-2565	177	2	,	,	PUNCT
cana-2565	177	3	521(7553	521(7553	NUM
cana-2565	177	4	)	)	PUNCT
cana-2565	177	5	,	,	PUNCT
cana-2565	177	6	436	436	NUM
cana-2565	177	7	-	-	SYM
cana-2565	177	8	444	444	NUM
cana-2565	177	9	.	.	PUNCT
cana-2565	178	1	[	[	X
cana-2565	178	2	10	10	NUM
cana-2565	178	3	]	]	X
cana-2565	178	4	montgomery	montgomery	PROPN
cana-2565	178	5	,	,	PUNCT
cana-2565	178	6	d.	d.	PROPN
cana-2565	178	7	c.	c.	PROPN
cana-2565	178	8	,	,	PUNCT
cana-2565	178	9	peck	peck	PROPN
cana-2565	178	10	,	,	PUNCT
cana-2565	178	11	e.	e.	PROPN
cana-2565	178	12	a.	a.	PROPN
cana-2565	178	13	,	,	PUNCT
cana-2565	178	14	&	&	CCONJ
cana-2565	178	15	vining	vine	VERB
cana-2565	178	16	,	,	PUNCT
cana-2565	178	17	g.	g.	PROPN
cana-2565	178	18	g.	g.	PROPN
cana-2565	178	19	(	(	PUNCT
cana-2565	178	20	2021	2021	NUM
cana-2565	178	21	)	)	PUNCT
cana-2565	178	22	.	.	PUNCT
cana-2565	179	1	introduction	introduction	NOUN
cana-2565	179	2	to	to	PART
cana-2565	179	3	linear	linear	VERB
cana-2565	179	4	regression	regression	NOUN
cana-2565	179	5	analysis	analysis	NOUN
cana-2565	179	6	.	.	PUNCT
cana-2565	180	1	wiley	wiley	NOUN
cana-2565	180	2	.	.	PUNCT
cana-2565	181	1	[	[	X
cana-2565	181	2	11	11	NUM
cana-2565	181	3	]	]	X
cana-2565	181	4	rajesh	rajesh	PROPN
cana-2565	181	5	,	,	PUNCT
cana-2565	181	6	p.	p.	PROPN
cana-2565	181	7	,	,	PUNCT
cana-2565	181	8	et	et	PROPN
cana-2565	181	9	al	al	PROPN
cana-2565	181	10	.	.	PROPN
cana-2565	181	11	(	(	PUNCT
cana-2565	181	12	2019	2019	NUM
cana-2565	181	13	)	)	PUNCT
cana-2565	181	14	.	.	PUNCT
cana-2565	182	1	comparative	comparative	ADJ
cana-2565	182	2	study	study	NOUN
cana-2565	182	3	of	of	ADP
cana-2565	182	4	decision	decision	NOUN
cana-2565	182	5	tree	tree	NOUN
cana-2565	182	6	approaches	approach	VERB
cana-2565	182	7	in	in	ADP
cana-2565	182	8	data	datum	NOUN
cana-2565	182	9	mining	mining	NOUN
cana-2565	182	10	using	use	VERB
cana-2565	182	11	chronic	chronic	ADJ
cana-2565	182	12	disease	disease	NOUN
cana-2565	182	13	indicators	indicator	NOUN
cana-2565	182	14	(	(	PUNCT
cana-2565	182	15	cdi	cdi	PROPN
cana-2565	182	16	)	)	PUNCT
cana-2565	182	17	data	data	PROPN
cana-2565	182	18	.	.	PUNCT
cana-2565	183	1	journal	journal	PROPN
cana-2565	183	2	of	of	ADP
cana-2565	183	3	computational	computational	ADJ
cana-2565	183	4	and	and	CCONJ
cana-2565	183	5	theoretical	theoretical	ADJ
cana-2565	183	6	nanoscience	nanoscience	NOUN
cana-2565	183	7	,	,	PUNCT
cana-2565	183	8	16(4	16(4	PROPN
cana-2565	183	9	)	)	PUNCT
cana-2565	183	10	,	,	PUNCT
cana-2565	183	11	1472	1472	NUM
cana-2565	183	12	-	-	SYM
cana-2565	183	13	1477	1477	NUM
cana-2565	183	14	.	.	PUNCT
cana-2565	184	1	[	[	X
cana-2565	184	2	12	12	NUM
cana-2565	184	3	]	]	X
cana-2565	184	4	rajesh	rajesh	PROPN
cana-2565	184	5	,	,	PUNCT
cana-2565	184	6	p.	p.	PROPN
cana-2565	184	7	,	,	PUNCT
cana-2565	184	8	karthikeyan	karthikeyan	PROPN
cana-2565	184	9	,	,	PUNCT
cana-2565	184	10	m.	m.	NOUN
cana-2565	184	11	(	(	PUNCT
cana-2565	184	12	2017	2017	NUM
cana-2565	184	13	)	)	PUNCT
cana-2565	184	14	.	.	PUNCT
cana-2565	185	1	a	a	DET
cana-2565	185	2	comparative	comparative	ADJ
cana-2565	185	3	study	study	NOUN
cana-2565	185	4	of	of	ADP
cana-2565	185	5	data	datum	NOUN
cana-2565	185	6	mining	mining	NOUN
cana-2565	185	7	algorithms	algorithm	NOUN
cana-2565	185	8	for	for	ADP
cana-2565	185	9	decision	decision	NOUN
cana-2565	185	10	tree	tree	NOUN
cana-2565	185	11	approaches	approach	NOUN
cana-2565	185	12	using	use	VERB
cana-2565	185	13	weka	weka	PROPN
cana-2565	185	14	tool	tool	PROPN
cana-2565	185	15	.	.	PUNCT
cana-2565	186	1	advances	advance	NOUN
cana-2565	186	2	in	in	ADP
cana-2565	186	3	natural	natural	ADJ
cana-2565	186	4	and	and	CCONJ
cana-2565	186	5	applied	applied	ADJ
cana-2565	186	6	sciences	science	NOUN
cana-2565	186	7	,	,	PUNCT
cana-2565	186	8	11(9	11(9	PROPN
cana-2565	186	9	)	)	PUNCT
cana-2565	186	10	,	,	PUNCT
cana-2565	186	11	230	230	NUM
cana-2565	186	12	-	-	SYM
cana-2565	186	13	243	243	NUM
cana-2565	186	14	.	.	PUNCT
cana-2565	187	1	[	[	X
cana-2565	187	2	13	13	NUM
cana-2565	187	3	]	]	X
cana-2565	187	4	rashid	rashid	PROPN
cana-2565	187	5	,	,	PUNCT
cana-2565	187	6	m.	m.	NOUN
cana-2565	187	7	,	,	PUNCT
cana-2565	187	8	et	et	PROPN
cana-2565	187	9	al	al	PROPN
cana-2565	187	10	.	.	PROPN
cana-2565	188	1	(	(	PUNCT
cana-2565	188	2	2022	2022	NUM
cana-2565	188	3	)	)	PUNCT
cana-2565	188	4	.	.	PUNCT
cana-2565	189	1	evaluating	evaluate	VERB
cana-2565	189	2	the	the	DET
cana-2565	189	3	performance	performance	NOUN
cana-2565	189	4	of	of	ADP
cana-2565	189	5	deep	deep	ADJ
cana-2565	189	6	learning	learning	NOUN
cana-2565	189	7	models	model	NOUN
cana-2565	189	8	in	in	ADP
cana-2565	189	9	parkinson	parkinson	NOUN
cana-2565	189	10	’s	’s	PART
cana-2565	189	11	disease	disease	NOUN
cana-2565	189	12	prediction	prediction	NOUN
cana-2565	189	13	.	.	PUNCT
cana-2565	190	1	ai	ai	VERB
cana-2565	190	2	in	in	ADP
cana-2565	190	3	medicine	medicine	NOUN
cana-2565	190	4	and	and	CCONJ
cana-2565	190	5	diagnostics	diagnostic	NOUN
cana-2565	190	6	,	,	PUNCT
cana-2565	190	7	19(4	19(4	NOUN
cana-2565	190	8	)	)	PUNCT
cana-2565	190	9	,	,	PUNCT
cana-2565	190	10	67	67	NUM
cana-2565	190	11	-	-	SYM
cana-2565	190	12	81	81	NUM
cana-2565	190	13	.	.	PUNCT
cana-2565	191	1	[	[	X
cana-2565	191	2	14	14	NUM
cana-2565	191	3	]	]	X
cana-2565	191	4	sakar	sakar	PROPN
cana-2565	191	5	,	,	PUNCT
cana-2565	191	6	c.	c.	PROPN
cana-2565	191	7	o.	o.	PROPN
cana-2565	191	8	,	,	PUNCT
cana-2565	191	9	et	et	PROPN
cana-2565	191	10	al	al	PROPN
cana-2565	191	11	.	.	PROPN
cana-2565	191	12	(	(	PUNCT
cana-2565	191	13	2013	2013	NUM
cana-2565	191	14	)	)	PUNCT
cana-2565	191	15	.	.	PUNCT
cana-2565	192	1	collection	collection	NOUN
cana-2565	192	2	and	and	CCONJ
cana-2565	192	3	analysis	analysis	NOUN
cana-2565	192	4	of	of	ADP
cana-2565	192	5	a	a	DET
cana-2565	192	6	parkinson	parkinson	NOUN
cana-2565	192	7	speech	speech	NOUN
cana-2565	192	8	dataset	dataset	VERB
cana-2565	192	9	with	with	ADP
cana-2565	192	10	multiple	multiple	ADJ
cana-2565	192	11	types	type	NOUN
cana-2565	192	12	of	of	ADP
cana-2565	192	13	sound	sound	ADJ
cana-2565	192	14	recordings	recording	NOUN
cana-2565	192	15	.	.	PUNCT
cana-2565	193	1	ieee	ieee	NOUN
cana-2565	193	2	transactions	transaction	NOUN
cana-2565	193	3	on	on	ADP
cana-2565	193	4	biomedical	biomedical	ADJ
cana-2565	193	5	engineering	engineering	NOUN
cana-2565	193	6	,	,	PUNCT
cana-2565	193	7	59(8	59(8	NUM
cana-2565	193	8	)	)	PUNCT
cana-2565	193	9	,	,	PUNCT
cana-2565	193	10	2187	2187	NUM
cana-2565	193	11	-	-	SYM
cana-2565	193	12	2194	2194	NUM
cana-2565	193	13	.	.	PUNCT
cana-2565	194	1	[	[	X
cana-2565	194	2	15	15	NUM
cana-2565	194	3	]	]	X
cana-2565	194	4	serrano	serrano	NOUN
cana-2565	194	5	-	-	PUNCT
cana-2565	194	6	gotarredona	gotarredona	PROPN
cana-2565	194	7	,	,	PUNCT
cana-2565	194	8	t.	t.	PROPN
cana-2565	194	9	,	,	PUNCT
cana-2565	194	10	et	et	PROPN
cana-2565	194	11	al	al	PROPN
cana-2565	194	12	.	.	PUNCT
cana-2565	194	13	(	(	PUNCT
cana-2565	194	14	2021	2021	NUM
cana-2565	194	15	)	)	PUNCT
cana-2565	194	16	.	.	PUNCT
cana-2565	195	1	advances	advance	NOUN
cana-2565	195	2	in	in	ADP
cana-2565	195	3	early	early	ADJ
cana-2565	195	4	diagnosis	diagnosis	NOUN
cana-2565	195	5	of	of	ADP
cana-2565	195	6	parkinson	parkinson	NOUN
cana-2565	195	7	’s	’s	PART
cana-2565	195	8	disease	disease	NOUN
cana-2565	195	9	using	use	VERB
cana-2565	195	10	ai	ai	VERB
cana-2565	195	11	techniques	technique	NOUN
cana-2565	195	12	.	.	PUNCT
cana-2565	196	1	journal	journal	PROPN
cana-2565	196	2	of	of	ADP
cana-2565	196	3	neurology	neurology	NOUN
cana-2565	196	4	and	and	CCONJ
cana-2565	196	5	ai	ai	PROPN
cana-2565	196	6	applications	application	NOUN
cana-2565	196	7	,	,	PUNCT
cana-2565	196	8	34(2	34(2	NUM
cana-2565	196	9	)	)	PUNCT
cana-2565	196	10	,	,	PUNCT
cana-2565	196	11	123	123	NUM
cana-2565	196	12	-	-	SYM
cana-2565	196	13	135	135	NUM
cana-2565	196	14	.	.	PUNCT
cana-2565	197	1	[	[	X
cana-2565	197	2	16	16	NUM
cana-2565	197	3	]	]	X
cana-2565	197	4	sokolova	sokolova	PROPN
cana-2565	197	5	,	,	PUNCT
cana-2565	197	6	m.	m.	NOUN
cana-2565	197	7	,	,	PUNCT
cana-2565	197	8	&	&	CCONJ
cana-2565	197	9	lapalme	lapalme	PROPN
cana-2565	197	10	,	,	PUNCT
cana-2565	197	11	g.	g.	PROPN
cana-2565	197	12	(	(	PUNCT
cana-2565	197	13	2009	2009	NUM
cana-2565	197	14	)	)	PUNCT
cana-2565	197	15	.	.	PUNCT
cana-2565	198	1	a	a	DET
cana-2565	198	2	systematic	systematic	ADJ
cana-2565	198	3	analysis	analysis	NOUN
cana-2565	198	4	of	of	ADP
cana-2565	198	5	performance	performance	NOUN
cana-2565	198	6	measures	measure	NOUN
cana-2565	198	7	for	for	ADP
cana-2565	198	8	classification	classification	NOUN
cana-2565	198	9	tasks	task	NOUN
cana-2565	198	10	.	.	PUNCT
cana-2565	199	1	information	information	NOUN
cana-2565	199	2	processing	processing	NOUN
cana-2565	199	3	&	&	CCONJ
cana-2565	199	4	management	management	NOUN
cana-2565	199	5	,	,	PUNCT
cana-2565	199	6	45(4	45(4	NUM
cana-2565	199	7	)	)	PUNCT
cana-2565	199	8	,	,	PUNCT
cana-2565	199	9	427	427	NUM
cana-2565	199	10	-	-	SYM
cana-2565	199	11	437	437	NUM
cana-2565	199	12	.	.	PUNCT
cana-2565	200	1	[	[	X
cana-2565	200	2	17	17	NUM
cana-2565	200	3	]	]	PUNCT
cana-2565	200	4	witten	witten	PROPN
cana-2565	200	5	,	,	PUNCT
cana-2565	200	6	i.	i.	PROPN
cana-2565	200	7	h.	h.	PROPN
cana-2565	200	8	,	,	PUNCT
cana-2565	200	9	frank	frank	PROPN
cana-2565	200	10	,	,	PUNCT
cana-2565	200	11	e.	e.	PROPN
cana-2565	200	12	,	,	PUNCT
cana-2565	200	13	hall	hall	PROPN
cana-2565	200	14	,	,	PUNCT
cana-2565	200	15	m.	m.	NOUN
cana-2565	200	16	a.	a.	PROPN
cana-2565	200	17	,	,	PUNCT
cana-2565	200	18	&	&	CCONJ
cana-2565	200	19	pal	pal	NOUN
cana-2565	200	20	,	,	PUNCT
cana-2565	200	21	c.	c.	PROPN
cana-2565	200	22	j.	j.	PROPN
cana-2565	200	23	(	(	PUNCT
cana-2565	200	24	2017	2017	NUM
cana-2565	200	25	)	)	PUNCT
cana-2565	200	26	.	.	PUNCT
cana-2565	201	1	data	datum	NOUN
cana-2565	201	2	mining	mining	NOUN
cana-2565	201	3	:	:	PUNCT
cana-2565	201	4	practical	practical	ADJ
cana-2565	201	5	machine	machine	NOUN
cana-2565	201	6	learning	learning	NOUN
cana-2565	201	7	tools	tool	NOUN
cana-2565	201	8	and	and	CCONJ
cana-2565	201	9	techniques	technique	NOUN
cana-2565	201	10	.	.	PUNCT
cana-2565	202	1	morgan	morgan	PROPN
cana-2565	202	2	kaufmann	kaufmann	PROPN
cana-2565	202	3	.	.	PUNCT
cana-2565	203	1	[	[	X
cana-2565	203	2	18	18	NUM
cana-2565	203	3	]	]	X
cana-2565	203	4	zhou	zhou	PROPN
cana-2565	203	5	,	,	PUNCT
cana-2565	203	6	x.	x.	PROPN
cana-2565	203	7	,	,	PUNCT
cana-2565	203	8	et	et	PROPN
cana-2565	203	9	al	al	PROPN
cana-2565	203	10	.	.	PROPN
cana-2565	203	11	(	(	PUNCT
cana-2565	203	12	2023	2023	NUM
cana-2565	203	13	)	)	PUNCT
cana-2565	203	14	.	.	PUNCT
cana-2565	204	1	optimized	optimize	VERB
cana-2565	204	2	deep	deep	ADJ
cana-2565	204	3	learning	learning	NOUN
cana-2565	204	4	models	model	NOUN
cana-2565	204	5	for	for	ADP
cana-2565	204	6	medical	medical	ADJ
cana-2565	204	7	diagnostics	diagnostic	NOUN
cana-2565	204	8	:	:	PUNCT
cana-2565	204	9	a	a	DET
cana-2565	204	10	review	review	NOUN
cana-2565	204	11	of	of	ADP
cana-2565	204	12	techniques	technique	NOUN
cana-2565	204	13	and	and	CCONJ
cana-2565	204	14	applications	application	NOUN
cana-2565	204	15	.	.	PUNCT
cana-2565	205	1	medical	medical	ADJ
cana-2565	205	2	ai	ai	PROPN
cana-2565	205	3	research	research	PROPN
cana-2565	205	4	journal	journal	PROPN
cana-2565	205	5	,	,	PUNCT
cana-2565	205	6	16(3	16(3	PROPN
cana-2565	205	7	)	)	PUNCT
cana-2565	205	8	,	,	PUNCT
cana-2565	205	9	78	78	NUM
cana-2565	205	10	-	-	SYM
cana-2565	205	11	98	98	NUM
cana-2565	205	12	.	.	PUNCT