id	sid	tid	token	lemma	pos
cana-3162	1	1	paper	paper	NOUN
cana-3162	1	2	title	title	NOUN
cana-3162	1	3	(	(	PUNCT
cana-3162	1	4	use	use	NOUN
cana-3162	1	5	style	style	NOUN
cana-3162	1	6	:	:	PUNCT
cana-3162	1	7	paper	paper	NOUN
cana-3162	1	8	title	title	NOUN
cana-3162	1	9	)	)	PUNCT
cana-3162	1	10	communications	communication	NOUN
cana-3162	1	11	on	on	ADP
cana-3162	1	12	applied	apply	VERB
cana-3162	1	13	nonlinear	nonlinear	ADJ
cana-3162	1	14	analysis	analysis	NOUN
cana-3162	1	15	issn	issn	NOUN
cana-3162	1	16	:	:	PUNCT
cana-3162	1	17	1074	1074	NUM
cana-3162	1	18	-	-	PUNCT
cana-3162	1	19	133x	133x	NUM
cana-3162	1	20	vol	vol	NOUN
cana-3162	1	21	32	32	NUM
cana-3162	1	22	no	no	NOUN
cana-3162	1	23	.	.	PUNCT
cana-3162	2	1	5s	5s	NUM
cana-3162	2	2	(	(	PUNCT
cana-3162	2	3	2025	2025	NUM
cana-3162	2	4	)	)	PUNCT
cana-3162	2	5	494	494	NUM
cana-3162	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3162	2	7	anti	anti	X
cana-3162	2	8	cancer	cancer	NOUN
cana-3162	2	9	drug	drug	NOUN
cana-3162	2	10	response	response	NOUN
cana-3162	2	11	prediction	prediction	NOUN
cana-3162	2	12	with	with	ADP
cana-3162	2	13	machine	machine	NOUN
cana-3162	2	14	learning	learning	NOUN
cana-3162	2	15	and	and	CCONJ
cana-3162	2	16	data	datum	NOUN
cana-3162	2	17	driven	drive	VERB
cana-3162	2	18	approaches	approach	NOUN
cana-3162	2	19	v.mahalakhsmi	v.mahalakhsmi	NOUN
cana-3162	2	20	assistant	assistant	NOUN
cana-3162	2	21	professor	professor	NOUN
cana-3162	2	22	,	,	PUNCT
cana-3162	2	23	department	department	NOUN
cana-3162	2	24	of	of	ADP
cana-3162	2	25	computer	computer	NOUN
cana-3162	2	26	science	science	NOUN
cana-3162	2	27	,	,	PUNCT
cana-3162	2	28	college	college	NOUN
cana-3162	2	29	of	of	ADP
cana-3162	2	30	engineering	engineering	NOUN
cana-3162	2	31	and	and	CCONJ
cana-3162	2	32	computer	computer	NOUN
cana-3162	2	33	sciences	sciences	PROPN
cana-3162	2	34	,	,	PUNCT
cana-3162	2	35	jazan	jazan	PROPN
cana-3162	2	36	university	university	PROPN
cana-3162	2	37	,	,	PUNCT
cana-3162	2	38	jazan	jazan	NOUN
cana-3162	2	39	,	,	PUNCT
cana-3162	2	40	saudiarabia	saudiarabia	NOUN
cana-3162	2	41	.	.	PUNCT
cana-3162	3	1	mlakshmi@jazanu.edu.sa	mlakshmi@jazanu.edu.sa	PROPN
cana-3162	3	2	article	article	PROPN
cana-3162	3	3	history	history	NOUN
cana-3162	3	4	:	:	PUNCT
cana-3162	3	5	received	receive	VERB
cana-3162	3	6	:	:	PUNCT
cana-3162	3	7	14	14	NUM
cana-3162	3	8	-	-	SYM
cana-3162	3	9	10	10	NUM
cana-3162	3	10	-	-	PUNCT
cana-3162	3	11	2024	2024	NUM
cana-3162	3	12	revised	revise	VERB
cana-3162	3	13	:	:	PUNCT
cana-3162	3	14	28	28	NUM
cana-3162	3	15	-	-	SYM
cana-3162	3	16	11	11	NUM
cana-3162	3	17	-	-	PUNCT
cana-3162	3	18	2024	2024	NUM
cana-3162	3	19	accepted	accept	VERB
cana-3162	3	20	:	:	PUNCT
cana-3162	3	21	10	10	NUM
cana-3162	3	22	-	-	SYM
cana-3162	3	23	12	12	NUM
cana-3162	3	24	-	-	PUNCT
cana-3162	3	25	2024	2024	NUM
cana-3162	3	26	abstract	abstract	NOUN
cana-3162	3	27	:	:	PUNCT
cana-3162	3	28	this	this	DET
cana-3162	3	29	research	research	NOUN
cana-3162	3	30	proposes	propose	VERB
cana-3162	3	31	employing	employ	VERB
cana-3162	3	32	sophisticated	sophisticated	ADJ
cana-3162	3	33	machine	machine	NOUN
cana-3162	3	34	learning	learning	NOUN
cana-3162	3	35	approaches	approach	NOUN
cana-3162	3	36	to	to	PART
cana-3162	3	37	improve	improve	VERB
cana-3162	3	38	feature	feature	NOUN
cana-3162	3	39	selection	selection	NOUN
cana-3162	3	40	,	,	PUNCT
cana-3162	3	41	model	model	NOUN
cana-3162	3	42	performance	performance	NOUN
cana-3162	3	43	,	,	PUNCT
cana-3162	3	44	and	and	CCONJ
cana-3162	3	45	guess	guess	VERB
cana-3162	3	46	accuracy	accuracy	NOUN
cana-3162	3	47	to	to	PART
cana-3162	3	48	predict	predict	VERB
cana-3162	3	49	cancer	cancer	NOUN
cana-3162	3	50	treatment	treatment	NOUN
cana-3162	3	51	outcomes	outcome	NOUN
cana-3162	3	52	.	.	PUNCT
cana-3162	4	1	ensemble	ensemble	ADJ
cana-3162	4	2	learning	learning	NOUN
cana-3162	4	3	,	,	PUNCT
cana-3162	4	4	svm	svm	ADJ
cana-3162	4	5	models	model	NOUN
cana-3162	4	6	,	,	PUNCT
cana-3162	4	7	decision	decision	NOUN
cana-3162	4	8	trees	tree	NOUN
cana-3162	4	9	,	,	PUNCT
cana-3162	4	10	and	and	CCONJ
cana-3162	4	11	bootstrapped	bootstrappe	VERB
cana-3162	4	12	examples	example	NOUN
cana-3162	4	13	improve	improve	VERB
cana-3162	4	14	accuracy	accuracy	NOUN
cana-3162	4	15	and	and	CCONJ
cana-3162	4	16	resilience	resilience	NOUN
cana-3162	4	17	.	.	PUNCT
cana-3162	5	1	reduced	reduce	VERB
cana-3162	5	2	impurity	impurity	NOUN
cana-3162	5	3	metrics	metric	NOUN
cana-3162	5	4	discover	discover	VERB
cana-3162	5	5	significant	significant	ADJ
cana-3162	5	6	features	feature	NOUN
cana-3162	5	7	,	,	PUNCT
cana-3162	5	8	lowering	lower	VERB
cana-3162	5	9	dimensions	dimension	NOUN
cana-3162	5	10	and	and	CCONJ
cana-3162	5	11	making	make	VERB
cana-3162	5	12	models	model	NOUN
cana-3162	5	13	simpler	simple	ADJ
cana-3162	5	14	to	to	PART
cana-3162	5	15	interpret	interpret	VERB
cana-3162	5	16	in	in	ADP
cana-3162	5	17	huge	huge	ADJ
cana-3162	5	18	datasets	dataset	NOUN
cana-3162	5	19	.	.	PUNCT
cana-3162	6	1	the	the	DET
cana-3162	6	2	recommended	recommend	VERB
cana-3162	6	3	solution	solution	NOUN
cana-3162	6	4	outperforms	outperform	VERB
cana-3162	6	5	others	other	NOUN
cana-3162	6	6	with	with	ADP
cana-3162	6	7	0.85	0.85	NUM
cana-3162	6	8	accuracy	accuracy	NOUN
cana-3162	6	9	,	,	PUNCT
cana-3162	6	10	0.83	0.83	NUM
cana-3162	6	11	precision	precision	NOUN
cana-3162	6	12	,	,	PUNCT
cana-3162	6	13	0.80	0.80	NUM
cana-3162	6	14	memory	memory	NOUN
cana-3162	6	15	,	,	PUNCT
cana-3162	6	16	and	and	CCONJ
cana-3162	6	17	0.81	0.81	NUM
cana-3162	6	18	f1	f1	NOUN
cana-3162	6	19	score	score	NOUN
cana-3162	6	20	.	.	PUNCT
cana-3162	7	1	the	the	DET
cana-3162	7	2	auc	auc	NOUN
cana-3162	7	3	-	-	PUNCT
cana-3162	7	4	roc	roc	NOUN
cana-3162	7	5	score	score	NOUN
cana-3162	7	6	of	of	ADP
cana-3162	7	7	0.87	0.87	NUM
cana-3162	7	8	indicates	indicate	VERB
cana-3162	7	9	that	that	SCONJ
cana-3162	7	10	these	these	DET
cana-3162	7	11	tests	test	NOUN
cana-3162	7	12	detect	detect	VERB
cana-3162	7	13	genuine	genuine	ADJ
cana-3162	7	14	drug	drug	NOUN
cana-3162	7	15	reactions	reaction	NOUN
cana-3162	7	16	well	well	ADV
cana-3162	7	17	.	.	PUNCT
cana-3162	8	1	the	the	DET
cana-3162	8	2	method	method	NOUN
cana-3162	8	3	effectively	effectively	ADV
cana-3162	8	4	reduces	reduce	VERB
cana-3162	8	5	the	the	DET
cana-3162	8	6	mean	mean	ADJ
cana-3162	8	7	absolute	absolute	ADJ
cana-3162	8	8	error	error	NOUN
cana-3162	8	9	to	to	ADP
cana-3162	8	10	0.30	0.30	NUM
cana-3162	8	11	.	.	PUNCT
cana-3162	9	1	this	this	DET
cana-3162	9	2	research	research	NOUN
cana-3162	9	3	highlights	highlight	NOUN
cana-3162	9	4	how	how	SCONJ
cana-3162	9	5	vital	vital	ADJ
cana-3162	9	6	it	it	PRON
cana-3162	9	7	is	be	AUX
cana-3162	9	8	to	to	PART
cana-3162	9	9	apply	apply	VERB
cana-3162	9	10	sophisticated	sophisticated	ADJ
cana-3162	9	11	machine	machine	NOUN
cana-3162	9	12	learning	learn	VERB
cana-3162	9	13	algorithms	algorithm	NOUN
cana-3162	9	14	to	to	PART
cana-3162	9	15	enhance	enhance	VERB
cana-3162	9	16	drug	drug	NOUN
cana-3162	9	17	predictions	prediction	NOUN
cana-3162	9	18	,	,	PUNCT
cana-3162	9	19	which	which	PRON
cana-3162	9	20	might	might	AUX
cana-3162	9	21	impact	impact	VERB
cana-3162	9	22	cancer	cancer	NOUN
cana-3162	9	23	patients	patient	NOUN
cana-3162	9	24	'	'	PART
cana-3162	9	25	choices	choice	NOUN
cana-3162	9	26	.	.	PUNCT
cana-3162	10	1	this	this	DET
cana-3162	10	2	strategy	strategy	NOUN
cana-3162	10	3	helps	help	VERB
cana-3162	10	4	us	we	PRON
cana-3162	10	5	comprehend	comprehend	VERB
cana-3162	10	6	cancer	cancer	NOUN
cana-3162	10	7	therapy	therapy	NOUN
cana-3162	10	8	changes	change	VERB
cana-3162	10	9	by	by	ADP
cana-3162	10	10	personalizing	personalize	VERB
cana-3162	10	11	treatment	treatment	NOUN
cana-3162	10	12	and	and	CCONJ
cana-3162	10	13	improving	improve	VERB
cana-3162	10	14	forecasts	forecast	NOUN
cana-3162	10	15	.	.	PUNCT
cana-3162	11	1	the	the	DET
cana-3162	11	2	goal	goal	NOUN
cana-3162	11	3	is	be	AUX
cana-3162	11	4	to	to	PART
cana-3162	11	5	improve	improve	VERB
cana-3162	11	6	patient	patient	ADJ
cana-3162	11	7	outcomes	outcome	NOUN
cana-3162	11	8	and	and	CCONJ
cana-3162	11	9	advance	advance	VERB
cana-3162	11	10	oncology	oncology	NOUN
cana-3162	11	11	.	.	PUNCT
cana-3162	12	1	keywords	keyword	NOUN
cana-3162	12	2	:	:	PUNCT
cana-3162	12	3	cancer	cancer	NOUN
cana-3162	12	4	treatment	treatment	NOUN
cana-3162	12	5	,	,	PUNCT
cana-3162	12	6	drug	drug	NOUN
cana-3162	12	7	response	response	NOUN
cana-3162	12	8	prediction	prediction	NOUN
cana-3162	12	9	,	,	PUNCT
cana-3162	12	10	ensemble	ensemble	ADJ
cana-3162	12	11	learning	learning	NOUN
cana-3162	12	12	,	,	PUNCT
cana-3162	12	13	feature	feature	NOUN
cana-3162	12	14	selection	selection	NOUN
cana-3162	12	15	,	,	PUNCT
cana-3162	12	16	machine	machine	NOUN
cana-3162	12	17	learning	learning	NOUN
cana-3162	12	18	,	,	PUNCT
cana-3162	12	19	precision	precision	NOUN
cana-3162	12	20	,	,	PUNCT
cana-3162	12	21	recall	recall	NOUN
cana-3162	12	22	,	,	PUNCT
cana-3162	12	23	support	support	NOUN
cana-3162	12	24	vector	vector	NOUN
cana-3162	12	25	machine	machine	NOUN
cana-3162	12	26	,	,	PUNCT
cana-3162	12	27	prediction	prediction	NOUN
cana-3162	12	28	accuracy	accuracy	NOUN
cana-3162	12	29	,	,	PUNCT
cana-3162	12	30	roc	roc	PROPN
cana-3162	12	31	curve	curve	PROPN
cana-3162	12	32	i.	i.	PROPN
cana-3162	12	33	introduction	introduction	NOUN
cana-3162	12	34	cancer	cancer	NOUN
cana-3162	12	35	is	be	AUX
cana-3162	12	36	one	one	NUM
cana-3162	12	37	of	of	ADP
cana-3162	12	38	the	the	DET
cana-3162	12	39	main	main	ADJ
cana-3162	12	40	causes	cause	NOUN
cana-3162	12	41	of	of	ADP
cana-3162	12	42	mortality	mortality	NOUN
cana-3162	12	43	worldwide	worldwide	ADV
cana-3162	12	44	,	,	PUNCT
cana-3162	12	45	with	with	ADP
cana-3162	12	46	millions	million	NOUN
cana-3162	12	47	of	of	ADP
cana-3162	12	48	new	new	ADJ
cana-3162	12	49	cases	case	NOUN
cana-3162	12	50	each	each	DET
cana-3162	12	51	year	year	NOUN
cana-3162	12	52	.	.	PUNCT
cana-3162	13	1	effective	effective	ADJ
cana-3162	13	2	cancer	cancer	NOUN
cana-3162	13	3	therapies	therapy	NOUN
cana-3162	13	4	are	be	AUX
cana-3162	13	5	challenging	challenge	VERB
cana-3162	13	6	due	due	ADJ
cana-3162	13	7	to	to	ADP
cana-3162	13	8	their	their	PRON
cana-3162	13	9	complexity	complexity	NOUN
cana-3162	13	10	[	[	X
cana-3162	13	11	1	1	NUM
cana-3162	13	12	]	]	PUNCT
cana-3162	13	13	.	.	PUNCT
cana-3162	14	1	with	with	ADP
cana-3162	14	2	the	the	DET
cana-3162	14	3	emergence	emergence	NOUN
cana-3162	14	4	of	of	ADP
cana-3162	14	5	customized	customized	ADJ
cana-3162	14	6	medicine	medicine	NOUN
cana-3162	14	7	,	,	PUNCT
cana-3162	14	8	knowing	know	VERB
cana-3162	14	9	how	how	SCONJ
cana-3162	14	10	cancer	cancer	NOUN
cana-3162	14	11	medications	medication	NOUN
cana-3162	14	12	impact	impact	VERB
cana-3162	14	13	various	various	ADJ
cana-3162	14	14	individuals	individual	NOUN
cana-3162	14	15	helps	help	VERB
cana-3162	14	16	improve	improve	VERB
cana-3162	14	17	treatment	treatment	NOUN
cana-3162	14	18	regimens	regimen	NOUN
cana-3162	14	19	.	.	PUNCT
cana-3162	15	1	machine	machine	NOUN
cana-3162	15	2	learning	learning	NOUN
cana-3162	15	3	and	and	CCONJ
cana-3162	15	4	data	data	NOUN
cana-3162	15	5	-	-	PUNCT
cana-3162	15	6	driven	drive	VERB
cana-3162	15	7	technologies	technology	NOUN
cana-3162	15	8	for	for	ADP
cana-3162	15	9	predicting	predict	VERB
cana-3162	15	10	cancer	cancer	NOUN
cana-3162	15	11	patients	patient	NOUN
cana-3162	15	12	'	'	PART
cana-3162	15	13	drug	drug	NOUN
cana-3162	15	14	reactions	reaction	NOUN
cana-3162	15	15	are	be	AUX
cana-3162	15	16	advancing	advance	VERB
cana-3162	15	17	rapidly	rapidly	ADV
cana-3162	15	18	and	and	CCONJ
cana-3162	15	19	might	might	AUX
cana-3162	15	20	lead	lead	VERB
cana-3162	15	21	to	to	ADP
cana-3162	15	22	personalized	personalized	ADJ
cana-3162	15	23	,	,	PUNCT
cana-3162	15	24	genetically	genetically	ADV
cana-3162	15	25	tailored	tailor	VERB
cana-3162	15	26	drugs	drug	NOUN
cana-3162	15	27	[	[	X
cana-3162	15	28	2	2	NUM
cana-3162	15	29	]	]	PUNCT
cana-3162	15	30	.	.	PUNCT
cana-3162	16	1	this	this	DET
cana-3162	16	2	introduction	introduction	NOUN
cana-3162	16	3	discusses	discuss	VERB
cana-3162	16	4	current	current	ADJ
cana-3162	16	5	advances	advance	NOUN
cana-3162	16	6	,	,	PUNCT
cana-3162	16	7	technique	technique	NOUN
cana-3162	16	8	concepts	concept	NOUN
cana-3162	16	9	,	,	PUNCT
cana-3162	16	10	probable	probable	ADJ
cana-3162	16	11	solutions	solution	NOUN
cana-3162	16	12	,	,	PUNCT
cana-3162	16	13	and	and	CCONJ
cana-3162	16	14	area	area	NOUN
cana-3162	16	15	contributions	contribution	NOUN
cana-3162	16	16	.	.	PUNCT
cana-3162	17	1	recent	recent	ADJ
cana-3162	17	2	advances	advance	NOUN
cana-3162	17	3	in	in	ADP
cana-3162	17	4	machine	machine	NOUN
cana-3162	17	5	learning	learning	NOUN
cana-3162	17	6	(	(	PUNCT
cana-3162	17	7	ml	ml	NOUN
cana-3162	17	8	)	)	PUNCT
cana-3162	17	9	and	and	CCONJ
cana-3162	17	10	data	data	NOUN
cana-3162	17	11	-	-	PUNCT
cana-3162	17	12	driven	drive	VERB
cana-3162	17	13	methodologies	methodology	NOUN
cana-3162	17	14	have	have	AUX
cana-3162	17	15	impacted	impact	VERB
cana-3162	17	16	cancer	cancer	NOUN
cana-3162	17	17	research	research	NOUN
cana-3162	17	18	and	and	CCONJ
cana-3162	17	19	other	other	ADJ
cana-3162	17	20	biological	biological	ADJ
cana-3162	17	21	fields	field	NOUN
cana-3162	17	22	[	[	X
cana-3162	17	23	3	3	NUM
cana-3162	17	24	]	]	PUNCT
cana-3162	17	25	.	.	PUNCT
cana-3162	18	1	high	high	ADJ
cana-3162	18	2	-	-	PUNCT
cana-3162	18	3	throughput	throughput	NOUN
cana-3162	18	4	technologies	technology	NOUN
cana-3162	18	5	like	like	ADP
cana-3162	18	6	ngs	ng	NOUN
cana-3162	18	7	generate	generate	VERB
cana-3162	18	8	a	a	DET
cana-3162	18	9	lot	lot	NOUN
cana-3162	18	10	of	of	ADP
cana-3162	18	11	molecular	molecular	ADJ
cana-3162	18	12	data	datum	NOUN
cana-3162	18	13	on	on	ADP
cana-3162	18	14	cancer	cancer	NOUN
cana-3162	18	15	cells	cell	NOUN
cana-3162	18	16	.	.	PUNCT
cana-3162	19	1	ml	ml	NOUN
cana-3162	19	2	models	model	NOUN
cana-3162	19	3	use	use	VERB
cana-3162	19	4	genetic	genetic	ADJ
cana-3162	19	5	,	,	PUNCT
cana-3162	19	6	transcriptomic	transcriptomic	ADJ
cana-3162	19	7	,	,	PUNCT
cana-3162	19	8	and	and	CCONJ
cana-3162	19	9	protein	protein	NOUN
cana-3162	19	10	data	datum	NOUN
cana-3162	19	11	to	to	PART
cana-3162	19	12	predict	predict	VERB
cana-3162	19	13	tumor	tumor	NOUN
cana-3162	19	14	treatment	treatment	NOUN
cana-3162	19	15	responses	response	NOUN
cana-3162	19	16	.	.	PUNCT
cana-3162	20	1	machine	machine	NOUN
cana-3162	20	2	learning	learning	NOUN
cana-3162	20	3	can	can	AUX
cana-3162	20	4	discover	discover	VERB
cana-3162	20	5	drug	drug	NOUN
cana-3162	20	6	-	-	PUNCT
cana-3162	20	7	working	work	VERB
cana-3162	20	8	indicators	indicator	NOUN
cana-3162	20	9	,	,	PUNCT
cana-3162	20	10	categorize	categorize	VERB
cana-3162	20	11	patients	patient	NOUN
cana-3162	20	12	into	into	ADP
cana-3162	20	13	responders	responder	NOUN
cana-3162	20	14	and	and	CCONJ
cana-3162	20	15	non	non	NOUN
cana-3162	20	16	-	-	NOUN
cana-3162	20	17	responders	responder	NOUN
cana-3162	20	18	,	,	PUNCT
cana-3162	20	19	and	and	CCONJ
cana-3162	20	20	predict	predict	VERB
cana-3162	20	21	outcomes	outcome	NOUN
cana-3162	20	22	based	base	VERB
cana-3162	20	23	on	on	ADP
cana-3162	20	24	historical	historical	ADJ
cana-3162	20	25	data	datum	NOUN
cana-3162	20	26	,	,	PUNCT
cana-3162	20	27	according	accord	VERB
cana-3162	20	28	to	to	ADP
cana-3162	20	29	studies	study	NOUN
cana-3162	20	30	.	.	PUNCT
cana-3162	21	1	forecast	forecast	NOUN
cana-3162	21	2	models	model	NOUN
cana-3162	21	3	often	often	ADV
cana-3162	21	4	employ	employ	VERB
cana-3162	21	5	decision	decision	NOUN
cana-3162	21	6	trees	tree	NOUN
cana-3162	21	7	,	,	PUNCT
cana-3162	21	8	random	random	ADJ
cana-3162	21	9	forests	forest	NOUN
cana-3162	21	10	,	,	PUNCT
cana-3162	21	11	svms	svms	NOUN
cana-3162	21	12	,	,	PUNCT
cana-3162	21	13	neural	neural	ADJ
cana-3162	21	14	networks	network	NOUN
cana-3162	21	15	,	,	PUNCT
cana-3162	21	16	and	and	CCONJ
cana-3162	21	17	ensemble	ensemble	ADJ
cana-3162	21	18	approaches	approach	NOUN
cana-3162	21	19	[	[	X
cana-3162	21	20	4	4	NUM
cana-3162	21	21	]	]	PUNCT
cana-3162	21	22	.	.	PUNCT
cana-3162	22	1	these	these	DET
cana-3162	22	2	methods	method	NOUN
cana-3162	22	3	improve	improve	VERB
cana-3162	22	4	cancer	cancer	NOUN
cana-3162	22	5	therapies	therapy	NOUN
cana-3162	22	6	and	and	CCONJ
cana-3162	22	7	help	help	AUX
cana-3162	22	8	identify	identify	VERB
cana-3162	22	9	drug	drug	NOUN
cana-3162	22	10	candidates	candidate	NOUN
cana-3162	22	11	.	.	PUNCT
cana-3162	23	1	multi	multi	ADJ
cana-3162	23	2	-	-	ADJ
cana-3162	23	3	omics	omics	ADJ
cana-3162	23	4	data	datum	NOUN
cana-3162	23	5	provides	provide	VERB
cana-3162	23	6	a	a	DET
cana-3162	23	7	complete	complete	ADJ
cana-3162	23	8	view	view	NOUN
cana-3162	23	9	of	of	ADP
cana-3162	23	10	the	the	DET
cana-3162	23	11	tumor	tumor	NOUN
cana-3162	23	12	microenvironment	microenvironment	NOUN
cana-3162	23	13	,	,	PUNCT
cana-3162	23	14	improving	improve	VERB
cana-3162	23	15	drug	drug	NOUN
cana-3162	23	16	prediction	prediction	NOUN
cana-3162	23	17	.	.	PUNCT
cana-3162	24	1	public	public	ADJ
cana-3162	24	2	datasets	dataset	NOUN
cana-3162	24	3	like	like	ADP
cana-3162	24	4	tcga	tcga	NOUN
cana-3162	24	5	and	and	CCONJ
cana-3162	24	6	gdsc	gdsc	ADJ
cana-3162	24	7	support	support	VERB
cana-3162	24	8	this	this	DET
cana-3162	24	9	study	study	NOUN
cana-3162	24	10	.	.	PUNCT
cana-3162	25	1	these	these	DET
cana-3162	25	2	databases	database	NOUN
cana-3162	25	3	include	include	VERB
cana-3162	25	4	massive	massive	ADJ
cana-3162	25	5	data	datum	NOUN
cana-3162	25	6	sets	set	NOUN
cana-3162	25	7	for	for	ADP
cana-3162	25	8	teaching	teach	VERB
cana-3162	25	9	machine	machine	NOUN
cana-3162	25	10	learning	learning	NOUN
cana-3162	25	11	algorithms	algorithm	NOUN
cana-3162	25	12	.	.	PUNCT
cana-3162	26	1	patient	patient	ADJ
cana-3162	26	2	organoids	organoid	NOUN
cana-3162	26	3	and	and	CCONJ
cana-3162	26	4	xenografts	xenografts	NOUN
cana-3162	26	5	simplify	simplify	VERB
cana-3162	26	6	drug	drug	NOUN
cana-3162	26	7	testing	testing	NOUN
cana-3162	26	8	and	and	CCONJ
cana-3162	26	9	validate	validate	VERB
cana-3162	26	10	forecast	forecast	NOUN
cana-3162	26	11	models	model	NOUN
cana-3162	26	12	[	[	X
cana-3162	26	13	5	5	NUM
cana-3162	26	14	]	]	PUNCT
cana-3162	26	15	.	.	PUNCT
cana-3162	27	1	the	the	DET
cana-3162	27	2	primary	primary	ADJ
cana-3162	27	3	principle	principle	NOUN
cana-3162	27	4	behind	behind	ADP
cana-3162	27	5	utilizing	utilize	VERB
cana-3162	27	6	machine	machine	NOUN
cana-3162	27	7	learning	learn	VERB
cana-3162	27	8	to	to	PART
cana-3162	27	9	predict	predict	VERB
cana-3162	27	10	therapeutic	therapeutic	ADJ
cana-3162	27	11	efficacy	efficacy	NOUN
cana-3162	27	12	is	be	AUX
cana-3162	27	13	that	that	SCONJ
cana-3162	27	14	each	each	DET
cana-3162	27	15	cancer	cancer	NOUN
cana-3162	27	16	patient	patient	NOUN
cana-3162	27	17	has	have	VERB
cana-3162	27	18	a	a	DET
cana-3162	27	19	unique	unique	ADJ
cana-3162	27	20	genetic	genetic	ADJ
cana-3162	27	21	and	and	CCONJ
cana-3162	27	22	molecular	molecular	ADJ
cana-3162	27	23	composition	composition	NOUN
cana-3162	27	24	that	that	PRON
cana-3162	27	25	impacts	impact	VERB
cana-3162	27	26	therapy	therapy	NOUN
cana-3162	27	27	.	.	PUNCT
cana-3162	28	1	ml	ml	PROPN
cana-3162	28	2	approaches	approach	NOUN
cana-3162	28	3	analyze	analyze	VERB
cana-3162	28	4	massive	massive	ADJ
cana-3162	28	5	cancer	cancer	NOUN
cana-3162	28	6	biology	biology	NOUN
cana-3162	28	7	data	datum	NOUN
cana-3162	28	8	sets	set	NOUN
cana-3162	28	9	to	to	PART
cana-3162	28	10	identify	identify	VERB
cana-3162	28	11	complicated	complicated	ADJ
cana-3162	28	12	patterns	pattern	NOUN
cana-3162	28	13	that	that	SCONJ
cana-3162	28	14	basic	basic	ADJ
cana-3162	28	15	statistics	statistic	NOUN
cana-3162	28	16	miss	miss	VERB
cana-3162	29	1	[	[	X
cana-3162	29	2	6	6	NUM
cana-3162	29	3	]	]	PUNCT
cana-3162	29	4	.	.	PUNCT
cana-3162	30	1	this	this	DET
cana-3162	30	2	approach	approach	NOUN
cana-3162	30	3	utilizes	utilize	VERB
cana-3162	30	4	supervised	supervise	VERB
cana-3162	30	5	learning	learn	VERB
cana-3162	30	6	to	to	PART
cana-3162	30	7	educate	educate	VERB
cana-3162	30	8	communications	communication	NOUN
cana-3162	30	9	on	on	ADP
cana-3162	30	10	applied	apply	VERB
cana-3162	30	11	nonlinear	nonlinear	ADJ
cana-3162	30	12	analysis	analysis	NOUN
cana-3162	30	13	issn	issn	NOUN
cana-3162	30	14	:	:	PUNCT
cana-3162	30	15	1074	1074	NUM
cana-3162	30	16	-	-	PUNCT
cana-3162	30	17	133x	133x	NUM
cana-3162	30	18	vol	vol	NOUN
cana-3162	30	19	32	32	NUM
cana-3162	30	20	no	no	NOUN
cana-3162	30	21	.	.	PUNCT
cana-3162	31	1	5s	5s	NUM
cana-3162	31	2	(	(	PUNCT
cana-3162	31	3	2025	2025	NUM
cana-3162	31	4	)	)	PUNCT
cana-3162	31	5	495	495	NUM
cana-3162	31	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3162	31	7	models	model	NOUN
cana-3162	31	8	on	on	ADP
cana-3162	31	9	identified	identify	VERB
cana-3162	31	10	data	datum	NOUN
cana-3162	31	11	,	,	PUNCT
cana-3162	31	12	including	include	VERB
cana-3162	31	13	genetic	genetic	ADJ
cana-3162	31	14	alterations	alteration	NOUN
cana-3162	31	15	,	,	PUNCT
cana-3162	31	16	expression	expression	NOUN
cana-3162	31	17	patterns	pattern	NOUN
cana-3162	31	18	,	,	PUNCT
cana-3162	31	19	and	and	CCONJ
cana-3162	31	20	treatment	treatment	NOUN
cana-3162	31	21	responses	response	NOUN
cana-3162	31	22	.	.	PUNCT
cana-3162	32	1	ram	ram	NOUN
cana-3162	32	2	establishes	establish	VERB
cana-3162	32	3	a	a	DET
cana-3162	32	4	connection	connection	NOUN
cana-3162	32	5	between	between	ADP
cana-3162	32	6	genetic	genetic	ADJ
cana-3162	32	7	markers	marker	NOUN
cana-3162	32	8	and	and	CCONJ
cana-3162	32	9	medication	medication	NOUN
cana-3162	32	10	,	,	PUNCT
cana-3162	32	11	thereby	thereby	ADV
cana-3162	32	12	assisting	assist	VERB
cana-3162	32	13	physicians	physician	NOUN
cana-3162	32	14	in	in	ADP
cana-3162	32	15	forecasting	forecast	VERB
cana-3162	32	16	patient	patient	ADJ
cana-3162	32	17	outcomes	outcome	NOUN
cana-3162	32	18	[	[	X
cana-3162	32	19	7	7	NUM
cana-3162	32	20	]	]	PUNCT
cana-3162	32	21	.	.	PUNCT
cana-3162	33	1	grouping	group	VERB
cana-3162	33	2	and	and	CCONJ
cana-3162	33	3	unsupervised	unsupervised	ADJ
cana-3162	33	4	learning	learning	NOUN
cana-3162	33	5	locate	locate	ADJ
cana-3162	33	6	patients	patient	NOUN
cana-3162	33	7	responsive	responsive	ADJ
cana-3162	33	8	to	to	ADP
cana-3162	33	9	the	the	DET
cana-3162	33	10	same	same	ADJ
cana-3162	33	11	drugs	drug	NOUN
cana-3162	33	12	.	.	PUNCT
cana-3162	34	1	the	the	DET
cana-3162	34	2	nonlinearity	nonlinearity	NOUN
cana-3162	34	3	and	and	CCONJ
cana-3162	34	4	large	large	ADJ
cana-3162	34	5	complexity	complexity	NOUN
cana-3162	34	6	of	of	ADP
cana-3162	34	7	biological	biological	ADJ
cana-3162	34	8	data	datum	NOUN
cana-3162	34	9	pose	pose	VERB
cana-3162	34	10	significant	significant	ADJ
cana-3162	34	11	challenges	challenge	NOUN
cana-3162	34	12	for	for	ADP
cana-3162	34	13	analysis	analysis	NOUN
cana-3162	34	14	.	.	PUNCT
cana-3162	35	1	machine	machine	NOUN
cana-3162	35	2	learning	learning	NOUN
cana-3162	35	3	may	may	AUX
cana-3162	35	4	construct	construct	VERB
cana-3162	35	5	models	model	NOUN
cana-3162	35	6	that	that	PRON
cana-3162	35	7	identify	identify	VERB
cana-3162	35	8	essential	essential	ADJ
cana-3162	35	9	qualities	quality	NOUN
cana-3162	35	10	,	,	PUNCT
cana-3162	35	11	simplifying	simplify	VERB
cana-3162	35	12	the	the	DET
cana-3162	35	13	issue	issue	NOUN
cana-3162	35	14	while	while	SCONJ
cana-3162	35	15	retaining	retain	VERB
cana-3162	35	16	crucial	crucial	ADJ
cana-3162	35	17	data	datum	NOUN
cana-3162	35	18	.	.	PUNCT
cana-3162	36	1	feature	feature	NOUN
cana-3162	36	2	selection	selection	NOUN
cana-3162	36	3	,	,	PUNCT
cana-3162	36	4	dimensionality	dimensionality	NOUN
cana-3162	36	5	reduction	reduction	NOUN
cana-3162	36	6	,	,	PUNCT
cana-3162	36	7	and	and	CCONJ
cana-3162	36	8	regularization	regularization	NOUN
cana-3162	36	9	improve	improve	VERB
cana-3162	36	10	models	model	NOUN
cana-3162	36	11	and	and	CCONJ
cana-3162	36	12	prevent	prevent	VERB
cana-3162	36	13	overfitting	overfitte	VERB
cana-3162	36	14	[	[	X
cana-3162	36	15	8	8	NUM
cana-3162	36	16	]	]	PUNCT
cana-3162	36	17	.	.	PUNCT
cana-3162	37	1	cross	cross	ADJ
cana-3162	37	2	-	-	ADJ
cana-3162	37	3	validation	validation	ADJ
cana-3162	37	4	ensures	ensure	NOUN
cana-3162	37	5	models	model	NOUN
cana-3162	37	6	work	work	VERB
cana-3162	37	7	with	with	ADP
cana-3162	37	8	fresh	fresh	ADJ
cana-3162	37	9	data	datum	NOUN
cana-3162	37	10	.	.	PUNCT
cana-3162	38	1	machine	machine	NOUN
cana-3162	38	2	learning	learning	NOUN
cana-3162	38	3	offers	offer	VERB
cana-3162	38	4	several	several	ADJ
cana-3162	38	5	intriguing	intriguing	ADJ
cana-3162	38	6	techniques	technique	NOUN
cana-3162	38	7	to	to	PART
cana-3162	38	8	improve	improve	VERB
cana-3162	38	9	medication	medication	NOUN
cana-3162	38	10	response	response	NOUN
cana-3162	38	11	predictions	prediction	NOUN
cana-3162	38	12	in	in	ADP
cana-3162	38	13	clinical	clinical	ADJ
cana-3162	38	14	settings	setting	NOUN
cana-3162	38	15	.	.	PUNCT
cana-3162	39	1	a	a	DET
cana-3162	39	2	multi	multi	ADJ
cana-3162	39	3	-	-	ADJ
cana-3162	39	4	omics	omics	ADJ
cana-3162	39	5	fusion	fusion	NOUN
cana-3162	39	6	of	of	ADP
cana-3162	39	7	genes	gene	NOUN
cana-3162	39	8	,	,	PUNCT
cana-3162	39	9	transcriptomics	transcriptomic	NOUN
cana-3162	39	10	,	,	PUNCT
cana-3162	39	11	and	and	CCONJ
cana-3162	39	12	proteins	protein	NOUN
cana-3162	39	13	is	be	AUX
cana-3162	39	14	one	one	NUM
cana-3162	39	15	of	of	ADP
cana-3162	39	16	the	the	DET
cana-3162	39	17	most	most	ADV
cana-3162	39	18	effective	effective	ADJ
cana-3162	39	19	approaches	approach	NOUN
cana-3162	39	20	to	to	ADP
cana-3162	39	21	displaying	display	VERB
cana-3162	39	22	tumor	tumor	NOUN
cana-3162	39	23	biology	biology	NOUN
cana-3162	39	24	[	[	X
cana-3162	39	25	9	9	NUM
cana-3162	39	26	]	]	PUNCT
cana-3162	39	27	.	.	PUNCT
cana-3162	40	1	deep	deep	ADJ
cana-3162	40	2	learning	learning	NOUN
cana-3162	40	3	systems	system	NOUN
cana-3162	40	4	like	like	ADP
cana-3162	40	5	cnns	cnn	NOUN
cana-3162	40	6	and	and	CCONJ
cana-3162	40	7	rnns	rnn	NOUN
cana-3162	40	8	automatically	automatically	ADV
cana-3162	40	9	draw	draw	VERB
cana-3162	40	10	highlevel	highlevel	ADJ
cana-3162	40	11	characteristics	characteristic	NOUN
cana-3162	40	12	to	to	PART
cana-3162	40	13	increase	increase	VERB
cana-3162	40	14	prediction	prediction	NOUN
cana-3162	40	15	accuracy	accuracy	NOUN
cana-3162	40	16	in	in	ADP
cana-3162	40	17	complicated	complicated	ADJ
cana-3162	40	18	,	,	PUNCT
cana-3162	40	19	nonlinear	nonlinear	ADJ
cana-3162	40	20	cancer	cancer	NOUN
cana-3162	40	21	data	datum	NOUN
cana-3162	40	22	interactions	interaction	NOUN
cana-3162	40	23	.	.	PUNCT
cana-3162	41	1	transfer	transfer	NOUN
cana-3162	41	2	learning	learn	VERB
cana-3162	41	3	moves	move	NOUN
cana-3162	41	4	data	datum	NOUN
cana-3162	41	5	from	from	ADP
cana-3162	41	6	similar	similar	ADJ
cana-3162	41	7	sources	source	NOUN
cana-3162	41	8	to	to	PART
cana-3162	41	9	predict	predict	VERB
cana-3162	41	10	cancers	cancer	NOUN
cana-3162	41	11	with	with	ADP
cana-3162	41	12	fewer	few	ADJ
cana-3162	41	13	data	datum	NOUN
cana-3162	41	14	points	point	NOUN
cana-3162	41	15	[	[	X
cana-3162	41	16	10	10	NUM
cana-3162	41	17	]	]	PUNCT
cana-3162	41	18	.	.	PUNCT
cana-3162	42	1	this	this	PRON
cana-3162	42	2	is	be	AUX
cana-3162	42	3	beneficial	beneficial	ADJ
cana-3162	42	4	for	for	ADP
cana-3162	42	5	cancers	cancer	NOUN
cana-3162	42	6	with	with	ADP
cana-3162	42	7	little	little	ADJ
cana-3162	42	8	labeled	label	VERB
cana-3162	42	9	data	datum	NOUN
cana-3162	42	10	.	.	PUNCT
cana-3162	43	1	experts	expert	NOUN
cana-3162	43	2	must	must	AUX
cana-3162	43	3	use	use	VERB
cana-3162	43	4	simple	simple	ADJ
cana-3162	43	5	procedures	procedure	NOUN
cana-3162	43	6	and	and	CCONJ
cana-3162	43	7	concentrate	concentrate	VERB
cana-3162	43	8	on	on	ADP
cana-3162	43	9	estimation	estimation	NOUN
cana-3162	43	10	elements	element	NOUN
cana-3162	43	11	to	to	PART
cana-3162	43	12	build	build	VERB
cana-3162	43	13	physicians	physician	NOUN
cana-3162	43	14	'	'	PART
cana-3162	43	15	confidence	confidence	NOUN
cana-3162	43	16	.	.	PUNCT
cana-3162	44	1	thus	thus	ADV
cana-3162	44	2	,	,	PUNCT
cana-3162	44	3	it	it	PRON
cana-3162	44	4	will	will	AUX
cana-3162	44	5	be	be	AUX
cana-3162	44	6	clear	clear	ADJ
cana-3162	44	7	.	.	PUNCT
cana-3162	45	1	we	we	PRON
cana-3162	45	2	are	be	AUX
cana-3162	45	3	developing	develop	VERB
cana-3162	45	4	machine	machine	NOUN
cana-3162	45	5	learning	learn	VERB
cana-3162	45	6	techniques	technique	NOUN
cana-3162	45	7	to	to	PART
cana-3162	45	8	anticipate	anticipate	VERB
cana-3162	45	9	medication	medication	NOUN
cana-3162	45	10	mixes	mix	NOUN
cana-3162	45	11	and	and	CCONJ
cana-3162	45	12	uncover	uncover	VERB
cana-3162	45	13	synergies	synergy	NOUN
cana-3162	45	14	that	that	PRON
cana-3162	45	15	may	may	AUX
cana-3162	45	16	enhance	enhance	VERB
cana-3162	45	17	outcomes	outcome	NOUN
cana-3162	45	18	and	and	CCONJ
cana-3162	45	19	reduce	reduce	VERB
cana-3162	45	20	toxicity	toxicity	NOUN
cana-3162	45	21	[	[	X
cana-3162	45	22	11	11	NUM
cana-3162	45	23	]	]	PUNCT
cana-3162	45	24	.	.	PUNCT
cana-3162	46	1	we	we	PRON
cana-3162	46	2	summarize	summarize	VERB
cana-3162	46	3	the	the	DET
cana-3162	46	4	key	key	ADJ
cana-3162	46	5	findings	finding	NOUN
cana-3162	46	6	of	of	ADP
cana-3162	46	7	this	this	DET
cana-3162	46	8	research	research	NOUN
cana-3162	46	9	below	below	ADV
cana-3162	46	10	:	:	PUNCT
cana-3162	46	11	a	a	DET
cana-3162	46	12	multiomics	multiomics	NOUN
cana-3162	46	13	-	-	PUNCT
cana-3162	46	14	based	base	VERB
cana-3162	46	15	machine	machine	NOUN
cana-3162	46	16	learning	learning	NOUN
cana-3162	46	17	system	system	NOUN
cana-3162	46	18	is	be	AUX
cana-3162	46	19	being	be	AUX
cana-3162	46	20	built	build	VERB
cana-3162	46	21	to	to	PART
cana-3162	46	22	improve	improve	VERB
cana-3162	46	23	predictions	prediction	NOUN
cana-3162	46	24	of	of	ADP
cana-3162	46	25	how	how	SCONJ
cana-3162	46	26	anti	anti	ADJ
cana-3162	46	27	-	-	ADJ
cana-3162	46	28	cancer	cancer	ADJ
cana-3162	46	29	drugs	drug	NOUN
cana-3162	46	30	will	will	AUX
cana-3162	46	31	work	work	VERB
cana-3162	46	32	.	.	PUNCT
cana-3162	47	1	we	we	PRON
cana-3162	47	2	are	be	AUX
cana-3162	47	3	also	also	ADV
cana-3162	47	4	coming	come	VERB
cana-3162	47	5	up	up	ADP
cana-3162	47	6	with	with	ADP
cana-3162	47	7	new	new	ADJ
cana-3162	47	8	ways	way	NOUN
cana-3162	47	9	to	to	PART
cana-3162	47	10	simplify	simplify	VERB
cana-3162	47	11	data	datum	NOUN
cana-3162	47	12	and	and	CCONJ
cana-3162	47	13	models	model	NOUN
cana-3162	47	14	,	,	PUNCT
cana-3162	47	15	using	use	VERB
cana-3162	47	16	deep	deep	ADJ
cana-3162	47	17	learning	learning	NOUN
cana-3162	47	18	to	to	PART
cana-3162	47	19	find	find	VERB
cana-3162	47	20	non	non	ADJ
cana-3162	47	21	-	-	ADJ
cana-3162	47	22	linear	linear	ADJ
cana-3162	47	23	patterns	pattern	NOUN
cana-3162	47	24	in	in	ADP
cana-3162	47	25	large	large	ADJ
cana-3162	47	26	biology	biology	NOUN
cana-3162	47	27	datasets	dataset	NOUN
cana-3162	47	28	to	to	PART
cana-3162	47	29	improve	improve	VERB
cana-3162	47	30	predictions	prediction	NOUN
cana-3162	47	31	of	of	ADP
cana-3162	47	32	how	how	SCONJ
cana-3162	47	33	anti	anti	ADJ
cana-3162	47	34	-	-	ADJ
cana-3162	47	35	cancer	cancer	ADJ
cana-3162	47	36	drugs	drug	NOUN
cana-3162	47	37	will	will	AUX
cana-3162	47	38	work	work	VERB
cana-3162	47	39	,	,	PUNCT
cana-3162	47	40	using	use	VERB
cana-3162	47	41	explainability	explainability	NOUN
cana-3162	47	42	techniques	technique	NOUN
cana-3162	47	43	to	to	PART
cana-3162	47	44	help	help	VERB
cana-3162	47	45	clinicians	clinician	NOUN
cana-3162	47	46	make	make	VERB
cana-3162	47	47	decisions	decision	NOUN
cana-3162	47	48	,	,	PUNCT
cana-3162	47	49	and	and	CCONJ
cana-3162	47	50	using	use	VERB
cana-3162	47	51	transfer	transfer	NOUN
cana-3162	47	52	learning	learn	VERB
cana-3162	47	53	to	to	PART
cana-3162	47	54	put	put	VERB
cana-3162	47	55	our	our	PRON
cana-3162	47	56	knowledge	knowledge	NOUN
cana-3162	47	57	into	into	ADP
cana-3162	47	58	practice	practice	NOUN
cana-3162	47	59	.	.	PUNCT
cana-3162	48	1	ii	ii	X
cana-3162	48	2	.	.	PROPN
cana-3162	48	3	related	relate	VERB
cana-3162	48	4	works	work	NOUN
cana-3162	48	5	in	in	ADP
cana-3162	48	6	recent	recent	ADJ
cana-3162	48	7	years	year	NOUN
cana-3162	48	8	,	,	PUNCT
cana-3162	48	9	machine	machine	NOUN
cana-3162	48	10	learning	learning	NOUN
cana-3162	48	11	and	and	CCONJ
cana-3162	48	12	data	data	NOUN
cana-3162	48	13	-	-	PUNCT
cana-3162	48	14	driven	drive	VERB
cana-3162	48	15	approaches	approach	NOUN
cana-3162	48	16	have	have	AUX
cana-3162	48	17	helped	help	VERB
cana-3162	48	18	predict	predict	VERB
cana-3162	48	19	cancer	cancer	NOUN
cana-3162	48	20	treatment	treatment	NOUN
cana-3162	48	21	outcomes	outcome	NOUN
cana-3162	48	22	,	,	PUNCT
cana-3162	48	23	which	which	PRON
cana-3162	48	24	is	be	AUX
cana-3162	48	25	important	important	ADJ
cana-3162	48	26	for	for	ADP
cana-3162	48	27	individualized	individualized	ADJ
cana-3162	48	28	therapy	therapy	NOUN
cana-3162	48	29	[	[	X
cana-3162	48	30	12	12	NUM
cana-3162	48	31	]	]	PUNCT
cana-3162	48	32	.	.	PUNCT
cana-3162	49	1	using	use	VERB
cana-3162	49	2	random	random	ADJ
cana-3162	49	3	forest	forest	NOUN
cana-3162	49	4	,	,	PUNCT
cana-3162	49	5	support	support	VERB
cana-3162	49	6	vector	vector	NOUN
cana-3162	49	7	machines	machine	NOUN
cana-3162	49	8	(	(	PUNCT
cana-3162	49	9	svm	svm	PROPN
cana-3162	49	10	)	)	PUNCT
cana-3162	49	11	,	,	PUNCT
cana-3162	49	12	deep	deep	ADJ
cana-3162	49	13	neural	neural	ADJ
cana-3162	49	14	networks	network	NOUN
cana-3162	49	15	(	(	PUNCT
cana-3162	49	16	dnn	dnn	PROPN
cana-3162	49	17	)	)	PUNCT
cana-3162	49	18	,	,	PUNCT
cana-3162	49	19	gradient	gradient	NOUN
cana-3162	49	20	boosting	boosting	NOUN
cana-3162	49	21	,	,	PUNCT
cana-3162	49	22	k	k	ADJ
cana-3162	49	23	-	-	PUNCT
cana-3162	49	24	nearest	near	ADJ
cana-3162	49	25	neighbors	neighbor	NOUN
cana-3162	49	26	,	,	PUNCT
cana-3162	49	27	elastic	elastic	ADJ
cana-3162	49	28	net	net	ADJ
cana-3162	49	29	regression	regression	NOUN
cana-3162	49	30	,	,	PUNCT
cana-3162	49	31	cnn	cnn	PROPN
cana-3162	49	32	,	,	PUNCT
cana-3162	49	33	xgboost	xgboost	ADV
cana-3162	49	34	,	,	PUNCT
cana-3162	49	35	bayesian	bayesian	NOUN
cana-3162	49	36	networks	network	NOUN
cana-3162	49	37	,	,	PUNCT
cana-3162	49	38	and	and	CCONJ
cana-3162	49	39	recurrent	recurrent	ADJ
cana-3162	49	40	neural	neural	ADJ
cana-3162	49	41	networks	network	NOUN
cana-3162	49	42	,	,	PUNCT
cana-3162	49	43	researchers	researcher	NOUN
cana-3162	49	44	have	have	AUX
cana-3162	49	45	made	make	VERB
cana-3162	49	46	it	it	PRON
cana-3162	49	47	easier	easy	ADJ
cana-3162	49	48	to	to	PART
cana-3162	49	49	predict	predict	VERB
cana-3162	49	50	how	how	SCONJ
cana-3162	49	51	drugs	drug	NOUN
cana-3162	49	52	will	will	AUX
cana-3162	49	53	work	work	VERB
cana-3162	49	54	.	.	PUNCT
cana-3162	50	1	each	each	DET
cana-3162	50	2	technique	technique	NOUN
cana-3162	50	3	handles	handle	VERB
cana-3162	50	4	complex	complex	ADJ
cana-3162	50	5	biological	biological	ADJ
cana-3162	50	6	data	datum	NOUN
cana-3162	50	7	differently	differently	ADV
cana-3162	50	8	and	and	CCONJ
cana-3162	50	9	provides	provide	VERB
cana-3162	50	10	essential	essential	ADJ
cana-3162	50	11	assessment	assessment	NOUN
cana-3162	50	12	tools	tool	NOUN
cana-3162	50	13	to	to	PART
cana-3162	50	14	evaluate	evaluate	VERB
cana-3162	50	15	their	their	PRON
cana-3162	50	16	efficacy	efficacy	NOUN
cana-3162	50	17	[	[	X
cana-3162	50	18	13	13	NUM
cana-3162	50	19	]	]	PUNCT
cana-3162	50	20	.	.	PUNCT
cana-3162	51	1	we	we	PRON
cana-3162	51	2	evaluate	evaluate	VERB
cana-3162	51	3	these	these	DET
cana-3162	51	4	drug	drug	NOUN
cana-3162	51	5	reaction	reaction	NOUN
cana-3162	51	6	data	datum	NOUN
cana-3162	51	7	classification	classification	NOUN
cana-3162	51	8	algorithms	algorithm	NOUN
cana-3162	51	9	using	use	VERB
cana-3162	51	10	accuracy	accuracy	NOUN
cana-3162	51	11	,	,	PUNCT
cana-3162	51	12	recall	recall	NOUN
cana-3162	51	13	,	,	PUNCT
cana-3162	51	14	f1	f1	NOUN
cana-3162	51	15	-	-	PUNCT
cana-3162	51	16	score	score	NOUN
cana-3162	51	17	,	,	PUNCT
cana-3162	51	18	auc	auc	NOUN
cana-3162	51	19	,	,	PUNCT
cana-3162	51	20	specificity	specificity	NOUN
cana-3162	51	21	,	,	PUNCT
cana-3162	51	22	and	and	CCONJ
cana-3162	51	23	sensitivity	sensitivity	NOUN
cana-3162	51	24	.	.	PUNCT
cana-3162	52	1	while	while	SCONJ
cana-3162	52	2	precision	precision	NOUN
cana-3162	52	3	and	and	CCONJ
cana-3162	52	4	recall	recall	NOUN
cana-3162	52	5	are	be	AUX
cana-3162	52	6	right	right	ADJ
cana-3162	52	7	estimations	estimation	NOUN
cana-3162	52	8	,	,	PUNCT
cana-3162	52	9	accuracy	accuracy	NOUN
cana-3162	52	10	is	be	AUX
cana-3162	52	11	the	the	DET
cana-3162	52	12	overall	overall	ADJ
cana-3162	52	13	number	number	NOUN
cana-3162	52	14	of	of	ADP
cana-3162	52	15	correct	correct	ADJ
cana-3162	52	16	answers	answer	NOUN
cana-3162	52	17	[	[	X
cana-3162	52	18	14	14	NUM
cana-3162	52	19	]	]	PUNCT
cana-3162	52	20	.	.	PUNCT
cana-3162	53	1	the	the	DET
cana-3162	53	2	f1	f1	ADJ
cana-3162	53	3	-	-	PUNCT
cana-3162	53	4	score	score	NOUN
cana-3162	53	5	balances	balance	NOUN
cana-3162	53	6	accuracy	accuracy	NOUN
cana-3162	53	7	and	and	CCONJ
cana-3162	53	8	recall	recall	NOUN
cana-3162	53	9	,	,	PUNCT
cana-3162	53	10	while	while	SCONJ
cana-3162	53	11	auc	auc	NOUN
cana-3162	53	12	demonstrates	demonstrate	VERB
cana-3162	53	13	how	how	SCONJ
cana-3162	53	14	effectively	effectively	ADV
cana-3162	53	15	the	the	DET
cana-3162	53	16	model	model	NOUN
cana-3162	53	17	distinguishes	distinguish	VERB
cana-3162	53	18	classes	class	NOUN
cana-3162	53	19	.	.	PUNCT
cana-3162	54	1	specificity	specificity	NOUN
cana-3162	54	2	and	and	CCONJ
cana-3162	54	3	sensitivity	sensitivity	NOUN
cana-3162	54	4	measure	measure	VERB
cana-3162	54	5	the	the	DET
cana-3162	54	6	model	model	NOUN
cana-3162	54	7	's	's	PART
cana-3162	54	8	ability	ability	NOUN
cana-3162	54	9	to	to	PART
cana-3162	54	10	discover	discover	VERB
cana-3162	54	11	real	real	ADJ
cana-3162	54	12	negatives	negative	NOUN
cana-3162	54	13	and	and	CCONJ
cana-3162	54	14	positives	positive	NOUN
cana-3162	54	15	.	.	PUNCT
cana-3162	55	1	cnn	cnn	PROPN
cana-3162	55	2	and	and	CCONJ
cana-3162	55	3	dnn	dnn	PROPN
cana-3162	55	4	outperform	outperform	VERB
cana-3162	55	5	the	the	DET
cana-3162	55	6	other	other	ADJ
cana-3162	55	7	approaches	approach	NOUN
cana-3162	55	8	in	in	ADP
cana-3162	55	9	accuracy	accuracy	NOUN
cana-3162	55	10	and	and	CCONJ
cana-3162	55	11	auc	auc	NOUN
cana-3162	55	12	,	,	PUNCT
cana-3162	55	13	indicating	indicate	VERB
cana-3162	55	14	they	they	PRON
cana-3162	55	15	can	can	AUX
cana-3162	55	16	handle	handle	VERB
cana-3162	55	17	complex	complex	ADJ
cana-3162	55	18	,	,	PUNCT
cana-3162	55	19	non	non	ADJ
cana-3162	55	20	-	-	ADJ
cana-3162	55	21	linear	linear	ADJ
cana-3162	55	22	data	data	NOUN
cana-3162	55	23	structures	structure	NOUN
cana-3162	55	24	[	[	X
cana-3162	55	25	15	15	NUM
cana-3162	55	26	]	]	PUNCT
cana-3162	55	27	.	.	PUNCT
cana-3162	56	1	gradient	gradient	NOUN
cana-3162	56	2	boosting	boosting	NOUN
cana-3162	56	3	and	and	CCONJ
cana-3162	56	4	xgboost	xgboost	ADV
cana-3162	56	5	also	also	ADV
cana-3162	56	6	perform	perform	VERB
cana-3162	56	7	well	well	ADV
cana-3162	56	8	in	in	ADP
cana-3162	56	9	many	many	ADJ
cana-3162	56	10	areas	area	NOUN
cana-3162	56	11	without	without	ADP
cana-3162	56	12	affecting	affect	VERB
cana-3162	56	13	f1	f1	NOUN
cana-3162	56	14	-	-	PUNCT
cana-3162	56	15	score	score	NOUN
cana-3162	56	16	,	,	PUNCT
cana-3162	56	17	precision	precision	NOUN
cana-3162	56	18	,	,	PUNCT
cana-3162	56	19	or	or	CCONJ
cana-3162	56	20	accuracy	accuracy	NOUN
cana-3162	56	21	.	.	PUNCT
cana-3162	57	1	these	these	DET
cana-3162	57	2	models	model	NOUN
cana-3162	57	3	are	be	AUX
cana-3162	57	4	evaluated	evaluate	VERB
cana-3162	57	5	using	use	VERB
cana-3162	57	6	error	error	NOUN
cana-3162	57	7	metrics	metric	NOUN
cana-3162	57	8	such	such	ADJ
cana-3162	57	9	as	as	ADP
cana-3162	57	10	mse	mse	PROPN
cana-3162	57	11	,	,	PUNCT
cana-3162	57	12	mae	mae	PROPN
cana-3162	57	13	,	,	PUNCT
cana-3162	57	14	rmse	rmse	NOUN
cana-3162	57	15	,	,	PUNCT
cana-3162	57	16	and	and	CCONJ
cana-3162	57	17	the	the	DET
cana-3162	57	18	r^2	r^2	PROPN
cana-3162	57	19	score	score	NOUN
cana-3162	57	20	.	.	PUNCT
cana-3162	58	1	these	these	DET
cana-3162	58	2	metrics	metric	NOUN
cana-3162	58	3	show	show	VERB
cana-3162	58	4	how	how	SCONJ
cana-3162	58	5	effectively	effectively	ADV
cana-3162	58	6	regression	regression	VERB
cana-3162	58	7	tasks	task	NOUN
cana-3162	58	8	using	use	VERB
cana-3162	58	9	continuous	continuous	ADJ
cana-3162	58	10	data	datum	NOUN
cana-3162	58	11	,	,	PUNCT
cana-3162	58	12	such	such	ADJ
cana-3162	58	13	as	as	ADP
cana-3162	58	14	medication	medication	NOUN
cana-3162	58	15	reaction	reaction	NOUN
cana-3162	58	16	quantities	quantity	NOUN
cana-3162	58	17	,	,	PUNCT
cana-3162	58	18	predict	predict	VERB
cana-3162	58	19	.	.	PUNCT
cana-3162	59	1	cnn	cnn	PROPN
cana-3162	59	2	and	and	CCONJ
cana-3162	59	3	dnn	dnn	PROPN
cana-3162	59	4	again	again	ADV
cana-3162	59	5	had	have	VERB
cana-3162	59	6	the	the	DET
cana-3162	59	7	lowest	low	ADJ
cana-3162	59	8	error	error	NOUN
cana-3162	59	9	rates	rate	NOUN
cana-3162	59	10	,	,	PUNCT
cana-3162	59	11	proving	prove	VERB
cana-3162	59	12	they	they	PRON
cana-3162	59	13	can	can	AUX
cana-3162	59	14	discover	discover	VERB
cana-3162	59	15	data	data	NOUN
cana-3162	59	16	patterns	pattern	NOUN
cana-3162	59	17	[	[	X
cana-3162	59	18	16	16	NUM
cana-3162	59	19	]	]	PUNCT
cana-3162	59	20	.	.	PUNCT
cana-3162	60	1	cnn	cnn	PROPN
cana-3162	60	2	outperforms	outperform	VERB
cana-3162	60	3	dnn	dnn	PROPN
cana-3162	60	4	and	and	CCONJ
cana-3162	60	5	xgboost	xgboost	ADV
cana-3162	60	6	in	in	ADP
cana-3162	60	7	the	the	DET
cana-3162	60	8	r^2	r^2	PROPN
cana-3162	60	9	score	score	NOUN
cana-3162	60	10	,	,	PUNCT
cana-3162	60	11	indicating	indicate	VERB
cana-3162	60	12	a	a	DET
cana-3162	60	13	superior	superior	ADJ
cana-3162	60	14	model	model	NOUN
cana-3162	60	15	explanation	explanation	NOUN
cana-3162	60	16	of	of	ADP
cana-3162	60	17	the	the	DET
cana-3162	60	18	answers	answer	NOUN
cana-3162	60	19	.	.	PUNCT
cana-3162	61	1	simple	simple	ADJ
cana-3162	61	2	models	model	NOUN
cana-3162	61	3	like	like	ADP
cana-3162	61	4	k	k	ADV
cana-3162	61	5	-	-	PUNCT
cana-3162	61	6	nearest	near	ADJ
cana-3162	61	7	neighbors	neighbor	NOUN
cana-3162	61	8	and	and	CCONJ
cana-3162	61	9	bayesian	bayesian	NOUN
cana-3162	61	10	networks	network	NOUN
cana-3162	61	11	have	have	VERB
cana-3162	61	12	higher	high	ADJ
cana-3162	61	13	error	error	NOUN
cana-3162	61	14	values	value	NOUN
cana-3162	61	15	for	for	ADP
cana-3162	61	16	complex	complex	ADJ
cana-3162	61	17	,	,	PUNCT
cana-3162	61	18	high	high	ADJ
cana-3162	61	19	-	-	PUNCT
cana-3162	61	20	dimensional	dimensional	ADJ
cana-3162	61	21	datasets	dataset	NOUN
cana-3162	61	22	.	.	PUNCT
cana-3162	62	1	this	this	PRON
cana-3162	62	2	illustrates	illustrate	VERB
cana-3162	62	3	their	their	PRON
cana-3162	62	4	boundaries	boundary	NOUN
cana-3162	62	5	.	.	PUNCT
cana-3162	63	1	in	in	ADP
cana-3162	63	2	general	general	ADJ
cana-3162	63	3	,	,	PUNCT
cana-3162	63	4	machine	machine	NOUN
cana-3162	63	5	learning	learning	NOUN
cana-3162	63	6	and	and	CCONJ
cana-3162	63	7	data	datum	NOUN
cana-3162	63	8	-	-	PUNCT
cana-3162	63	9	driven	drive	VERB
cana-3162	63	10	anticancer	anticancer	NOUN
cana-3162	63	11	medication	medication	NOUN
cana-3162	63	12	prediction	prediction	NOUN
cana-3162	63	13	methods	method	NOUN
cana-3162	63	14	are	be	AUX
cana-3162	63	15	improving	improve	VERB
cana-3162	63	16	[	[	X
cana-3162	63	17	17	17	NUM
cana-3162	63	18	]	]	PUNCT
cana-3162	63	19	.	.	PUNCT
cana-3162	64	1	this	this	PRON
cana-3162	64	2	implies	imply	VERB
cana-3162	64	3	more	more	ADV
cana-3162	64	4	precise	precise	ADJ
cana-3162	64	5	,	,	PUNCT
cana-3162	64	6	data	data	NOUN
cana-3162	64	7	-	-	PUNCT
cana-3162	64	8	driven	drive	VERB
cana-3162	64	9	cancer	cancer	NOUN
cana-3162	64	10	therapies	therapy	NOUN
cana-3162	64	11	are	be	AUX
cana-3162	64	12	accessible	accessible	ADJ
cana-3162	64	13	.	.	PUNCT
cana-3162	65	1	comparing	compare	VERB
cana-3162	65	2	techniques	technique	NOUN
cana-3162	65	3	across	across	ADP
cana-3162	65	4	several	several	ADJ
cana-3162	65	5	success	success	NOUN
cana-3162	65	6	indicators	indicator	NOUN
cana-3162	65	7	reveals	reveal	VERB
cana-3162	65	8	their	their	PRON
cana-3162	65	9	strengths	strength	NOUN
cana-3162	65	10	and	and	CCONJ
cana-3162	65	11	downsides	downside	NOUN
cana-3162	65	12	.	.	PUNCT
cana-3162	66	1	this	this	PRON
cana-3162	66	2	enables	enable	VERB
cana-3162	66	3	specialists	specialist	NOUN
cana-3162	66	4	to	to	PART
cana-3162	66	5	choose	choose	VERB
cana-3162	66	6	the	the	DET
cana-3162	66	7	optimal	optimal	ADJ
cana-3162	66	8	solution	solution	NOUN
cana-3162	66	9	for	for	ADP
cana-3162	66	10	each	each	DET
cana-3162	66	11	issue	issue	NOUN
cana-3162	66	12	.	.	PUNCT
cana-3162	67	1	communications	communication	NOUN
cana-3162	67	2	on	on	ADP
cana-3162	67	3	applied	apply	VERB
cana-3162	67	4	nonlinear	nonlinear	ADJ
cana-3162	67	5	analysis	analysis	NOUN
cana-3162	67	6	issn	issn	NOUN
cana-3162	67	7	:	:	PUNCT
cana-3162	67	8	1074	1074	NUM
cana-3162	67	9	-	-	PUNCT
cana-3162	67	10	133x	133x	NUM
cana-3162	67	11	vol	vol	NOUN
cana-3162	67	12	32	32	NUM
cana-3162	67	13	no	no	NOUN
cana-3162	67	14	.	.	PUNCT
cana-3162	68	1	5s	5s	NUM
cana-3162	68	2	(	(	PUNCT
cana-3162	68	3	2025	2025	NUM
cana-3162	68	4	)	)	PUNCT
cana-3162	68	5	496	496	NUM
cana-3162	68	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3162	68	7	table	table	NOUN
cana-3162	68	8	1	1	NUM
cana-3162	68	9	.	.	PUNCT
cana-3162	68	10	performance	performance	NOUN
cana-3162	68	11	evaluation	evaluation	NOUN
cana-3162	68	12	metrics	metric	NOUN
cana-3162	68	13	for	for	ADP
cana-3162	68	14	machine	machine	NOUN
cana-3162	68	15	learning	learning	NOUN
cana-3162	68	16	methods	method	NOUN
cana-3162	68	17	in	in	ADP
cana-3162	68	18	anti	anti	ADJ
cana-3162	68	19	-	-	ADJ
cana-3162	68	20	cancer	cancer	ADJ
cana-3162	68	21	drug	drug	NOUN
cana-3162	68	22	response	response	NOUN
cana-3162	68	23	prediction	prediction	NOUN
cana-3162	68	24	method	method	NOUN
cana-3162	68	25	accuracy	accuracy	NOUN
cana-3162	68	26	(	(	PUNCT
cana-3162	68	27	%	%	INTJ
cana-3162	68	28	)	)	PUNCT
cana-3162	68	29	precision	precision	NOUN
cana-3162	68	30	(	(	PUNCT
cana-3162	68	31	%	%	INTJ
cana-3162	68	32	)	)	PUNCT
cana-3162	68	33	recall	recall	NOUN
cana-3162	68	34	(	(	PUNCT
cana-3162	68	35	%	%	INTJ
cana-3162	68	36	)	)	PUNCT
cana-3162	68	37	f1score	f1score	NOUN
cana-3162	68	38	(	(	PUNCT
cana-3162	68	39	%	%	INTJ
cana-3162	68	40	)	)	PUNCT
cana-3162	68	41	auc	auc	NOUN
cana-3162	68	42	(	(	PUNCT
cana-3162	68	43	%	%	INTJ
cana-3162	68	44	)	)	PUNCT
cana-3162	68	45	specificity	specificity	NOUN
cana-3162	68	46	(	(	PUNCT
cana-3162	68	47	%	%	INTJ
cana-3162	68	48	)	)	PUNCT
cana-3162	68	49	sensitivity	sensitivity	NOUN
cana-3162	68	50	(	(	PUNCT
cana-3162	68	51	%	%	INTJ
cana-3162	68	52	)	)	PUNCT
cana-3162	68	53	random	random	ADJ
cana-3162	68	54	forest	forest	NOUN
cana-3162	68	55	85.2	85.2	NUM
cana-3162	68	56	84.1	84.1	NUM
cana-3162	68	57	83.5	83.5	NUM
cana-3162	68	58	83.8	83.8	NUM
cana-3162	68	59	89.5	89.5	NUM
cana-3162	68	60	87.3	87.3	NUM
cana-3162	68	61	83.5	83.5	NUM
cana-3162	68	62	support	support	NOUN
cana-3162	68	63	vector	vector	NOUN
cana-3162	68	64	machines	machine	NOUN
cana-3162	68	65	83.7	83.7	NUM
cana-3162	68	66	82.3	82.3	NUM
cana-3162	68	67	81.4	81.4	NUM
cana-3162	68	68	81.8	81.8	NUM
cana-3162	68	69	87.9	87.9	NUM
cana-3162	68	70	85.7	85.7	NUM
cana-3162	68	71	81.4	81.4	NUM
cana-3162	68	72	deep	deep	ADJ
cana-3162	68	73	neural	neural	ADJ
cana-3162	68	74	networks	network	NOUN
cana-3162	68	75	88.5	88.5	NUM
cana-3162	68	76	87.4	87.4	NUM
cana-3162	68	77	86.2	86.2	NUM
cana-3162	68	78	86.8	86.8	NUM
cana-3162	68	79	90.3	90.3	NUM
cana-3162	68	80	88.7	88.7	NUM
cana-3162	68	81	86.2	86.2	NUM
cana-3162	68	82	gradient	gradient	NOUN
cana-3162	68	83	boosting	boost	VERB
cana-3162	68	84	87.1	87.1	NUM
cana-3162	68	85	86.0	86.0	NUM
cana-3162	68	86	85.1	85.1	NUM
cana-3162	68	87	85.5	85.5	NUM
cana-3162	68	88	89.7	89.7	NUM
cana-3162	68	89	88.0	88.0	NUM
cana-3162	68	90	85.1	85.1	NUM
cana-3162	68	91	k	k	NOUN
cana-3162	68	92	-	-	PUNCT
cana-3162	68	93	nearest	near	ADJ
cana-3162	68	94	neighbors	neighbor	NOUN
cana-3162	68	95	80.3	80.3	NUM
cana-3162	68	96	79.0	79.0	NUM
cana-3162	68	97	78.1	78.1	NUM
cana-3162	68	98	78.5	78.5	NUM
cana-3162	68	99	84.5	84.5	NUM
cana-3162	68	100	82.4	82.4	NUM
cana-3162	68	101	78.1	78.1	NUM
cana-3162	68	102	elastic	elastic	ADJ
cana-3162	68	103	net	net	ADJ
cana-3162	68	104	regression	regression	NOUN
cana-3162	68	105	82.9	82.9	NUM
cana-3162	68	106	81.8	81.8	NUM
cana-3162	68	107	80.7	80.7	NUM
cana-3162	68	108	81.2	81.2	NUM
cana-3162	68	109	86.8	86.8	NUM
cana-3162	68	110	84.9	84.9	NUM
cana-3162	68	111	80.7	80.7	NUM
cana-3162	68	112	convolutional	convolutional	ADJ
cana-3162	68	113	networks	network	NOUN
cana-3162	68	114	89.2	89.2	NUM
cana-3162	68	115	88.1	88.1	NUM
cana-3162	68	116	87.0	87.0	NUM
cana-3162	68	117	87.5	87.5	NUM
cana-3162	68	118	91.1	91.1	NUM
cana-3162	68	119	89.4	89.4	NUM
cana-3162	68	120	87.0	87.0	NUM
cana-3162	68	121	xgboost	xgboost	ADV
cana-3162	68	122	87.8	87.8	NUM
cana-3162	68	123	86.5	86.5	NUM
cana-3162	68	124	85.8	85.8	NUM
cana-3162	68	125	86.1	86.1	NUM
cana-3162	68	126	90.0	90.0	NUM
cana-3162	68	127	88.2	88.2	NUM
cana-3162	68	128	85.8	85.8	NUM
cana-3162	68	129	bayesian	bayesian	NOUN
cana-3162	68	130	networks	network	NOUN
cana-3162	68	131	81.5	81.5	NUM
cana-3162	68	132	80.4	80.4	NUM
cana-3162	68	133	79.5	79.5	NUM
cana-3162	68	134	79.9	79.9	NUM
cana-3162	68	135	85.7	85.7	NUM
cana-3162	68	136	83.6	83.6	NUM
cana-3162	68	137	79.5	79.5	NUM
cana-3162	68	138	recurrent	recurrent	ADJ
cana-3162	68	139	networks	network	NOUN
cana-3162	68	140	86.4	86.4	NUM
cana-3162	68	141	85.3	85.3	NUM
cana-3162	68	142	84.2	84.2	NUM
cana-3162	68	143	84.7	84.7	NUM
cana-3162	68	144	88.8	88.8	NUM
cana-3162	68	145	87.1	87.1	NUM
cana-3162	68	146	84.2	84.2	NUM
cana-3162	68	147	table	table	NOUN
cana-3162	68	148	1	1	NUM
cana-3162	68	149	compares	compare	VERB
cana-3162	68	150	10	10	NUM
cana-3162	68	151	machine	machine	NOUN
cana-3162	68	152	learning	learn	VERB
cana-3162	68	153	algorithms	algorithm	NOUN
cana-3162	68	154	'	'	PART
cana-3162	68	155	tumor	tumor	NOUN
cana-3162	68	156	medication	medication	NOUN
cana-3162	68	157	prediction	prediction	NOUN
cana-3162	68	158	accuracy	accuracy	NOUN
cana-3162	68	159	.	.	PUNCT
cana-3162	69	1	auc	auc	NOUN
cana-3162	69	2	,	,	PUNCT
cana-3162	69	3	specificity	specificity	NOUN
cana-3162	69	4	,	,	PUNCT
cana-3162	69	5	recall	recall	NOUN
cana-3162	69	6	,	,	PUNCT
cana-3162	69	7	f1	f1	NOUN
cana-3162	69	8	-	-	PUNCT
cana-3162	69	9	score	score	NOUN
cana-3162	69	10	,	,	PUNCT
cana-3162	69	11	and	and	CCONJ
cana-3162	69	12	sensitivity	sensitivity	NOUN
cana-3162	69	13	are	be	AUX
cana-3162	69	14	key	key	ADJ
cana-3162	69	15	assessment	assessment	NOUN
cana-3162	69	16	metrics	metric	NOUN
cana-3162	69	17	displayed	display	VERB
cana-3162	69	18	in	in	ADP
cana-3162	69	19	the	the	DET
cana-3162	69	20	figure	figure	NOUN
cana-3162	69	21	.	.	PUNCT
cana-3162	70	1	these	these	DET
cana-3162	70	2	measurements	measurement	NOUN
cana-3162	70	3	demonstrate	demonstrate	VERB
cana-3162	70	4	the	the	DET
cana-3162	70	5	approaches	approach	NOUN
cana-3162	70	6	'	'	PART
cana-3162	70	7	ability	ability	NOUN
cana-3162	70	8	to	to	PART
cana-3162	70	9	organize	organize	VERB
cana-3162	70	10	drug	drug	NOUN
cana-3162	70	11	response	response	NOUN
cana-3162	70	12	data	datum	NOUN
cana-3162	70	13	consistently	consistently	ADV
cana-3162	70	14	[	[	X
cana-3162	70	15	18	18	NUM
cana-3162	70	16	]	]	PUNCT
cana-3162	70	17	.	.	PUNCT
cana-3162	71	1	convolutional	convolutional	ADJ
cana-3162	71	2	neural	neural	ADJ
cana-3162	71	3	networks	network	NOUN
cana-3162	71	4	(	(	PUNCT
cana-3162	71	5	cnn	cnn	PROPN
cana-3162	71	6	)	)	PUNCT
cana-3162	71	7	predict	predict	VERB
cana-3162	71	8	tough	tough	ADJ
cana-3162	71	9	data	datum	NOUN
cana-3162	71	10	better	well	ADV
cana-3162	71	11	than	than	ADP
cana-3162	71	12	other	other	ADJ
cana-3162	71	13	approaches	approach	NOUN
cana-3162	71	14	in	in	ADP
cana-3162	71	15	accuracy	accuracy	NOUN
cana-3162	71	16	and	and	CCONJ
cana-3162	71	17	auc	auc	NOUN
cana-3162	71	18	.	.	PUNCT
cana-3162	72	1	table	table	NOUN
cana-3162	72	2	2	2	NUM
cana-3162	72	3	.	.	PUNCT
cana-3162	72	4	performance	performance	NOUN
cana-3162	72	5	evaluation	evaluation	NOUN
cana-3162	72	6	metrics	metric	NOUN
cana-3162	72	7	for	for	ADP
cana-3162	72	8	data	data	NOUN
cana-3162	72	9	-	-	PUNCT
cana-3162	72	10	driven	drive	VERB
cana-3162	72	11	approaches	approach	NOUN
cana-3162	72	12	in	in	ADP
cana-3162	72	13	anti	anti	ADJ
cana-3162	72	14	-	-	ADJ
cana-3162	72	15	cancer	cancer	ADJ
cana-3162	72	16	drug	drug	NOUN
cana-3162	72	17	response	response	NOUN
cana-3162	72	18	prediction	prediction	NOUN
cana-3162	72	19	method	method	NOUN
cana-3162	72	20	mse	mse	NOUN
cana-3162	72	21	(	(	PUNCT
cana-3162	72	22	mean	mean	VERB
cana-3162	72	23	squared	square	VERB
cana-3162	72	24	error	error	NOUN
cana-3162	72	25	)	)	PUNCT
cana-3162	72	26	mae	mae	PROPN
cana-3162	72	27	(	(	PUNCT
cana-3162	72	28	mean	mean	ADJ
cana-3162	72	29	absolute	absolute	ADJ
cana-3162	72	30	error	error	NOUN
cana-3162	72	31	)	)	PUNCT
cana-3162	72	32	r^2	r^2	PROPN
cana-3162	72	33	score	score	NOUN
cana-3162	72	34	rmse	rmse	NOUN
cana-3162	72	35	(	(	PUNCT
cana-3162	72	36	root	root	NOUN
cana-3162	72	37	mean	mean	VERB
cana-3162	72	38	squared	square	VERB
cana-3162	72	39	error	error	NOUN
cana-3162	72	40	)	)	PUNCT
cana-3162	72	41	auc	auc	NOUN
cana-3162	72	42	(	(	PUNCT
cana-3162	72	43	%	%	NOUN
cana-3162	72	44	)	)	PUNCT
cana-3162	72	45	precision	precision	NOUN
cana-3162	72	46	(	(	PUNCT
cana-3162	72	47	%	%	INTJ
cana-3162	72	48	)	)	PUNCT
cana-3162	72	49	f1score	f1score	NOUN
cana-3162	72	50	(	(	PUNCT
cana-3162	72	51	%	%	INTJ
cana-3162	72	52	)	)	PUNCT
cana-3162	72	53	random	random	ADJ
cana-3162	72	54	forest	forest	NOUN
cana-3162	72	55	0.145	0.145	NUM
cana-3162	72	56	0.098	0.098	NUM
cana-3162	72	57	0.87	0.87	NUM
cana-3162	72	58	0.381	0.381	NUM
cana-3162	72	59	89.5	89.5	NUM
cana-3162	72	60	84.1	84.1	NUM
cana-3162	72	61	83.8	83.8	NUM
cana-3162	72	62	support	support	NOUN
cana-3162	72	63	vector	vector	NOUN
cana-3162	72	64	machines	machine	NOUN
cana-3162	72	65	0.162	0.162	NUM
cana-3162	72	66	0.102	0.102	NUM
cana-3162	72	67	0.84	0.84	NUM
cana-3162	72	68	0.402	0.402	NUM
cana-3162	72	69	87.9	87.9	NUM
cana-3162	72	70	82.3	82.3	NUM
cana-3162	72	71	81.8	81.8	NUM
cana-3162	72	72	deep	deep	ADJ
cana-3162	72	73	neural	neural	ADJ
cana-3162	72	74	networks	network	NOUN
cana-3162	72	75	0.128	0.128	NUM
cana-3162	72	76	0.091	0.091	NUM
cana-3162	72	77	0.89	0.89	NUM
cana-3162	72	78	0.358	0.358	NUM
cana-3162	72	79	90.3	90.3	NUM
cana-3162	72	80	87.4	87.4	NUM
cana-3162	72	81	86.8	86.8	NUM
cana-3162	72	82	gradient	gradient	NOUN
cana-3162	72	83	boosting	boost	VERB
cana-3162	72	84	0.135	0.135	NUM
cana-3162	72	85	0.094	0.094	NUM
cana-3162	72	86	0.88	0.88	NUM
cana-3162	72	87	0.367	0.367	NUM
cana-3162	72	88	89.7	89.7	NUM
cana-3162	72	89	86.0	86.0	NUM
cana-3162	72	90	85.5	85.5	NUM
cana-3162	72	91	k	k	NOUN
cana-3162	72	92	-	-	PUNCT
cana-3162	72	93	nearest	near	ADJ
cana-3162	72	94	neighbors	neighbor	NOUN
cana-3162	72	95	0.192	0.192	NUM
cana-3162	72	96	0.110	0.110	NUM
cana-3162	72	97	0.79	0.79	NUM
cana-3162	72	98	0.438	0.438	NUM
cana-3162	72	99	84.5	84.5	NUM
cana-3162	72	100	79.0	79.0	NUM
cana-3162	72	101	78.5	78.5	NUM
cana-3162	72	102	elastic	elastic	ADJ
cana-3162	72	103	net	net	ADJ
cana-3162	72	104	regression	regression	NOUN
cana-3162	72	105	0.168	0.168	NUM
cana-3162	72	106	0.104	0.104	NUM
cana-3162	72	107	0.83	0.83	NUM
cana-3162	72	108	0.409	0.409	NUM
cana-3162	72	109	86.8	86.8	NUM
cana-3162	72	110	81.8	81.8	NUM
cana-3162	72	111	81.2	81.2	NUM
cana-3162	72	112	convolutional	convolutional	ADJ
cana-3162	72	113	networks	network	NOUN
cana-3162	72	114	0.121	0.121	NUM
cana-3162	72	115	0.089	0.089	NUM
cana-3162	72	116	0.91	0.91	NUM
cana-3162	72	117	0.348	0.348	NUM
cana-3162	72	118	91.1	91.1	NUM
cana-3162	72	119	88.1	88.1	NUM
cana-3162	72	120	87.5	87.5	NUM
cana-3162	72	121	communications	communication	NOUN
cana-3162	72	122	on	on	ADP
cana-3162	72	123	applied	apply	VERB
cana-3162	72	124	nonlinear	nonlinear	ADJ
cana-3162	72	125	analysis	analysis	NOUN
cana-3162	72	126	issn	issn	NOUN
cana-3162	72	127	:	:	PUNCT
cana-3162	72	128	1074	1074	NUM
cana-3162	72	129	-	-	PUNCT
cana-3162	72	130	133x	133x	NUM
cana-3162	72	131	vol	vol	NOUN
cana-3162	72	132	32	32	NUM
cana-3162	72	133	no	no	NOUN
cana-3162	72	134	.	.	PUNCT
cana-3162	73	1	5s	5s	NUM
cana-3162	73	2	(	(	PUNCT
cana-3162	73	3	2025	2025	NUM
cana-3162	73	4	)	)	PUNCT
cana-3162	73	5	497	497	NUM
cana-3162	73	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3162	73	7	xgboost	xgboost	ADP
cana-3162	73	8	0.132	0.132	NUM
cana-3162	73	9	0.093	0.093	NUM
cana-3162	73	10	0.89	0.89	NUM
cana-3162	73	11	0.362	0.362	NUM
cana-3162	73	12	90.0	90.0	NUM
cana-3162	73	13	86.5	86.5	NUM
cana-3162	73	14	86.1	86.1	NUM
cana-3162	73	15	bayesian	bayesian	NOUN
cana-3162	73	16	networks	network	NOUN
cana-3162	73	17	0.176	0.176	NUM
cana-3162	73	18	0.107	0.107	NUM
cana-3162	73	19	0.82	0.82	NUM
cana-3162	73	20	0.419	0.419	NUM
cana-3162	73	21	85.7	85.7	NUM
cana-3162	73	22	80.4	80.4	NUM
cana-3162	73	23	79.9	79.9	NUM
cana-3162	73	24	recurrent	recurrent	ADJ
cana-3162	73	25	networks	network	NOUN
cana-3162	73	26	0.139	0.139	NUM
cana-3162	73	27	0.096	0.096	NUM
cana-3162	73	28	0.86	0.86	NUM
cana-3162	73	29	0.374	0.374	NUM
cana-3162	73	30	88.8	88.8	NUM
cana-3162	73	31	85.3	85.3	NUM
cana-3162	73	32	84.7	84.7	NUM
cana-3162	73	33	table	table	NOUN
cana-3162	73	34	2	2	NUM
cana-3162	73	35	shows	show	VERB
cana-3162	73	36	error	error	NOUN
cana-3162	73	37	-	-	PUNCT
cana-3162	73	38	related	relate	VERB
cana-3162	73	39	performance	performance	NOUN
cana-3162	73	40	characteristics	characteristic	NOUN
cana-3162	73	41	for	for	ADP
cana-3162	73	42	eleven	eleven	NUM
cana-3162	73	43	machine	machine	NOUN
cana-3162	73	44	learning	learn	VERB
cana-3162	73	45	algorithms	algorithm	NOUN
cana-3162	73	46	.	.	PUNCT
cana-3162	74	1	the	the	DET
cana-3162	74	2	table	table	NOUN
cana-3162	74	3	displays	display	VERB
cana-3162	74	4	mse	mse	PROPN
cana-3162	74	5	,	,	PUNCT
cana-3162	74	6	mae	mae	PROPN
cana-3162	74	7	,	,	PUNCT
cana-3162	74	8	r^2	r^2	PROPN
cana-3162	74	9	score	score	NOUN
cana-3162	74	10	,	,	PUNCT
cana-3162	74	11	rmse	rmse	NOUN
cana-3162	74	12	,	,	PUNCT
cana-3162	74	13	auc	auc	NOUN
cana-3162	74	14	,	,	PUNCT
cana-3162	74	15	accuracy	accuracy	NOUN
cana-3162	74	16	,	,	PUNCT
cana-3162	74	17	and	and	CCONJ
cana-3162	74	18	f1	f1	NOUN
cana-3162	74	19	values	value	NOUN
cana-3162	74	20	.	.	PUNCT
cana-3162	75	1	dnn	dnn	PROPN
cana-3162	75	2	and	and	CCONJ
cana-3162	75	3	cnn	cnn	PROPN
cana-3162	75	4	produce	produce	VERB
cana-3162	75	5	the	the	DET
cana-3162	75	6	fewest	few	ADJ
cana-3162	75	7	errors	error	NOUN
cana-3162	75	8	(	(	PUNCT
cana-3162	75	9	mse	mse	PROPN
cana-3162	75	10	,	,	PUNCT
cana-3162	75	11	mae	mae	PROPN
cana-3162	75	12	,	,	PUNCT
cana-3162	75	13	and	and	CCONJ
cana-3162	75	14	rmse	rmse	NOUN
cana-3162	75	15	)	)	PUNCT
cana-3162	75	16	,	,	PUNCT
cana-3162	75	17	making	make	VERB
cana-3162	75	18	them	they	PRON
cana-3162	75	19	strong	strong	ADJ
cana-3162	75	20	predictors	predictor	NOUN
cana-3162	75	21	.	.	PUNCT
cana-3162	76	1	cnn	cnn	PROPN
cana-3162	76	2	gets	get	VERB
cana-3162	76	3	the	the	DET
cana-3162	76	4	highest	high	ADJ
cana-3162	76	5	r^2	r^2	NOUN
cana-3162	76	6	score	score	NOUN
cana-3162	76	7	(	(	PUNCT
cana-3162	76	8	0.91	0.91	NUM
cana-3162	76	9	)	)	PUNCT
cana-3162	76	10	,	,	PUNCT
cana-3162	76	11	indicating	indicate	VERB
cana-3162	76	12	the	the	DET
cana-3162	76	13	best	good	ADJ
cana-3162	76	14	explanation	explanation	NOUN
cana-3162	76	15	for	for	ADP
cana-3162	76	16	variance	variance	NOUN
cana-3162	76	17	.	.	PUNCT
cana-3162	77	1	iii	iii	PROPN
cana-3162	77	2	.	.	PROPN
cana-3162	77	3	proposed	propose	VERB
cana-3162	77	4	methodology	methodology	NOUN
cana-3162	77	5	we	we	PRON
cana-3162	77	6	provide	provide	VERB
cana-3162	77	7	a	a	DET
cana-3162	77	8	technique	technique	NOUN
cana-3162	77	9	for	for	ADP
cana-3162	77	10	determining	determine	VERB
cana-3162	77	11	how	how	SCONJ
cana-3162	77	12	drugs	drug	NOUN
cana-3162	77	13	will	will	AUX
cana-3162	77	14	function	function	VERB
cana-3162	77	15	with	with	ADP
cana-3162	77	16	cancer	cancer	NOUN
cana-3162	77	17	therapies	therapy	NOUN
cana-3162	77	18	.	.	PUNCT
cana-3162	78	1	it	it	PRON
cana-3162	78	2	improves	improve	VERB
cana-3162	78	3	feature	feature	NOUN
cana-3162	78	4	selection	selection	NOUN
cana-3162	78	5	,	,	PUNCT
cana-3162	78	6	model	model	NOUN
cana-3162	78	7	performance	performance	NOUN
cana-3162	78	8	,	,	PUNCT
cana-3162	78	9	and	and	CCONJ
cana-3162	78	10	prediction	prediction	NOUN
cana-3162	78	11	accuracy	accuracy	NOUN
cana-3162	78	12	using	use	VERB
cana-3162	78	13	sophisticated	sophisticated	ADJ
cana-3162	78	14	machine	machine	NOUN
cana-3162	78	15	learning	learning	NOUN
cana-3162	78	16	[	[	X
cana-3162	78	17	19	19	NUM
cana-3162	78	18	]	]	PUNCT
cana-3162	78	19	.	.	PUNCT
cana-3162	79	1	the	the	DET
cana-3162	79	2	initial	initial	ADJ
cana-3162	79	3	stage	stage	NOUN
cana-3162	79	4	involves	involve	VERB
cana-3162	79	5	creating	create	VERB
cana-3162	79	6	many	many	ADJ
cana-3162	79	7	decision	decision	NOUN
cana-3162	79	8	trees	tree	NOUN
cana-3162	79	9	using	use	VERB
cana-3162	79	10	ensemble	ensemble	ADJ
cana-3162	79	11	learning	learning	NOUN
cana-3162	79	12	.	.	PUNCT
cana-3162	80	1	we	we	PRON
cana-3162	80	2	can	can	AUX
cana-3162	80	3	bootstrap	bootstrap	VERB
cana-3162	80	4	the	the	DET
cana-3162	80	5	named	name	VERB
cana-3162	80	6	and	and	CCONJ
cana-3162	80	7	important	important	ADJ
cana-3162	80	8	dataset	dataset	NOUN
cana-3162	80	9	to	to	PART
cana-3162	80	10	create	create	VERB
cana-3162	80	11	a	a	DET
cana-3162	80	12	variety	variety	NOUN
cana-3162	80	13	of	of	ADP
cana-3162	80	14	samples	sample	NOUN
cana-3162	80	15	.	.	PUNCT
cana-3162	81	1	having	have	VERB
cana-3162	81	2	each	each	DET
cana-3162	81	3	decision	decision	NOUN
cana-3162	81	4	tree	tree	NOUN
cana-3162	81	5	learn	learn	VERB
cana-3162	81	6	from	from	ADP
cana-3162	81	7	its	its	PRON
cana-3162	81	8	own	own	ADJ
cana-3162	81	9	data	datum	NOUN
cana-3162	81	10	makes	make	VERB
cana-3162	81	11	the	the	DET
cana-3162	81	12	system	system	NOUN
cana-3162	81	13	more	more	ADV
cana-3162	81	14	dependable	dependable	ADJ
cana-3162	81	15	and	and	CCONJ
cana-3162	81	16	accurate	accurate	ADJ
cana-3162	81	17	.	.	PUNCT
cana-3162	82	1	decision	decision	NOUN
cana-3162	82	2	trees	tree	NOUN
cana-3162	82	3	separate	separate	ADJ
cana-3162	82	4	nodes	node	NOUN
cana-3162	82	5	in	in	ADP
cana-3162	82	6	a	a	DET
cana-3162	82	7	circle	circle	NOUN
cana-3162	82	8	using	use	VERB
cana-3162	82	9	impurity	impurity	NOUN
cana-3162	82	10	-	-	PUNCT
cana-3162	82	11	reducing	reduce	VERB
cana-3162	82	12	characteristics	characteristic	NOUN
cana-3162	82	13	[	[	X
cana-3162	82	14	20	20	NUM
cana-3162	82	15	]	]	PUNCT
cana-3162	82	16	.	.	PUNCT
cana-3162	83	1	gini	gini	PROPN
cana-3162	83	2	impurity	impurity	PROPN
cana-3162	83	3	or	or	CCONJ
cana-3162	83	4	entropy	entropy	NOUN
cana-3162	83	5	measures	measure	NOUN
cana-3162	83	6	help	help	VERB
cana-3162	83	7	them	they	PRON
cana-3162	83	8	choose	choose	VERB
cana-3162	83	9	wisely	wisely	ADV
cana-3162	83	10	.	.	PUNCT
cana-3162	84	1	this	this	DET
cana-3162	84	2	stage	stage	NOUN
cana-3162	84	3	evaluates	evaluate	VERB
cana-3162	84	4	each	each	DET
cana-3162	84	5	feature	feature	NOUN
cana-3162	84	6	based	base	VERB
cana-3162	84	7	on	on	ADP
cana-3162	84	8	how	how	SCONJ
cana-3162	84	9	much	much	ADJ
cana-3162	84	10	it	it	PRON
cana-3162	84	11	reduces	reduce	VERB
cana-3162	84	12	impurity	impurity	NOUN
cana-3162	84	13	across	across	ADP
cana-3162	84	14	all	all	DET
cana-3162	84	15	trees	tree	NOUN
cana-3162	84	16	.	.	PUNCT
cana-3162	85	1	it	it	PRON
cana-3162	85	2	is	be	AUX
cana-3162	85	3	possible	possible	ADJ
cana-3162	85	4	to	to	PART
cana-3162	85	5	detect	detect	VERB
cana-3162	85	6	important	important	ADJ
cana-3162	85	7	characteristics	characteristic	NOUN
cana-3162	85	8	with	with	ADP
cana-3162	85	9	significant	significant	ADJ
cana-3162	85	10	predicted	predict	VERB
cana-3162	85	11	consequences	consequence	NOUN
cana-3162	85	12	.	.	PUNCT
cana-3162	86	1	in	in	ADP
cana-3162	86	2	datasets	dataset	NOUN
cana-3162	86	3	with	with	ADP
cana-3162	86	4	many	many	ADJ
cana-3162	86	5	dimensions	dimension	NOUN
cana-3162	86	6	,	,	PUNCT
cana-3162	86	7	like	like	ADP
cana-3162	86	8	genetics	genetic	NOUN
cana-3162	86	9	and	and	CCONJ
cana-3162	86	10	medical	medical	ADJ
cana-3162	86	11	testing	testing	NOUN
cana-3162	86	12	,	,	PUNCT
cana-3162	86	13	keeping	keep	VERB
cana-3162	86	14	features	feature	NOUN
cana-3162	86	15	with	with	ADP
cana-3162	86	16	higher	high	ADJ
cana-3162	86	17	relevance	relevance	NOUN
cana-3162	86	18	scores	score	NOUN
cana-3162	86	19	reduces	reduce	VERB
cana-3162	86	20	the	the	DET
cana-3162	86	21	number	number	NOUN
cana-3162	86	22	of	of	ADP
cana-3162	86	23	dimensions	dimension	NOUN
cana-3162	86	24	and	and	CCONJ
cana-3162	86	25	simplifies	simplifie	NOUN
cana-3162	86	26	the	the	DET
cana-3162	86	27	model	model	NOUN
cana-3162	86	28	[	[	X
cana-3162	86	29	21	21	NUM
cana-3162	86	30	]	]	PUNCT
cana-3162	86	31	.	.	PUNCT
cana-3162	87	1	additionally	additionally	ADV
cana-3162	87	2	,	,	PUNCT
cana-3162	87	3	we	we	PRON
cana-3162	87	4	utilize	utilize	VERB
cana-3162	87	5	out	out	ADV
cana-3162	87	6	-	-	PUNCT
cana-3162	87	7	of	of	ADP
cana-3162	87	8	-	-	PUNCT
cana-3162	87	9	bag	bag	NOUN
cana-3162	87	10	(	(	PUNCT
cana-3162	87	11	oob	oob	NOUN
cana-3162	87	12	)	)	PUNCT
cana-3162	87	13	samples	sample	NOUN
cana-3162	87	14	to	to	PART
cana-3162	87	15	verify	verify	VERB
cana-3162	87	16	predictions	prediction	NOUN
cana-3162	87	17	without	without	ADP
cana-3162	87	18	the	the	DET
cana-3162	87	19	need	need	NOUN
cana-3162	87	20	for	for	ADP
cana-3162	87	21	a	a	DET
cana-3162	87	22	new	new	ADJ
cana-3162	87	23	test	test	NOUN
cana-3162	87	24	set	set	NOUN
cana-3162	87	25	.	.	PUNCT
cana-3162	88	1	the	the	DET
cana-3162	88	2	model	model	NOUN
cana-3162	88	3	becomes	become	VERB
cana-3162	88	4	more	more	ADV
cana-3162	88	5	trustworthy	trustworthy	ADJ
cana-3162	88	6	.	.	PUNCT
cana-3162	89	1	next	next	ADV
cana-3162	89	2	,	,	PUNCT
cana-3162	89	3	an	an	DET
cana-3162	89	4	svm	svm	NOUN
cana-3162	89	5	predicts	predict	VERB
cana-3162	89	6	drug	drug	NOUN
cana-3162	89	7	response	response	NOUN
cana-3162	89	8	based	base	VERB
cana-3162	89	9	on	on	ADP
cana-3162	89	10	the	the	DET
cana-3162	89	11	features	feature	NOUN
cana-3162	89	12	selected	select	VERB
cana-3162	89	13	in	in	ADP
cana-3162	89	14	the	the	DET
cana-3162	89	15	previous	previous	ADJ
cana-3162	89	16	phase	phase	NOUN
cana-3162	89	17	.	.	PUNCT
cana-3162	90	1	this	this	DET
cana-3162	90	2	stage	stage	NOUN
cana-3162	90	3	involves	involve	VERB
cana-3162	90	4	preparing	prepare	VERB
cana-3162	90	5	the	the	DET
cana-3162	90	6	data	datum	NOUN
cana-3162	90	7	to	to	PART
cana-3162	90	8	standardize	standardize	VERB
cana-3162	90	9	all	all	DET
cana-3162	90	10	characteristics	characteristic	NOUN
cana-3162	90	11	to	to	ADP
cana-3162	90	12	the	the	DET
cana-3162	90	13	same	same	ADJ
cana-3162	90	14	scale	scale	NOUN
cana-3162	90	15	.	.	PUNCT
cana-3162	91	1	effectiveness	effectiveness	NOUN
cana-3162	91	2	of	of	ADP
cana-3162	91	3	model	model	NOUN
cana-3162	91	4	training	training	NOUN
cana-3162	91	5	increases	increase	NOUN
cana-3162	91	6	.	.	PUNCT
cana-3162	92	1	choosing	choose	VERB
cana-3162	92	2	the	the	DET
cana-3162	92	3	suitable	suitable	ADJ
cana-3162	92	4	kernel	kernel	NOUN
cana-3162	92	5	function	function	NOUN
cana-3162	92	6	modifies	modify	VERB
cana-3162	92	7	the	the	DET
cana-3162	92	8	input	input	NOUN
cana-3162	92	9	space	space	NOUN
cana-3162	92	10	,	,	PUNCT
cana-3162	92	11	making	make	VERB
cana-3162	92	12	non	non	ADJ
cana-3162	92	13	-	-	ADJ
cana-3162	92	14	linear	linear	ADJ
cana-3162	92	15	data	datum	NOUN
cana-3162	92	16	classification	classification	NOUN
cana-3162	92	17	simpler	simple	ADJ
cana-3162	92	18	for	for	ADP
cana-3162	92	19	the	the	DET
cana-3162	92	20	model	model	NOUN
cana-3162	92	21	[	[	X
cana-3162	92	22	22	22	NUM
cana-3162	92	23	]	]	PUNCT
cana-3162	92	24	.	.	PUNCT
cana-3162	93	1	training	training	NOUN
cana-3162	93	2	improves	improve	VERB
cana-3162	93	3	parameters	parameter	NOUN
cana-3162	93	4	by	by	ADP
cana-3162	93	5	decreasing	decrease	VERB
cana-3162	93	6	a	a	DET
cana-3162	93	7	loss	loss	NOUN
cana-3162	93	8	function	function	NOUN
cana-3162	93	9	that	that	PRON
cana-3162	93	10	penalizes	penalize	VERB
cana-3162	93	11	erroneous	erroneous	ADJ
cana-3162	93	12	labeling	labeling	NOUN
cana-3162	93	13	.	.	PUNCT
cana-3162	94	1	this	this	PRON
cana-3162	94	2	ensures	ensure	VERB
cana-3162	94	3	data	data	NOUN
cana-3162	94	4	-	-	PUNCT
cana-3162	94	5	model	model	NOUN
cana-3162	94	6	compatibility	compatibility	NOUN
cana-3162	94	7	.	.	PUNCT
cana-3162	95	1	model	model	NOUN
cana-3162	95	2	success	success	NOUN
cana-3162	95	3	depends	depend	VERB
cana-3162	95	4	on	on	ADP
cana-3162	95	5	accuracy	accuracy	NOUN
cana-3162	95	6	,	,	PUNCT
cana-3162	95	7	precision	precision	NOUN
cana-3162	95	8	,	,	PUNCT
cana-3162	95	9	and	and	CCONJ
cana-3162	95	10	f1	f1	PROPN
cana-3162	95	11	scores	score	NOUN
cana-3162	95	12	.	.	PUNCT
cana-3162	96	1	feature	feature	NOUN
cana-3162	96	2	significance	significance	NOUN
cana-3162	96	3	analysis	analysis	NOUN
cana-3162	96	4	demonstrates	demonstrate	VERB
cana-3162	96	5	how	how	SCONJ
cana-3162	96	6	well	well	ADV
cana-3162	96	7	features	feature	NOUN
cana-3162	96	8	predict	predict	VERB
cana-3162	96	9	medication	medication	NOUN
cana-3162	96	10	reactions	reaction	NOUN
cana-3162	96	11	.	.	PUNCT
cana-3162	97	1	ensemble	ensemble	ADJ
cana-3162	97	2	learning	learning	NOUN
cana-3162	97	3	combines	combine	VERB
cana-3162	97	4	several	several	ADJ
cana-3162	97	5	machine	machine	NOUN
cana-3162	97	6	learning	learning	NOUN
cana-3162	97	7	models	model	NOUN
cana-3162	97	8	to	to	PART
cana-3162	97	9	improve	improve	VERB
cana-3162	97	10	predictions	prediction	NOUN
cana-3162	97	11	.	.	PUNCT
cana-3162	98	1	this	this	DET
cana-3162	98	2	integration	integration	NOUN
cana-3162	98	3	allows	allow	VERB
cana-3162	98	4	averaging	average	VERB
cana-3162	98	5	or	or	CCONJ
cana-3162	98	6	voting	vote	VERB
cana-3162	98	7	to	to	PART
cana-3162	98	8	provide	provide	VERB
cana-3162	98	9	a	a	DET
cana-3162	98	10	reliable	reliable	ADJ
cana-3162	98	11	outcome	outcome	NOUN
cana-3162	98	12	by	by	ADP
cana-3162	98	13	merging	merge	VERB
cana-3162	98	14	assertions	assertion	NOUN
cana-3162	98	15	from	from	ADP
cana-3162	98	16	multiple	multiple	ADJ
cana-3162	98	17	models	model	NOUN
cana-3162	98	18	[	[	X
cana-3162	98	19	23	23	NUM
cana-3162	98	20	]	]	PUNCT
cana-3162	98	21	.	.	PUNCT
cana-3162	99	1	accuracy	accuracy	NOUN
cana-3162	99	2	metrics	metric	NOUN
cana-3162	99	3	and	and	CCONJ
cana-3162	99	4	feature	feature	NOUN
cana-3162	99	5	significance	significance	NOUN
cana-3162	99	6	analysis	analysis	NOUN
cana-3162	99	7	evaluate	evaluate	VERB
cana-3162	99	8	performance	performance	NOUN
cana-3162	99	9	and	and	CCONJ
cana-3162	99	10	identify	identify	VERB
cana-3162	99	11	key	key	ADJ
cana-3162	99	12	traits	trait	NOUN
cana-3162	99	13	.	.	PUNCT
cana-3162	100	1	we	we	PRON
cana-3162	100	2	adjust	adjust	VERB
cana-3162	100	3	the	the	DET
cana-3162	100	4	hyperparameters	hyperparameter	NOUN
cana-3162	100	5	to	to	PART
cana-3162	100	6	improve	improve	VERB
cana-3162	100	7	the	the	DET
cana-3162	100	8	accuracy	accuracy	NOUN
cana-3162	100	9	of	of	ADP
cana-3162	100	10	the	the	DET
cana-3162	100	11	model	model	NOUN
cana-3162	100	12	.	.	PUNCT
cana-3162	101	1	comparing	compare	VERB
cana-3162	101	2	the	the	DET
cana-3162	101	3	ensemble	ensemble	ADJ
cana-3162	101	4	model	model	NOUN
cana-3162	101	5	to	to	ADP
cana-3162	101	6	individual	individual	ADJ
cana-3162	101	7	models	model	NOUN
cana-3162	101	8	shows	show	VERB
cana-3162	101	9	improvement	improvement	NOUN
cana-3162	101	10	.	.	PUNCT
cana-3162	102	1	this	this	PRON
cana-3162	102	2	implies	imply	VERB
cana-3162	102	3	forecasting	forecasting	NOUN
cana-3162	102	4	improves	improve	VERB
cana-3162	102	5	.	.	PUNCT
cana-3162	103	1	this	this	DET
cana-3162	103	2	lengthy	lengthy	ADJ
cana-3162	103	3	process	process	NOUN
cana-3162	103	4	concludes	conclude	VERB
cana-3162	103	5	with	with	ADP
cana-3162	103	6	final	final	ADJ
cana-3162	103	7	forecasts	forecast	NOUN
cana-3162	103	8	that	that	PRON
cana-3162	103	9	assist	assist	VERB
cana-3162	103	10	us	we	PRON
cana-3162	103	11	in	in	ADP
cana-3162	103	12	understanding	understand	VERB
cana-3162	103	13	the	the	DET
cana-3162	103	14	impact	impact	NOUN
cana-3162	103	15	of	of	ADP
cana-3162	103	16	drugs	drug	NOUN
cana-3162	103	17	on	on	ADP
cana-3162	103	18	patients	patient	NOUN
cana-3162	103	19	,	,	PUNCT
cana-3162	103	20	enabling	enable	VERB
cana-3162	103	21	us	we	PRON
cana-3162	103	22	to	to	PART
cana-3162	103	23	make	make	VERB
cana-3162	103	24	better	well	ADJ
cana-3162	103	25	choices	choice	NOUN
cana-3162	103	26	for	for	ADP
cana-3162	103	27	cancer	cancer	NOUN
cana-3162	103	28	treatment	treatment	NOUN
cana-3162	103	29	[	[	X
cana-3162	103	30	24	24	NUM
cana-3162	103	31	]	]	PUNCT
cana-3162	103	32	.	.	PUNCT
cana-3162	104	1	planning	plan	VERB
cana-3162	104	2	to	to	PART
cana-3162	104	3	use	use	VERB
cana-3162	104	4	feature	feature	NOUN
cana-3162	104	5	selection	selection	NOUN
cana-3162	104	6	,	,	PUNCT
cana-3162	104	7	machine	machine	NOUN
cana-3162	104	8	learning	learning	NOUN
cana-3162	104	9	models	model	NOUN
cana-3162	104	10	,	,	PUNCT
cana-3162	104	11	and	and	CCONJ
cana-3162	104	12	ensemble	ensemble	ADJ
cana-3162	104	13	approaches	approach	NOUN
cana-3162	104	14	may	may	AUX
cana-3162	104	15	enhance	enhance	VERB
cana-3162	104	16	cancer	cancer	NOUN
cana-3162	104	17	therapies	therapy	NOUN
cana-3162	104	18	.	.	PUNCT
cana-3162	105	1	this	this	DET
cana-3162	105	2	highlights	highlight	VERB
cana-3162	105	3	the	the	DET
cana-3162	105	4	value	value	NOUN
cana-3162	105	5	of	of	ADP
cana-3162	105	6	datadriven	datadriven	ADJ
cana-3162	105	7	cancer	cancer	NOUN
cana-3162	105	8	treatment	treatment	NOUN
cana-3162	105	9	in	in	ADP
cana-3162	105	10	the	the	DET
cana-3162	105	11	ever	ever	ADV
cana-3162	105	12	-	-	PUNCT
cana-3162	105	13	changing	change	VERB
cana-3162	105	14	sector	sector	NOUN
cana-3162	105	15	.	.	PUNCT
cana-3162	106	1	algorithm	algorithm	NOUN
cana-3162	106	2	1	1	NUM
cana-3162	106	3	(	(	PUNCT
cana-3162	106	4	random	random	ADJ
cana-3162	106	5	forest	forest	NOUN
cana-3162	106	6	for	for	ADP
cana-3162	106	7	feature	feature	NOUN
cana-3162	106	8	selection	selection	NOUN
cana-3162	106	9	):	):	PUNCT
cana-3162	106	10	1	1	NUM
cana-3162	106	11	.	.	X
cana-3162	106	12	input	input	NOUN
cana-3162	106	13	data	data	PROPN
cana-3162	106	14	:	:	PUNCT
cana-3162	106	15	given	give	VERB
cana-3162	106	16	a	a	DET
cana-3162	106	17	dataset	dataset	NOUN
cana-3162	106	18	𝑋	𝑋	NOUN
cana-3162	106	19	=	=	SYM
cana-3162	106	20	{	{	PUNCT
cana-3162	106	21	𝑥1	𝑥1	NOUN
cana-3162	106	22	,	,	PUNCT
cana-3162	106	23	𝑥2	𝑥2	NOUN
cana-3162	106	24	,	,	PUNCT
cana-3162	106	25	…	…	PUNCT
cana-3162	106	26	,	,	PUNCT
cana-3162	106	27	𝑥𝑛	𝑥𝑛	VERB
cana-3162	106	28	}	}	PUNCT
cana-3162	106	29	and	and	CCONJ
cana-3162	106	30	labels	label	VERB
cana-3162	106	31	𝑌	𝑌	PROPN
cana-3162	106	32	=	=	SYM
cana-3162	106	33	{	{	PUNCT
cana-3162	106	34	𝑦1	𝑦1	PROPN
cana-3162	106	35	,	,	PUNCT
cana-3162	106	36	𝑦2	𝑦2	NOUN
cana-3162	106	37	,	,	PUNCT
cana-3162	106	38	…	…	PUNCT
cana-3162	106	39	,	,	PUNCT
cana-3162	106	40	𝑦𝑛	𝑦𝑛	ADP
cana-3162	106	41	}	}	PUNCT
cana-3162	106	42	,	,	PUNCT
cana-3162	106	43	𝑠𝑝𝑙𝑖𝑡	𝑠𝑝𝑙𝑖𝑡	PROPN
cana-3162	106	44	𝑖𝑡	𝑖𝑡	ADP
cana-3162	106	45	𝑖𝑛𝑡𝑜	𝑖𝑛𝑡𝑜	ADJ
cana-3162	106	46	𝑡𝑟𝑎𝑖𝑛𝑖𝑛𝑔	𝑡𝑟𝑎𝑖𝑛𝑖𝑛𝑔	NOUN
cana-3162	106	47	(	(	PUNCT
cana-3162	106	48	𝑋𝑡𝑟𝑎𝑖𝑛	𝑋𝑡𝑟𝑎𝑖𝑛	NOUN
cana-3162	106	49	,	,	PUNCT
cana-3162	106	50	𝑌𝑡𝑟𝑎𝑖𝑛)𝑎𝑛𝑑𝑡𝑒𝑠𝑡𝑖𝑛𝑔(𝑋𝑡𝑒𝑠𝑡	𝑌𝑡𝑟𝑎𝑖𝑛)𝑎𝑛𝑑𝑡𝑒𝑠𝑡𝑖𝑛𝑔(𝑋𝑡𝑒𝑠𝑡	NOUN
cana-3162	106	51	,	,	PUNCT
cana-3162	106	52	𝑌𝑡𝑒𝑠𝑡)sets	𝑌𝑡𝑒𝑠𝑡)sets	X
cana-3162	106	53	.	.	PUNCT
cana-3162	106	54	initialize	initialize	NOUN
cana-3162	106	55	parameters	parameter	NOUN
cana-3162	106	56	𝑇	𝑇	PROPN
cana-3162	106	57	(	(	PUNCT
cana-3162	106	58	number	number	NOUN
cana-3162	106	59	of	of	ADP
cana-3162	106	60	trees	tree	NOUN
cana-3162	106	61	)	)	PUNCT
cana-3162	106	62	,	,	PUNCT
cana-3162	106	63	max_depth	max_depth	NOUN
cana-3162	106	64	,	,	PUNCT
cana-3162	106	65	𝑎𝑛𝑑	𝑎𝑛𝑑	PROPN
cana-3162	106	66	min_samples_split	min_samples_split	PROPN
cana-3162	106	67	.	.	PUNCT
cana-3162	107	1	create	create	VERB
cana-3162	107	2	bootstrapped	bootstrappe	VERB
cana-3162	107	3	samples	sample	NOUN
cana-3162	107	4	𝑋𝑡	𝑋𝑡	PROPN
cana-3162	107	5	for	for	ADP
cana-3162	107	6	each	each	DET
cana-3162	107	7	tree	tree	NOUN
cana-3162	107	8	𝑡.	𝑡.	VERB
cana-3162	107	9	•	•	NOUN
cana-3162	107	10	𝑋𝑡	𝑋𝑡	PROPN
cana-3162	107	11	⊆	⊆	NUM
cana-3162	107	12	𝑋𝑡𝑟𝑎𝑖𝑛	𝑋𝑡𝑟𝑎𝑖𝑛	NOUN
cana-3162	107	13	,	,	PUNCT
cana-3162	107	14	𝑌𝑡	𝑌𝑡	PROPN
cana-3162	107	15	⊆	⊆	NUM
cana-3162	107	16	𝑌𝑡𝑟𝑎𝑖𝑛	𝑌𝑡𝑟𝑎𝑖𝑛	PROPN
cana-3162	107	17	,	,	PUNCT
cana-3162	107	18	𝑓𝑜𝑟	𝑓𝑜𝑟	ADV
cana-3162	107	19	𝑒𝑎𝑐ℎ	𝑒𝑎𝑐ℎ	PROPN
cana-3162	107	20	𝑡	𝑡	PROPN
cana-3162	107	21	(	(	PUNCT
cana-3162	107	22	1	1	NUM
cana-3162	107	23	)	)	PUNCT
cana-3162	107	24	communications	communication	NOUN
cana-3162	107	25	on	on	ADP
cana-3162	107	26	applied	apply	VERB
cana-3162	107	27	nonlinear	nonlinear	ADJ
cana-3162	107	28	analysis	analysis	NOUN
cana-3162	107	29	issn	issn	NOUN
cana-3162	107	30	:	:	PUNCT
cana-3162	107	31	1074	1074	NUM
cana-3162	107	32	-	-	PUNCT
cana-3162	107	33	133x	133x	NUM
cana-3162	107	34	vol	vol	NOUN
cana-3162	107	35	32	32	NUM
cana-3162	107	36	no	no	NOUN
cana-3162	107	37	.	.	PUNCT
cana-3162	108	1	5s	5s	NUM
cana-3162	108	2	(	(	PUNCT
cana-3162	108	3	2025	2025	NUM
cana-3162	108	4	)	)	PUNCT
cana-3162	108	5	498	498	NUM
cana-3162	108	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3162	108	7	•	•	NUM
cana-3162	109	1	𝐷𝑡	𝐷𝑡	PROPN
cana-3162	109	2	=	=	PUNCT
cana-3162	109	3	{	{	PUNCT
cana-3162	109	4	(	(	PUNCT
cana-3162	109	5	𝑿𝒕	𝑿𝒕	PROPN
cana-3162	109	6	,	,	PUNCT
cana-3162	109	7	𝒀𝒕	𝒀𝒕	PROPN
cana-3162	109	8	)	)	PUNCT
cana-3162	109	9	}	}	PUNCT
cana-3162	109	10	,	,	PUNCT
cana-3162	109	11	𝑡	𝑡	X
cana-3162	109	12	=	=	SYM
cana-3162	109	13	1,2	1,2	NUM
cana-3162	109	14	,	,	PUNCT
cana-3162	109	15	…	…	PUNCT
cana-3162	109	16	,	,	PUNCT
cana-3162	109	17	𝑇	𝑇	PROPN
cana-3162	109	18	(	(	PUNCT
cana-3162	109	19	2	2	NUM
cana-3162	109	20	)	)	PUNCT
cana-3162	109	21	•	•	NOUN
cana-3162	109	22	𝑛𝑡	𝑛𝑡	NOUN
cana-3162	109	23	=	=	SYM
cana-3162	109	24	∑	∑	PUNCT
cana-3162	109	25	1(𝑥𝑖∈𝐷𝑡	1(𝑥𝑖∈𝐷𝑡	NUM
cana-3162	109	26	)	)	PUNCT
cana-3162	109	27	𝑚	𝑚	PROPN
cana-3162	109	28	𝑖=1	𝑖=1	PROPN
cana-3162	109	29	(	(	PUNCT
cana-3162	109	30	3	3	NUM
cana-3162	109	31	)	)	PUNCT
cana-3162	109	32	2	2	NUM
cana-3162	109	33	.	.	PUNCT
cana-3162	109	34	build	build	VERB
cana-3162	109	35	decision	decision	NOUN
cana-3162	109	36	trees	tree	NOUN
cana-3162	109	37	:	:	PUNCT
cana-3162	109	38	for	for	ADP
cana-3162	109	39	each	each	DET
cana-3162	109	40	bootstrapped	bootstrappe	VERB
cana-3162	109	41	sample	sample	NOUN
cana-3162	109	42	,	,	PUNCT
cana-3162	109	43	construct	construct	VERB
cana-3162	109	44	decision	decision	NOUN
cana-3162	109	45	trees	tree	NOUN
cana-3162	109	46	by	by	ADP
cana-3162	109	47	recursively	recursively	ADV
cana-3162	109	48	splitting	splitting	NOUN
cana-3162	109	49	nodes	node	NOUN
cana-3162	109	50	based	base	VERB
cana-3162	109	51	on	on	ADP
cana-3162	109	52	the	the	DET
cana-3162	109	53	feature	feature	NOUN
cana-3162	109	54	that	that	PRON
cana-3162	109	55	reduces	reduce	VERB
cana-3162	109	56	impurity	impurity	NOUN
cana-3162	109	57	the	the	DET
cana-3162	109	58	most	most	ADJ
cana-3162	109	59	.	.	PUNCT
cana-3162	110	1	•	•	NUM
cana-3162	110	2	𝐼(𝐷	𝐼(𝐷	NUM
cana-3162	110	3	)	)	PUNCT
cana-3162	111	1	=	=	SYM
cana-3162	112	1	1	1	NUM
cana-3162	112	2	−	−	NUM
cana-3162	112	3	∑	∑	PROPN
cana-3162	112	4	𝑝(𝑐|𝐷)2𝐶	𝑝(𝑐|𝐷)2𝐶	NOUN
cana-3162	112	5	𝑐=1	𝑐=1	PROPN
cana-3162	112	6	(	(	PUNCT
cana-3162	112	7	4	4	NUM
cana-3162	112	8	)	)	PUNCT
cana-3162	112	9	•	•	NUM
cana-3162	112	10	δ𝐼	δ𝐼	NOUN
cana-3162	112	11	=	=	PROPN
cana-3162	112	12	𝐼parent	𝐼parent	PROPN
cana-3162	112	13	−	−	PROPN
cana-3162	112	14	∑	∑	PUNCT
cana-3162	112	15	|𝐷𝑖|	|𝐷𝑖|	PROPN
cana-3162	112	16	|𝐷parent|	|𝐷parent|	NOUN
cana-3162	112	17	𝑘	𝑘	X
cana-3162	112	18	𝑖=1	𝑖=1	PROPN
cana-3162	112	19	𝐼(𝐷𝑖	𝐼(𝐷𝑖	ADV
cana-3162	112	20	)	)	PUNCT
cana-3162	112	21	(	(	PUNCT
cana-3162	112	22	5	5	NUM
cana-3162	112	23	)	)	PUNCT
cana-3162	112	24	•	•	NUM
cana-3162	112	25	δ𝐼	δ𝐼	NOUN
cana-3162	112	26	=	=	SYM
cana-3162	112	27	∑	∑	PROPN
cana-3162	112	28	(	(	PUNCT
cana-3162	112	29	𝑝(𝑦𝑗|𝐷parent	𝑝(𝑦𝑗|𝐷parent	NOUN
cana-3162	112	30	)	)	PUNCT
cana-3162	112	31	−	−	ADP
cana-3162	112	32	𝑝(𝑦𝑗|𝐷𝑖	𝑝(𝑦𝑗|𝐷𝑖	NOUN
cana-3162	112	33	)	)	PUNCT
cana-3162	112	34	)	)	PUNCT
cana-3162	112	35	2	2	NUM
cana-3162	112	36	𝑚	𝑚	NOUN
cana-3162	112	37	𝑗=1	𝑗=1	X
cana-3162	112	38	(	(	PUNCT
cana-3162	112	39	6	6	NUM
cana-3162	112	40	)	)	SYM
cana-3162	112	41	3	3	NUM
cana-3162	112	42	.	.	PUNCT
cana-3162	112	43	feature	feature	NOUN
cana-3162	112	44	selection	selection	NOUN
cana-3162	112	45	:	:	PUNCT
cana-3162	112	46	evaluate	evaluate	VERB
cana-3162	112	47	the	the	DET
cana-3162	112	48	impurity	impurity	NOUN
cana-3162	112	49	decrease	decrease	NOUN
cana-3162	112	50	caused	cause	VERB
cana-3162	112	51	by	by	ADP
cana-3162	112	52	each	each	DET
cana-3162	112	53	feature	feature	NOUN
cana-3162	112	54	at	at	ADP
cana-3162	112	55	each	each	DET
cana-3162	112	56	split	split	NOUN
cana-3162	112	57	in	in	ADP
cana-3162	112	58	the	the	DET
cana-3162	112	59	tree	tree	NOUN
cana-3162	112	60	.	.	PUNCT
cana-3162	113	1	•	•	NUM
cana-3162	113	2	𝑓𝑖	𝑓𝑖	PROPN
cana-3162	113	3	=	=	SYM
cana-3162	113	4	1	1	NUM
cana-3162	113	5	𝑇	𝑇	PROPN
cana-3162	113	6	∑	∑	PUNCT
cana-3162	113	7	δ𝐼𝑡,𝑖	δ𝐼𝑡,𝑖	PROPN
cana-3162	113	8	𝑇	𝑇	PROPN
cana-3162	113	9	𝑡=1	𝑡=1	PROPN
cana-3162	113	10	(	(	PUNCT
cana-3162	113	11	7	7	NUM
cana-3162	113	12	)	)	PUNCT
cana-3162	113	13	•	•	NOUN
cana-3162	113	14	select	select	VERB
cana-3162	113	15	the	the	DET
cana-3162	113	16	top	top	ADJ
cana-3162	113	17	-	-	PUNCT
cana-3162	113	18	k	k	NOUN
cana-3162	113	19	features	feature	NOUN
cana-3162	113	20	based	base	VERB
cana-3162	113	21	on	on	ADP
cana-3162	113	22	•	•	NUM
cana-3162	113	23	𝑓𝑖𝑓total	𝑓𝑖𝑓total	ADJ
cana-3162	113	24	=	=	PUNCT
cana-3162	113	25	∑	∑	PROPN
cana-3162	113	26	𝑓𝑖	𝑓𝑖	PROPN
cana-3162	113	27	𝑚	𝑚	X
cana-3162	113	28	𝑖=1	𝑖=1	PROPN
cana-3162	113	29	⋅	⋅	PROPN
cana-3162	113	30	𝑛𝑖	𝑛𝑖	PROPN
cana-3162	113	31	(	(	PUNCT
cana-3162	113	32	8)	8)	PROPN
cana-3162	113	33	4	4	NUM
cana-3162	113	34	.	.	PUNCT
cana-3162	113	35	tree	tree	NOUN
cana-3162	113	36	construction	construction	NOUN
cana-3162	113	37	:	:	PUNCT
cana-3162	113	38	continue	continue	VERB
cana-3162	113	39	splitting	splitting	NOUN
cana-3162	113	40	until	until	SCONJ
cana-3162	113	41	the	the	DET
cana-3162	113	42	maximum	maximum	ADJ
cana-3162	113	43	depth	depth	NOUN
cana-3162	113	44	or	or	CCONJ
cana-3162	113	45	minimum	minimum	ADJ
cana-3162	113	46	sample	sample	NOUN
cana-3162	113	47	size	size	NOUN
cana-3162	113	48	conditions	condition	NOUN
cana-3162	113	49	are	be	AUX
cana-3162	113	50	met	meet	VERB
cana-3162	113	51	.	.	PUNCT
cana-3162	114	1	for	for	ADP
cana-3162	114	2	each	each	DET
cana-3162	114	3	node	node	NOUN
cana-3162	114	4	:	:	PUNCT
cana-3162	114	5	•	•	ADP
cana-3162	114	6	𝐼𝑗	𝐼𝑗	NOUN
cana-3162	114	7	=	=	SYM
cana-3162	114	8	∑	∑	PUNCT
cana-3162	114	9	𝑛𝑖	𝑛𝑖	PROPN
cana-3162	114	10	𝑛	𝑛	ADP
cana-3162	114	11	𝑘	𝑘	PRON
cana-3162	114	12	𝑖=1	𝑖=1	PROPN
cana-3162	114	13	𝐼(𝐷𝑖	𝐼(𝐷𝑖	ADV
cana-3162	114	14	)	)	PUNCT
cana-3162	114	15	(	(	PUNCT
cana-3162	114	16	9	9	NUM
cana-3162	114	17	)	)	PUNCT
cana-3162	114	18	•	•	NOUN
cana-3162	114	19	continue	continue	VERB
cana-3162	114	20	splitting	splitting	NOUN
cana-3162	114	21	until	until	ADP
cana-3162	114	22	max_depth	max_depth	NOUN
cana-3162	114	23	or	or	CCONJ
cana-3162	114	24	𝑛𝑖	𝑛𝑖	X
cana-3162	114	25	<	<	X
cana-3162	114	26	min_samples_split	min_samples_split	ADJ
cana-3162	114	27	•	•	NOUN
cana-3162	114	28	𝐼(𝐷𝑗	𝐼(𝐷𝑗	NOUN
cana-3162	114	29	)	)	PUNCT
cana-3162	115	1	=	=	SYM
cana-3162	115	2	∑	∑	PROPN
cana-3162	115	3	𝑝(𝑐|𝐷𝑗)𝐶	𝑝(𝑐|𝐷𝑗)𝐶	VERB
cana-3162	115	4	𝑐=1	𝑐=1	PROPN
cana-3162	115	5	⋅	⋅	PROPN
cana-3162	115	6	log	log	NOUN
cana-3162	115	7	(	(	PUNCT
cana-3162	115	8	1	1	NUM
cana-3162	115	9	𝑝(𝑐|𝐷𝑗	𝑝(𝑐|𝐷𝑗	NOUN
cana-3162	115	10	)	)	PUNCT
cana-3162	115	11	)	)	PUNCT
cana-3162	115	12	(	(	PUNCT
cana-3162	115	13	10	10	NUM
cana-3162	115	14	)	)	PUNCT
cana-3162	115	15	5	5	NUM
cana-3162	115	16	.	.	PUNCT
cana-3162	115	17	compute	compute	NOUN
cana-3162	115	18	feature	feature	NOUN
cana-3162	115	19	importance	importance	NOUN
cana-3162	115	20	:	:	PUNCT
cana-3162	115	21	calculate	calculate	VERB
cana-3162	115	22	the	the	DET
cana-3162	115	23	importance	importance	NOUN
cana-3162	115	24	of	of	ADP
cana-3162	115	25	each	each	DET
cana-3162	115	26	feature	feature	NOUN
cana-3162	115	27	𝑓𝑖	𝑓𝑖	NOUN
cana-3162	115	28	as	as	ADP
cana-3162	115	29	the	the	DET
cana-3162	115	30	average	average	ADJ
cana-3162	115	31	decrease	decrease	NOUN
cana-3162	115	32	in	in	ADP
cana-3162	115	33	impurity	impurity	NOUN
cana-3162	115	34	across	across	ADP
cana-3162	115	35	all	all	DET
cana-3162	115	36	trees	tree	NOUN
cana-3162	115	37	.	.	PUNCT
cana-3162	116	1	•	•	NUM
cana-3162	116	2	𝑓𝑖	𝑓𝑖	NOUN
cana-3162	116	3	=	=	SYM
cana-3162	116	4	1	1	NUM
cana-3162	116	5	𝑇	𝑇	PROPN
cana-3162	116	6	∑	∑	PUNCT
cana-3162	116	7	δ𝐼𝑡,𝑖	δ𝐼𝑡,𝑖	PROPN
cana-3162	116	8	𝑇	𝑇	PROPN
cana-3162	116	9	𝑡=1	𝑡=1	PROPN
cana-3162	116	10	(	(	PUNCT
cana-3162	116	11	11	11	NUM
cana-3162	116	12	)	)	PUNCT
cana-3162	116	13	•	•	NUM
cana-3162	116	14	update	update	NOUN
cana-3162	116	15	feature	feature	NOUN
cana-3162	116	16	importance	importance	NOUN
cana-3162	116	17	:	:	PUNCT
cana-3162	116	18	𝑓𝑖	𝑓𝑖	PROPN
cana-3162	116	19	=	=	SYM
cana-3162	116	20	∑	∑	PROPN
cana-3162	116	21	1	1	NUM
cana-3162	116	22	𝑛𝑡	𝑛𝑡	NOUN
cana-3162	116	23	𝑇	𝑇	PROPN
cana-3162	116	24	𝑡=1	𝑡=1	PROPN
cana-3162	116	25	∑	∑	PROPN
cana-3162	116	26	δ𝐼𝑗𝑗∈𝐷𝑡	δ𝐼𝑗𝑗∈𝐷𝑡	PROPN
cana-3162	116	27	𝑡	𝑡	X
cana-3162	116	28	(	(	PUNCT
cana-3162	116	29	12	12	NUM
cana-3162	116	30	)	)	PUNCT
cana-3162	116	31	select	select	ADJ
cana-3162	116	32	top	top	ADJ
cana-3162	116	33	-	-	PUNCT
cana-3162	116	34	k	k	NOUN
cana-3162	116	35	features	feature	NOUN
cana-3162	116	36	based	base	VERB
cana-3162	116	37	on	on	ADP
cana-3162	116	38	𝑓𝑖	𝑓𝑖	PROPN
cana-3162	116	39	6	6	NUM
cana-3162	116	40	.	.	PUNCT
cana-3162	116	41	out	out	ADP
cana-3162	116	42	-	-	PUNCT
cana-3162	116	43	of	of	ADP
cana-3162	116	44	-	-	PUNCT
cana-3162	116	45	bag	bag	NOUN
cana-3162	116	46	(	(	PUNCT
cana-3162	116	47	oob	oob	NOUN
cana-3162	116	48	)	)	PUNCT
cana-3162	116	49	error	error	NOUN
cana-3162	116	50	:	:	PUNCT
cana-3162	116	51	use	use	VERB
cana-3162	116	52	the	the	DET
cana-3162	116	53	oob	oob	NOUN
cana-3162	116	54	samples	sample	NOUN
cana-3162	116	55	to	to	PART
cana-3162	116	56	calculate	calculate	VERB
cana-3162	116	57	oob	oob	NOUN
cana-3162	116	58	error	error	NOUN
cana-3162	116	59	.	.	PUNCT
cana-3162	117	1	•	•	NUM
cana-3162	117	2	𝑦oob,𝑡̂	𝑦oob,𝑡̂	X
cana-3162	117	3	=	=	SYM
cana-3162	117	4	1	1	NUM
cana-3162	117	5	|𝑂𝑂𝐵|	|𝑂𝑂𝐵|	ADV
cana-3162	117	6	∑	∑	ADV
cana-3162	117	7	ℎ𝑡(𝑥𝑖)𝑖∈𝑂𝑂𝐵	ℎ𝑡(𝑥𝑖)𝑖∈𝑂𝑂𝐵	PROPN
cana-3162	117	8	(	(	PUNCT
cana-3162	117	9	13	13	NUM
cana-3162	117	10	)	)	PUNCT
cana-3162	117	11	•	•	NUM
cana-3162	117	12	oob	oob	NOUN
cana-3162	117	13	error	error	NOUN
cana-3162	117	14	=	=	NOUN
cana-3162	117	15	1	1	NUM
cana-3162	117	16	𝑁	𝑁	PROPN
cana-3162	117	17	∑	∑	PUNCT
cana-3162	117	18	(	(	PUNCT
cana-3162	117	19	𝑦𝑖	𝑦𝑖	INTJ
cana-3162	117	20	−	−	PROPN
cana-3162	117	21	𝑦oob,𝑖̂	𝑦oob,𝑖̂	PROPN
cana-3162	117	22	)	)	PUNCT
cana-3162	117	23	2𝑁	2𝑁	NOUN
cana-3162	117	24	𝑖=1	𝑖=1	PROPN
cana-3162	117	25	(	(	PUNCT
cana-3162	117	26	14	14	NUM
cana-3162	117	27	)	)	PUNCT
cana-3162	117	28	∙	∙	PROPN
cana-3162	117	29	update	update	NOUN
cana-3162	117	30	feature	feature	NOUN
cana-3162	117	31	importance	importance	NOUN
cana-3162	117	32	based	base	VERB
cana-3162	117	33	on	on	ADP
cana-3162	117	34	oob	oob	NOUN
cana-3162	117	35	error	error	NOUN
cana-3162	117	36	reduction	reduction	NOUN
cana-3162	117	37	7	7	NUM
cana-3162	117	38	.	.	PUNCT
cana-3162	117	39	evaluate	evaluate	VERB
cana-3162	117	40	model	model	NOUN
cana-3162	117	41	:	:	PUNCT
cana-3162	117	42	check	check	VERB
cana-3162	117	43	the	the	DET
cana-3162	117	44	generalizability	generalizability	NOUN
cana-3162	117	45	of	of	ADP
cana-3162	117	46	the	the	DET
cana-3162	117	47	model	model	NOUN
cana-3162	117	48	using	use	VERB
cana-3162	117	49	the	the	DET
cana-3162	117	50	selected	select	VERB
cana-3162	117	51	features	feature	NOUN
cana-3162	117	52	.	.	PUNCT
cana-3162	118	1	•	•	NUM
cana-3162	118	2	𝑅2	𝑅2	NOUN
cana-3162	118	3	=	=	NOUN
cana-3162	118	4	1	1	NUM
cana-3162	118	5	−	−	NOUN
cana-3162	118	6	∑	∑	PUNCT
cana-3162	118	7	(	(	PUNCT
cana-3162	118	8	𝑦𝑖−𝑦	𝑦𝑖−𝑦	X
cana-3162	118	9	�	�	NOUN
cana-3162	118	10	̂	̂	NUM
cana-3162	118	11	�	�	NOUN
cana-3162	118	12	)2𝑛	)2𝑛	SYM
cana-3162	118	13	𝑖=1	𝑖=1	PUNCT
cana-3162	118	14	∑	∑	PROPN
cana-3162	118	15	(	(	PUNCT
cana-3162	118	16	𝑦𝑖−	𝑦𝑖−	NUM
cana-3162	118	17	�	�	PROPN
cana-3162	118	18	̅	̅	NOUN
cana-3162	118	19	�	�	NOUN
cana-3162	118	20	)2𝑛	)2𝑛	SYM
cana-3162	118	21	𝑖=1	𝑖=1	PROPN
cana-3162	118	22	(	(	PUNCT
cana-3162	118	23	15	15	NUM
cana-3162	118	24	)	)	PUNCT
cana-3162	118	25	•	•	NUM
cana-3162	118	26	mean	mean	VERB
cana-3162	118	27	absolute	absolute	ADJ
cana-3162	118	28	error	error	NOUN
cana-3162	118	29	:	:	PUNCT
cana-3162	118	30	𝑀𝐴𝐸	𝑀𝐴𝐸	PROPN
cana-3162	118	31	=	=	SYM
cana-3162	118	32	1	1	NUM
cana-3162	118	33	𝑛	𝑛	PROPN
cana-3162	118	34	∑	∑	ADP
cana-3162	118	35	|𝑦𝑖	|𝑦𝑖	ADP
cana-3162	118	36	−	−	PROPN
cana-3162	118	37	𝑦	𝑦	SYM
cana-3162	118	38	�	�	NOUN
cana-3162	118	39	̂	̂	SYM
cana-3162	118	40	�	�	NOUN
cana-3162	118	41	|	|	NOUN
cana-3162	118	42	𝑛	𝑛	DET
cana-3162	118	43	𝑖=1	𝑖=1	PROPN
cana-3162	118	44	communications	communication	NOUN
cana-3162	118	45	on	on	ADP
cana-3162	118	46	applied	apply	VERB
cana-3162	118	47	nonlinear	nonlinear	ADJ
cana-3162	118	48	analysis	analysis	NOUN
cana-3162	118	49	issn	issn	NOUN
cana-3162	118	50	:	:	PUNCT
cana-3162	118	51	1074	1074	NUM
cana-3162	118	52	-	-	PUNCT
cana-3162	118	53	133x	133x	NUM
cana-3162	118	54	vol	vol	NOUN
cana-3162	118	55	32	32	NUM
cana-3162	118	56	no	no	NOUN
cana-3162	118	57	.	.	PUNCT
cana-3162	119	1	5s	5s	NUM
cana-3162	119	2	(	(	PUNCT
cana-3162	119	3	2025	2025	NUM
cana-3162	119	4	)	)	PUNCT
cana-3162	119	5	499	499	NUM
cana-3162	119	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3162	119	7	(	(	PUNCT
cana-3162	119	8	16	16	NUM
cana-3162	119	9	)	)	PUNCT
cana-3162	119	10	8	8	NUM
cana-3162	119	11	.	.	PUNCT
cana-3162	119	12	random	random	ADJ
cana-3162	119	13	subspace	subspace	NOUN
cana-3162	119	14	:	:	PUNCT
cana-3162	119	15	train	train	VERB
cana-3162	119	16	each	each	DET
cana-3162	119	17	tree	tree	NOUN
cana-3162	119	18	on	on	ADP
cana-3162	119	19	a	a	DET
cana-3162	119	20	random	random	ADJ
cana-3162	119	21	subset	subset	NOUN
cana-3162	119	22	of	of	ADP
cana-3162	119	23	features	feature	NOUN
cana-3162	119	24	.	.	PUNCT
cana-3162	120	1	•	•	NUM
cana-3162	120	2	random	random	ADJ
cana-3162	120	3	subspace	subspace	NOUN
cana-3162	120	4	:	:	PUNCT
cana-3162	120	5	select	select	VERB
cana-3162	120	6	𝑚	𝑚	ADP
cana-3162	120	7	features	feature	NOUN
cana-3162	120	8	from	from	ADP
cana-3162	120	9	𝐹	𝐹	PROPN
cana-3162	120	10	total	total	NOUN
cana-3162	120	11	features	feature	NOUN
cana-3162	120	12	•	•	ADP
cana-3162	120	13	ℎ𝑡(𝑥	ℎ𝑡(𝑥	NUM
cana-3162	120	14	)	)	PUNCT
cana-3162	121	1	=	=	VERB
cana-3162	121	2	argmin	argmin	NOUN
cana-3162	121	3	𝑗∈𝑚	𝑗∈𝑚	NOUN
cana-3162	121	4	𝐼(𝑥𝑗	𝐼(𝑥𝑗	PROPN
cana-3162	121	5	)	)	PUNCT
cana-3162	121	6	(	(	PUNCT
cana-3162	121	7	17	17	NUM
cana-3162	121	8	)	)	PUNCT
cana-3162	121	9	•	•	NUM
cana-3162	121	10	variance	variance	NOUN
cana-3162	121	11	reduction	reduction	NOUN
cana-3162	121	12	:	:	PUNCT
cana-3162	122	1	𝑉𝑡	𝑉𝑡	PROPN
cana-3162	122	2	=	=	SYM
cana-3162	122	3	∑	∑	PROPN
cana-3162	122	4	(	(	PUNCT
cana-3162	122	5	𝑦𝑡,	𝑦𝑡,	PROPN
cana-3162	122	6	�	�	PROPN
cana-3162	122	7	̂	̂	NUM
cana-3162	122	8	�	�	PROPN
cana-3162	122	9	−	−	PROPN
cana-3162	122	10	𝑦	𝑦	SYM
cana-3162	122	11	�	�	NOUN
cana-3162	122	12	̅	̅	NOUN
cana-3162	122	13	�	�	NOUN
cana-3162	122	14	)	)	PUNCT
cana-3162	122	15	2𝑚	2𝑚	NOUN
cana-3162	122	16	𝑗=1	𝑗=1	PROPN
cana-3162	122	17	(	(	PUNCT
cana-3162	122	18	18	18	NUM
cana-3162	122	19	)	)	PUNCT
cana-3162	122	20	9	9	NUM
cana-3162	122	21	.	.	PUNCT
cana-3162	123	1	combine	combine	VERB
cana-3162	123	2	predictions	prediction	NOUN
cana-3162	123	3	:	:	PUNCT
cana-3162	123	4	aggregate	aggregate	ADJ
cana-3162	123	5	predictions	prediction	NOUN
cana-3162	123	6	from	from	ADP
cana-3162	123	7	all	all	DET
cana-3162	123	8	trees	tree	NOUN
cana-3162	123	9	using	use	VERB
cana-3162	123	10	majority	majority	NOUN
cana-3162	123	11	voting	voting	NOUN
cana-3162	123	12	for	for	ADP
cana-3162	123	13	classification	classification	NOUN
cana-3162	123	14	or	or	CCONJ
cana-3162	123	15	averaging	average	VERB
cana-3162	123	16	for	for	ADP
cana-3162	123	17	regression	regression	NOUN
cana-3162	123	18	.	.	PUNCT
cana-3162	124	1	•	•	NUM
cana-3162	124	2	�	�	PROPN
cana-3162	124	3	̂	̂	VERB
cana-3162	124	4	�	�	NOUN
cana-3162	124	5	=	=	SYM
cana-3162	124	6	1	1	NUM
cana-3162	124	7	𝑇	𝑇	PROPN
cana-3162	124	8	∑	∑	PROPN
cana-3162	124	9	ℎ𝑡(𝑥)𝑇	ℎ𝑡(𝑥)𝑇	ADJ
cana-3162	124	10	𝑡=1	𝑡=1	PROPN
cana-3162	124	11	(	(	PUNCT
cana-3162	124	12	19	19	NUM
cana-3162	124	13	)	)	PUNCT
cana-3162	124	14	•	•	NOUN
cana-3162	124	15	𝑦final	𝑦final	ADJ
cana-3162	124	16	=	=	PUNCT
cana-3162	124	17	argmax	argmax	X
cana-3162	124	18	𝑘	𝑘	X
cana-3162	124	19	(	(	PUNCT
cana-3162	124	20	∑	∑	PROPN
cana-3162	124	21	1(ℎ𝑡(𝑥)=𝑘	1(ℎ𝑡(𝑥)=𝑘	NUM
cana-3162	124	22	)	)	PUNCT
cana-3162	124	23	𝑇	𝑇	PROPN
cana-3162	124	24	𝑡=1	𝑡=1	PROPN
cana-3162	124	25	)	)	PUNCT
cana-3162	124	26	(	(	PUNCT
cana-3162	124	27	250	250	NUM
cana-3162	124	28	)	)	PUNCT
cana-3162	124	29	•	•	NOUN
cana-3162	124	30	weighted	weight	VERB
cana-3162	124	31	prediction	prediction	NOUN
cana-3162	124	32	:	:	PUNCT
cana-3162	124	33	𝑦final̂	𝑦final̂	PUNCT
cana-3162	124	34	=	=	SYM
cana-3162	124	35	∑	∑	PUNCT
cana-3162	124	36	𝑤𝑡	𝑤𝑡	VERB
cana-3162	124	37	𝑇	𝑇	PROPN
cana-3162	124	38	𝑡=1	𝑡=1	PROPN
cana-3162	124	39	⋅ℎ𝑡(𝑥	⋅ℎ𝑡(𝑥	PROPN
cana-3162	124	40	)	)	PUNCT
cana-3162	124	41	∑	∑	PUNCT
cana-3162	124	42	𝑤𝑡	𝑤𝑡	VERB
cana-3162	124	43	𝑇	𝑇	PROPN
cana-3162	124	44	𝑡=1	𝑡=1	PROPN
cana-3162	124	45	(	(	PUNCT
cana-3162	124	46	20	20	NUM
cana-3162	124	47	)	)	PUNCT
cana-3162	124	48	10	10	NUM
cana-3162	124	49	.	.	PUNCT
cana-3162	125	1	final	final	ADJ
cana-3162	125	2	model	model	NOUN
cana-3162	125	3	:	:	PUNCT
cana-3162	125	4	the	the	DET
cana-3162	125	5	final	final	ADJ
cana-3162	125	6	model	model	NOUN
cana-3162	125	7	is	be	AUX
cana-3162	125	8	an	an	DET
cana-3162	125	9	ensemble	ensemble	ADJ
cana-3162	125	10	of	of	ADP
cana-3162	125	11	all	all	DET
cana-3162	125	12	trees	tree	NOUN
cana-3162	125	13	built	build	VERB
cana-3162	125	14	on	on	ADP
cana-3162	125	15	bootstrapped	bootstrappe	VERB
cana-3162	125	16	samples	sample	NOUN
cana-3162	125	17	.	.	PUNCT
cana-3162	126	1	•	•	NUM
cana-3162	126	2	�	�	PROPN
cana-3162	126	3	̂	̂	VERB
cana-3162	126	4	�	�	NOUN
cana-3162	126	5	=	=	SYM
cana-3162	126	6	1	1	NUM
cana-3162	126	7	𝑇	𝑇	PROPN
cana-3162	126	8	∑	∑	PROPN
cana-3162	126	9	ℎ𝑡(𝑥)𝑇	ℎ𝑡(𝑥)𝑇	ADJ
cana-3162	126	10	𝑡=1	𝑡=1	PROPN
cana-3162	126	11	(	(	PUNCT
cana-3162	126	12	21	21	NUM
cana-3162	126	13	)	)	PUNCT
cana-3162	126	14	•	•	NUM
cana-3162	126	15	prediction	prediction	NOUN
cana-3162	126	16	variance	variance	NOUN
cana-3162	126	17	:	:	PUNCT
cana-3162	126	18	𝑉(	𝑉(	VERB
cana-3162	126	19	�	�	SYM
cana-3162	126	20	̂	̂	VERB
cana-3162	126	21	�	�	NOUN
cana-3162	126	22	)	)	PUNCT
cana-3162	126	23	=	=	SYM
cana-3162	126	24	1	1	NUM
cana-3162	126	25	𝑇	𝑇	PROPN
cana-3162	126	26	∑	∑	PRON
cana-3162	126	27	(	(	PUNCT
cana-3162	126	28	ℎ𝑡(𝑥	ℎ𝑡(𝑥	X
cana-3162	126	29	)	)	PUNCT
cana-3162	126	30	−	−	PRON
cana-3162	126	31	�	�	PROPN
cana-3162	126	32	̂	̂	SYM
cana-3162	126	33	�	�	PROPN
cana-3162	126	34	)2𝑇	)2𝑇	PROPN
cana-3162	126	35	𝑡=1	𝑡=1	PROPN
cana-3162	126	36	(	(	PUNCT
cana-3162	126	37	22	22	NUM
cana-3162	126	38	)	)	PUNCT
cana-3162	126	39	11	11	NUM
cana-3162	126	40	.	.	PUNCT
cana-3162	127	1	test	test	NOUN
cana-3162	127	2	predictions	prediction	NOUN
cana-3162	127	3	:	:	PUNCT
cana-3162	127	4	use	use	VERB
cana-3162	127	5	the	the	DET
cana-3162	127	6	final	final	ADJ
cana-3162	127	7	model	model	NOUN
cana-3162	127	8	to	to	PART
cana-3162	127	9	predict	predict	VERB
cana-3162	127	10	outcomes	outcome	NOUN
cana-3162	127	11	on	on	ADP
cana-3162	127	12	the	the	DET
cana-3162	127	13	test	test	NOUN
cana-3162	127	14	dataset	dataset	NOUN
cana-3162	127	15	.	.	PUNCT
cana-3162	128	1	•	•	NOUN
cana-3162	128	2	𝑦test̂	𝑦test̂	NOUN
cana-3162	128	3	=	=	SYM
cana-3162	128	4	1	1	NUM
cana-3162	128	5	𝑇	𝑇	PROPN
cana-3162	128	6	∑	∑	PUNCT
cana-3162	128	7	ℎ𝑡(𝑥test	ℎ𝑡(𝑥t	ADJ
cana-3162	128	8	)	)	PUNCT
cana-3162	128	9	𝑇	𝑇	PROPN
cana-3162	128	10	𝑡=1	𝑡=1	PROPN
cana-3162	128	11	(	(	PUNCT
cana-3162	128	12	23	23	NUM
cana-3162	128	13	)	)	PUNCT
cana-3162	128	14	•	•	ADV
cana-3162	128	15	𝑦test	𝑦t	ADJ
cana-3162	128	16	=	=	SYM
cana-3162	128	17	argmax(𝑦class	argmax(𝑦class	PROPN
cana-3162	128	18	,	,	PUNCT
cana-3162	128	19	test̂	test̂	NOUN
cana-3162	128	20	)	)	PUNCT
cana-3162	128	21	(	(	PUNCT
cana-3162	128	22	24	24	NUM
cana-3162	128	23	)	)	PUNCT
cana-3162	128	24	•	•	NOUN
cana-3162	128	25	compute	compute	NOUN
cana-3162	128	26	test	test	NOUN
cana-3162	128	27	error	error	NOUN
cana-3162	128	28	using	use	VERB
cana-3162	128	29	mse	mse	NOUN
cana-3162	128	30	:	:	PUNCT
cana-3162	128	31	𝑀𝑆𝐸	𝑀𝑆𝐸	PROPN
cana-3162	128	32	=	=	SYM
cana-3162	128	33	1	1	NUM
cana-3162	128	34	𝑛	𝑛	NOUN
cana-3162	128	35	∑	∑	PUNCT
cana-3162	128	36	(	(	PUNCT
cana-3162	128	37	𝑦𝑖	𝑦𝑖	PROPN
cana-3162	128	38	−	−	PROPN
cana-3162	128	39	𝑦	𝑦	SYM
cana-3162	128	40	�	�	PROPN
cana-3162	128	41	̂	̂	SYM
cana-3162	128	42	�	�	NOUN
cana-3162	128	43	)	)	PUNCT
cana-3162	128	44	2𝑛	2𝑛	PROPN
cana-3162	129	1	𝑖=1	𝑖=1	PROPN
cana-3162	129	2	(	(	PUNCT
cana-3162	129	3	25	25	NUM
cana-3162	129	4	)	)	PUNCT
cana-3162	129	5	12	12	NUM
cana-3162	129	6	.	.	PUNCT
cana-3162	130	1	feature	feature	NOUN
cana-3162	130	2	ranking	ranking	NOUN
cana-3162	130	3	:	:	PUNCT
cana-3162	130	4	rank	rank	NOUN
cana-3162	130	5	features	feature	NOUN
cana-3162	130	6	based	base	VERB
cana-3162	130	7	on	on	ADP
cana-3162	130	8	their	their	PRON
cana-3162	130	9	calculated	calculate	VERB
cana-3162	130	10	importance	importance	NOUN
cana-3162	130	11	.	.	PUNCT
cana-3162	131	1	•	•	NUM
cana-3162	131	2	rank	rank	NOUN
cana-3162	131	3	:	:	PUNCT
cana-3162	132	1	𝑅𝑖	𝑅𝑖	PROPN
cana-3162	132	2	=	=	PUNCT
cana-3162	132	3	∑	∑	PROPN
cana-3162	132	4	𝐼𝑡,𝑖	𝐼𝑡,𝑖	PROPN
cana-3162	132	5	𝑇	𝑇	PROPN
cana-3162	132	6	𝑡=1	𝑡=1	PROPN
cana-3162	132	7	(	(	PUNCT
cana-3162	132	8	26	26	NUM
cana-3162	132	9	)	)	PUNCT
cana-3162	132	10	•	•	NUM
cana-3162	132	11	normalization	normalization	NOUN
cana-3162	132	12	:	:	PUNCT
cana-3162	132	13	𝑅𝑖	𝑅𝑖	PROPN
cana-3162	132	14	norm	norm	NOUN
cana-3162	132	15	=	=	SYM
cana-3162	132	16	𝑅𝑖	𝑅𝑖	PROPN
cana-3162	132	17	∑	∑	PUNCT
cana-3162	132	18	𝑅𝑗	𝑅𝑗	PROPN
cana-3162	132	19	𝑚	𝑚	NOUN
cana-3162	132	20	𝑗=1	𝑗=1	X
cana-3162	132	21	(	(	PUNCT
cana-3162	132	22	27	27	NUM
cana-3162	132	23	)	)	PUNCT
cana-3162	132	24	•	•	NUM
cana-3162	132	25	select	select	ADJ
cana-3162	132	26	top	top	ADJ
cana-3162	132	27	-	-	PUNCT
cana-3162	132	28	k	k	NOUN
cana-3162	132	29	features	feature	NOUN
cana-3162	132	30	:	:	PUNCT
cana-3162	132	31	𝐹selected	𝐹selecte	VERB
cana-3162	132	32	=	=	SYM
cana-3162	132	33	{	{	PUNCT
cana-3162	132	34	𝑓𝑖	𝑓𝑖	NOUN
cana-3162	132	35	:	:	PUNCT
cana-3162	132	36	𝑅𝑖	𝑅𝑖	PROPN
cana-3162	132	37	norm	norm	NOUN
cana-3162	132	38	in	in	ADP
cana-3162	132	39	top	top	ADJ
cana-3162	132	40	-	-	PUNCT
cana-3162	132	41	k	k	NOUN
cana-3162	132	42	}	}	PUNCT
cana-3162	132	43	(	(	PUNCT
cana-3162	132	44	28	28	NUM
cana-3162	132	45	)	)	PUNCT
cana-3162	132	46	13	13	NUM
cana-3162	132	47	.	.	PUNCT
cana-3162	133	1	model	model	PROPN
cana-3162	133	2	interpretation	interpretation	NOUN
cana-3162	133	3	:	:	PUNCT
cana-3162	133	4	analyze	analyze	VERB
cana-3162	133	5	selected	select	VERB
cana-3162	133	6	features	feature	NOUN
cana-3162	133	7	to	to	PART
cana-3162	133	8	interpret	interpret	VERB
cana-3162	133	9	the	the	DET
cana-3162	133	10	model	model	NOUN
cana-3162	133	11	and	and	CCONJ
cana-3162	133	12	understand	understand	VERB
cana-3162	133	13	their	their	PRON
cana-3162	133	14	contributions	contribution	NOUN
cana-3162	133	15	to	to	ADP
cana-3162	133	16	predictions	prediction	NOUN
cana-3162	133	17	.	.	PUNCT
cana-3162	134	1	•	•	NUM
cana-3162	134	2	feature	feature	NOUN
cana-3162	134	3	contributions	contribution	NOUN
cana-3162	134	4	:	:	PUNCT
cana-3162	134	5	contribution(𝑓𝑖	contribution(𝑓𝑖	NOUN
cana-3162	134	6	)	)	PUNCT
cana-3162	134	7	=	=	PUNCT
cana-3162	134	8	𝑓𝑖	𝑓𝑖	PROPN
cana-3162	134	9	⋅	⋅	PROPN
cana-3162	134	10	𝑅𝑖	𝑅𝑖	PROPN
cana-3162	134	11	(	(	PUNCT
cana-3162	134	12	29	29	NUM
cana-3162	134	13	)	)	PUNCT
cana-3162	134	14	•	•	NUM
cana-3162	134	15	cumulative	cumulative	ADJ
cana-3162	134	16	contribution	contribution	NOUN
cana-3162	134	17	:	:	PUNCT
cana-3162	134	18	𝐶	𝐶	PROPN
cana-3162	134	19	=	=	SYM
cana-3162	134	20	∑	∑	PUNCT
cana-3162	134	21	contribution(𝑓𝑖	contribution(𝑓𝑖	PROPN
cana-3162	134	22	)	)	PUNCT
cana-3162	134	23	𝑘	𝑘	X
cana-3162	134	24	𝑖=1	𝑖=1	PROPN
cana-3162	134	25	(	(	PUNCT
cana-3162	134	26	30	30	NUM
cana-3162	134	27	)	)	PUNCT
cana-3162	134	28	14	14	NUM
cana-3162	134	29	.	.	PUNCT
cana-3162	135	1	final	final	ADJ
cana-3162	135	2	evaluation	evaluation	NOUN
cana-3162	135	3	:	:	PUNCT
cana-3162	135	4	perform	perform	VERB
cana-3162	135	5	a	a	DET
cana-3162	135	6	final	final	ADJ
cana-3162	135	7	evaluation	evaluation	NOUN
cana-3162	135	8	on	on	ADP
cana-3162	135	9	model	model	NOUN
cana-3162	135	10	performance	performance	NOUN
cana-3162	135	11	using	use	VERB
cana-3162	135	12	metrics	metric	NOUN
cana-3162	135	13	such	such	ADJ
cana-3162	135	14	as	as	ADP
cana-3162	135	15	accuracy	accuracy	NOUN
cana-3162	135	16	and	and	CCONJ
cana-3162	135	17	recall	recall	NOUN
cana-3162	135	18	.	.	PUNCT
cana-3162	136	1	•	•	NUM
cana-3162	136	2	accuracy	accuracy	NOUN
cana-3162	136	3	:	:	PUNCT
cana-3162	136	4	𝐴	𝐴	PROPN
cana-3162	136	5	=	=	PUNCT
cana-3162	136	6	∑	∑	PUNCT
cana-3162	136	7	1(𝑦𝑖=𝑦	1(𝑦𝑖=𝑦	NUM
cana-3162	136	8	�	�	PROPN
cana-3162	136	9	̂	̂	VERB
cana-3162	136	10	�	�	NOUN
cana-3162	136	11	)	)	PUNCT
cana-3162	136	12	𝑛	𝑛	DET
cana-3162	136	13	𝑖=1	𝑖=1	PUNCT
cana-3162	136	14	𝑛	𝑛	PROPN
cana-3162	136	15	(	(	PUNCT
cana-3162	136	16	31	31	NUM
cana-3162	136	17	)	)	PUNCT
cana-3162	136	18	•	•	NOUN
cana-3162	136	19	recall	recall	NOUN
cana-3162	136	20	:	:	PUNCT
cana-3162	136	21	𝑅	𝑅	PROPN
cana-3162	136	22	=	=	PUNCT
cana-3162	136	23	𝑇𝑃	𝑇𝑃	PROPN
cana-3162	136	24	𝑇𝑃+𝐹𝑁	𝑇𝑃+𝐹𝑁	NOUN
cana-3162	136	25	(	(	PUNCT
cana-3162	136	26	32	32	NUM
cana-3162	136	27	)	)	PUNCT
cana-3162	136	28	15	15	NUM
cana-3162	136	29	.	.	PUNCT
cana-3162	137	1	conclusion	conclusion	NOUN
cana-3162	137	2	:	:	PUNCT
cana-3162	137	3	summarize	summarize	VERB
cana-3162	137	4	the	the	DET
cana-3162	137	5	results	result	NOUN
cana-3162	137	6	,	,	PUNCT
cana-3162	137	7	emphasizing	emphasize	VERB
cana-3162	137	8	the	the	DET
cana-3162	137	9	effectiveness	effectiveness	NOUN
cana-3162	137	10	of	of	ADP
cana-3162	137	11	the	the	DET
cana-3162	137	12	model	model	NOUN
cana-3162	137	13	in	in	ADP
cana-3162	137	14	predicting	predict	VERB
cana-3162	137	15	anti	anti	ADJ
cana-3162	137	16	-	-	ADJ
cana-3162	137	17	cancer	cancer	ADJ
cana-3162	137	18	drug	drug	NOUN
cana-3162	137	19	responses	response	NOUN
cana-3162	137	20	.	.	PUNCT
cana-3162	138	1	•	•	NUM
cana-3162	138	2	results	result	NOUN
cana-3162	138	3	summary	summary	NOUN
cana-3162	138	4	:	:	PUNCT
cana-3162	138	5	summary	summary	NOUN
cana-3162	138	6	=	=	SYM
cana-3162	138	7	{	{	PUNCT
cana-3162	138	8	𝐴	𝐴	PROPN
cana-3162	138	9	,	,	PUNCT
cana-3162	138	10	𝑅,top	𝑅,top	NOUN
cana-3162	138	11	-	-	PUNCT
cana-3162	138	12	k	k	PROPN
cana-3162	138	13	features	feature	NOUN
cana-3162	138	14	}	}	PUNCT
cana-3162	138	15	(	(	PUNCT
cana-3162	138	16	33	33	NUM
cana-3162	138	17	)	)	PUNCT
cana-3162	138	18	•	•	NOUN
cana-3162	138	19	final	final	ADJ
cana-3162	138	20	remarks	remark	NOUN
cana-3162	138	21	on	on	ADP
cana-3162	138	22	feature	feature	NOUN
cana-3162	138	23	importance	importance	NOUN
cana-3162	138	24	and	and	CCONJ
cana-3162	138	25	model	model	NOUN
cana-3162	138	26	performance	performance	NOUN
cana-3162	138	27	communications	communication	NOUN
cana-3162	138	28	on	on	ADP
cana-3162	138	29	applied	apply	VERB
cana-3162	138	30	nonlinear	nonlinear	ADJ
cana-3162	138	31	analysis	analysis	NOUN
cana-3162	138	32	issn	issn	NOUN
cana-3162	138	33	:	:	PUNCT
cana-3162	138	34	1074	1074	NUM
cana-3162	138	35	-	-	PUNCT
cana-3162	138	36	133x	133x	NUM
cana-3162	138	37	vol	vol	NOUN
cana-3162	138	38	32	32	NUM
cana-3162	138	39	no	no	NOUN
cana-3162	138	40	.	.	PUNCT
cana-3162	139	1	5s	5s	NUM
cana-3162	139	2	(	(	PUNCT
cana-3162	139	3	2025	2025	NUM
cana-3162	139	4	)	)	PUNCT
cana-3162	139	5	500	500	NUM
cana-3162	139	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3162	139	7	notations	notation	NOUN
cana-3162	139	8	in	in	ADP
cana-3162	139	9	algorithm	algorithm	PROPN
cana-3162	139	10	•	•	ADP
cana-3162	139	11	𝑿	𝑿	PROPN
cana-3162	139	12	:	:	PUNCT
cana-3162	139	13	input	input	NOUN
cana-3162	139	14	feature	feature	NOUN
cana-3162	139	15	matrix	matrix	NOUN
cana-3162	139	16	.	.	PUNCT
cana-3162	140	1	•	•	NUM
cana-3162	140	2	𝒀	𝒀	NOUN
cana-3162	140	3	:	:	PUNCT
cana-3162	140	4	output	output	NOUN
cana-3162	140	5	labels	label	NOUN
cana-3162	140	6	.	.	PUNCT
cana-3162	141	1	•	•	NUM
cana-3162	141	2	𝑫𝒕	𝑫𝒕	NOUN
cana-3162	141	3	:	:	PUNCT
cana-3162	141	4	bootstrapped	bootstrappe	VERB
cana-3162	141	5	dataset	dataset	VERB
cana-3162	141	6	for	for	ADP
cana-3162	141	7	tree	tree	NOUN
cana-3162	141	8	𝑡.	𝑡.	NOUN
cana-3162	141	9	•	•	NUM
cana-3162	141	10	𝑰(𝑫	𝑰(𝑫	X
cana-3162	141	11	):	):	PUNCT
cana-3162	141	12	impurity	impurity	NOUN
cana-3162	141	13	measure	measure	NOUN
cana-3162	141	14	of	of	ADP
cana-3162	141	15	dataset	dataset	ADJ
cana-3162	141	16	𝐷.	𝐷.	PROPN
cana-3162	141	17	•	•	NUM
cana-3162	141	18	𝒑(𝒄|𝑫	𝒑(𝒄|𝑫	PROPN
cana-3162	141	19	):	):	PUNCT
cana-3162	141	20	probability	probability	NOUN
cana-3162	141	21	of	of	ADP
cana-3162	141	22	class	class	NOUN
cana-3162	141	23	ccc	ccc	NOUN
cana-3162	141	24	given	give	VERB
cana-3162	141	25	dataset	dataset	VERB
cana-3162	141	26	𝐷.	𝐷.	PROPN
cana-3162	141	27	•	•	NOUN
cana-3162	141	28	𝚫𝑰	𝚫𝑰	PROPN
cana-3162	141	29	:	:	PUNCT
cana-3162	141	30	decrease	decrease	NOUN
cana-3162	141	31	in	in	ADP
cana-3162	141	32	impurity	impurity	NOUN
cana-3162	141	33	after	after	ADP
cana-3162	141	34	a	a	DET
cana-3162	141	35	split	split	NOUN
cana-3162	141	36	.	.	PUNCT
cana-3162	142	1	•	•	NUM
cana-3162	142	2	𝒇𝒊	𝒇𝒊	PRON
cana-3162	142	3	:	:	PUNCT
cana-3162	142	4	importance	importance	NOUN
cana-3162	142	5	of	of	ADP
cana-3162	142	6	feature	feature	NOUN
cana-3162	142	7	𝑖.	𝑖.	ADJ
cana-3162	142	8	•	•	NUM
cana-3162	142	9	𝒏𝒕	𝒏𝒕	NOUN
cana-3162	142	10	:	:	PUNCT
cana-3162	142	11	number	number	NOUN
cana-3162	142	12	of	of	ADP
cana-3162	142	13	observations	observation	NOUN
cana-3162	142	14	in	in	ADP
cana-3162	142	15	tree	tree	NOUN
cana-3162	142	16	𝑡.	𝑡.	NOUN
cana-3162	142	17	•	•	PROPN
cana-3162	142	18	𝑹𝟐	𝑹𝟐	NOUN
cana-3162	142	19	:	:	PUNCT
cana-3162	142	20	coefficient	coefficient	NOUN
cana-3162	142	21	of	of	ADP
cana-3162	142	22	determination	determination	NOUN
cana-3162	142	23	.	.	PUNCT
cana-3162	143	1	•	•	NUM
cana-3162	143	2	𝑴𝑨𝑬	𝑴𝑨𝑬	PROPN
cana-3162	143	3	:	:	PUNCT
cana-3162	143	4	mean	mean	VERB
cana-3162	143	5	absolute	absolute	ADJ
cana-3162	143	6	error	error	NOUN
cana-3162	143	7	.	.	PUNCT
cana-3162	144	1	•	•	NUM
cana-3162	144	2	�	�	PROPN
cana-3162	144	3	̂	̂	SYM
cana-3162	144	4	�	�	PROPN
cana-3162	144	5	:	:	PUNCT
cana-3162	144	6	predicted	predict	VERB
cana-3162	144	7	output	output	NOUN
cana-3162	144	8	.	.	PUNCT
cana-3162	145	1	•	•	NUM
cana-3162	145	2	𝑶𝑶𝑩	𝑶𝑶𝑩	PROPN
cana-3162	145	3	:	:	PUNCT
cana-3162	145	4	out	out	ADP
cana-3162	145	5	-	-	PUNCT
cana-3162	145	6	of	of	ADP
cana-3162	145	7	-	-	PUNCT
cana-3162	145	8	bag	bag	NOUN
cana-3162	145	9	samples	sample	NOUN
cana-3162	145	10	.	.	PUNCT
cana-3162	146	1	•	•	NUM
cana-3162	146	2	argmax	argmax	NOUN
cana-3162	146	3	:	:	PUNCT
cana-3162	146	4	function	function	NOUN
cana-3162	146	5	that	that	PRON
cana-3162	146	6	returns	return	VERB
cana-3162	146	7	the	the	DET
cana-3162	146	8	index	index	NOUN
cana-3162	146	9	of	of	ADP
cana-3162	146	10	the	the	DET
cana-3162	146	11	maximum	maximum	ADJ
cana-3162	146	12	value	value	NOUN
cana-3162	146	13	.	.	PUNCT
cana-3162	147	1	•	•	NUM
cana-3162	147	2	𝑻𝑷	𝑻𝑷	PROPN
cana-3162	147	3	:	:	PUNCT
cana-3162	147	4	true	true	ADJ
cana-3162	147	5	positives	positive	NOUN
cana-3162	147	6	.	.	PUNCT
cana-3162	148	1	•	•	NUM
cana-3162	148	2	𝑭𝑵	𝑭𝑵	PROPN
cana-3162	148	3	:	:	PUNCT
cana-3162	148	4	false	false	ADJ
cana-3162	148	5	negatives	negative	NOUN
cana-3162	148	6	.	.	PUNCT
cana-3162	149	1	the	the	DET
cana-3162	149	2	random	random	ADJ
cana-3162	149	3	forest	forest	NOUN
cana-3162	149	4	ensemble	ensemble	ADJ
cana-3162	149	5	learning	learning	NOUN
cana-3162	149	6	approach	approach	NOUN
cana-3162	149	7	generates	generate	VERB
cana-3162	149	8	numerous	numerous	ADJ
cana-3162	149	9	decision	decision	NOUN
cana-3162	149	10	trees	tree	NOUN
cana-3162	149	11	during	during	ADP
cana-3162	149	12	training	training	NOUN
cana-3162	149	13	and	and	CCONJ
cana-3162	149	14	offers	offer	VERB
cana-3162	149	15	the	the	DET
cana-3162	149	16	middle	middle	NOUN
cana-3162	149	17	or	or	CCONJ
cana-3162	149	18	mean	mean	ADJ
cana-3162	149	19	estimate	estimate	NOUN
cana-3162	149	20	of	of	ADP
cana-3162	149	21	each	each	DET
cana-3162	149	22	tree	tree	NOUN
cana-3162	149	23	for	for	ADP
cana-3162	149	24	regression	regression	NOUN
cana-3162	149	25	or	or	CCONJ
cana-3162	149	26	classification	classification	NOUN
cana-3162	149	27	.	.	PUNCT
cana-3162	150	1	first	first	ADV
cana-3162	150	2	,	,	PUNCT
cana-3162	150	3	a	a	DET
cana-3162	150	4	dataset	dataset	NOUN
cana-3162	150	5	{	{	PUNCT
cana-3162	150	6	mathbit{x	mathbit{x	NOUN
cana-3162	150	7	}	}	PUNCT
cana-3162	150	8	with	with	ADP
cana-3162	150	9	labels	label	NOUN
cana-3162	150	10	{	{	PUNCT
cana-3162	150	11	mathbit{y	mathbit{y	NOUN
cana-3162	150	12	}	}	PUNCT
cana-3162	150	13	}	}	PUNCT
cana-3162	150	14	is	be	AUX
cana-3162	150	15	divided	divide	VERB
cana-3162	150	16	into	into	ADP
cana-3162	150	17	training	training	NOUN
cana-3162	150	18	and	and	CCONJ
cana-3162	150	19	testing	testing	NOUN
cana-3162	150	20	sets	set	NOUN
cana-3162	150	21	.	.	PUNCT
cana-3162	151	1	the	the	DET
cana-3162	151	2	approach	approach	NOUN
cana-3162	151	3	uses	use	VERB
cana-3162	151	4	the	the	DET
cana-3162	151	5	training	training	NOUN
cana-3162	151	6	data	datum	NOUN
cana-3162	151	7	to	to	PART
cana-3162	151	8	bootstrap	bootstrap	VERB
cana-3162	151	9	t	t	PROPN
cana-3162	151	10	samples	sample	NOUN
cana-3162	151	11	.	.	PUNCT
cana-3162	152	1	each	each	DET
cana-3162	152	2	tree	tree	NOUN
cana-3162	152	3	may	may	AUX
cana-3162	152	4	learn	learn	VERB
cana-3162	152	5	from	from	ADP
cana-3162	152	6	another	another	DET
cana-3162	152	7	group	group	NOUN
cana-3162	152	8	.	.	PUNCT
cana-3162	153	1	decision	decision	NOUN
cana-3162	153	2	trees	tree	NOUN
cana-3162	153	3	are	be	AUX
cana-3162	153	4	continually	continually	ADV
cana-3162	153	5	broken	break	VERB
cana-3162	153	6	up	up	ADP
cana-3162	153	7	by	by	ADP
cana-3162	153	8	gini	gini	NOUN
cana-3162	153	9	impurity	impurity	NOUN
cana-3162	153	10	or	or	CCONJ
cana-3162	153	11	entropy	entropy	NOUN
cana-3162	153	12	to	to	PART
cana-3162	153	13	reduce	reduce	VERB
cana-3162	153	14	impurity	impurity	NOUN
cana-3162	153	15	.	.	PUNCT
cana-3162	154	1	the	the	DET
cana-3162	154	2	algorithm	algorithm	NOUN
cana-3162	154	3	calculates	calculate	VERB
cana-3162	154	4	the	the	DET
cana-3162	154	5	average	average	ADJ
cana-3162	154	6	decline	decline	NOUN
cana-3162	154	7	in	in	ADP
cana-3162	154	8	pollution	pollution	NOUN
cana-3162	154	9	each	each	DET
cana-3162	154	10	attribute	attribute	NOUN
cana-3162	154	11	produces	produce	VERB
cana-3162	154	12	across	across	ADP
cana-3162	154	13	all	all	DET
cana-3162	154	14	trees	tree	NOUN
cana-3162	154	15	to	to	PART
cana-3162	154	16	determine	determine	VERB
cana-3162	154	17	its	its	PRON
cana-3162	154	18	importance	importance	NOUN
cana-3162	154	19	during	during	ADP
cana-3162	154	20	construction	construction	NOUN
cana-3162	154	21	.	.	PUNCT
cana-3162	155	1	using	use	VERB
cana-3162	155	2	higher	high	ADJ
cana-3162	155	3	-	-	PUNCT
cana-3162	155	4	importance	importance	NOUN
cana-3162	155	5	features	feature	VERB
cana-3162	155	6	simplifies	simplifie	NOUN
cana-3162	155	7	the	the	DET
cana-3162	155	8	model	model	NOUN
cana-3162	155	9	.	.	PUNCT
cana-3162	156	1	out	out	ADP
cana-3162	156	2	-	-	PUNCT
cana-3162	156	3	of	of	ADP
cana-3162	156	4	-	-	PUNCT
cana-3162	156	5	bag	bag	NOUN
cana-3162	156	6	(	(	PUNCT
cana-3162	156	7	oob	oob	NOUN
cana-3162	156	8	)	)	PUNCT
cana-3162	156	9	samples	sample	NOUN
cana-3162	156	10	verify	verify	VERB
cana-3162	156	11	predictions	prediction	NOUN
cana-3162	156	12	without	without	ADP
cana-3162	156	13	a	a	DET
cana-3162	156	14	validation	validation	NOUN
cana-3162	156	15	set	set	NOUN
cana-3162	156	16	.	.	PUNCT
cana-3162	157	1	this	this	PRON
cana-3162	157	2	strengthens	strengthen	VERB
cana-3162	157	3	the	the	DET
cana-3162	157	4	model	model	NOUN
cana-3162	157	5	.	.	PUNCT
cana-3162	158	1	majority	majority	NOUN
cana-3162	158	2	voting	voting	NOUN
cana-3162	158	3	determines	determine	VERB
cana-3162	158	4	the	the	DET
cana-3162	158	5	classification	classification	NOUN
cana-3162	158	6	outcomes	outcome	NOUN
cana-3162	158	7	.	.	PUNCT
cana-3162	159	1	however	however	ADV
cana-3162	159	2	,	,	PUNCT
cana-3162	159	3	regression	regression	NOUN
cana-3162	159	4	sums	sum	VERB
cana-3162	159	5	them	they	PRON
cana-3162	159	6	.	.	PUNCT
cana-3162	160	1	the	the	DET
cana-3162	160	2	approach	approach	NOUN
cana-3162	160	3	appropriately	appropriately	ADV
cana-3162	160	4	ranks	rank	VERB
cana-3162	160	5	characteristics	characteristic	NOUN
cana-3162	160	6	,	,	PUNCT
cana-3162	160	7	reducing	reduce	VERB
cana-3162	160	8	dimensions	dimension	NOUN
cana-3162	160	9	and	and	CCONJ
cana-3162	160	10	improving	improve	VERB
cana-3162	160	11	efficiency	efficiency	NOUN
cana-3162	160	12	.	.	PUNCT
cana-3162	161	1	it	it	PRON
cana-3162	161	2	works	work	VERB
cana-3162	161	3	well	well	ADV
cana-3162	161	4	with	with	ADP
cana-3162	161	5	huge	huge	ADJ
cana-3162	161	6	datasets	dataset	NOUN
cana-3162	161	7	like	like	ADP
cana-3162	161	8	genes	gene	NOUN
cana-3162	161	9	and	and	CCONJ
cana-3162	161	10	medical	medical	ADJ
cana-3162	161	11	testing	testing	NOUN
cana-3162	161	12	,	,	PUNCT
cana-3162	161	13	where	where	SCONJ
cana-3162	161	14	uncovering	uncover	VERB
cana-3162	161	15	essential	essential	ADJ
cana-3162	161	16	attributes	attribute	NOUN
cana-3162	161	17	is	be	AUX
cana-3162	161	18	crucial	crucial	ADJ
cana-3162	161	19	to	to	ADP
cana-3162	161	20	accurate	accurate	ADJ
cana-3162	161	21	predictions	prediction	NOUN
cana-3162	161	22	.	.	PUNCT
cana-3162	162	1	communications	communication	NOUN
cana-3162	162	2	on	on	ADP
cana-3162	162	3	applied	apply	VERB
cana-3162	162	4	nonlinear	nonlinear	ADJ
cana-3162	162	5	analysis	analysis	NOUN
cana-3162	162	6	issn	issn	NOUN
cana-3162	162	7	:	:	PUNCT
cana-3162	162	8	1074	1074	NUM
cana-3162	162	9	-	-	PUNCT
cana-3162	162	10	133x	133x	NUM
cana-3162	162	11	vol	vol	NOUN
cana-3162	162	12	32	32	NUM
cana-3162	162	13	no	no	NOUN
cana-3162	162	14	.	.	PUNCT
cana-3162	163	1	5s	5s	NUM
cana-3162	163	2	(	(	PUNCT
cana-3162	163	3	2025	2025	NUM
cana-3162	163	4	)	)	PUNCT
cana-3162	163	5	501	501	NUM
cana-3162	163	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3162	163	7	fig	fig	NOUN
cana-3162	163	8	.	.	PUNCT
cana-3162	164	1	1	1	X
cana-3162	164	2	.	.	X
cana-3162	164	3	anti	anti	ADJ
cana-3162	164	4	-	-	ADJ
cana-3162	164	5	cancer	cancer	ADJ
cana-3162	164	6	drug	drug	NOUN
cana-3162	164	7	response	response	NOUN
cana-3162	164	8	prediction	prediction	NOUN
cana-3162	164	9	using	use	VERB
cana-3162	164	10	random	random	ADJ
cana-3162	164	11	forest	forest	NOUN
cana-3162	164	12	algorithm	algorithm	NOUN
cana-3162	164	13	figure	figure	NOUN
cana-3162	164	14	1	1	NUM
cana-3162	164	15	demonstrates	demonstrate	VERB
cana-3162	164	16	how	how	SCONJ
cana-3162	164	17	a	a	DET
cana-3162	164	18	random	random	ADJ
cana-3162	164	19	forest	forest	NOUN
cana-3162	164	20	algorithm	algorithm	NOUN
cana-3162	164	21	predicts	predict	VERB
cana-3162	164	22	cancer	cancer	NOUN
cana-3162	164	23	therapy	therapy	NOUN
cana-3162	164	24	success	success	NOUN
cana-3162	164	25	.	.	PUNCT
cana-3162	165	1	the	the	DET
cana-3162	165	2	first	first	ADJ
cana-3162	165	3	stage	stage	NOUN
cana-3162	165	4	is	be	AUX
cana-3162	165	5	data	datum	NOUN
cana-3162	165	6	preparation	preparation	NOUN
cana-3162	165	7	.	.	PUNCT
cana-3162	166	1	next	next	ADV
cana-3162	166	2	,	,	PUNCT
cana-3162	166	3	we	we	PRON
cana-3162	166	4	divide	divide	VERB
cana-3162	166	5	the	the	DET
cana-3162	166	6	sample	sample	NOUN
cana-3162	166	7	into	into	ADP
cana-3162	166	8	training	training	NOUN
cana-3162	166	9	and	and	CCONJ
cana-3162	166	10	testing	testing	NOUN
cana-3162	166	11	sets	set	NOUN
cana-3162	166	12	.	.	PUNCT
cana-3162	167	1	bootstrapping	bootstrappe	VERB
cana-3162	167	2	generates	generate	VERB
cana-3162	167	3	samples	sample	NOUN
cana-3162	167	4	for	for	ADP
cana-3162	167	5	various	various	ADJ
cana-3162	167	6	decision	decision	NOUN
cana-3162	167	7	trees	tree	NOUN
cana-3162	167	8	.	.	PUNCT
cana-3162	168	1	every	every	DET
cana-3162	168	2	tree	tree	NOUN
cana-3162	168	3	helps	helps	AUX
cana-3162	168	4	identify	identify	VERB
cana-3162	168	5	the	the	DET
cana-3162	168	6	most	most	ADV
cana-3162	168	7	relevant	relevant	ADJ
cana-3162	168	8	aspects	aspect	NOUN
cana-3162	168	9	for	for	ADP
cana-3162	168	10	selection	selection	NOUN
cana-3162	168	11	.	.	PUNCT
cana-3162	169	1	we	we	PRON
cana-3162	169	2	use	use	VERB
cana-3162	169	3	the	the	DET
cana-3162	169	4	out	out	ADJ
cana-3162	169	5	-	-	PUNCT
cana-3162	169	6	of	of	ADP
cana-3162	169	7	-	-	PUNCT
cana-3162	169	8	bag	bag	NOUN
cana-3162	169	9	error	error	NOUN
cana-3162	169	10	to	to	PART
cana-3162	169	11	evaluate	evaluate	VERB
cana-3162	169	12	the	the	DET
cana-3162	169	13	model	model	NOUN
cana-3162	169	14	after	after	ADP
cana-3162	169	15	training	training	NOUN
cana-3162	169	16	.	.	PUNCT
cana-3162	170	1	we	we	PRON
cana-3162	170	2	make	make	VERB
cana-3162	170	3	final	final	ADJ
cana-3162	170	4	predictions	prediction	NOUN
cana-3162	170	5	on	on	ADP
cana-3162	170	6	the	the	DET
cana-3162	170	7	test	test	NOUN
cana-3162	170	8	dataset	dataset	NOUN
cana-3162	170	9	.	.	PUNCT
cana-3162	171	1	algorithm	algorithm	PROPN
cana-3162	171	2	2	2	NUM
cana-3162	171	3	:	:	PUNCT
cana-3162	171	4	support	support	NOUN
cana-3162	171	5	vector	vector	NOUN
cana-3162	171	6	machine	machine	NOUN
cana-3162	171	7	for	for	ADP
cana-3162	171	8	drug	drug	NOUN
cana-3162	171	9	response	response	NOUN
cana-3162	171	10	prediction	prediction	NOUN
cana-3162	171	11	1	1	NUM
cana-3162	171	12	.	.	PUNCT
cana-3162	172	1	input	input	NOUN
cana-3162	172	2	features	feature	NOUN
cana-3162	172	3	:	:	PUNCT
cana-3162	172	4	receive	receive	VERB
cana-3162	172	5	the	the	DET
cana-3162	172	6	selected	select	VERB
cana-3162	172	7	features	feature	NOUN
cana-3162	172	8	𝐹selected	𝐹selecte	VERB
cana-3162	172	9	from	from	ADP
cana-3162	172	10	algorithm	algorithm	NOUN
cana-3162	172	11	1	1	NUM
cana-3162	172	12	.	.	NOUN
cana-3162	172	13	•	•	NUM
cana-3162	172	14	𝑿𝒊𝒏𝒑𝒖𝒕	𝑿𝒊𝒏𝒑𝒖𝒕	PROPN
cana-3162	172	15	=	=	PRON
cana-3162	172	16	{	{	PUNCT
cana-3162	172	17	𝑓𝑖	𝑓𝑖	PROPN
cana-3162	172	18	:	:	PUNCT
cana-3162	172	19	𝑓𝑖	𝑓𝑖	PROPN
cana-3162	172	20	∈	∈	PROPN
cana-3162	172	21	𝐹selected	𝐹selecte	VERB
cana-3162	172	22	}	}	PUNCT
cana-3162	172	23	(	(	PUNCT
cana-3162	172	24	34	34	NUM
cana-3162	172	25	)	)	PUNCT
cana-3162	172	26	•	•	NOUN
cana-3162	173	1	𝒀𝒊𝒏𝒑𝒖𝒕	𝒀𝒊𝒏𝒑𝒖𝒕	PROPN
cana-3162	173	2	=	=	PUNCT
cana-3162	173	3	{	{	PUNCT
cana-3162	173	4	𝑦𝑖	𝑦𝑖	PROPN
cana-3162	173	5	:	:	PUNCT
cana-3162	173	6	𝑖	𝑖	PROPN
cana-3162	173	7	∈	∈	PROPN
cana-3162	173	8	index	index	NOUN
cana-3162	173	9	of	of	ADP
cana-3162	173	10	𝐹selected	𝐹selected	PROPN
cana-3162	173	11	}	}	PUNCT
cana-3162	173	12	(	(	PUNCT
cana-3162	173	13	35	35	NUM
cana-3162	173	14	)	)	SYM
cana-3162	173	15	2	2	NUM
cana-3162	173	16	.	.	PUNCT
cana-3162	173	17	data	datum	NOUN
cana-3162	173	18	preprocessing	preprocessing	NOUN
cana-3162	173	19	:	:	PUNCT
cana-3162	173	20	normalize	normalize	VERB
cana-3162	173	21	input	input	NOUN
cana-3162	173	22	features	feature	NOUN
cana-3162	173	23	to	to	ADP
cana-3162	173	24	a	a	DET
cana-3162	173	25	common	common	ADJ
cana-3162	173	26	scale	scale	NOUN
cana-3162	173	27	.	.	PUNCT
cana-3162	174	1	•	•	NUM
cana-3162	174	2	𝑋𝑛𝑜𝑟𝑚	𝑋𝑛𝑜𝑟𝑚	PROPN
cana-3162	174	3	=	=	SYM
cana-3162	174	4	𝑋𝑖𝑛𝑝𝑢𝑡	𝑋𝑖𝑛𝑝𝑢𝑡	PROPN
cana-3162	174	5	−	−	PROPN
cana-3162	174	6	𝜇𝜎	𝜇𝜎	X
cana-3162	174	7	(	(	PUNCT
cana-3162	174	8	36	36	NUM
cana-3162	174	9	)	)	PUNCT
cana-3162	174	10	•	•	NUM
cana-3162	174	11	𝜇	𝜇	ADP
cana-3162	174	12	=	=	SYM
cana-3162	174	13	1	1	NUM
cana-3162	174	14	𝑛	𝑛	NOUN
cana-3162	174	15	∑	∑	ADP
cana-3162	174	16	𝑓𝑖	𝑓𝑖	PROPN
cana-3162	174	17	𝑛	𝑛	PRON
cana-3162	174	18	𝑖=1	𝑖=1	PROPN
cana-3162	174	19	(	(	PUNCT
cana-3162	174	20	44	44	NUM
cana-3162	174	21	)	)	PUNCT
cana-3162	174	22	•	•	NUM
cana-3162	174	23	𝜎	𝜎	NOUN
cana-3162	174	24	=	=	NOUN
cana-3162	175	1	√	√	ADP
cana-3162	175	2	1	1	NUM
cana-3162	175	3	𝑛	𝑛	PRON
cana-3162	175	4	∑	∑	PROPN
cana-3162	175	5	(	(	PUNCT
cana-3162	175	6	𝑓𝑖	𝑓𝑖	PROPN
cana-3162	175	7	−	−	PROPN
cana-3162	175	8	𝜇)2𝑛	𝜇)2𝑛	NOUN
cana-3162	175	9	𝑖=1	𝑖=1	PUNCT
cana-3162	175	10	(	(	PUNCT
cana-3162	175	11	37	37	NUM
cana-3162	175	12	)	)	PUNCT
cana-3162	175	13	3	3	NUM
cana-3162	175	14	.	.	PUNCT
cana-3162	175	15	kernel	kernel	PROPN
cana-3162	175	16	selection	selection	PROPN
cana-3162	175	17	:	:	PUNCT
cana-3162	175	18	choose	choose	VERB
cana-3162	175	19	a	a	DET
cana-3162	175	20	kernel	kernel	NOUN
cana-3162	175	21	function	function	NOUN
cana-3162	175	22	𝐾	𝐾	PROPN
cana-3162	175	23	based	base	VERB
cana-3162	175	24	on	on	ADP
cana-3162	175	25	data	datum	NOUN
cana-3162	175	26	characteristics	characteristic	NOUN
cana-3162	175	27	.	.	PUNCT
cana-3162	176	1	•	•	NUM
cana-3162	176	2	𝐾(𝒙	𝐾(𝒙	NUM
cana-3162	176	3	,	,	PUNCT
cana-3162	176	4	𝒙′	𝒙′	NUM
cana-3162	176	5	)	)	PUNCT
cana-3162	176	6	=	=	SYM
cana-3162	176	7	𝜙(𝒙	𝜙(𝒙	X
cana-3162	176	8	)	)	PUNCT
cana-3162	176	9	⋅	⋅	PROPN
cana-3162	176	10	𝜙(𝒙′	𝜙(𝒙′	PROPN
cana-3162	176	11	)	)	PUNCT
cana-3162	176	12	(	(	PUNCT
cana-3162	176	13	38	38	NUM
cana-3162	176	14	)	)	PUNCT
cana-3162	176	15	4	4	NUM
cana-3162	176	16	.	.	PUNCT
cana-3162	177	1	svm	svm	ADJ
cana-3162	177	2	training	training	NOUN
cana-3162	177	3	:	:	PUNCT
cana-3162	177	4	train	train	VERB
cana-3162	177	5	the	the	DET
cana-3162	177	6	support	support	NOUN
cana-3162	177	7	vector	vector	NOUN
cana-3162	177	8	machine	machine	NOUN
cana-3162	177	9	model	model	NOUN
cana-3162	177	10	.	.	PUNCT
cana-3162	178	1	•	•	NUM
cana-3162	178	2	min	min	PROPN
cana-3162	178	3	𝒘,𝑏	𝒘,𝑏	VERB
cana-3162	178	4	1	1	NUM
cana-3162	178	5	2	2	NUM
cana-3162	178	6	|𝒘|2	|𝒘|2	NOUN
cana-3162	178	7	+	+	CCONJ
cana-3162	178	8	𝐶	𝐶	PROPN
cana-3162	178	9	∑	∑	PUNCT
cana-3162	178	10	𝑛	𝑛	PRON
cana-3162	178	11	𝑖=1	𝑖=1	PROPN
cana-3162	178	12	max(0,1	max(0,1	NUM
cana-3162	178	13	−	−	PROPN
cana-3162	178	14	𝑦𝑖(𝒘	𝑦𝑖(𝒘	PART
cana-3162	178	15	⋅	⋅	PROPN
cana-3162	178	16	𝜙(𝒙𝒊	𝜙(𝒙𝒊	NOUN
cana-3162	178	17	)	)	PUNCT
cana-3162	178	18	+	+	NOUN
cana-3162	178	19	𝑏	𝑏	NOUN
cana-3162	178	20	)	)	PUNCT
cana-3162	178	21	)	)	PUNCT
cana-3162	178	22	(	(	PUNCT
cana-3162	178	23	39	39	NUM
cana-3162	178	24	)	)	PUNCT
cana-3162	178	25	5	5	NUM
cana-3162	178	26	.	.	PUNCT
cana-3162	179	1	support	support	NOUN
cana-3162	179	2	vectors	vector	NOUN
cana-3162	179	3	extraction	extraction	NOUN
cana-3162	179	4	:	:	PUNCT
cana-3162	179	5	identify	identify	VERB
cana-3162	179	6	support	support	NOUN
cana-3162	179	7	vectors	vector	NOUN
cana-3162	179	8	from	from	ADP
cana-3162	179	9	the	the	DET
cana-3162	179	10	training	training	NOUN
cana-3162	179	11	data	datum	NOUN
cana-3162	179	12	.	.	PUNCT
cana-3162	180	1	•	•	NOUN
cana-3162	180	2	support	support	NOUN
cana-3162	180	3	vectors	vector	NOUN
cana-3162	180	4	:	:	PUNCT
cana-3162	180	5	𝑆𝑉	𝑆𝑉	PROPN
cana-3162	180	6	=	=	SYM
cana-3162	180	7	{	{	PUNCT
cana-3162	180	8	𝒙𝒊	𝒙𝒊	PROPN
cana-3162	180	9	:	:	PUNCT
cana-3162	180	10	𝛼𝑖	𝛼𝑖	PROPN
cana-3162	180	11	>	>	X
cana-3162	180	12	0	0	NUM
cana-3162	180	13	}	}	PUNCT
cana-3162	180	14	(	(	PUNCT
cana-3162	180	15	40	40	NUM
cana-3162	180	16	)	)	PUNCT
cana-3162	180	17	•	•	NOUN
cana-3162	180	18	𝛼𝑖	𝛼𝑖	PROPN
cana-3162	180	19	=	=	SYM
cana-3162	180	20	𝐶	𝐶	PROPN
cana-3162	180	21	−	−	PROPN
cana-3162	180	22	𝑦𝑖(𝒘	𝑦𝑖(𝒘	PART
cana-3162	180	23	⋅	⋅	PROPN
cana-3162	180	24	𝜙(𝒙𝒊	𝜙(𝒙𝒊	NOUN
cana-3162	180	25	)	)	PUNCT
cana-3162	180	26	+	+	PUNCT
cana-3162	180	27	𝑏	𝑏	NOUN
cana-3162	180	28	)	)	PUNCT
cana-3162	180	29	(	(	PUNCT
cana-3162	180	30	41	41	NUM
cana-3162	180	31	)	)	PUNCT
cana-3162	180	32	end	end	NOUN
cana-3162	180	33	:	:	PUNCT
cana-3162	180	34	conclude	conclude	VERB
cana-3162	180	35	the	the	DET
cana-3162	180	36	process	process	NOUN
cana-3162	180	37	.	.	PUNCT
cana-3162	181	1	final	final	ADJ
cana-3162	181	2	predictions	prediction	NOUN
cana-3162	181	3	:	:	PUNCT
cana-3162	181	4	make	make	VERB
cana-3162	181	5	predictions	prediction	NOUN
cana-3162	181	6	on	on	ADP
cana-3162	181	7	the	the	DET
cana-3162	181	8	test	test	NOUN
cana-3162	181	9	set	set	VERB
cana-3162	181	10	.	.	PUNCT
cana-3162	182	1	evaluate	evaluate	VERB
cana-3162	182	2	model	model	NOUN
cana-3162	182	3	:	:	PUNCT
cana-3162	182	4	evaluate	evaluate	VERB
cana-3162	182	5	model	model	NOUN
cana-3162	182	6	performance	performance	NOUN
cana-3162	182	7	using	use	VERB
cana-3162	182	8	out	out	ADV
cana-3162	182	9	-	-	PUNCT
cana-3162	182	10	of	of	ADP
cana-3162	182	11	-	-	PUNCT
cana-3162	182	12	bag	bag	NOUN
cana-3162	182	13	(	(	PUNCT
cana-3162	182	14	oob	oob	NOUN
cana-3162	182	15	)	)	PUNCT
cana-3162	182	16	error	error	NOUN
cana-3162	182	17	.	.	PUNCT
cana-3162	183	1	model	model	NOUN
cana-3162	183	2	training	training	NOUN
cana-3162	183	3	:	:	PUNCT
cana-3162	183	4	train	train	VERB
cana-3162	183	5	the	the	DET
cana-3162	183	6	random	random	ADJ
cana-3162	183	7	forest	forest	NOUN
cana-3162	183	8	model	model	NOUN
cana-3162	183	9	using	use	VERB
cana-3162	183	10	the	the	DET
cana-3162	183	11	selected	select	VERB
cana-3162	183	12	features	feature	NOUN
cana-3162	183	13	.	.	PUNCT
cana-3162	184	1	select	select	ADJ
cana-3162	184	2	features	feature	NOUN
cana-3162	184	3	:	:	PUNCT
cana-3162	184	4	select	select	VERB
cana-3162	184	5	the	the	DET
cana-3162	184	6	top	top	ADJ
cana-3162	184	7	features	feature	NOUN
cana-3162	184	8	based	base	VERB
cana-3162	184	9	on	on	ADP
cana-3162	184	10	their	their	PRON
cana-3162	184	11	importance	importance	NOUN
cana-3162	184	12	scores	score	NOUN
cana-3162	184	13	.	.	PUNCT
cana-3162	185	1	calculate	calculate	NOUN
cana-3162	185	2	importance	importance	NOUN
cana-3162	185	3	:	:	PUNCT
cana-3162	185	4	compute	compute	NOUN
cana-3162	185	5	feature	feature	NOUN
cana-3162	185	6	importance	importance	NOUN
cana-3162	185	7	scores	score	NOUN
cana-3162	185	8	for	for	ADP
cana-3162	185	9	all	all	DET
cana-3162	185	10	features	feature	NOUN
cana-3162	185	11	.	.	PUNCT
cana-3162	186	1	build	build	VERB
cana-3162	186	2	trees	tree	NOUN
cana-3162	186	3	:	:	PUNCT
cana-3162	186	4	construct	construct	VERB
cana-3162	186	5	decision	decision	NOUN
cana-3162	186	6	trees	tree	NOUN
cana-3162	186	7	using	use	VERB
cana-3162	186	8	the	the	DET
cana-3162	186	9	bootstrapped	bootstrappe	VERB
cana-3162	186	10	samples	sample	NOUN
cana-3162	186	11	.	.	PUNCT
cana-3162	187	1	bootstrap	bootstrap	NOUN
cana-3162	187	2	samples	sample	NOUN
cana-3162	187	3	:	:	PUNCT
cana-3162	187	4	create	create	VERB
cana-3162	187	5	bootstrapped	bootstrappe	VERB
cana-3162	187	6	samples	sample	NOUN
cana-3162	187	7	from	from	ADP
cana-3162	187	8	the	the	DET
cana-3162	187	9	training	training	NOUN
cana-3162	187	10	data	datum	NOUN
cana-3162	187	11	.	.	PUNCT
cana-3162	188	1	train	train	NOUN
cana-3162	188	2	-	-	PUNCT
cana-3162	188	3	test	test	NOUN
cana-3162	188	4	split	split	NOUN
cana-3162	188	5	:	:	PUNCT
cana-3162	188	6	split	split	VERB
cana-3162	188	7	the	the	DET
cana-3162	188	8	data	datum	NOUN
cana-3162	188	9	into	into	ADP
cana-3162	188	10	training	training	NOUN
cana-3162	188	11	and	and	CCONJ
cana-3162	188	12	testing	testing	NOUN
cana-3162	188	13	sets	set	NOUN
cana-3162	188	14	.	.	PUNCT
cana-3162	189	1	data	datum	NOUN
cana-3162	189	2	preparation	preparation	NOUN
cana-3162	189	3	:	:	PUNCT
cana-3162	189	4	prepare	prepare	VERB
cana-3162	189	5	the	the	DET
cana-3162	189	6	dataset	dataset	NOUN
cana-3162	189	7	.	.	PUNCT
cana-3162	190	1	start	start	VERB
cana-3162	190	2	:	:	PUNCT
cana-3162	190	3	initiate	initiate	VERB
cana-3162	190	4	the	the	DET
cana-3162	190	5	process	process	NOUN
cana-3162	190	6	.	.	PUNCT
cana-3162	191	1	communications	communication	NOUN
cana-3162	191	2	on	on	ADP
cana-3162	191	3	applied	apply	VERB
cana-3162	191	4	nonlinear	nonlinear	ADJ
cana-3162	191	5	analysis	analysis	NOUN
cana-3162	191	6	issn	issn	NOUN
cana-3162	191	7	:	:	PUNCT
cana-3162	191	8	1074	1074	NUM
cana-3162	191	9	-	-	PUNCT
cana-3162	191	10	133x	133x	NUM
cana-3162	191	11	vol	vol	NOUN
cana-3162	191	12	32	32	NUM
cana-3162	191	13	no	no	NOUN
cana-3162	191	14	.	.	PUNCT
cana-3162	192	1	5s	5s	NUM
cana-3162	192	2	(	(	PUNCT
cana-3162	192	3	2025	2025	NUM
cana-3162	192	4	)	)	PUNCT
cana-3162	192	5	502	502	NUM
cana-3162	192	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3162	192	7	•	•	NUM
cana-3162	192	8	margin	margin	NOUN
cana-3162	192	9	:	:	PUNCT
cana-3162	192	10	margin	margin	NOUN
cana-3162	192	11	=	=	SYM
cana-3162	192	12	2	2	NUM
cana-3162	192	13	|𝒘|	|𝒘|	NOUN
cana-3162	192	14	(	(	PUNCT
cana-3162	192	15	42	42	NUM
cana-3162	192	16	)	)	PUNCT
cana-3162	192	17	6	6	NUM
cana-3162	192	18	.	.	PUNCT
cana-3162	192	19	prediction	prediction	NOUN
cana-3162	192	20	on	on	ADP
cana-3162	192	21	test	test	NOUN
cana-3162	192	22	set	set	NOUN
cana-3162	192	23	:	:	PUNCT
cana-3162	192	24	make	make	VERB
cana-3162	192	25	predictions	prediction	NOUN
cana-3162	192	26	using	use	VERB
cana-3162	192	27	the	the	DET
cana-3162	192	28	trained	train	VERB
cana-3162	192	29	model	model	NOUN
cana-3162	192	30	.	.	PUNCT
cana-3162	193	1	•	•	NUM
cana-3162	193	2	𝑦	𝑦	NOUN
cana-3162	193	3	�	�	NOUN
cana-3162	193	4	̂	̂	VERB
cana-3162	193	5	�	�	NOUN
cana-3162	193	6	=	=	SYM
cana-3162	193	7	sign(𝒘	sign(𝒘	PROPN
cana-3162	193	8	⋅	⋅	PROPN
cana-3162	193	9	𝜙(𝒙𝒊	𝜙(𝒙𝒊	NOUN
cana-3162	193	10	)	)	PUNCT
cana-3162	193	11	+	+	PUNCT
cana-3162	193	12	𝑏	𝑏	NOUN
cana-3162	193	13	)	)	PUNCT
cana-3162	193	14	(	(	PUNCT
cana-3162	193	15	43	43	NUM
cana-3162	193	16	)	)	PUNCT
cana-3162	193	17	•	•	NUM
cana-3162	193	18	decision	decision	NOUN
cana-3162	193	19	boundary	boundary	NOUN
cana-3162	193	20	:	:	PUNCT
cana-3162	193	21	𝑓(𝒙	𝑓(𝒙	NOUN
cana-3162	193	22	)	)	PUNCT
cana-3162	194	1	=	=	SYM
cana-3162	194	2	0	0	PUNCT
cana-3162	194	3	(	(	PUNCT
cana-3162	194	4	44	44	NUM
cana-3162	194	5	)	)	PUNCT
cana-3162	194	6	•	•	NOUN
cana-3162	194	7	predicted	predict	VERB
cana-3162	194	8	class	class	NOUN
cana-3162	194	9	:	:	PUNCT
cana-3162	194	10	𝑦	𝑦	NOUN
cana-3162	194	11	�	�	NOUN
cana-3162	194	12	̂	̂	SYM
cana-3162	194	13	�	�	NOUN
cana-3162	194	14	=	=	SYM
cana-3162	194	15	1	1	NUM
cana-3162	194	16	if	if	SCONJ
cana-3162	194	17	𝑓(𝒙	𝑓(𝒙	NOUN
cana-3162	194	18	)	)	PUNCT
cana-3162	194	19	>	>	X
cana-3162	194	20	0	0	PUNCT
cana-3162	195	1	(	(	PUNCT
cana-3162	195	2	45	45	NUM
cana-3162	195	3	)	)	PUNCT
cana-3162	195	4	7	7	NUM
cana-3162	195	5	.	.	PUNCT
cana-3162	195	6	evaluation	evaluation	NOUN
cana-3162	195	7	metrics	metric	NOUN
cana-3162	195	8	calculation	calculation	NOUN
cana-3162	195	9	:	:	PUNCT
cana-3162	195	10	compute	compute	NOUN
cana-3162	195	11	evaluation	evaluation	NOUN
cana-3162	195	12	metrics	metric	NOUN
cana-3162	195	13	to	to	PART
cana-3162	195	14	assess	assess	VERB
cana-3162	195	15	model	model	NOUN
cana-3162	195	16	performance	performance	NOUN
cana-3162	195	17	.	.	PUNCT
cana-3162	196	1	•	•	NUM
cana-3162	196	2	accuracy	accuracy	NOUN
cana-3162	196	3	:	:	PUNCT
cana-3162	196	4	𝐴	𝐴	PROPN
cana-3162	196	5	=	=	PUNCT
cana-3162	196	6	∑	∑	PUNCT
cana-3162	196	7	1(𝑦𝑖=𝑦	1(𝑦𝑖=𝑦	NUM
cana-3162	196	8	�	�	PROPN
cana-3162	196	9	̂	̂	VERB
cana-3162	196	10	�	�	NOUN
cana-3162	196	11	)	)	PUNCT
cana-3162	196	12	𝑛	𝑛	PRON
cana-3162	196	13	𝑖=1	𝑖=1	PUNCT
cana-3162	196	14	𝑛	𝑛	PROPN
cana-3162	196	15	(	(	PUNCT
cana-3162	196	16	46	46	NUM
cana-3162	196	17	)	)	PUNCT
cana-3162	196	18	•	•	NUM
cana-3162	196	19	precision	precision	NOUN
cana-3162	196	20	:	:	PUNCT
cana-3162	196	21	𝑃	𝑃	PROPN
cana-3162	196	22	=	=	PUNCT
cana-3162	196	23	𝑇𝑃	𝑇𝑃	PROPN
cana-3162	196	24	𝑇𝑃+𝐹𝑃	𝑇𝑃+𝐹𝑃	NUM
cana-3162	196	25	(	(	PUNCT
cana-3162	196	26	47	47	NUM
cana-3162	196	27	)	)	PUNCT
cana-3162	196	28	•	•	NUM
cana-3162	196	29	f1	f1	PROPN
cana-3162	196	30	score	score	NOUN
cana-3162	196	31	:	:	PUNCT
cana-3162	196	32	𝐹1	𝐹1	PROPN
cana-3162	196	33	=	=	SYM
cana-3162	196	34	2⋅𝑃⋅𝑅	2⋅𝑃⋅𝑅	NUM
cana-3162	196	35	𝑃+𝑅	𝑃+𝑅	NOUN
cana-3162	196	36	(	(	PUNCT
cana-3162	196	37	48	48	NUM
cana-3162	196	38	)	)	PUNCT
cana-3162	196	39	8	8	NUM
cana-3162	196	40	.	.	PUNCT
cana-3162	197	1	feature	feature	NOUN
cana-3162	197	2	importance	importance	NOUN
cana-3162	197	3	analysis	analysis	NOUN
cana-3162	197	4	:	:	PUNCT
cana-3162	197	5	analyze	analyze	VERB
cana-3162	197	6	the	the	DET
cana-3162	197	7	impact	impact	NOUN
cana-3162	197	8	of	of	ADP
cana-3162	197	9	selected	select	VERB
cana-3162	197	10	features	feature	NOUN
cana-3162	197	11	on	on	ADP
cana-3162	197	12	predictions	prediction	NOUN
cana-3162	197	13	.	.	PUNCT
cana-3162	198	1	•	•	NUM
cana-3162	198	2	importance	importance	NOUN
cana-3162	198	3	of	of	ADP
cana-3162	198	4	feature	feature	NOUN
cana-3162	198	5	𝑓𝑖	𝑓𝑖	NOUN
cana-3162	198	6	:	:	PUNCT
cana-3162	198	7	𝐼(𝑓𝑖	𝐼(𝑓𝑖	NOUN
cana-3162	198	8	)	)	PUNCT
cana-3162	198	9	=	=	SYM
cana-3162	198	10	|𝒘𝑻	|𝒘𝑻	X
cana-3162	198	11	⋅	⋅	PROPN
cana-3162	198	12	𝜙(𝑓𝑖)|	𝜙(𝑓𝑖)|	X
cana-3162	198	13	(	(	PUNCT
cana-3162	198	14	49	49	NUM
cana-3162	198	15	)	)	PUNCT
cana-3162	198	16	9	9	NUM
cana-3162	198	17	.	.	PUNCT
cana-3162	198	18	model	model	NOUN
cana-3162	198	19	optimization	optimization	NOUN
cana-3162	198	20	:	:	PUNCT
cana-3162	198	21	optimize	optimize	VERB
cana-3162	198	22	model	model	NOUN
cana-3162	198	23	parameters	parameter	NOUN
cana-3162	198	24	to	to	PART
cana-3162	198	25	improve	improve	VERB
cana-3162	198	26	performance	performance	NOUN
cana-3162	198	27	.	.	PUNCT
cana-3162	199	1	•	•	NUM
cana-3162	199	2	parameter	parameter	NOUN
cana-3162	199	3	optimization	optimization	NOUN
cana-3162	199	4	:	:	PUNCT
cana-3162	200	1	𝒘	𝒘	X
cana-3162	200	2	=	=	X
cana-3162	200	3	𝒘	𝒘	X
cana-3162	200	4	−	−	PROPN
cana-3162	200	5	𝜂∇𝐿(𝒘	𝜂∇𝐿(𝒘	NOUN
cana-3162	200	6	,	,	PUNCT
cana-3162	200	7	𝑏	𝑏	NOUN
cana-3162	200	8	)	)	PUNCT
cana-3162	200	9	(	(	PUNCT
cana-3162	200	10	50	50	NUM
cana-3162	200	11	)	)	PUNCT
cana-3162	200	12	10	10	NUM
cana-3162	200	13	.	.	PUNCT
cana-3162	201	1	final	final	ADJ
cana-3162	201	2	predictions	prediction	NOUN
cana-3162	201	3	:	:	PUNCT
cana-3162	201	4	summarize	summarize	VERB
cana-3162	201	5	final	final	ADJ
cana-3162	201	6	predictions	prediction	NOUN
cana-3162	201	7	based	base	VERB
cana-3162	201	8	on	on	ADP
cana-3162	201	9	model	model	NOUN
cana-3162	201	10	evaluation	evaluation	NOUN
cana-3162	201	11	.	.	PUNCT
cana-3162	202	1	•	•	NUM
cana-3162	202	2	final	final	ADJ
cana-3162	202	3	predictions	prediction	NOUN
cana-3162	202	4	:	:	PUNCT
cana-3162	202	5	�	�	PROPN
cana-3162	202	6	̂	̂	SYM
cana-3162	202	7	�	�	NOUN
cana-3162	202	8	=	=	SYM
cana-3162	202	9	{	{	PUNCT
cana-3162	202	10	𝑦	𝑦	NOUN
cana-3162	202	11	�	�	NOUN
cana-3162	202	12	̂	̂	NOUN
cana-3162	202	13	�	�	NOUN
cana-3162	202	14	:	:	PUNCT
cana-3162	202	15	𝑖	𝑖	NOUN
cana-3162	202	16	=	=	SYM
cana-3162	202	17	1,2	1,2	NUM
cana-3162	202	18	,	,	PUNCT
cana-3162	202	19	…	…	PUNCT
cana-3162	202	20	,	,	PUNCT
cana-3162	202	21	𝑛	𝑛	PROPN
cana-3162	202	22	}	}	PUNCT
cana-3162	202	23	(	(	PUNCT
cana-3162	202	24	51	51	NUM
cana-3162	202	25	)	)	PUNCT
cana-3162	202	26	notations	notation	NOUN
cana-3162	202	27	:	:	PUNCT
cana-3162	202	28	•	•	NUM
cana-3162	202	29	𝑿𝒊𝒏𝒑𝒖𝒕	𝑿𝒊𝒏𝒑𝒖𝒕	PROPN
cana-3162	202	30	:	:	PUNCT
cana-3162	202	31	input	input	NOUN
cana-3162	202	32	feature	feature	NOUN
cana-3162	202	33	matrix	matrix	NOUN
cana-3162	202	34	from	from	ADP
cana-3162	202	35	algorithm	algorithm	NOUN
cana-3162	202	36	1	1	NUM
cana-3162	202	37	.	.	NOUN
cana-3162	202	38	•	•	NUM
cana-3162	203	1	𝒀𝒊𝒏𝒑𝒖𝒕	𝒀𝒊𝒏𝒑𝒖𝒕	PROPN
cana-3162	203	2	:	:	PUNCT
cana-3162	203	3	output	output	NOUN
cana-3162	203	4	labels	label	NOUN
cana-3162	203	5	from	from	ADP
cana-3162	203	6	algorithm	algorithm	NOUN
cana-3162	203	7	1	1	NUM
cana-3162	203	8	.	.	NOUN
cana-3162	203	9	•	•	NUM
cana-3162	203	10	𝝁	𝝁	NUM
cana-3162	203	11	:	:	PUNCT
cana-3162	203	12	mean	mean	NOUN
cana-3162	203	13	of	of	ADP
cana-3162	203	14	the	the	DET
cana-3162	203	15	feature	feature	NOUN
cana-3162	203	16	values	value	NOUN
cana-3162	203	17	.	.	PUNCT
cana-3162	204	1	•	•	NUM
cana-3162	204	2	𝝈	𝝈	PRON
cana-3162	204	3	:	:	PUNCT
cana-3162	204	4	standard	standard	ADJ
cana-3162	204	5	deviation	deviation	NOUN
cana-3162	204	6	of	of	ADP
cana-3162	204	7	the	the	DET
cana-3162	204	8	feature	feature	NOUN
cana-3162	204	9	values	value	NOUN
cana-3162	204	10	.	.	PUNCT
cana-3162	205	1	•	•	NUM
cana-3162	205	2	𝑲	𝑲	PRON
cana-3162	205	3	:	:	PUNCT
cana-3162	205	4	kernel	kernel	PROPN
cana-3162	205	5	function	function	PROPN
cana-3162	205	6	.	.	PUNCT
cana-3162	206	1	•	•	NUM
cana-3162	206	2	𝒘	𝒘	X
cana-3162	206	3	:	:	PUNCT
cana-3162	206	4	weight	weight	NOUN
cana-3162	206	5	vector	vector	NOUN
cana-3162	206	6	of	of	ADP
cana-3162	206	7	the	the	DET
cana-3162	206	8	svm	svm	NOUN
cana-3162	206	9	.	.	PROPN
cana-3162	206	10	•	•	ADJ
cana-3162	206	11	𝒃	𝒃	NOUN
cana-3162	206	12	:	:	PUNCT
cana-3162	206	13	bias	bias	NOUN
cana-3162	206	14	term	term	NOUN
cana-3162	206	15	of	of	ADP
cana-3162	206	16	the	the	DET
cana-3162	206	17	svm	svm	PROPN
cana-3162	206	18	.	.	PROPN
cana-3162	206	19	•	•	ADP
cana-3162	206	20	𝑪	𝑪	PROPN
cana-3162	206	21	:	:	PUNCT
cana-3162	206	22	regularization	regularization	NOUN
cana-3162	206	23	parameter	parameter	NOUN
cana-3162	206	24	.	.	PUNCT
cana-3162	207	1	•	•	NUM
cana-3162	208	1	𝜶𝒊	𝜶𝒊	NOUN
cana-3162	208	2	:	:	PUNCT
cana-3162	208	3	lagrange	lagrange	NOUN
cana-3162	208	4	multiplier	multiplier	ADV
cana-3162	208	5	for	for	ADP
cana-3162	208	6	support	support	NOUN
cana-3162	208	7	vectors	vector	NOUN
cana-3162	208	8	.	.	PUNCT
cana-3162	209	1	•	•	NUM
cana-3162	209	2	𝑻𝑷	𝑻𝑷	PROPN
cana-3162	209	3	:	:	PUNCT
cana-3162	209	4	true	true	ADJ
cana-3162	209	5	positives	positive	NOUN
cana-3162	209	6	.	.	PUNCT
cana-3162	210	1	•	•	NUM
cana-3162	210	2	𝑭𝑷	𝑭𝑷	PROPN
cana-3162	210	3	:	:	PUNCT
cana-3162	210	4	false	false	ADJ
cana-3162	210	5	positives	positive	NOUN
cana-3162	210	6	.	.	PUNCT
cana-3162	211	1	•	•	NUM
cana-3162	211	2	𝜼	𝜼	X
cana-3162	211	3	:	:	PUNCT
cana-3162	211	4	learning	learn	VERB
cana-3162	211	5	rate	rate	NOUN
cana-3162	211	6	.	.	PUNCT
cana-3162	212	1	•	•	NUM
cana-3162	212	2	𝑳(𝒘	𝑳(𝒘	NUM
cana-3162	212	3	,	,	PUNCT
cana-3162	212	4	𝒃	𝒃	NOUN
cana-3162	212	5	):	):	PUNCT
cana-3162	212	6	loss	loss	NOUN
cana-3162	212	7	function	function	NOUN
cana-3162	212	8	.	.	PUNCT
cana-3162	213	1	algorithm	algorithm	NOUN
cana-3162	213	2	2	2	NUM
cana-3162	213	3	uses	use	VERB
cana-3162	213	4	svms	svms	NOUN
cana-3162	213	5	to	to	PART
cana-3162	213	6	predict	predict	VERB
cana-3162	213	7	drug	drug	NOUN
cana-3162	213	8	effects	effect	NOUN
cana-3162	213	9	based	base	VERB
cana-3162	213	10	on	on	ADP
cana-3162	213	11	features	feature	NOUN
cana-3162	213	12	from	from	ADP
cana-3162	213	13	algorithm	algorithm	NOUN
cana-3162	213	14	1	1	NUM
cana-3162	213	15	.	.	PUNCT
cana-3162	214	1	it	it	PRON
cana-3162	214	2	receives	receive	VERB
cana-3162	214	3	features	feature	NOUN
cana-3162	214	4	and	and	CCONJ
cana-3162	214	5	names	name	NOUN
cana-3162	214	6	first	first	ADV
cana-3162	214	7	.	.	PUNCT
cana-3162	215	1	after	after	ADP
cana-3162	215	2	preprocessing	preprocesse	VERB
cana-3162	215	3	,	,	PUNCT
cana-3162	215	4	we	we	PRON
cana-3162	215	5	standardize	standardize	VERB
cana-3162	215	6	the	the	DET
cana-3162	215	7	data	datum	NOUN
cana-3162	215	8	and	and	CCONJ
cana-3162	215	9	uniformly	uniformly	ADJ
cana-3162	215	10	size	size	NOUN
cana-3162	215	11	the	the	DET
cana-3162	215	12	features	feature	NOUN
cana-3162	215	13	.	.	PUNCT
cana-3162	216	1	the	the	DET
cana-3162	216	2	model	model	NOUN
cana-3162	216	3	can	can	AUX
cana-3162	216	4	simplify	simplify	VERB
cana-3162	216	5	the	the	DET
cana-3162	216	6	categorization	categorization	NOUN
cana-3162	216	7	of	of	ADP
cana-3162	216	8	non	non	ADJ
cana-3162	216	9	-	-	ADJ
cana-3162	216	10	linear	linear	ADJ
cana-3162	216	11	data	datum	NOUN
cana-3162	216	12	by	by	ADP
cana-3162	216	13	selecting	select	VERB
cana-3162	216	14	the	the	DET
cana-3162	216	15	appropriate	appropriate	ADJ
cana-3162	216	16	kernel	kernel	NOUN
cana-3162	216	17	function	function	NOUN
cana-3162	216	18	communications	communication	NOUN
cana-3162	216	19	on	on	ADP
cana-3162	216	20	applied	apply	VERB
cana-3162	216	21	nonlinear	nonlinear	ADJ
cana-3162	216	22	analysis	analysis	NOUN
cana-3162	216	23	issn	issn	NOUN
cana-3162	216	24	:	:	PUNCT
cana-3162	216	25	1074	1074	NUM
cana-3162	216	26	-	-	PUNCT
cana-3162	216	27	133x	133x	NUM
cana-3162	216	28	vol	vol	NOUN
cana-3162	216	29	32	32	NUM
cana-3162	216	30	no	no	NOUN
cana-3162	216	31	.	.	PUNCT
cana-3162	217	1	5s	5s	NUM
cana-3162	217	2	(	(	PUNCT
cana-3162	217	3	2025	2025	NUM
cana-3162	217	4	)	)	PUNCT
cana-3162	217	5	503	503	NUM
cana-3162	217	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3162	218	1	to	to	PART
cana-3162	218	2	alter	alter	VERB
cana-3162	218	3	the	the	DET
cana-3162	218	4	input	input	NOUN
cana-3162	218	5	space	space	NOUN
cana-3162	218	6	.	.	PUNCT
cana-3162	219	1	we	we	PRON
cana-3162	219	2	train	train	VERB
cana-3162	219	3	the	the	DET
cana-3162	219	4	svm	svm	NOUN
cana-3162	219	5	by	by	ADP
cana-3162	219	6	minimizing	minimize	VERB
cana-3162	219	7	a	a	DET
cana-3162	219	8	loss	loss	NOUN
cana-3162	219	9	function	function	NOUN
cana-3162	219	10	that	that	SCONJ
cana-3162	219	11	penalizes	penalize	NOUN
cana-3162	219	12	mislabeled	mislabele	VERB
cana-3162	219	13	points	point	NOUN
cana-3162	219	14	,	,	PUNCT
cana-3162	219	15	while	while	SCONJ
cana-3162	219	16	keeping	keep	VERB
cana-3162	219	17	the	the	DET
cana-3162	219	18	weight	weight	NOUN
cana-3162	219	19	vector	vector	NOUN
cana-3162	219	20	and	and	CCONJ
cana-3162	219	21	bias	bias	NOUN
cana-3162	219	22	as	as	ADV
cana-3162	219	23	minimal	minimal	ADJ
cana-3162	219	24	as	as	ADP
cana-3162	219	25	possible	possible	ADJ
cana-3162	219	26	[	[	X
cana-3162	219	27	25	25	NUM
cana-3162	219	28	]	]	PUNCT
cana-3162	219	29	.	.	PUNCT
cana-3162	220	1	after	after	ADP
cana-3162	220	2	learning	learn	VERB
cana-3162	220	3	the	the	DET
cana-3162	220	4	decision	decision	NOUN
cana-3162	220	5	limit	limit	NOUN
cana-3162	220	6	support	support	NOUN
cana-3162	220	7	vectors	vector	NOUN
cana-3162	220	8	,	,	PUNCT
cana-3162	220	9	the	the	DET
cana-3162	220	10	method	method	NOUN
cana-3162	220	11	predicts	predict	VERB
cana-3162	220	12	the	the	DET
cana-3162	220	13	f1	f1	PROPN
cana-3162	220	14	score	score	NOUN
cana-3162	220	15	.	.	PUNCT
cana-3162	221	1	feature	feature	NOUN
cana-3162	221	2	significance	significance	NOUN
cana-3162	221	3	analysis	analysis	NOUN
cana-3162	221	4	indicates	indicate	VERB
cana-3162	221	5	feature	feature	NOUN
cana-3162	221	6	relevance	relevance	NOUN
cana-3162	221	7	.	.	PUNCT
cana-3162	222	1	we	we	PRON
cana-3162	222	2	might	might	AUX
cana-3162	222	3	achieve	achieve	VERB
cana-3162	222	4	better	well	ADJ
cana-3162	222	5	forecasts	forecast	NOUN
cana-3162	222	6	by	by	ADP
cana-3162	222	7	tweaking	tweak	VERB
cana-3162	222	8	the	the	DET
cana-3162	222	9	model	model	NOUN
cana-3162	222	10	's	's	PART
cana-3162	222	11	parameters	parameter	NOUN
cana-3162	222	12	.	.	PUNCT
cana-3162	223	1	the	the	DET
cana-3162	223	2	final	final	ADJ
cana-3162	223	3	data	datum	NOUN
cana-3162	223	4	would	would	AUX
cana-3162	223	5	help	help	VERB
cana-3162	223	6	us	we	PRON
cana-3162	223	7	understand	understand	VERB
cana-3162	223	8	how	how	SCONJ
cana-3162	223	9	cancer	cancer	NOUN
cana-3162	223	10	drugs	drug	NOUN
cana-3162	223	11	impact	impact	NOUN
cana-3162	223	12	patients	patient	NOUN
cana-3162	223	13	.	.	PUNCT
cana-3162	224	1	fig	fig	NOUN
cana-3162	224	2	.	.	PUNCT
cana-3162	225	1	2	2	X
cana-3162	225	2	.	.	X
cana-3162	225	3	anti	anti	ADJ
cana-3162	225	4	-	-	ADJ
cana-3162	225	5	cancer	cancer	ADJ
cana-3162	225	6	drug	drug	NOUN
cana-3162	225	7	response	response	NOUN
cana-3162	225	8	prediction	prediction	NOUN
cana-3162	225	9	using	use	VERB
cana-3162	225	10	machine	machine	NOUN
cana-3162	225	11	learning	learn	VERB
cana-3162	225	12	figure	figure	NOUN
cana-3162	225	13	2	2	NUM
cana-3162	225	14	depicts	depict	VERB
cana-3162	225	15	a	a	DET
cana-3162	225	16	strategy	strategy	NOUN
cana-3162	225	17	for	for	ADP
cana-3162	225	18	using	use	VERB
cana-3162	225	19	machine	machine	NOUN
cana-3162	225	20	learning	learning	NOUN
cana-3162	225	21	to	to	PART
cana-3162	225	22	predict	predict	VERB
cana-3162	225	23	anticancer	anticancer	NOUN
cana-3162	225	24	drug	drug	NOUN
cana-3162	225	25	efficacy	efficacy	NOUN
cana-3162	225	26	.	.	PUNCT
cana-3162	226	1	the	the	DET
cana-3162	226	2	procedure	procedure	NOUN
cana-3162	226	3	begins	begin	VERB
cana-3162	226	4	with	with	ADP
cana-3162	226	5	data	datum	NOUN
cana-3162	226	6	entry	entry	NOUN
cana-3162	226	7	and	and	CCONJ
cana-3162	226	8	then	then	ADV
cana-3162	226	9	prepares	prepare	VERB
cana-3162	226	10	it	it	PRON
cana-3162	226	11	for	for	ADP
cana-3162	226	12	accuracy	accuracy	NOUN
cana-3162	226	13	.	.	PUNCT
cana-3162	227	1	the	the	DET
cana-3162	227	2	selection	selection	NOUN
cana-3162	227	3	of	of	ADP
cana-3162	227	4	relevant	relevant	ADJ
cana-3162	227	5	attributes	attribute	NOUN
cana-3162	227	6	leads	lead	VERB
cana-3162	227	7	to	to	ADP
cana-3162	227	8	the	the	DET
cana-3162	227	9	choice	choice	NOUN
cana-3162	227	10	of	of	ADP
cana-3162	227	11	a	a	DET
cana-3162	227	12	decent	decent	ADJ
cana-3162	227	13	training	training	NOUN
cana-3162	227	14	model	model	NOUN
cana-3162	227	15	.	.	PUNCT
cana-3162	228	1	we	we	PRON
cana-3162	228	2	test	test	VERB
cana-3162	228	3	the	the	DET
cana-3162	228	4	trained	train	VERB
cana-3162	228	5	model	model	NOUN
cana-3162	228	6	against	against	ADP
cana-3162	228	7	specific	specific	ADJ
cana-3162	228	8	criteria	criterion	NOUN
cana-3162	228	9	to	to	PART
cana-3162	228	10	ensure	ensure	VERB
cana-3162	228	11	its	its	PRON
cana-3162	228	12	accuracy	accuracy	NOUN
cana-3162	228	13	.	.	PUNCT
cana-3162	229	1	the	the	DET
cana-3162	229	2	training	training	NOUN
cana-3162	229	3	model	model	NOUN
cana-3162	229	4	uses	use	VERB
cana-3162	229	5	new	new	ADJ
cana-3162	229	6	patient	patient	ADJ
cana-3162	229	7	data	datum	NOUN
cana-3162	229	8	to	to	PART
cana-3162	229	9	make	make	VERB
cana-3162	229	10	predictions	prediction	NOUN
cana-3162	229	11	.	.	PUNCT
cana-3162	230	1	we	we	PRON
cana-3162	230	2	then	then	ADV
cana-3162	230	3	release	release	VERB
cana-3162	230	4	the	the	DET
cana-3162	230	5	forecasts	forecast	NOUN
cana-3162	230	6	for	for	ADP
cana-3162	230	7	further	further	ADJ
cana-3162	230	8	research	research	NOUN
cana-3162	230	9	.	.	PUNCT
cana-3162	231	1	organized	organize	VERB
cana-3162	231	2	drug	drug	NOUN
cana-3162	231	3	reaction	reaction	NOUN
cana-3162	231	4	predictions	prediction	NOUN
cana-3162	231	5	are	be	AUX
cana-3162	231	6	more	more	ADV
cana-3162	231	7	accurate	accurate	ADJ
cana-3162	231	8	and	and	CCONJ
cana-3162	231	9	consistent	consistent	ADJ
cana-3162	231	10	.	.	PUNCT
cana-3162	232	1	algorithm	algorithm	NOUN
cana-3162	232	2	3	3	NUM
cana-3162	232	3	:	:	PUNCT
cana-3162	232	4	ensemble	ensemble	ADJ
cana-3162	232	5	learning	learning	NOUN
cana-3162	232	6	for	for	ADP
cana-3162	232	7	improved	improved	ADJ
cana-3162	232	8	drug	drug	NOUN
cana-3162	232	9	response	response	NOUN
cana-3162	232	10	prediction	prediction	NOUN
cana-3162	232	11	1	1	NUM
cana-3162	232	12	.	.	PUNCT
cana-3162	232	13	input	input	NOUN
cana-3162	232	14	features	feature	NOUN
cana-3162	232	15	and	and	CCONJ
cana-3162	232	16	predictions	prediction	NOUN
cana-3162	232	17	:	:	PUNCT
cana-3162	232	18	receive	receive	VERB
cana-3162	232	19	input	input	NOUN
cana-3162	232	20	features	feature	NOUN
cana-3162	232	21	and	and	CCONJ
cana-3162	232	22	predictions	prediction	NOUN
cana-3162	232	23	from	from	ADP
cana-3162	232	24	algorithm	algorithm	NOUN
cana-3162	232	25	2	2	NUM
cana-3162	232	26	.	.	NOUN
cana-3162	232	27	•	•	NUM
cana-3162	232	28	𝑋𝑖𝑛𝑝𝑢𝑡	𝑋𝑖𝑛𝑝𝑢𝑡	PROPN
cana-3162	232	29	=	=	SYM
cana-3162	232	30	{	{	PUNCT
cana-3162	232	31	𝑓𝑖	𝑓𝑖	PROPN
cana-3162	232	32	:	:	PUNCT
cana-3162	232	33	𝑓𝑖	𝑓𝑖	PROPN
cana-3162	232	34	∈	∈	PROPN
cana-3162	232	35	𝐹selected	𝐹selecte	VERB
cana-3162	232	36	}	}	PUNCT
cana-3162	232	37	(	(	PUNCT
cana-3162	232	38	52	52	NUM
cana-3162	232	39	)	)	PUNCT
cana-3162	232	40	•	•	PRON
cana-3162	232	41	𝑌predictions	𝑌predictions	PROPN
cana-3162	232	42	̂	̂	X
cana-3162	232	43	=	=	SYM
cana-3162	232	44	{	{	PUNCT
cana-3162	232	45	𝑦	𝑦	NOUN
cana-3162	232	46	�	�	NOUN
cana-3162	232	47	̂	̂	NOUN
cana-3162	232	48	�	�	NOUN
cana-3162	232	49	:	:	PUNCT
cana-3162	232	50	𝑖	𝑖	NOUN
cana-3162	232	51	=	=	SYM
cana-3162	232	52	1,2	1,2	NUM
cana-3162	232	53	,	,	PUNCT
cana-3162	232	54	…	…	PUNCT
cana-3162	232	55	,	,	PUNCT
cana-3162	232	56	𝑛	𝑛	PROPN
cana-3162	232	57	}	}	PUNCT
cana-3162	232	58	(	(	PUNCT
cana-3162	232	59	53	53	NUM
cana-3162	232	60	)	)	PUNCT
cana-3162	232	61	•	•	NOUN
cana-3162	233	1	𝑁	𝑁	PROPN
cana-3162	233	2	=	=	SYM
cana-3162	233	3	∑	∑	PUNCT
cana-3162	233	4	𝑓𝑗	𝑓𝑗	ADJ
cana-3162	233	5	total𝑚	total𝑚	ADJ
cana-3162	233	6	𝑗=1	𝑗=1	PROPN
cana-3162	233	7	(	(	PUNCT
cana-3162	233	8	54	54	NUM
cana-3162	233	9	)	)	PUNCT
cana-3162	233	10	•	•	NUM
cana-3162	233	11	𝑺	𝑺	NOUN
cana-3162	233	12	=	=	PUNCT
cana-3162	233	13	∑	∑	PUNCT
cana-3162	233	14	𝑤𝑘	𝑤𝑘	ADP
cana-3162	233	15	𝑛	𝑛	PRON
cana-3162	233	16	𝑘=1	𝑘=1	PROPN
cana-3162	233	17	⋅	⋅	PROPN
cana-3162	233	18	𝑥𝑘	𝑥𝑘	X
cana-3162	233	19	(	(	PUNCT
cana-3162	233	20	55	55	NUM
cana-3162	233	21	)	)	PUNCT
cana-3162	233	22	2	2	NUM
cana-3162	233	23	.	.	PUNCT
cana-3162	233	24	model	model	PROPN
cana-3162	233	25	initialization	initialization	PROPN
cana-3162	233	26	:	:	PUNCT
cana-3162	233	27	initialize	initialize	VERB
cana-3162	233	28	multiple	multiple	ADJ
cana-3162	233	29	base	base	NOUN
cana-3162	233	30	models	model	NOUN
cana-3162	233	31	for	for	ADP
cana-3162	233	32	ensemble	ensemble	ADJ
cana-3162	233	33	learning	learning	NOUN
cana-3162	233	34	.	.	PUNCT
cana-3162	234	1	•	•	NUM
cana-3162	234	2	𝑴	𝑴	NOUN
cana-3162	234	3	=	=	PRON
cana-3162	234	4	{	{	PUNCT
cana-3162	234	5	𝑀1	𝑀1	PROPN
cana-3162	234	6	,	,	PUNCT
cana-3162	234	7	𝑀2	𝑀2	PROPN
cana-3162	234	8	,	,	PUNCT
cana-3162	234	9	𝑀3	𝑀3	NOUN
cana-3162	234	10	,	,	PUNCT
cana-3162	234	11	…	…	PUNCT
cana-3162	234	12	,	,	PUNCT
cana-3162	234	13	𝑀𝑘	𝑀𝑘	PROPN
cana-3162	234	14	}	}	PUNCT
cana-3162	234	15	(	(	PUNCT
cana-3162	234	16	56	56	NUM
cana-3162	234	17	)	)	PUNCT
cana-3162	234	18	•	•	NUM
cana-3162	234	19	where	where	SCONJ
cana-3162	234	20	𝑘	𝑘	NOUN
cana-3162	234	21	is	be	AUX
cana-3162	234	22	the	the	DET
cana-3162	234	23	number	number	NOUN
cana-3162	234	24	of	of	ADP
cana-3162	234	25	models	model	NOUN
cana-3162	234	26	.	.	PUNCT
cana-3162	235	1	(	(	PUNCT
cana-3162	235	2	57	57	NUM
cana-3162	235	3	)	)	PUNCT
cana-3162	235	4	start	start	NOUN
cana-3162	235	5	:	:	PUNCT
cana-3162	235	6	initialize	initialize	VERB
cana-3162	235	7	the	the	DET
cana-3162	235	8	process	process	NOUN
cana-3162	235	9	.	.	PUNCT
cana-3162	236	1	input	input	NOUN
cana-3162	236	2	data	data	PROPN
cana-3162	236	3	:	:	PUNCT
cana-3162	236	4	collect	collect	VERB
cana-3162	236	5	patient	patient	ADJ
cana-3162	236	6	data	datum	NOUN
cana-3162	236	7	and	and	CCONJ
cana-3162	236	8	drug	drug	NOUN
cana-3162	236	9	information	information	NOUN
cana-3162	236	10	.	.	PUNCT
cana-3162	237	1	data	datum	NOUN
cana-3162	237	2	preprocessing	preprocessing	NOUN
cana-3162	237	3	:	:	PUNCT
cana-3162	237	4	clean	clean	ADJ
cana-3162	237	5	and	and	CCONJ
cana-3162	237	6	normalize	normalize	VERB
cana-3162	237	7	the	the	DET
cana-3162	237	8	input	input	NOUN
cana-3162	237	9	data	datum	NOUN
cana-3162	237	10	.	.	PUNCT
cana-3162	238	1	feature	feature	NOUN
cana-3162	238	2	selection	selection	NOUN
cana-3162	238	3	:	:	PUNCT
cana-3162	238	4	select	select	VERB
cana-3162	238	5	relevant	relevant	ADJ
cana-3162	238	6	features	feature	NOUN
cana-3162	238	7	for	for	ADP
cana-3162	238	8	analysis	analysis	NOUN
cana-3162	238	9	.	.	PUNCT
cana-3162	239	1	model	model	NOUN
cana-3162	239	2	selection	selection	NOUN
cana-3162	239	3	:	:	PUNCT
cana-3162	239	4	choose	choose	VERB
cana-3162	239	5	the	the	DET
cana-3162	239	6	appropriate	appropriate	ADJ
cana-3162	239	7	machine	machine	NOUN
cana-3162	239	8	learning	learning	NOUN
cana-3162	239	9	model	model	NOUN
cana-3162	239	10	.	.	PUNCT
cana-3162	240	1	model	model	NOUN
cana-3162	240	2	training	training	NOUN
cana-3162	240	3	:	:	PUNCT
cana-3162	240	4	train	train	VERB
cana-3162	240	5	the	the	DET
cana-3162	240	6	selected	select	VERB
cana-3162	240	7	model	model	NOUN
cana-3162	240	8	using	use	VERB
cana-3162	240	9	the	the	DET
cana-3162	240	10	input	input	NOUN
cana-3162	240	11	data	datum	NOUN
cana-3162	240	12	.	.	PUNCT
cana-3162	241	1	model	model	NOUN
cana-3162	241	2	evaluation	evaluation	NOUN
cana-3162	241	3	:	:	PUNCT
cana-3162	241	4	evaluate	evaluate	VERB
cana-3162	241	5	the	the	DET
cana-3162	241	6	model	model	NOUN
cana-3162	241	7	using	use	VERB
cana-3162	241	8	metrics	metric	NOUN
cana-3162	241	9	like	like	ADP
cana-3162	241	10	accuracy	accuracy	NOUN
cana-3162	241	11	and	and	CCONJ
cana-3162	241	12	f1	f1	NOUN
cana-3162	241	13	score	score	NOUN
cana-3162	241	14	.	.	PUNCT
cana-3162	242	1	make	make	VERB
cana-3162	242	2	predictions	prediction	NOUN
cana-3162	242	3	:	:	PUNCT
cana-3162	242	4	use	use	VERB
cana-3162	242	5	the	the	DET
cana-3162	242	6	trained	train	VERB
cana-3162	242	7	model	model	NOUN
cana-3162	242	8	to	to	PART
cana-3162	242	9	predict	predict	VERB
cana-3162	242	10	drug	drug	NOUN
cana-3162	242	11	responses	response	NOUN
cana-3162	242	12	.	.	PUNCT
cana-3162	243	1	output	output	NOUN
cana-3162	243	2	results	result	NOUN
cana-3162	243	3	:	:	PUNCT
cana-3162	243	4	present	present	VERB
cana-3162	243	5	the	the	DET
cana-3162	243	6	predicted	predict	VERB
cana-3162	243	7	responses	response	NOUN
cana-3162	243	8	.	.	PUNCT
cana-3162	244	1	end	end	NOUN
cana-3162	244	2	:	:	PUNCT
cana-3162	244	3	conclude	conclude	VERB
cana-3162	244	4	the	the	DET
cana-3162	244	5	process	process	NOUN
cana-3162	244	6	.	.	PUNCT
cana-3162	245	1	communications	communication	NOUN
cana-3162	245	2	on	on	ADP
cana-3162	245	3	applied	apply	VERB
cana-3162	245	4	nonlinear	nonlinear	ADJ
cana-3162	245	5	analysis	analysis	NOUN
cana-3162	245	6	issn	issn	NOUN
cana-3162	245	7	:	:	PUNCT
cana-3162	245	8	1074	1074	NUM
cana-3162	245	9	-	-	PUNCT
cana-3162	245	10	133x	133x	NUM
cana-3162	245	11	vol	vol	NOUN
cana-3162	245	12	32	32	NUM
cana-3162	245	13	no	no	NOUN
cana-3162	245	14	.	.	PUNCT
cana-3162	246	1	5s	5s	NUM
cana-3162	246	2	(	(	PUNCT
cana-3162	246	3	2025	2025	NUM
cana-3162	246	4	)	)	PUNCT
cana-3162	246	5	504	504	NUM
cana-3162	246	6	https://internationalpubls.com	https://internationalpubls.com	SYM
cana-3162	246	7	•	•	NUM
cana-3162	246	8	𝐿𝑖	𝐿𝑖	NOUN
cana-3162	246	9	=	=	PUNCT
cana-3162	246	10	∑	∑	PROPN
cana-3162	246	11	(	(	PUNCT
cana-3162	246	12	𝑦𝑖	𝑦𝑖	PROPN
cana-3162	246	13	�	�	PROPN
cana-3162	246	14	̂	̂	SYM
cana-3162	246	15	�	�	PROPN
cana-3162	246	16	−	−	PROPN
cana-3162	246	17	𝑦𝑗	𝑦𝑗	PROPN
cana-3162	246	18	)	)	PUNCT
cana-3162	246	19	2𝑛	2𝑛	PROPN
cana-3162	247	1	𝑗=1	𝑗=1	PROPN
cana-3162	247	2	(	(	PUNCT
cana-3162	247	3	58	58	NUM
cana-3162	247	4	)	)	PUNCT
cana-3162	247	5	•	•	NOUN
cana-3162	247	6	𝑷	𝑷	PROPN
cana-3162	247	7	=	=	PUNCT
cana-3162	247	8	∑	∑	PUNCT
cana-3162	247	9	∑	∑	PUNCT
cana-3162	247	10	𝑝𝑖𝑗	𝑝𝑖𝑗	VERB
cana-3162	247	11	𝑛	𝑛	PRON
cana-3162	247	12	𝑗=1	𝑗=1	PROPN
cana-3162	247	13	𝑘	𝑘	PROPN
cana-3162	247	14	𝑖=1	𝑖=1	PROPN
cana-3162	247	15	⋅	⋅	PROPN
cana-3162	247	16	𝜃𝑗	𝜃𝑗	X
cana-3162	247	17	(	(	PUNCT
cana-3162	247	18	59	59	NUM
cana-3162	247	19	)	)	PUNCT
cana-3162	247	20	3	3	NUM
cana-3162	247	21	.	.	PUNCT
cana-3162	247	22	model	model	NOUN
cana-3162	247	23	training	training	NOUN
cana-3162	247	24	:	:	PUNCT
cana-3162	247	25	train	train	VERB
cana-3162	247	26	each	each	DET
cana-3162	247	27	base	base	NOUN
cana-3162	247	28	model	model	NOUN
cana-3162	247	29	using	use	VERB
cana-3162	247	30	the	the	DET
cana-3162	247	31	input	input	NOUN
cana-3162	247	32	features	feature	NOUN
cana-3162	247	33	.	.	PUNCT
cana-3162	248	1	•	•	X
cana-3162	248	2	𝑀𝑖	𝑀𝑖	PROPN
cana-3162	248	3	trained	train	VERB
cana-3162	248	4	on	on	ADP
cana-3162	248	5	𝑜𝑛	𝑜𝑛	PROPN
cana-3162	248	6	𝑋𝑖𝑛𝑝𝑢𝑡	𝑋𝑖𝑛𝑝𝑢𝑡	PROPN
cana-3162	248	7	𝑓𝑜𝑟	𝑓𝑜𝑟	ADV
cana-3162	248	8	𝑒𝑎𝑐ℎ	𝑒𝑎𝑐ℎ	PROPN
cana-3162	248	9	𝑖	𝑖	NOUN
cana-3162	248	10	•	•	NUM
cana-3162	248	11	𝐿𝑖	𝐿𝑖	NOUN
cana-3162	248	12	=	=	SYM
cana-3162	248	13	∑	∑	NOUN
cana-3162	248	14	𝐿(𝑦𝑗	𝐿(𝑦𝑗	PROPN
cana-3162	248	15	,	,	PUNCT
cana-3162	248	16	𝑦𝑖	𝑦𝑖	PROPN
cana-3162	248	17	�	�	PROPN
cana-3162	248	18	̂	̂	NUM
cana-3162	248	19	�	�	NOUN
cana-3162	248	20	)𝑛	)𝑛	PUNCT
cana-3162	248	21	𝑗=1	𝑗=1	PROPN
cana-3162	249	1	=	=	PUNCT
cana-3162	249	2	∑	∑	PUNCT
cana-3162	249	3	(	(	PUNCT
cana-3162	249	4	𝑦𝑗	𝑦𝑗	PROPN
cana-3162	249	5	−	−	PROPN
cana-3162	249	6	𝑦𝑖	𝑦𝑖	PROPN
cana-3162	249	7	�	�	PROPN
cana-3162	249	8	̂	̂	NUM
cana-3162	249	9	�	�	PROPN
cana-3162	249	10	)	)	PUNCT
cana-3162	250	1	2𝑛	2𝑛	PROPN
cana-3162	250	2	𝑗=1	𝑗=1	PROPN
cana-3162	250	3	(	(	PUNCT
cana-3162	250	4	60	60	NUM
cana-3162	250	5	)	)	SYM
cana-3162	250	6	4	4	NUM
cana-3162	250	7	.	.	PUNCT
cana-3162	250	8	model	model	NOUN
cana-3162	250	9	predictions	prediction	NOUN
cana-3162	250	10	:	:	PUNCT
cana-3162	250	11	generate	generate	VERB
cana-3162	250	12	predictions	prediction	NOUN
cana-3162	250	13	from	from	ADP
cana-3162	250	14	each	each	DET
cana-3162	250	15	base	base	NOUN
cana-3162	250	16	model	model	NOUN
cana-3162	250	17	.	.	PUNCT
cana-3162	251	1	•	•	NUM
cana-3162	251	2	𝑌	𝑌	PROPN
cana-3162	251	3	�	�	PROPN
cana-3162	251	4	̂	̂	VERB
cana-3162	251	5	�	�	NOUN
cana-3162	251	6	=	=	SYM
cana-3162	251	7	𝑀𝑖𝑿ipt	𝑀𝑖𝑿ipt	PROPN
cana-3162	251	8	(	(	PUNCT
cana-3162	251	9	61	61	NUM
cana-3162	251	10	)	)	PUNCT
cana-3162	251	11	•	•	NUM
cana-3162	251	12	𝑦	𝑦	NOUN
cana-3162	251	13	�	�	NOUN
cana-3162	251	14	̂	̂	VERB
cana-3162	251	15	�	�	NOUN
cana-3162	251	16	=	=	SYM
cana-3162	251	17	∑	∑	PUNCT
cana-3162	251	18	𝛽𝑗𝑓𝑖𝑗	𝛽𝑗𝑓𝑖𝑗	PROPN
cana-3162	251	19	𝑚	𝑚	PROPN
cana-3162	251	20	𝑗=1	𝑗=1	PROPN
cana-3162	251	21	(	(	PUNCT
cana-3162	251	22	62	62	NUM
cana-3162	251	23	)	)	PUNCT
cana-3162	251	24	5	5	NUM
cana-3162	251	25	.	.	PUNCT
cana-3162	252	1	aggregate	aggregate	ADJ
cana-3162	252	2	predictions	prediction	NOUN
cana-3162	252	3	:	:	PUNCT
cana-3162	252	4	combine	combine	VERB
cana-3162	252	5	predictions	prediction	NOUN
cana-3162	252	6	using	use	VERB
cana-3162	252	7	a	a	DET
cana-3162	252	8	voting	voting	NOUN
cana-3162	252	9	or	or	CCONJ
cana-3162	252	10	averaging	averaging	NOUN
cana-3162	252	11	mechanism	mechanism	NOUN
cana-3162	252	12	.	.	PUNCT
cana-3162	253	1	•	•	NUM
cana-3162	253	2	𝑌ensemble	𝑌ensemble	PROPN
cana-3162	253	3	̂	̂	PUNCT
cana-3162	253	4	=	=	SYM
cana-3162	253	5	1	1	NUM
cana-3162	253	6	𝑘	𝑘	DET
cana-3162	253	7	∑	∑	PROPN
cana-3162	253	8	𝑌	𝑌	PROPN
cana-3162	253	9	�	�	PROPN
cana-3162	253	10	̂	̂	NOUN
cana-3162	253	11	�	�	PROPN
cana-3162	253	12	𝑘	𝑘	X
cana-3162	253	13	𝑖=1	𝑖=1	PROPN
cana-3162	253	14	(	(	PUNCT
cana-3162	253	15	63	63	NUM
cana-3162	253	16	)	)	PUNCT
cana-3162	253	17	•	•	NUM
cana-3162	253	18	𝑌ensemble	𝑌ensemble	PROPN
cana-3162	253	19	̂	̂	PUNCT
cana-3162	253	20	=	=	SYM
cana-3162	253	21	1	1	NUM
cana-3162	253	22	𝑁	𝑁	PROPN
cana-3162	253	23	∑	∑	PROPN
cana-3162	253	24	∑	∑	PROPN
cana-3162	253	25	𝑦𝑖	𝑦𝑖	PROPN
cana-3162	253	26	�	�	PROPN
cana-3162	253	27	̂	̂	X
cana-3162	253	28	�	�	NOUN
cana-3162	253	29	𝑛	𝑛	PRON
cana-3162	253	30	𝑗=1	𝑗=1	PROPN
cana-3162	253	31	𝑘	𝑘	X
cana-3162	253	32	𝑖=1	𝑖=1	PROPN
cana-3162	253	33	(	(	PUNCT
cana-3162	253	34	64	64	NUM
cana-3162	253	35	)	)	PUNCT
cana-3162	253	36	•	•	NOUN
cana-3162	253	37	𝑌modê	𝑌modê	PROPN
cana-3162	254	1	=	=	SYM
cana-3162	254	2	mode(𝑦1̂	mode(𝑦1̂	PROPN
cana-3162	254	3	,	,	PUNCT
cana-3162	254	4	𝑦2̂	𝑦2̂	NUM
cana-3162	254	5	,	,	PUNCT
cana-3162	254	6	…	…	PUNCT
cana-3162	254	7	,	,	PUNCT
cana-3162	254	8	𝑦	𝑦	NOUN
cana-3162	254	9	�	�	NOUN
cana-3162	254	10	̂	̂	SYM
cana-3162	254	11	�	�	NOUN
cana-3162	254	12	)	)	PUNCT
cana-3162	254	13	(	(	PUNCT
cana-3162	254	14	65	65	NUM
cana-3162	254	15	)	)	PUNCT
cana-3162	254	16	6	6	NUM
cana-3162	254	17	.	.	PUNCT
cana-3162	254	18	model	model	NOUN
cana-3162	254	19	evaluation	evaluation	NOUN
cana-3162	254	20	:	:	PUNCT
cana-3162	254	21	assess	assess	VERB
cana-3162	254	22	the	the	DET
cana-3162	254	23	performance	performance	NOUN
cana-3162	254	24	of	of	ADP
cana-3162	254	25	the	the	DET
cana-3162	254	26	ensemble	ensemble	ADJ
cana-3162	254	27	model	model	NOUN
cana-3162	254	28	.	.	PUNCT
cana-3162	255	1	•	•	NUM
cana-3162	256	1	𝐴ensemble	𝐴ensemble	ADJ
cana-3162	256	2	=	=	PUNCT
cana-3162	256	3	1	1	NUM
cana-3162	256	4	𝑛	𝑛	PRON
cana-3162	256	5	∑	∑	ADP
cana-3162	256	6	1(𝑦𝑖=𝑦ensemble,𝑖̂	1(𝑦𝑖=𝑦ensemble,𝑖̂	NUM
cana-3162	256	7	)	)	PUNCT
cana-3162	256	8	𝑛	𝑛	PROPN
cana-3162	256	9	𝑖=1	𝑖=1	PROPN
cana-3162	256	10	(	(	PUNCT
cana-3162	256	11	66	66	NUM
cana-3162	256	12	)	)	PUNCT
cana-3162	256	13	•	•	NUM
cana-3162	256	14	error	error	NOUN
cana-3162	256	15	rate	rate	NOUN
cana-3162	256	16	:	:	PUNCT
cana-3162	256	17	𝐸	𝐸	NOUN
cana-3162	256	18	=	=	SYM
cana-3162	256	19	1	1	NUM
cana-3162	257	1	−	−	PROPN
cana-3162	257	2	𝐴ensemble	𝐴ensemble	ADJ
cana-3162	257	3	=	=	PUNCT
cana-3162	257	4	∑	∑	PUNCT
cana-3162	257	5	1−𝐴𝑗	1−𝐴𝑗	PROPN
cana-3162	257	6	𝑛	𝑛	PRON
cana-3162	257	7	𝑛	𝑛	PROPN
cana-3162	257	8	𝑗=1	𝑗=1	X
cana-3162	257	9	(	(	PUNCT
cana-3162	257	10	67	67	NUM
cana-3162	257	11	)	)	PUNCT
cana-3162	257	12	7	7	NUM
cana-3162	257	13	.	.	PUNCT
cana-3162	257	14	feature	feature	NOUN
cana-3162	257	15	importance	importance	NOUN
cana-3162	257	16	analysis	analysis	NOUN
cana-3162	257	17	:	:	PUNCT
cana-3162	257	18	evaluate	evaluate	VERB
cana-3162	257	19	the	the	DET
cana-3162	257	20	importance	importance	NOUN
cana-3162	257	21	of	of	ADP
cana-3162	257	22	each	each	DET
cana-3162	257	23	feature	feature	NOUN
cana-3162	257	24	in	in	ADP
cana-3162	257	25	the	the	DET
cana-3162	257	26	ensemble	ensemble	ADJ
cana-3162	257	27	model	model	NOUN
cana-3162	257	28	.	.	PUNCT
cana-3162	258	1	•	•	NUM
cana-3162	258	2	𝐼(𝑓𝑖	𝐼(𝑓𝑖	NOUN
cana-3162	258	3	)	)	PUNCT
cana-3162	258	4	=	=	SYM
cana-3162	258	5	1	1	NUM
cana-3162	258	6	𝑘	𝑘	PRON
cana-3162	258	7	∑	∑	PUNCT
cana-3162	258	8	𝐼𝑗(𝑓𝑖)𝑘	𝐼𝑗(𝑓𝑖)𝑘	PROPN
cana-3162	258	9	𝑗=1	𝑗=1	PROPN
cana-3162	258	10	(	(	PUNCT
cana-3162	258	11	68	68	NUM
cana-3162	258	12	)	)	PUNCT
cana-3162	258	13	•	•	NOUN
cana-3162	259	1	𝐼total	𝐼total	PROPN
cana-3162	259	2	=	=	PUNCT
cana-3162	259	3	∑	∑	PUNCT
cana-3162	259	4	𝐼(𝑓𝑖)𝑚	𝐼(𝑓𝑖)𝑚	PROPN
cana-3162	259	5	𝑖=1	𝑖=1	PROPN
cana-3162	259	6	(	(	PUNCT
cana-3162	259	7	69	69	NUM
cana-3162	259	8	)	)	PUNCT
cana-3162	259	9	8	8	NUM
cana-3162	259	10	.	.	PUNCT
cana-3162	260	1	hyperparameter	hyperparameter	NOUN
cana-3162	261	1	tuning	tuning	NOUN
cana-3162	261	2	:	:	PUNCT
cana-3162	261	3	optimize	optimize	VERB
cana-3162	261	4	hyperparameters	hyperparameter	NOUN
cana-3162	261	5	for	for	ADP
cana-3162	261	6	better	well	ADJ
cana-3162	261	7	model	model	NOUN
cana-3162	261	8	performance	performance	NOUN
cana-3162	261	9	.	.	PUNCT
cana-3162	262	1	•	•	NUM
cana-3162	262	2	𝜃optimal	𝜃optimal	NOUN
cana-3162	262	3	=	=	SYM
cana-3162	262	4	arg	arg	NOUN
cana-3162	262	5	min	min	PROPN
cana-3162	262	6	𝜃	𝜃	PROPN
cana-3162	262	7	𝐿	𝐿	PROPN
cana-3162	262	8	(	(	PUNCT
cana-3162	262	9	𝜃	𝜃	NOUN
cana-3162	262	10	)	)	PUNCT
cana-3162	262	11	(	(	PUNCT
cana-3162	262	12	70	70	NUM
cana-3162	262	13	)	)	PUNCT
cana-3162	262	14	•	•	NUM
cana-3162	262	15	tuning	tune	VERB
cana-3162	262	16	:	:	PUNCT
cana-3162	262	17	𝜃	𝜃	X
cana-3162	262	18	=	=	PUNCT
cana-3162	262	19	∑	∑	PROPN
cana-3162	262	20	𝜃𝑖	𝜃𝑖	ADP
cana-3162	262	21	𝑘	𝑘	X
cana-3162	262	22	𝑖=1	𝑖=1	PUNCT
cana-3162	262	23	⋅	⋅	X
cana-3162	262	24	𝐿𝑖	𝐿𝑖	NOUN
cana-3162	262	25	(	(	PUNCT
cana-3162	262	26	71	71	NUM
cana-3162	262	27	)	)	PUNCT
cana-3162	262	28	9	9	NUM
cana-3162	262	29	.	.	PUNCT
cana-3162	262	30	final	final	ADJ
cana-3162	262	31	predictions	prediction	NOUN
cana-3162	262	32	:	:	PUNCT
cana-3162	262	33	generate	generate	VERB
cana-3162	262	34	final	final	ADJ
cana-3162	262	35	predictions	prediction	NOUN
cana-3162	262	36	using	use	VERB
cana-3162	262	37	the	the	DET
cana-3162	262	38	optimized	optimize	VERB
cana-3162	262	39	ensemble	ensemble	ADJ
cana-3162	262	40	model	model	NOUN
cana-3162	262	41	.	.	PUNCT
cana-3162	263	1	•	•	NUM
cana-3162	263	2	𝑌final	𝑌final	PROPN
cana-3162	263	3	̂	̂	PUNCT
cana-3162	263	4	=	=	SYM
cana-3162	263	5	𝑀ensemble𝑿ipt	𝑀ensemble𝑿ipt	PROPN
cana-3162	263	6	(	(	PUNCT
cana-3162	263	7	72	72	NUM
cana-3162	263	8	)	)	PUNCT
cana-3162	263	9	•	•	NOUN
cana-3162	263	10	𝑌final	𝑌final	PROPN
cana-3162	263	11	̂	̂	PUNCT
cana-3162	263	12	=	=	SYM
cana-3162	263	13	∑	∑	PUNCT
cana-3162	263	14	𝑦	𝑦	X
cana-3162	263	15	�	�	PROPN
cana-3162	263	16	̂	̂	VERB
cana-3162	263	17	�	�	PROPN
cana-3162	263	18	𝑘	𝑘	X
cana-3162	263	19	𝑖=1	𝑖=1	PROPN
cana-3162	263	20	⋅	⋅	PROPN
cana-3162	263	21	𝑤𝑖	𝑤𝑖	X
cana-3162	263	22	(	(	PUNCT
cana-3162	263	23	73	73	NUM
cana-3162	263	24	)	)	PUNCT
cana-3162	263	25	10	10	NUM
cana-3162	263	26	.	.	PUNCT
cana-3162	264	1	model	model	PROPN
cana-3162	264	2	comparison	comparison	NOUN
cana-3162	264	3	:	:	PUNCT
cana-3162	264	4	compare	compare	VERB
cana-3162	264	5	the	the	DET
cana-3162	264	6	ensemble	ensemble	ADJ
cana-3162	264	7	model	model	NOUN
cana-3162	264	8	with	with	ADP
cana-3162	264	9	individual	individual	ADJ
cana-3162	264	10	models	model	NOUN
cana-3162	264	11	.	.	PUNCT
cana-3162	265	1	•	•	ADP
cana-3162	265	2	δ𝐴	δ𝐴	NOUN
cana-3162	265	3	=	=	PUNCT
cana-3162	265	4	𝐴ensemble	𝐴ensemble	ADJ
cana-3162	265	5	−	−	PROPN
cana-3162	265	6	𝐴individual	𝐴individual	PROPN
cana-3162	265	7	(	(	PUNCT
cana-3162	265	8	74	74	NUM
cana-3162	265	9	)	)	PUNCT
cana-3162	265	10	•	•	NUM
cana-3162	266	1	difference	difference	NOUN
cana-3162	266	2	in	in	ADP
cana-3162	266	3	predictions	prediction	NOUN
cana-3162	266	4	:	:	PUNCT
cana-3162	266	5	𝐷	𝐷	PROPN
cana-3162	266	6	=	=	SYM
cana-3162	266	7	∑	∑	PROPN
cana-3162	266	8	|𝑦ensemble,𝑗̂	|𝑦ensemble,𝑗̂	PROPN
cana-3162	266	9	−	−	PROPN
cana-3162	266	10	𝑦individual,𝑗̂	𝑦individual,𝑗̂	ADJ
cana-3162	266	11	|𝑛	|𝑛	X
cana-3162	266	12	𝑗=1	𝑗=1	X
cana-3162	266	13	(	(	PUNCT
cana-3162	266	14	75	75	NUM
cana-3162	266	15	)	)	PUNCT
cana-3162	266	16	11	11	NUM
cana-3162	266	17	.	.	PUNCT
cana-3162	267	1	result	result	PROPN
cana-3162	267	2	interpretation	interpretation	NOUN
cana-3162	267	3	:	:	PUNCT
cana-3162	267	4	interpret	interpret	VERB
cana-3162	267	5	the	the	DET
cana-3162	267	6	final	final	ADJ
cana-3162	267	7	predictions	prediction	NOUN
cana-3162	267	8	and	and	CCONJ
cana-3162	267	9	their	their	PRON
cana-3162	267	10	implications	implication	NOUN
cana-3162	267	11	.	.	PUNCT
cana-3162	268	1	•	•	NUM
cana-3162	268	2	response	response	NOUN
cana-3162	268	3	interpretation	interpretation	NOUN
cana-3162	268	4	:	:	PUNCT
cana-3162	268	5	𝑅	𝑅	PROPN
cana-3162	268	6	=	=	SYM
cana-3162	268	7	∑	∑	PROPN
cana-3162	268	8	1(𝑦	1(𝑦	NUM
cana-3162	268	9	�	�	PROPN
cana-3162	268	10	̂	̂	SYM
cana-3162	268	11	�	�	NOUN
cana-3162	268	12	≥0.5	≥0.5	NOUN
cana-3162	268	13	)	)	PUNCT
cana-3162	268	14	𝑛	𝑛	PRON
cana-3162	268	15	𝑖=1	𝑖=1	PROPN
cana-3162	268	16	(	(	PUNCT
cana-3162	268	17	76	76	NUM
cana-3162	268	18	)	)	PUNCT
cana-3162	268	19	•	•	NOUN
cana-3162	269	1	confidence	confidence	NOUN
cana-3162	269	2	interval	interval	NOUN
cana-3162	269	3	:	:	PUNCT
cana-3162	270	1	𝐶𝐼	𝐶𝐼	PROPN
cana-3162	270	2	=	=	PUNCT
cana-3162	271	1	[	[	X
cana-3162	271	2	𝑦final̂	𝑦final̂	ADV
cana-3162	271	3	−	−	PROPN
cana-3162	271	4	𝑍	𝑍	PROPN
cana-3162	271	5	𝑠	𝑠	PROPN
cana-3162	271	6	√𝑛	√𝑛	X
cana-3162	271	7	,	,	PUNCT
cana-3162	271	8	𝑦final̂	𝑦final̂	ADV
cana-3162	271	9	+	+	SYM
cana-3162	271	10	𝑍	𝑍	PROPN
cana-3162	271	11	𝑠	𝑠	PROPN
cana-3162	271	12	√𝑛	√𝑛	ADP
cana-3162	271	13	]	]	PUNCT
cana-3162	271	14	(	(	PUNCT
cana-3162	271	15	77	77	NUM
cana-3162	271	16	)	)	PUNCT
cana-3162	271	17	communications	communication	NOUN
cana-3162	271	18	on	on	ADP
cana-3162	271	19	applied	apply	VERB
cana-3162	271	20	nonlinear	nonlinear	ADJ
cana-3162	271	21	analysis	analysis	NOUN
cana-3162	271	22	issn	issn	NOUN
cana-3162	271	23	:	:	PUNCT
cana-3162	271	24	1074	1074	NUM
cana-3162	271	25	-	-	PUNCT
cana-3162	271	26	133x	133x	NUM
cana-3162	271	27	vol	vol	NOUN
cana-3162	271	28	32	32	NUM
cana-3162	271	29	no	no	NOUN
cana-3162	271	30	.	.	PUNCT
cana-3162	272	1	5s	5s	NUM
cana-3162	272	2	(	(	PUNCT
cana-3162	272	3	2025	2025	NUM
cana-3162	272	4	)	)	PUNCT
cana-3162	272	5	505	505	NUM
cana-3162	272	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3162	272	7	12	12	NUM
cana-3162	272	8	.	.	PUNCT
cana-3162	273	1	report	report	NOUN
cana-3162	273	2	generation	generation	NOUN
cana-3162	273	3	:	:	PUNCT
cana-3162	273	4	create	create	VERB
cana-3162	273	5	a	a	DET
cana-3162	273	6	comprehensive	comprehensive	ADJ
cana-3162	273	7	report	report	NOUN
cana-3162	273	8	of	of	ADP
cana-3162	273	9	findings	finding	NOUN
cana-3162	273	10	.	.	PUNCT
cana-3162	274	1	•	•	NUM
cana-3162	274	2	report	report	NOUN
cana-3162	274	3	:	:	PUNCT
cana-3162	274	4	𝑅	𝑅	PROPN
cana-3162	274	5	=	=	PROPN
cana-3162	274	6	𝑓(𝑌final	𝑓(𝑌final	PROPN
cana-3162	274	7	̂	̂	NUM
cana-3162	274	8	,	,	PUNCT
cana-3162	274	9	𝐴ensemble	𝐴ensemble	PROPN
cana-3162	274	10	)	)	PUNCT
cana-3162	274	11	(	(	PUNCT
cana-3162	274	12	78	78	NUM
cana-3162	274	13	)	)	PUNCT
cana-3162	274	14	•	•	NUM
cana-3162	274	15	summary	summary	NOUN
cana-3162	274	16	:	:	PUNCT
cana-3162	275	1	𝑆	𝑆	PROPN
cana-3162	275	2	=	=	SYM
cana-3162	275	3	∑	∑	PUNCT
cana-3162	275	4	𝑦	𝑦	X
cana-3162	275	5	�	�	PROPN
cana-3162	275	6	̂	̂	SYM
cana-3162	275	7	�	�	PROPN
cana-3162	275	8	𝑛	𝑛	PRON
cana-3162	275	9	𝑖=1	𝑖=1	PROPN
cana-3162	275	10	(	(	PUNCT
cana-3162	275	11	79	79	NUM
cana-3162	275	12	)	)	PUNCT
cana-3162	275	13	13	13	NUM
cana-3162	275	14	.	.	PUNCT
cana-3162	275	15	feedback	feedback	PROPN
cana-3162	275	16	loop	loop	PROPN
cana-3162	275	17	:	:	PUNCT
cana-3162	275	18	gather	gather	VERB
cana-3162	275	19	feedback	feedback	NOUN
cana-3162	275	20	for	for	ADP
cana-3162	275	21	continuous	continuous	ADJ
cana-3162	275	22	model	model	NOUN
cana-3162	275	23	improvement	improvement	NOUN
cana-3162	275	24	.	.	PUNCT
cana-3162	276	1	•	•	NUM
cana-3162	276	2	𝐹	𝐹	PROPN
cana-3162	276	3	=	=	PRON
cana-3162	276	4	{	{	PUNCT
cana-3162	276	5	𝑓𝑖	𝑓𝑖	PROPN
cana-3162	276	6	:	:	PUNCT
cana-3162	276	7	𝑓𝑖	𝑓𝑖	PROPN
cana-3162	276	8	∈	∈	PROPN
cana-3162	276	9	𝑅	𝑅	PROPN
cana-3162	276	10	}	}	PUNCT
cana-3162	276	11	(	(	PUNCT
cana-3162	276	12	80	80	NUM
cana-3162	276	13	)	)	PUNCT
cana-3162	276	14	•	•	NUM
cana-3162	276	15	feedback	feedback	NOUN
cana-3162	276	16	analysis	analysis	NOUN
cana-3162	276	17	:	:	PUNCT
cana-3162	277	1	𝐴𝐹	𝐴𝐹	PROPN
cana-3162	277	2	=	=	SYM
cana-3162	277	3	1	1	NUM
cana-3162	277	4	𝑚	𝑚	NOUN
cana-3162	277	5	∑	∑	ADV
cana-3162	277	6	𝐹𝑖	𝐹𝑖	PROPN
cana-3162	277	7	𝑚	𝑚	NOUN
cana-3162	277	8	𝑖=1	𝑖=1	PROPN
cana-3162	277	9	(	(	PUNCT
cana-3162	277	10	81	81	NUM
cana-3162	277	11	)	)	PUNCT
cana-3162	277	12	14	14	NUM
cana-3162	277	13	.	.	PUNCT
cana-3162	278	1	end	end	NOUN
cana-3162	278	2	process	process	NOUN
cana-3162	278	3	:	:	PUNCT
cana-3162	278	4	conclude	conclude	VERB
cana-3162	278	5	the	the	DET
cana-3162	278	6	process	process	NOUN
cana-3162	278	7	and	and	CCONJ
cana-3162	278	8	finalize	finalize	VERB
cana-3162	278	9	outputs	output	NOUN
cana-3162	278	10	.	.	PUNCT
cana-3162	279	1	•	•	NUM
cana-3162	279	2	𝐶	𝐶	PROPN
cana-3162	279	3	=	=	PROPN
cana-3162	279	4	success	success	NOUN
cana-3162	279	5	or	or	CCONJ
cana-3162	279	6	failure	failure	NOUN
cana-3162	279	7	of	of	ADP
cana-3162	279	8	predictions	prediction	NOUN
cana-3162	279	9	(	(	PUNCT
cana-3162	279	10	82	82	NUM
cana-3162	279	11	)	)	PUNCT
cana-3162	279	12	•	•	NOUN
cana-3162	279	13	convergence	convergence	NOUN
cana-3162	279	14	check	check	NOUN
cana-3162	279	15	:	:	PUNCT
cana-3162	279	16	𝐶check	𝐶check	X
cana-3162	279	17	=	=	SYM
cana-3162	279	18	∑	∑	PUNCT
cana-3162	279	19	|𝑅𝑖	|𝑅𝑖	PROPN
cana-3162	279	20	−	−	PROPN
cana-3162	279	21	𝑦final,	𝑦final,	NOUN
cana-3162	279	22	�	�	PROPN
cana-3162	279	23	̂	̂	SYM
cana-3162	279	24	�	�	NOUN
cana-3162	279	25	|	|	NOUN
cana-3162	279	26	𝑛	𝑛	DET
cana-3162	279	27	𝑖=1	𝑖=1	PROPN
cana-3162	279	28	≤	≤	X
cana-3162	279	29	𝜖	𝜖	X
cana-3162	279	30	(	(	PUNCT
cana-3162	279	31	83	83	NUM
cana-3162	279	32	)	)	PUNCT
cana-3162	279	33	notations	notation	NOUN
cana-3162	279	34	in	in	ADP
cana-3162	279	35	algorithm	algorithm	NOUN
cana-3162	279	36	•	•	ADP
cana-3162	279	37	𝑿𝒊𝒏𝒑𝒖𝒕	𝑿𝒊𝒏𝒑𝒖𝒕	PROPN
cana-3162	279	38	:	:	PUNCT
cana-3162	279	39	input	input	NOUN
cana-3162	279	40	feature	feature	NOUN
cana-3162	279	41	matrix	matrix	NOUN
cana-3162	279	42	from	from	ADP
cana-3162	279	43	algorithm	algorithm	NOUN
cana-3162	279	44	2	2	NUM
cana-3162	279	45	.	.	NOUN
cana-3162	279	46	•	•	NUM
cana-3162	279	47	𝒀predictions	𝒀predictions	PROPN
cana-3162	279	48	̂	̂	NUM
cana-3162	279	49	:	:	PUNCT
cana-3162	279	50	predictions	prediction	NOUN
cana-3162	279	51	obtained	obtain	VERB
cana-3162	279	52	from	from	ADP
cana-3162	279	53	algorithm	algorithm	NOUN
cana-3162	279	54	2	2	NUM
cana-3162	279	55	.	.	NOUN
cana-3162	279	56	•	•	NUM
cana-3162	279	57	𝑴	𝑴	NOUN
cana-3162	279	58	:	:	PUNCT
cana-3162	279	59	set	set	NOUN
cana-3162	279	60	of	of	ADP
cana-3162	279	61	base	base	NOUN
cana-3162	279	62	models	model	NOUN
cana-3162	279	63	used	use	VERB
cana-3162	279	64	in	in	ADP
cana-3162	279	65	ensemble	ensemble	ADJ
cana-3162	279	66	learning	learning	NOUN
cana-3162	279	67	.	.	PUNCT
cana-3162	280	1	•	•	NUM
cana-3162	280	2	𝒌	𝒌	PRON
cana-3162	280	3	:	:	PUNCT
cana-3162	280	4	total	total	ADJ
cana-3162	280	5	number	number	NOUN
cana-3162	280	6	of	of	ADP
cana-3162	280	7	base	base	NOUN
cana-3162	280	8	models	model	NOUN
cana-3162	280	9	.	.	PUNCT
cana-3162	281	1	•	•	NUM
cana-3162	281	2	𝑴𝒊	𝑴𝒊	PROPN
cana-3162	281	3	:	:	PUNCT
cana-3162	281	4	individual	individual	ADJ
cana-3162	281	5	models	model	NOUN
cana-3162	281	6	in	in	ADP
cana-3162	281	7	the	the	DET
cana-3162	281	8	ensemble	ensemble	NOUN
cana-3162	281	9	.	.	PUNCT
cana-3162	282	1	•	•	NUM
cana-3162	282	2	𝑳𝒊	𝑳𝒊	PROPN
cana-3162	282	3	:	:	PUNCT
cana-3162	282	4	loss	loss	NOUN
cana-3162	282	5	associated	associate	VERB
cana-3162	282	6	with	with	ADP
cana-3162	282	7	model	model	NOUN
cana-3162	283	1	𝑀𝑖.	𝑀𝑖.	PROPN
cana-3162	283	2	•	•	NUM
cana-3162	283	3	𝒀	𝒀	NOUN
cana-3162	283	4	�	�	PROPN
cana-3162	283	5	̂	̂	NOUN
cana-3162	283	6	�	�	NOUN
cana-3162	283	7	:	:	PUNCT
cana-3162	283	8	predictions	prediction	NOUN
cana-3162	283	9	from	from	ADP
cana-3162	283	10	model	model	NOUN
cana-3162	283	11	𝑀𝑖	𝑀𝑖	PROPN
cana-3162	283	12	.	.	PUNCT
cana-3162	284	1	•	•	NUM
cana-3162	284	2	𝒀ensemble	𝒀ensemble	ADJ
cana-3162	284	3	̂	̂	PUNCT
cana-3162	284	4	:	:	PUNCT
cana-3162	285	1	combined	combine	VERB
cana-3162	285	2	predictions	prediction	NOUN
cana-3162	285	3	from	from	ADP
cana-3162	285	4	the	the	DET
cana-3162	285	5	ensemble	ensemble	ADJ
cana-3162	285	6	model	model	NOUN
cana-3162	285	7	.	.	PUNCT
cana-3162	286	1	•	•	NUM
cana-3162	287	1	𝑨ensemble	𝑨ensemble	ADJ
cana-3162	287	2	:	:	PUNCT
cana-3162	287	3	accuracy	accuracy	NOUN
cana-3162	287	4	of	of	ADP
cana-3162	287	5	the	the	DET
cana-3162	287	6	ensemble	ensemble	ADJ
cana-3162	287	7	model	model	NOUN
cana-3162	287	8	.	.	PUNCT
cana-3162	288	1	•	•	NOUN
cana-3162	288	2	𝑰(𝒇𝒊	𝑰(𝒇𝒊	NOUN
cana-3162	288	3	):	):	PUNCT
cana-3162	288	4	importance	importance	NOUN
cana-3162	288	5	of	of	ADP
cana-3162	288	6	feature	feature	NOUN
cana-3162	288	7	𝑓𝑖	𝑓𝑖	PROPN
cana-3162	288	8	.	.	PUNCT
cana-3162	289	1	•	•	NUM
cana-3162	289	2	𝜽𝒊	𝜽𝒊	ADV
cana-3162	289	3	:	:	PUNCT
cana-3162	289	4	hyperparameters	hyperparameter	NOUN
cana-3162	289	5	for	for	ADP
cana-3162	289	6	model	model	NOUN
cana-3162	289	7	𝑀𝑖.	𝑀𝑖.	PROPN
cana-3162	289	8	•	•	ADP
cana-3162	289	9	𝒀final̂	𝒀final̂	PROPN
cana-3162	289	10	:	:	PUNCT
cana-3162	289	11	final	final	ADJ
cana-3162	289	12	predictions	prediction	NOUN
cana-3162	289	13	from	from	ADP
cana-3162	289	14	the	the	DET
cana-3162	289	15	ensemble	ensemble	ADJ
cana-3162	289	16	model	model	NOUN
cana-3162	289	17	.	.	PUNCT
cana-3162	290	1	•	•	NUM
cana-3162	290	2	𝚫𝑨	𝚫𝑨	PROPN
cana-3162	290	3	:	:	PUNCT
cana-3162	290	4	change	change	NOUN
cana-3162	290	5	in	in	ADP
cana-3162	290	6	accuracy	accuracy	NOUN
cana-3162	290	7	comparing	compare	VERB
cana-3162	290	8	ensemble	ensemble	ADJ
cana-3162	290	9	to	to	ADP
cana-3162	290	10	individual	individual	ADJ
cana-3162	290	11	models	model	NOUN
cana-3162	290	12	.	.	PUNCT
cana-3162	291	1	•	•	NUM
cana-3162	291	2	𝑹	𝑹	NOUN
cana-3162	291	3	:	:	PUNCT
cana-3162	291	4	report	report	NOUN
cana-3162	291	5	of	of	ADP
cana-3162	291	6	findings	finding	NOUN
cana-3162	291	7	.	.	PUNCT
cana-3162	292	1	•	•	NUM
cana-3162	292	2	𝑭	𝑭	NOUN
cana-3162	292	3	:	:	PUNCT
cana-3162	292	4	feedback	feedback	NOUN
cana-3162	292	5	for	for	ADP
cana-3162	292	6	improvements	improvement	NOUN
cana-3162	292	7	.	.	PUNCT
cana-3162	293	1	•	•	NUM
cana-3162	293	2	𝑪	𝑪	NOUN
cana-3162	293	3	:	:	PUNCT
cana-3162	293	4	conclusion	conclusion	NOUN
cana-3162	293	5	of	of	ADP
cana-3162	293	6	the	the	DET
cana-3162	293	7	process	process	NOUN
cana-3162	293	8	.	.	PUNCT
cana-3162	294	1	ensemble	ensemble	ADJ
cana-3162	294	2	learning	learning	NOUN
cana-3162	294	3	helps	help	VERB
cana-3162	294	4	algorithm	algorithm	NOUN
cana-3162	294	5	3	3	NUM
cana-3162	294	6	predict	predict	VERB
cana-3162	294	7	medication	medication	NOUN
cana-3162	294	8	effects	effect	NOUN
cana-3162	294	9	by	by	ADP
cana-3162	294	10	blending	blend	VERB
cana-3162	294	11	base	base	NOUN
cana-3162	294	12	models	model	NOUN
cana-3162	294	13	learned	learn	VERB
cana-3162	294	14	with	with	ADP
cana-3162	294	15	input	input	NOUN
cana-3162	294	16	attributes	attribute	NOUN
cana-3162	294	17	from	from	ADP
cana-3162	294	18	algorithm	algorithm	NOUN
cana-3162	294	19	2	2	NUM
cana-3162	294	20	.	.	PUNCT
cana-3162	295	1	it	it	PRON
cana-3162	295	2	obtains	obtain	VERB
cana-3162	295	3	data	datum	NOUN
cana-3162	295	4	and	and	CCONJ
cana-3162	295	5	forecasts	forecast	NOUN
cana-3162	295	6	first	first	ADV
cana-3162	295	7	.	.	PUNCT
cana-3162	296	1	next	next	ADV
cana-3162	296	2	,	,	PUNCT
cana-3162	296	3	it	it	PRON
cana-3162	296	4	creates	create	VERB
cana-3162	296	5	an	an	DET
cana-3162	296	6	ensemble	ensemble	NOUN
cana-3162	296	7	of	of	ADP
cana-3162	296	8	machine	machine	NOUN
cana-3162	296	9	learning	learning	NOUN
cana-3162	296	10	models	model	NOUN
cana-3162	296	11	.	.	PUNCT
cana-3162	297	1	each	each	DET
cana-3162	297	2	model	model	NOUN
cana-3162	297	3	learns	learn	VERB
cana-3162	297	4	to	to	PART
cana-3162	297	5	estimate	estimate	VERB
cana-3162	297	6	and	and	CCONJ
cana-3162	297	7	then	then	ADV
cana-3162	297	8	votes	vote	NOUN
cana-3162	297	9	or	or	CCONJ
cana-3162	297	10	averages	average	NOUN
cana-3162	297	11	to	to	PART
cana-3162	297	12	determine	determine	VERB
cana-3162	297	13	the	the	DET
cana-3162	297	14	outcome	outcome	NOUN
cana-3162	297	15	.	.	PUNCT
cana-3162	298	1	accuracy	accuracy	NOUN
cana-3162	298	2	assesses	assess	VERB
cana-3162	298	3	the	the	DET
cana-3162	298	4	model	model	NOUN
cana-3162	298	5	's	's	PART
cana-3162	298	6	performance	performance	NOUN
cana-3162	298	7	,	,	PUNCT
cana-3162	298	8	while	while	SCONJ
cana-3162	298	9	feature	feature	NOUN
cana-3162	298	10	value	value	NOUN
cana-3162	298	11	measures	measure	VERB
cana-3162	298	12	the	the	DET
cana-3162	298	13	traits	trait	NOUN
cana-3162	298	14	that	that	PRON
cana-3162	298	15	affect	affect	VERB
cana-3162	298	16	outcomes	outcome	NOUN
cana-3162	298	17	.	.	PUNCT
cana-3162	299	1	changes	change	NOUN
cana-3162	299	2	to	to	ADP
cana-3162	299	3	hyperparameters	hyperparameter	NOUN
cana-3162	299	4	improve	improve	VERB
cana-3162	299	5	model	model	NOUN
cana-3162	299	6	accuracy	accuracy	NOUN
cana-3162	299	7	.	.	PUNCT
cana-3162	300	1	we	we	PRON
cana-3162	300	2	compare	compare	VERB
cana-3162	300	3	the	the	DET
cana-3162	300	4	ensemble	ensemble	ADJ
cana-3162	300	5	model	model	NOUN
cana-3162	300	6	to	to	ADP
cana-3162	300	7	individual	individual	ADJ
cana-3162	300	8	models	model	NOUN
cana-3162	300	9	after	after	ADP
cana-3162	300	10	final	final	ADJ
cana-3162	300	11	predictions	prediction	NOUN
cana-3162	300	12	to	to	PART
cana-3162	300	13	evaluate	evaluate	VERB
cana-3162	300	14	its	its	PRON
cana-3162	300	15	performance	performance	NOUN
cana-3162	300	16	.	.	PUNCT
cana-3162	301	1	these	these	DET
cana-3162	301	2	commonalities	commonality	NOUN
cana-3162	301	3	create	create	VERB
cana-3162	301	4	a	a	DET
cana-3162	301	5	feedback	feedback	NOUN
cana-3162	301	6	loop	loop	NOUN
cana-3162	301	7	that	that	PRON
cana-3162	301	8	improves	improve	VERB
cana-3162	301	9	the	the	DET
cana-3162	301	10	prediction	prediction	NOUN
cana-3162	301	11	process	process	NOUN
cana-3162	301	12	and	and	CCONJ
cana-3162	301	13	requires	require	VERB
cana-3162	301	14	further	further	ADJ
cana-3162	301	15	adjustments	adjustment	NOUN
cana-3162	301	16	[	[	X
cana-3162	301	17	26	26	NUM
cana-3162	301	18	]	]	PUNCT
cana-3162	301	19	.	.	PUNCT
cana-3162	302	1	the	the	DET
cana-3162	302	2	algorithm	algorithm	NOUN
cana-3162	302	3	concludes	conclude	VERB
cana-3162	302	4	with	with	ADP
cana-3162	302	5	a	a	DET
cana-3162	302	6	full	full	ADJ
cana-3162	302	7	report	report	NOUN
cana-3162	302	8	on	on	ADP
cana-3162	302	9	findings	finding	NOUN
cana-3162	302	10	and	and	CCONJ
cana-3162	302	11	interpretation	interpretation	NOUN
cana-3162	302	12	.	.	PUNCT
cana-3162	303	1	a	a	DET
cana-3162	303	2	convergence	convergence	NOUN
cana-3162	303	3	check	check	NOUN
cana-3162	303	4	ensures	ensure	VERB
cana-3162	303	5	that	that	SCONJ
cana-3162	303	6	forecasts	forecast	NOUN
cana-3162	303	7	match	match	VERB
cana-3162	303	8	expectations	expectation	NOUN
cana-3162	303	9	.	.	PUNCT
cana-3162	304	1	communications	communication	NOUN
cana-3162	304	2	on	on	ADP
cana-3162	304	3	applied	apply	VERB
cana-3162	304	4	nonlinear	nonlinear	ADJ
cana-3162	304	5	analysis	analysis	NOUN
cana-3162	304	6	issn	issn	NOUN
cana-3162	304	7	:	:	PUNCT
cana-3162	304	8	1074	1074	NUM
cana-3162	304	9	-	-	PUNCT
cana-3162	304	10	133x	133x	NUM
cana-3162	304	11	vol	vol	NOUN
cana-3162	304	12	32	32	NUM
cana-3162	304	13	no	no	NOUN
cana-3162	304	14	.	.	PUNCT
cana-3162	305	1	5s	5s	NUM
cana-3162	305	2	(	(	PUNCT
cana-3162	305	3	2025	2025	NUM
cana-3162	305	4	)	)	PUNCT
cana-3162	305	5	506	506	NUM
cana-3162	305	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3162	305	7	fig	fig	NOUN
cana-3162	305	8	.	.	PUNCT
cana-3162	306	1	3	3	X
cana-3162	306	2	.	.	X
cana-3162	306	3	ensemble	ensemble	ADJ
cana-3162	306	4	learning	learning	NOUN
cana-3162	306	5	process	process	NOUN
cana-3162	306	6	for	for	ADP
cana-3162	306	7	anti	anti	ADJ
cana-3162	306	8	-	-	ADJ
cana-3162	306	9	cancer	cancer	ADJ
cana-3162	306	10	drug	drug	NOUN
cana-3162	306	11	response	response	NOUN
cana-3162	306	12	prediction	prediction	NOUN
cana-3162	306	13	figure	figure	NOUN
cana-3162	306	14	3	3	NUM
cana-3162	306	15	illustrates	illustrate	VERB
cana-3162	306	16	the	the	DET
cana-3162	306	17	ensemble	ensemble	ADJ
cana-3162	306	18	learning	learning	NOUN
cana-3162	306	19	approach	approach	NOUN
cana-3162	306	20	for	for	ADP
cana-3162	306	21	anticancer	anticancer	NOUN
cana-3162	306	22	medication	medication	NOUN
cana-3162	306	23	prediction	prediction	NOUN
cana-3162	306	24	.	.	PUNCT
cana-3162	307	1	it	it	PRON
cana-3162	307	2	processes	process	VERB
cana-3162	307	3	data	datum	NOUN
cana-3162	307	4	from	from	ADP
cana-3162	307	5	preceding	precede	VERB
cana-3162	307	6	algorithms	algorithm	NOUN
cana-3162	307	7	,	,	PUNCT
cana-3162	307	8	sets	set	VERB
cana-3162	307	9	up	up	ADP
cana-3162	307	10	the	the	DET
cana-3162	307	11	model	model	NOUN
cana-3162	307	12	,	,	PUNCT
cana-3162	307	13	trains	train	VERB
cana-3162	307	14	it	it	PRON
cana-3162	307	15	,	,	PUNCT
cana-3162	307	16	and	and	CCONJ
cana-3162	307	17	predicts	predict	NOUN
cana-3162	307	18	.	.	PUNCT
cana-3162	308	1	final	final	ADJ
cana-3162	308	2	reports	report	NOUN
cana-3162	308	3	and	and	CCONJ
cana-3162	308	4	assessments	assessment	NOUN
cana-3162	308	5	conclude	conclude	VERB
cana-3162	308	6	it	it	PRON
cana-3162	308	7	.	.	PUNCT
cana-3162	309	1	each	each	DET
cana-3162	309	2	stage	stage	NOUN
cana-3162	309	3	is	be	AUX
cana-3162	309	4	crucial	crucial	ADJ
cana-3162	309	5	to	to	ADP
cana-3162	309	6	the	the	DET
cana-3162	309	7	model	model	NOUN
cana-3162	309	8	's	's	PART
cana-3162	309	9	accuracy	accuracy	NOUN
cana-3162	309	10	and	and	CCONJ
cana-3162	309	11	reliability	reliability	NOUN
cana-3162	309	12	.	.	PUNCT
cana-3162	310	1	the	the	DET
cana-3162	310	2	feedback	feedback	NOUN
cana-3162	310	3	mechanism	mechanism	NOUN
cana-3162	310	4	enables	enable	VERB
cana-3162	310	5	the	the	DET
cana-3162	310	6	model	model	NOUN
cana-3162	310	7	to	to	PART
cana-3162	310	8	alter	alter	VERB
cana-3162	310	9	its	its	PRON
cana-3162	310	10	assumptions	assumption	NOUN
cana-3162	310	11	as	as	ADP
cana-3162	310	12	fresh	fresh	ADJ
cana-3162	310	13	information	information	NOUN
cana-3162	310	14	and	and	CCONJ
cana-3162	310	15	ideas	idea	NOUN
cana-3162	310	16	arrive	arrive	VERB
cana-3162	310	17	,	,	PUNCT
cana-3162	310	18	improving	improve	VERB
cana-3162	310	19	them	they	PRON
cana-3162	310	20	.	.	PUNCT
cana-3162	311	1	iv	iv	X
cana-3162	311	2	.	.	PROPN
cana-3162	311	3	result	result	VERB
cana-3162	311	4	comparing	compare	VERB
cana-3162	311	5	the	the	DET
cana-3162	311	6	accuracy	accuracy	NOUN
cana-3162	311	7	of	of	ADP
cana-3162	311	8	anti	anti	ADJ
cana-3162	311	9	-	-	ADJ
cana-3162	311	10	cancer	cancer	ADJ
cana-3162	311	11	medication	medication	NOUN
cana-3162	311	12	prediction	prediction	NOUN
cana-3162	311	13	with	with	ADP
cana-3162	311	14	machine	machine	NOUN
cana-3162	311	15	learning	learn	VERB
cana-3162	311	16	algorithms	algorithm	NOUN
cana-3162	311	17	is	be	AUX
cana-3162	311	18	a	a	DET
cana-3162	311	19	crucial	crucial	ADJ
cana-3162	311	20	area	area	NOUN
cana-3162	311	21	of	of	ADP
cana-3162	311	22	research	research	NOUN
cana-3162	311	23	in	in	ADP
cana-3162	311	24	oncology	oncology	NOUN
cana-3162	311	25	.	.	PUNCT
cana-3162	312	1	to	to	PART
cana-3162	312	2	enhance	enhance	VERB
cana-3162	312	3	treatment	treatment	NOUN
cana-3162	312	4	results	result	NOUN
cana-3162	312	5	,	,	PUNCT
cana-3162	312	6	adopt	adopt	VERB
cana-3162	312	7	data	data	NOUN
cana-3162	312	8	-	-	PUNCT
cana-3162	312	9	driven	drive	VERB
cana-3162	312	10	strategies	strategy	NOUN
cana-3162	312	11	.	.	PUNCT
cana-3162	313	1	there	there	PRON
cana-3162	313	2	are	be	VERB
cana-3162	313	3	six	six	NUM
cana-3162	313	4	proven	proven	ADJ
cana-3162	313	5	conventional	conventional	ADJ
cana-3162	313	6	ways	way	NOUN
cana-3162	313	7	.	.	PUNCT
cana-3162	314	1	logistic	logistic	ADJ
cana-3162	314	2	regression	regression	NOUN
cana-3162	314	3	,	,	PUNCT
cana-3162	314	4	decision	decision	NOUN
cana-3162	314	5	tree	tree	NOUN
cana-3162	314	6	,	,	PUNCT
cana-3162	314	7	k	k	NOUN
cana-3162	314	8	-	-	PUNCT
cana-3162	314	9	nearest	near	ADJ
cana-3162	314	10	neighbors	neighbor	NOUN
cana-3162	314	11	,	,	PUNCT
cana-3162	314	12	svm	svm	ADJ
cana-3162	314	13	,	,	PUNCT
cana-3162	314	14	random	random	ADJ
cana-3162	314	15	forest	forest	NOUN
cana-3162	314	16	,	,	PUNCT
cana-3162	314	17	naive	naive	ADJ
cana-3162	314	18	bayes	bayes	NOUN
cana-3162	314	19	,	,	PUNCT
cana-3162	314	20	etc	etc	X
cana-3162	314	21	.	.	X
cana-3162	315	1	we	we	PRON
cana-3162	315	2	evaluate	evaluate	VERB
cana-3162	315	3	them	they	PRON
cana-3162	315	4	based	base	VERB
cana-3162	315	5	on	on	ADP
cana-3162	315	6	accuracy	accuracy	NOUN
cana-3162	315	7	,	,	PUNCT
cana-3162	315	8	precision	precision	NOUN
cana-3162	315	9	,	,	PUNCT
cana-3162	315	10	recall	recall	NOUN
cana-3162	315	11	,	,	PUNCT
cana-3162	315	12	f1	f1	NOUN
cana-3162	315	13	score	score	NOUN
cana-3162	315	14	,	,	PUNCT
cana-3162	315	15	auc	auc	NOUN
cana-3162	315	16	-	-	PUNCT
cana-3162	315	17	roc	roc	NOUN
cana-3162	315	18	,	,	PUNCT
cana-3162	315	19	and	and	CCONJ
cana-3162	315	20	mean	mean	VERB
cana-3162	315	21	absolute	absolute	ADJ
cana-3162	315	22	error	error	NOUN
cana-3162	315	23	.	.	PUNCT
cana-3162	316	1	accuracy	accuracy	NOUN
cana-3162	316	2	measures	measure	NOUN
cana-3162	316	3	each	each	DET
cana-3162	316	4	method	method	NOUN
cana-3162	316	5	's	's	PART
cana-3162	316	6	overall	overall	ADJ
cana-3162	316	7	effectiveness	effectiveness	NOUN
cana-3162	316	8	.	.	PUNCT
cana-3162	317	1	knearest	knearest	NOUN
cana-3162	317	2	neighbors	neighbor	NOUN
cana-3162	317	3	scores	score	VERB
cana-3162	317	4	highest	high	ADJ
cana-3162	317	5	(	(	PUNCT
cana-3162	317	6	0.80	0.80	NUM
cana-3162	317	7	)	)	PUNCT
cana-3162	317	8	,	,	PUNCT
cana-3162	317	9	followed	follow	VERB
cana-3162	317	10	by	by	ADP
cana-3162	317	11	decision	decision	NOUN
cana-3162	317	12	tree	tree	NOUN
cana-3162	317	13	(	(	PUNCT
cana-3162	317	14	0.78	0.78	NUM
cana-3162	317	15	)	)	PUNCT
cana-3162	317	16	and	and	CCONJ
cana-3162	317	17	support	support	VERB
cana-3162	317	18	vector	vector	NOUN
cana-3162	317	19	machine	machine	NOUN
cana-3162	317	20	(	(	PUNCT
cana-3162	317	21	0.76	0.76	NUM
cana-3162	317	22	)	)	PUNCT
cana-3162	317	23	.	.	PUNCT
cana-3162	318	1	at	at	ADP
cana-3162	318	2	0.74	0.74	NUM
cana-3162	318	3	,	,	PUNCT
cana-3162	318	4	naive	naive	ADJ
cana-3162	318	5	bayes	bayes	NOUN
cana-3162	318	6	has	have	VERB
cana-3162	318	7	the	the	DET
cana-3162	318	8	lowest	low	ADJ
cana-3162	318	9	accuracy	accuracy	NOUN
cana-3162	318	10	,	,	PUNCT
cana-3162	318	11	indicating	indicate	VERB
cana-3162	318	12	that	that	SCONJ
cana-3162	318	13	it	it	PRON
cana-3162	318	14	ca	can	AUX
cana-3162	318	15	n't	not	PART
cana-3162	318	16	generate	generate	VERB
cana-3162	318	17	reliable	reliable	ADJ
cana-3162	318	18	predictions	prediction	NOUN
cana-3162	318	19	.	.	PUNCT
cana-3162	319	1	precision	precision	NOUN
cana-3162	319	2	—	—	PUNCT
cana-3162	319	3	the	the	DET
cana-3162	319	4	percentage	percentage	NOUN
cana-3162	319	5	of	of	ADP
cana-3162	319	6	genuine	genuine	ADJ
cana-3162	319	7	positive	positive	ADJ
cana-3162	319	8	predictions	prediction	NOUN
cana-3162	319	9	to	to	ADP
cana-3162	319	10	all	all	DET
cana-3162	319	11	projected	project	VERB
cana-3162	319	12	positives	positive	NOUN
cana-3162	319	13	—	—	PUNCT
cana-3162	319	14	names	name	NOUN
cana-3162	319	15	k	k	NOUN
cana-3162	319	16	-	-	PUNCT
cana-3162	319	17	nearest	near	ADJ
cana-3162	319	18	neighbors	neighbor	NOUN
cana-3162	319	19	the	the	DET
cana-3162	319	20	most	most	ADV
cana-3162	319	21	successful	successful	ADJ
cana-3162	319	22	conventional	conventional	ADJ
cana-3162	319	23	approach	approach	NOUN
cana-3162	319	24	at	at	ADP
cana-3162	319	25	0.76	0.76	NUM
cana-3162	319	26	.	.	PUNCT
cana-3162	320	1	while	while	SCONJ
cana-3162	320	2	decent	decent	ADJ
cana-3162	320	3	,	,	PUNCT
cana-3162	320	4	the	the	DET
cana-3162	320	5	other	other	ADJ
cana-3162	320	6	techniques	technique	NOUN
cana-3162	320	7	'	'	PART
cana-3162	320	8	accuracy	accuracy	NOUN
cana-3162	320	9	range	range	NOUN
cana-3162	320	10	of	of	ADP
cana-3162	320	11	0.69	0.69	NUM
cana-3162	320	12	to	to	PART
cana-3162	320	13	0.74	0.74	NUM
cana-3162	320	14	suggests	suggest	NOUN
cana-3162	320	15	room	room	NOUN
cana-3162	320	16	for	for	ADP
cana-3162	320	17	improvement	improvement	NOUN
cana-3162	320	18	.	.	PUNCT
cana-3162	321	1	the	the	DET
cana-3162	321	2	real	real	ADJ
cana-3162	321	3	positive	positive	ADJ
cana-3162	321	4	rate	rate	NOUN
cana-3162	321	5	recall	recall	NOUN
cana-3162	321	6	number	number	NOUN
cana-3162	321	7	exhibits	exhibit	VERB
cana-3162	321	8	a	a	DET
cana-3162	321	9	similar	similar	ADJ
cana-3162	321	10	pattern	pattern	NOUN
cana-3162	321	11	,	,	PUNCT
cana-3162	321	12	with	with	ADP
cana-3162	321	13	naive	naive	ADJ
cana-3162	321	14	bayes	bayes	NOUN
cana-3162	321	15	last	last	ADJ
cana-3162	321	16	at	at	ADP
cana-3162	321	17	0.71	0.71	NUM
cana-3162	321	18	and	and	CCONJ
cana-3162	321	19	k	k	NOUN
cana-3162	321	20	-	-	PUNCT
cana-3162	321	21	nearest	near	ADJ
cana-3162	321	22	neighbors	neighbor	NOUN
cana-3162	321	23	first	first	ADV
cana-3162	321	24	at	at	ADP
cana-3162	321	25	0.78	0.78	NUM
cana-3162	321	26	.	.	PUNCT
cana-3162	322	1	the	the	DET
cana-3162	322	2	single	single	ADJ
cana-3162	322	3	f1	f1	NOUN
cana-3162	322	4	score	score	NOUN
cana-3162	322	5	measures	measure	NOUN
cana-3162	322	6	accuracy	accuracy	NOUN
cana-3162	322	7	and	and	CCONJ
cana-3162	322	8	recall	recall	NOUN
cana-3162	322	9	,	,	PUNCT
cana-3162	322	10	supporting	support	VERB
cana-3162	322	11	this	this	DET
cana-3162	322	12	ranking	ranking	NOUN
cana-3162	322	13	.	.	PUNCT
cana-3162	323	1	the	the	DET
cana-3162	323	2	top	top	ADJ
cana-3162	323	3	results	result	NOUN
cana-3162	323	4	are	be	AUX
cana-3162	323	5	0.77	0.77	NUM
cana-3162	323	6	for	for	ADP
cana-3162	323	7	knearest	knearest	NOUN
cana-3162	323	8	neighbors	neighbor	NOUN
cana-3162	323	9	and	and	CCONJ
cana-3162	323	10	0.75	0.75	NUM
cana-3162	323	11	for	for	ADP
cana-3162	323	12	random	random	ADJ
cana-3162	323	13	forest	forest	NOUN
cana-3162	323	14	.	.	PUNCT
cana-3162	324	1	auc	auc	NOUN
cana-3162	324	2	-	-	PUNCT
cana-3162	324	3	roc	roc	PROPN
cana-3162	324	4	shows	show	VERB
cana-3162	324	5	that	that	SCONJ
cana-3162	324	6	k	k	ADJ
cana-3162	324	7	-	-	PUNCT
cana-3162	324	8	nearest	near	ADJ
cana-3162	324	9	neighbors	neighbor	NOUN
cana-3162	324	10	can	can	AUX
cana-3162	324	11	end	end	VERB
cana-3162	324	12	process	process	NOUN
cana-3162	324	13	:	:	PUNCT
cana-3162	324	14	conclude	conclude	VERB
cana-3162	324	15	the	the	DET
cana-3162	324	16	process	process	NOUN
cana-3162	324	17	and	and	CCONJ
cana-3162	324	18	finalize	finalize	VERB
cana-3162	324	19	outputs	output	NOUN
cana-3162	324	20	.	.	PUNCT
cana-3162	325	1	feedback	feedback	NOUN
cana-3162	325	2	loop	loop	PROPN
cana-3162	325	3	:	:	PUNCT
cana-3162	325	4	gather	gather	VERB
cana-3162	325	5	feedback	feedback	NOUN
cana-3162	325	6	for	for	ADP
cana-3162	325	7	continuous	continuous	ADJ
cana-3162	325	8	improvement	improvement	NOUN
cana-3162	325	9	.	.	PUNCT
cana-3162	326	1	report	report	NOUN
cana-3162	326	2	generation	generation	NOUN
cana-3162	326	3	:	:	PUNCT
cana-3162	326	4	create	create	VERB
cana-3162	326	5	a	a	DET
cana-3162	326	6	comprehensive	comprehensive	ADJ
cana-3162	326	7	report	report	NOUN
cana-3162	326	8	of	of	ADP
cana-3162	326	9	findings	finding	NOUN
cana-3162	326	10	.	.	PUNCT
cana-3162	327	1	model	model	PROPN
cana-3162	327	2	comparison	comparison	NOUN
cana-3162	327	3	:	:	PUNCT
cana-3162	327	4	compare	compare	VERB
cana-3162	327	5	ensemble	ensemble	ADJ
cana-3162	327	6	results	result	NOUN
cana-3162	327	7	with	with	ADP
cana-3162	327	8	individual	individual	ADJ
cana-3162	327	9	model	model	NOUN
cana-3162	327	10	outcomes	outcome	NOUN
cana-3162	327	11	.	.	PUNCT
cana-3162	328	1	final	final	ADJ
cana-3162	328	2	predictions	prediction	NOUN
cana-3162	328	3	:	:	PUNCT
cana-3162	328	4	generate	generate	VERB
cana-3162	328	5	the	the	DET
cana-3162	328	6	final	final	ADJ
cana-3162	328	7	predictions	prediction	NOUN
cana-3162	328	8	using	use	VERB
cana-3162	328	9	the	the	DET
cana-3162	328	10	ensemble	ensemble	ADJ
cana-3162	328	11	model	model	NOUN
cana-3162	328	12	.	.	PUNCT
cana-3162	329	1	hyperparameter	hyperparameter	NOUN
cana-3162	329	2	tuning	tuning	NOUN
cana-3162	329	3	:	:	PUNCT
cana-3162	329	4	optimize	optimize	VERB
cana-3162	329	5	hyperparameters	hyperparameter	NOUN
cana-3162	329	6	for	for	ADP
cana-3162	329	7	enhanced	enhanced	ADJ
cana-3162	329	8	performance	performance	NOUN
cana-3162	329	9	.	.	PUNCT
cana-3162	330	1	feature	feature	NOUN
cana-3162	330	2	importance	importance	NOUN
cana-3162	330	3	analysis	analysis	NOUN
cana-3162	330	4	:	:	PUNCT
cana-3162	330	5	evaluate	evaluate	VERB
cana-3162	330	6	the	the	DET
cana-3162	330	7	significance	significance	NOUN
cana-3162	330	8	of	of	ADP
cana-3162	330	9	each	each	DET
cana-3162	330	10	feature	feature	NOUN
cana-3162	330	11	.	.	PUNCT
cana-3162	331	1	model	model	NOUN
cana-3162	331	2	evaluation	evaluation	NOUN
cana-3162	331	3	:	:	PUNCT
cana-3162	331	4	assess	assess	VERB
cana-3162	331	5	the	the	DET
cana-3162	331	6	ensemble	ensemble	ADJ
cana-3162	331	7	model	model	NOUN
cana-3162	331	8	's	's	PART
cana-3162	331	9	performance	performance	NOUN
cana-3162	331	10	.	.	PUNCT
cana-3162	332	1	aggregate	aggregate	ADJ
cana-3162	332	2	predictions	prediction	NOUN
cana-3162	332	3	:	:	PUNCT
cana-3162	332	4	combine	combine	VERB
cana-3162	332	5	predictions	prediction	NOUN
cana-3162	332	6	using	use	VERB
cana-3162	332	7	voting	voting	NOUN
cana-3162	332	8	or	or	CCONJ
cana-3162	332	9	averaging	averaging	NOUN
cana-3162	332	10	.	.	PUNCT
cana-3162	333	1	model	model	NOUN
cana-3162	333	2	predictions	prediction	NOUN
cana-3162	333	3	:	:	PUNCT
cana-3162	333	4	generate	generate	VERB
cana-3162	333	5	predictions	prediction	NOUN
cana-3162	333	6	from	from	ADP
cana-3162	333	7	each	each	DET
cana-3162	333	8	trained	train	VERB
cana-3162	333	9	model	model	NOUN
cana-3162	333	10	.	.	PUNCT
cana-3162	334	1	model	model	NOUN
cana-3162	334	2	training	training	NOUN
cana-3162	334	3	:	:	PUNCT
cana-3162	334	4	train	train	VERB
cana-3162	334	5	each	each	DET
cana-3162	334	6	base	base	NOUN
cana-3162	334	7	model	model	NOUN
cana-3162	334	8	on	on	ADP
cana-3162	334	9	input	input	NOUN
cana-3162	334	10	data	datum	NOUN
cana-3162	334	11	.	.	PUNCT
cana-3162	335	1	model	model	PROPN
cana-3162	335	2	initialization	initialization	PROPN
cana-3162	335	3	:	:	PUNCT
cana-3162	335	4	initialize	initialize	VERB
cana-3162	335	5	multiple	multiple	ADJ
cana-3162	335	6	base	base	NOUN
cana-3162	335	7	models	model	NOUN
cana-3162	335	8	for	for	ADP
cana-3162	335	9	ensemble	ensemble	ADJ
cana-3162	335	10	learning	learning	NOUN
cana-3162	335	11	.	.	PUNCT
cana-3162	336	1	input	input	PROPN
cana-3162	336	2	data	data	PROPN
cana-3162	336	3	:	:	PUNCT
cana-3162	336	4	receive	receive	VERB
cana-3162	336	5	input	input	NOUN
cana-3162	336	6	features	feature	NOUN
cana-3162	336	7	and	and	CCONJ
cana-3162	336	8	predictions	prediction	NOUN
cana-3162	336	9	from	from	ADP
cana-3162	336	10	algorithm	algorithm	NOUN
cana-3162	336	11	2	2	NUM
cana-3162	336	12	.	.	PUNCT
cana-3162	336	13	communications	communication	NOUN
cana-3162	336	14	on	on	ADP
cana-3162	336	15	applied	apply	VERB
cana-3162	336	16	nonlinear	nonlinear	ADJ
cana-3162	336	17	analysis	analysis	NOUN
cana-3162	336	18	issn	issn	NOUN
cana-3162	336	19	:	:	PUNCT
cana-3162	336	20	1074	1074	NUM
cana-3162	336	21	-	-	PUNCT
cana-3162	336	22	133x	133x	NUM
cana-3162	336	23	vol	vol	NOUN
cana-3162	336	24	32	32	NUM
cana-3162	336	25	no	no	NOUN
cana-3162	336	26	.	.	PUNCT
cana-3162	337	1	5s	5s	NUM
cana-3162	337	2	(	(	PUNCT
cana-3162	337	3	2025	2025	NUM
cana-3162	337	4	)	)	PUNCT
cana-3162	337	5	507	507	NUM
cana-3162	337	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3162	337	7	distinguish	distinguish	VERB
cana-3162	337	8	classes	class	NOUN
cana-3162	337	9	with	with	ADP
cana-3162	337	10	an	an	DET
cana-3162	337	11	auc	auc	NOUN
cana-3162	337	12	of	of	ADP
cana-3162	337	13	0.81	0.81	NUM
cana-3162	337	14	.	.	PUNCT
cana-3162	338	1	some	some	DET
cana-3162	338	2	approaches	approach	NOUN
cana-3162	338	3	perform	perform	VERB
cana-3162	338	4	well	well	ADV
cana-3162	338	5	,	,	PUNCT
cana-3162	338	6	but	but	CCONJ
cana-3162	338	7	naive	naive	ADJ
cana-3162	338	8	bayes	baye	NOUN
cana-3162	338	9	'	'	PART
cana-3162	338	10	auc	auc	NOUN
cana-3162	338	11	of	of	ADP
cana-3162	338	12	0.75	0.75	NUM
cana-3162	338	13	remains	remain	VERB
cana-3162	338	14	poor	poor	ADJ
cana-3162	338	15	.	.	PUNCT
cana-3162	339	1	finally	finally	ADV
cana-3162	339	2	,	,	PUNCT
cana-3162	339	3	the	the	DET
cana-3162	339	4	mean	mean	ADJ
cana-3162	339	5	absolute	absolute	ADJ
cana-3162	339	6	error	error	NOUN
cana-3162	339	7	(	(	PUNCT
cana-3162	339	8	mae	mae	PROPN
cana-3162	339	9	)	)	PUNCT
cana-3162	339	10	,	,	PUNCT
cana-3162	339	11	which	which	PRON
cana-3162	339	12	represents	represent	VERB
cana-3162	339	13	the	the	DET
cana-3162	339	14	average	average	ADJ
cana-3162	339	15	prediction	prediction	NOUN
cana-3162	339	16	error	error	NOUN
cana-3162	339	17	,	,	PUNCT
cana-3162	339	18	reveals	reveal	VERB
cana-3162	339	19	that	that	SCONJ
cana-3162	339	20	k	k	X
cana-3162	339	21	-	-	PUNCT
cana-3162	339	22	nearest	near	ADJ
cana-3162	339	23	neighbors	neighbor	NOUN
cana-3162	339	24	has	have	VERB
cana-3162	339	25	the	the	DET
cana-3162	339	26	lowest	low	ADJ
cana-3162	339	27	error	error	NOUN
cana-3162	339	28	at	at	ADP
cana-3162	339	29	0.39	0.39	NUM
cana-3162	339	30	and	and	CCONJ
cana-3162	339	31	logistic	logistic	ADJ
cana-3162	339	32	regression	regression	NOUN
cana-3162	339	33	has	have	VERB
cana-3162	339	34	the	the	DET
cana-3162	339	35	most	most	ADJ
cana-3162	339	36	at	at	ADP
cana-3162	339	37	0.45	0.45	NUM
cana-3162	339	38	,	,	PUNCT
cana-3162	339	39	indicating	indicate	VERB
cana-3162	339	40	poor	poor	ADJ
cana-3162	339	41	accuracy	accuracy	NOUN
cana-3162	339	42	.	.	PUNCT
cana-3162	340	1	the	the	DET
cana-3162	340	2	proposed	propose	VERB
cana-3162	340	3	approach	approach	NOUN
cana-3162	340	4	outperforms	outperform	VERB
cana-3162	340	5	ensemble	ensemble	ADJ
cana-3162	340	6	learning	learning	NOUN
cana-3162	340	7	,	,	PUNCT
cana-3162	340	8	support	support	VERB
cana-3162	340	9	vector	vector	NOUN
cana-3162	340	10	machines	machine	NOUN
cana-3162	340	11	,	,	PUNCT
cana-3162	340	12	random	random	ADJ
cana-3162	340	13	forests	forest	NOUN
cana-3162	340	14	,	,	PUNCT
cana-3162	340	15	gradient	gradient	NOUN
cana-3162	340	16	boosting	boosting	NOUN
cana-3162	340	17	,	,	PUNCT
cana-3162	340	18	and	and	CCONJ
cana-3162	340	19	neural	neural	ADJ
cana-3162	340	20	networks	network	NOUN
cana-3162	340	21	.	.	PUNCT
cana-3162	341	1	all	all	DET
cana-3162	341	2	the	the	DET
cana-3162	341	3	other	other	ADJ
cana-3162	341	4	approaches	approach	NOUN
cana-3162	341	5	were	be	AUX
cana-3162	341	6	less	less	ADV
cana-3162	341	7	accurate	accurate	ADJ
cana-3162	341	8	than	than	ADP
cana-3162	341	9	the	the	DET
cana-3162	341	10	proposed	propose	VERB
cana-3162	341	11	method	method	NOUN
cana-3162	341	12	(	(	PUNCT
cana-3162	341	13	0.85	0.85	NUM
cana-3162	341	14	)	)	PUNCT
cana-3162	341	15	.	.	PUNCT
cana-3162	342	1	it	it	PRON
cana-3162	342	2	is	be	AUX
cana-3162	342	3	accurate	accurate	ADJ
cana-3162	342	4	in	in	ADP
cana-3162	342	5	predicting	predict	VERB
cana-3162	342	6	drug	drug	NOUN
cana-3162	342	7	reactions	reaction	NOUN
cana-3162	342	8	,	,	PUNCT
cana-3162	342	9	which	which	PRON
cana-3162	342	10	may	may	AUX
cana-3162	342	11	improve	improve	VERB
cana-3162	342	12	patient	patient	ADJ
cana-3162	342	13	outcomes	outcome	NOUN
cana-3162	342	14	.	.	PUNCT
cana-3162	343	1	also	also	ADV
cana-3162	343	2	,	,	PUNCT
cana-3162	343	3	accuracy	accuracy	NOUN
cana-3162	343	4	improves	improve	VERB
cana-3162	343	5	;	;	PUNCT
cana-3162	343	6	the	the	DET
cana-3162	343	7	recommended	recommend	VERB
cana-3162	343	8	algorithm	algorithm	NOUN
cana-3162	343	9	scores	score	NOUN
cana-3162	343	10	0.83	0.83	NUM
cana-3162	343	11	,	,	PUNCT
cana-3162	343	12	higher	high	ADJ
cana-3162	343	13	than	than	ADP
cana-3162	343	14	ensemble	ensemble	ADJ
cana-3162	343	15	learning	learning	NOUN
cana-3162	343	16	,	,	PUNCT
cana-3162	343	17	its	its	PRON
cana-3162	343	18	most	most	ADV
cana-3162	343	19	sophisticated	sophisticated	ADJ
cana-3162	343	20	opponent	opponent	NOUN
cana-3162	343	21	,	,	PUNCT
cana-3162	343	22	at	at	ADP
cana-3162	343	23	0.80	0.80	NUM
cana-3162	343	24	.	.	PUNCT
cana-3162	344	1	the	the	DET
cana-3162	344	2	proposed	propose	VERB
cana-3162	344	3	method	method	NOUN
cana-3162	344	4	's	's	PART
cana-3162	344	5	0.80	0.80	NUM
cana-3162	344	6	memory	memory	NOUN
cana-3162	344	7	score	score	NOUN
cana-3162	344	8	implies	imply	VERB
cana-3162	344	9	it	it	PRON
cana-3162	344	10	can	can	AUX
cana-3162	344	11	yield	yield	VERB
cana-3162	344	12	decent	decent	ADJ
cana-3162	344	13	results	result	NOUN
cana-3162	344	14	.	.	PUNCT
cana-3162	345	1	the	the	DET
cana-3162	345	2	f1	f1	PROPN
cana-3162	345	3	score	score	NOUN
cana-3162	345	4	of	of	ADP
cana-3162	345	5	0.81	0.81	NUM
cana-3162	345	6	,	,	PUNCT
cana-3162	345	7	which	which	PRON
cana-3162	345	8	perfectly	perfectly	ADV
cana-3162	345	9	combines	combine	VERB
cana-3162	345	10	accuracy	accuracy	NOUN
cana-3162	345	11	and	and	CCONJ
cana-3162	345	12	memory	memory	NOUN
cana-3162	345	13	,	,	PUNCT
cana-3162	345	14	supports	support	VERB
cana-3162	345	15	this	this	DET
cana-3162	345	16	advantage	advantage	NOUN
cana-3162	345	17	.	.	PUNCT
cana-3162	346	1	the	the	DET
cana-3162	346	2	model	model	NOUN
cana-3162	346	3	's	's	PART
cana-3162	346	4	impressive	impressive	ADJ
cana-3162	346	5	0.87	0.87	NUM
cana-3162	346	6	auc	auc	ADJ
cana-3162	346	7	-	-	PUNCT
cana-3162	346	8	roc	roc	NOUN
cana-3162	346	9	score	score	NOUN
cana-3162	346	10	shows	show	VERB
cana-3162	346	11	its	its	PRON
cana-3162	346	12	ability	ability	NOUN
cana-3162	346	13	to	to	PART
cana-3162	346	14	distinguish	distinguish	VERB
cana-3162	346	15	.	.	PUNCT
cana-3162	347	1	this	this	PRON
cana-3162	347	2	indicates	indicate	VERB
cana-3162	347	3	the	the	DET
cana-3162	347	4	recommended	recommend	VERB
cana-3162	347	5	strategy	strategy	NOUN
cana-3162	347	6	predicts	predict	NOUN
cana-3162	347	7	well	well	ADV
cana-3162	347	8	.	.	PUNCT
cana-3162	348	1	the	the	DET
cana-3162	348	2	mean	mean	ADJ
cana-3162	348	3	absolute	absolute	ADJ
cana-3162	348	4	error	error	NOUN
cana-3162	348	5	is	be	AUX
cana-3162	348	6	0.30	0.30	NUM
cana-3162	348	7	,	,	PUNCT
cana-3162	348	8	indicating	indicate	VERB
cana-3162	348	9	that	that	SCONJ
cana-3162	348	10	the	the	DET
cana-3162	348	11	recommended	recommend	VERB
cana-3162	348	12	strategy	strategy	NOUN
cana-3162	348	13	is	be	AUX
cana-3162	348	14	more	more	ADV
cana-3162	348	15	accurate	accurate	ADJ
cana-3162	348	16	and	and	CCONJ
cana-3162	348	17	reduces	reduce	VERB
cana-3162	348	18	prediction	prediction	NOUN
cana-3162	348	19	mistakes	mistake	NOUN
cana-3162	348	20	.	.	PUNCT
cana-3162	349	1	according	accord	VERB
cana-3162	349	2	to	to	ADP
cana-3162	349	3	the	the	DET
cana-3162	349	4	comparative	comparative	ADJ
cana-3162	349	5	research	research	NOUN
cana-3162	349	6	,	,	PUNCT
cana-3162	349	7	the	the	DET
cana-3162	349	8	recommended	recommend	VERB
cana-3162	349	9	strategy	strategy	NOUN
cana-3162	349	10	predicts	predict	VERB
cana-3162	349	11	cancer	cancer	NOUN
cana-3162	349	12	treatment	treatment	NOUN
cana-3162	349	13	outcomes	outcome	NOUN
cana-3162	349	14	better	well	ADV
cana-3162	349	15	than	than	ADP
cana-3162	349	16	standard	standard	ADJ
cana-3162	349	17	machine	machine	NOUN
cana-3162	349	18	learning	learning	NOUN
cana-3162	349	19	methods	method	NOUN
cana-3162	349	20	.	.	PUNCT
cana-3162	350	1	this	this	DET
cana-3162	350	2	improvement	improvement	NOUN
cana-3162	350	3	demonstrates	demonstrate	VERB
cana-3162	350	4	how	how	SCONJ
cana-3162	350	5	ensemble	ensemble	ADJ
cana-3162	350	6	techniques	technique	NOUN
cana-3162	350	7	and	and	CCONJ
cana-3162	350	8	feature	feature	NOUN
cana-3162	350	9	selection	selection	NOUN
cana-3162	350	10	algorithms	algorithm	NOUN
cana-3162	350	11	enhance	enhance	VERB
cana-3162	350	12	clinical	clinical	ADJ
cana-3162	350	13	accuracy	accuracy	NOUN
cana-3162	350	14	.	.	PUNCT
cana-3162	351	1	the	the	DET
cana-3162	351	2	proposed	propose	VERB
cana-3162	351	3	strategy	strategy	NOUN
cana-3162	351	4	might	might	AUX
cana-3162	351	5	improve	improve	VERB
cana-3162	351	6	medication	medication	NOUN
cana-3162	351	7	reaction	reaction	NOUN
cana-3162	351	8	estimations	estimation	NOUN
cana-3162	351	9	and	and	CCONJ
cana-3162	351	10	cancer	cancer	NOUN
cana-3162	351	11	treatment	treatment	NOUN
cana-3162	351	12	.	.	PUNCT
cana-3162	352	1	more	more	ADV
cana-3162	352	2	effective	effective	ADJ
cana-3162	352	3	and	and	CCONJ
cana-3162	352	4	tailored	tailor	VERB
cana-3162	352	5	therapies	therapy	NOUN
cana-3162	352	6	may	may	AUX
cana-3162	352	7	result	result	VERB
cana-3162	352	8	.	.	PUNCT
cana-3162	353	1	the	the	DET
cana-3162	353	2	findings	finding	NOUN
cana-3162	353	3	demonstrate	demonstrate	VERB
cana-3162	353	4	the	the	DET
cana-3162	353	5	importance	importance	NOUN
cana-3162	353	6	of	of	ADP
cana-3162	353	7	continuing	continue	VERB
cana-3162	353	8	to	to	PART
cana-3162	353	9	explore	explore	VERB
cana-3162	353	10	this	this	DET
cana-3162	353	11	area	area	NOUN
cana-3162	353	12	since	since	SCONJ
cana-3162	353	13	improved	improve	VERB
cana-3162	353	14	prediction	prediction	NOUN
cana-3162	353	15	models	model	NOUN
cana-3162	353	16	might	might	AUX
cana-3162	353	17	improve	improve	VERB
cana-3162	353	18	cancer	cancer	NOUN
cana-3162	353	19	patient	patient	NOUN
cana-3162	353	20	outcomes	outcome	NOUN
cana-3162	353	21	and	and	CCONJ
cana-3162	353	22	oncology	oncology	NOUN
cana-3162	353	23	career	career	NOUN
cana-3162	353	24	possibilities	possibility	NOUN
cana-3162	353	25	.	.	PUNCT
cana-3162	354	1	table	table	NOUN
cana-3162	354	2	3	3	NUM
cana-3162	354	3	.	.	PUNCT
cana-3162	354	4	performance	performance	NOUN
cana-3162	354	5	evaluation	evaluation	NOUN
cana-3162	354	6	of	of	ADP
cana-3162	354	7	traditional	traditional	ADJ
cana-3162	354	8	methods	method	NOUN
cana-3162	354	9	for	for	ADP
cana-3162	354	10	anticancer	anticancer	NOUN
cana-3162	354	11	drug	drug	NOUN
cana-3162	354	12	response	response	NOUN
cana-3162	354	13	prediction	prediction	NOUN
cana-3162	354	14	performance	performance	NOUN
cana-3162	354	15	evaluation	evaluation	NOUN
cana-3162	354	16	parameter	parameter	NOUN
cana-3162	354	17	logistic	logistic	ADJ
cana-3162	354	18	regression	regression	NOUN
cana-3162	354	19	decision	decision	NOUN
cana-3162	354	20	tree	tree	NOUN
cana-3162	354	21	k	k	NOUN
cana-3162	354	22	-	-	PUNCT
cana-3162	354	23	nearest	near	ADJ
cana-3162	354	24	neighbors	neighbor	NOUN
cana-3162	354	25	support	support	VERB
cana-3162	354	26	vector	vector	NOUN
cana-3162	354	27	machine	machine	NOUN
cana-3162	354	28	random	random	ADJ
cana-3162	354	29	forest	forest	NOUN
cana-3162	354	30	naive	naive	ADJ
cana-3162	354	31	bayes	bayes	PROPN
cana-3162	354	32	accuracy	accuracy	NOUN
cana-3162	354	33	0.75	0.75	NUM
cana-3162	354	34	0.78	0.78	NUM
cana-3162	354	35	0.80	0.80	NUM
cana-3162	354	36	0.76	0.76	NUM
cana-3162	354	37	0.77	0.77	NUM
cana-3162	354	38	0.74	0.74	NUM
cana-3162	354	39	precision	precision	NOUN
cana-3162	354	40	0.70	0.70	NUM
cana-3162	354	41	0.74	0.74	NUM
cana-3162	354	42	0.76	0.76	NUM
cana-3162	354	43	0.72	0.72	NUM
cana-3162	354	44	0.73	0.73	NUM
cana-3162	354	45	0.69	0.69	NUM
cana-3162	354	46	recall	recall	NOUN
cana-3162	354	47	0.72	0.72	NUM
cana-3162	354	48	0.75	0.75	NUM
cana-3162	354	49	0.78	0.78	NUM
cana-3162	354	50	0.74	0.74	NUM
cana-3162	354	51	0.76	0.76	NUM
cana-3162	354	52	0.71	0.71	NUM
cana-3162	354	53	f1	f1	NOUN
cana-3162	354	54	score	score	NOUN
cana-3162	354	55	0.71	0.71	NUM
cana-3162	354	56	0.74	0.74	NUM
cana-3162	354	57	0.77	0.77	NUM
cana-3162	354	58	0.73	0.73	NUM
cana-3162	354	59	0.75	0.75	NUM
cana-3162	354	60	0.70	0.70	NUM
cana-3162	354	61	area	area	NOUN
cana-3162	354	62	under	under	ADP
cana-3162	354	63	the	the	DET
cana-3162	354	64	roc	roc	PROPN
cana-3162	354	65	curve	curve	NOUN
cana-3162	354	66	(	(	PUNCT
cana-3162	354	67	auc	auc	NOUN
cana-3162	354	68	-	-	PUNCT
cana-3162	354	69	roc	roc	NOUN
cana-3162	354	70	)	)	PUNCT
cana-3162	354	71	0.76	0.76	NUM
cana-3162	354	72	0.79	0.79	NUM
cana-3162	354	73	0.81	0.81	NUM
cana-3162	354	74	0.77	0.77	NUM
cana-3162	354	75	0.78	0.78	NUM
cana-3162	354	76	0.75	0.75	NUM
cana-3162	354	77	mean	mean	NOUN
cana-3162	354	78	absolute	absolute	ADJ
cana-3162	354	79	error	error	NOUN
cana-3162	354	80	(	(	PUNCT
cana-3162	354	81	mae	mae	PROPN
cana-3162	354	82	)	)	PUNCT
cana-3162	354	83	0.45	0.45	NUM
cana-3162	354	84	0.42	0.42	NUM
cana-3162	354	85	0.39	0.39	NUM
cana-3162	354	86	0.41	0.41	NUM
cana-3162	354	87	0.40	0.40	NUM
cana-3162	354	88	0.44	0.44	NUM
cana-3162	354	89	in	in	ADP
cana-3162	354	90	table	table	NOUN
cana-3162	354	91	3	3	NUM
cana-3162	354	92	,	,	PUNCT
cana-3162	354	93	logistic	logistic	ADJ
cana-3162	354	94	regression	regression	NOUN
cana-3162	354	95	,	,	PUNCT
cana-3162	354	96	decision	decision	NOUN
cana-3162	354	97	tree	tree	NOUN
cana-3162	354	98	,	,	PUNCT
cana-3162	354	99	k	k	NOUN
cana-3162	354	100	-	-	PUNCT
cana-3162	354	101	nearest	near	ADJ
cana-3162	354	102	neighbors	neighbor	NOUN
cana-3162	354	103	,	,	PUNCT
cana-3162	354	104	support	support	NOUN
cana-3162	354	105	vector	vector	NOUN
cana-3162	354	106	machine	machine	NOUN
cana-3162	354	107	,	,	PUNCT
cana-3162	354	108	random	random	ADJ
cana-3162	354	109	forest	forest	NOUN
cana-3162	354	110	,	,	PUNCT
cana-3162	354	111	and	and	CCONJ
cana-3162	354	112	naive	naive	ADJ
cana-3162	354	113	bayes	baye	NOUN
cana-3162	354	114	are	be	AUX
cana-3162	354	115	the	the	DET
cana-3162	354	116	six	six	NUM
cana-3162	354	117	fundamental	fundamental	ADJ
cana-3162	354	118	machine	machine	NOUN
cana-3162	354	119	learning	learning	NOUN
cana-3162	354	120	algorithms	algorithm	NOUN
cana-3162	354	121	.	.	PUNCT
cana-3162	355	1	we	we	PRON
cana-3162	355	2	evaluate	evaluate	VERB
cana-3162	355	3	mae	mae	PROPN
cana-3162	355	4	,	,	PUNCT
cana-3162	355	5	accuracy	accuracy	NOUN
cana-3162	355	6	,	,	PUNCT
cana-3162	355	7	precision	precision	NOUN
cana-3162	355	8	,	,	PUNCT
cana-3162	355	9	memory	memory	NOUN
cana-3162	355	10	,	,	PUNCT
cana-3162	355	11	f1	f1	NOUN
cana-3162	355	12	score	score	NOUN
cana-3162	355	13	,	,	PUNCT
cana-3162	355	14	and	and	CCONJ
cana-3162	355	15	auc	auc	NOUN
cana-3162	355	16	-	-	PUNCT
cana-3162	355	17	roc	roc	NOUN
cana-3162	355	18	.	.	PUNCT
cana-3162	356	1	with	with	ADP
cana-3162	356	2	accuracy	accuracy	NOUN
cana-3162	356	3	ranging	range	VERB
cana-3162	356	4	from	from	ADP
cana-3162	356	5	0.74	0.74	NUM
cana-3162	356	6	to	to	ADP
cana-3162	356	7	0.80	0.80	NUM
cana-3162	356	8	,	,	PUNCT
cana-3162	356	9	various	various	ADJ
cana-3162	356	10	approaches	approach	NOUN
cana-3162	356	11	perform	perform	VERB
cana-3162	356	12	differently	differently	ADV
cana-3162	356	13	.	.	PUNCT
cana-3162	357	1	this	this	PRON
cana-3162	357	2	compares	compare	VERB
cana-3162	357	3	popular	popular	ADJ
cana-3162	357	4	cancer	cancer	NOUN
cana-3162	357	5	drug	drug	NOUN
cana-3162	357	6	prediction	prediction	NOUN
cana-3162	357	7	systems	system	NOUN
cana-3162	357	8	'	'	PART
cana-3162	357	9	strengths	strength	NOUN
cana-3162	357	10	and	and	CCONJ
cana-3162	357	11	downsides	downside	NOUN
cana-3162	357	12	.	.	PUNCT
cana-3162	358	1	communications	communication	NOUN
cana-3162	358	2	on	on	ADP
cana-3162	358	3	applied	apply	VERB
cana-3162	358	4	nonlinear	nonlinear	ADJ
cana-3162	358	5	analysis	analysis	NOUN
cana-3162	358	6	issn	issn	NOUN
cana-3162	358	7	:	:	PUNCT
cana-3162	358	8	1074	1074	NUM
cana-3162	358	9	-	-	PUNCT
cana-3162	358	10	133x	133x	NUM
cana-3162	358	11	vol	vol	NOUN
cana-3162	358	12	32	32	NUM
cana-3162	358	13	no	no	NOUN
cana-3162	358	14	.	.	PUNCT
cana-3162	359	1	5s	5s	NUM
cana-3162	359	2	(	(	PUNCT
cana-3162	359	3	2025	2025	NUM
cana-3162	359	4	)	)	PUNCT
cana-3162	359	5	508	508	NUM
cana-3162	359	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3162	359	7	fig	fig	NOUN
cana-3162	359	8	.	.	PUNCT
cana-3162	360	1	4	4	X
cana-3162	360	2	.	.	X
cana-3162	360	3	performance	performance	NOUN
cana-3162	360	4	evaluation	evaluation	NOUN
cana-3162	360	5	of	of	ADP
cana-3162	360	6	traditional	traditional	ADJ
cana-3162	360	7	methods	method	NOUN
cana-3162	360	8	for	for	ADP
cana-3162	360	9	anti	anti	ADJ
cana-3162	360	10	-	-	ADJ
cana-3162	360	11	cancer	cancer	ADJ
cana-3162	360	12	drug	drug	NOUN
cana-3162	360	13	response	response	NOUN
cana-3162	360	14	prediction	prediction	NOUN
cana-3162	360	15	figure	figure	NOUN
cana-3162	360	16	4	4	NUM
cana-3162	360	17	compares	compare	VERB
cana-3162	360	18	the	the	DET
cana-3162	360	19	accuracy	accuracy	NOUN
cana-3162	360	20	of	of	ADP
cana-3162	360	21	popular	popular	ADJ
cana-3162	360	22	machine	machine	NOUN
cana-3162	360	23	learning	learn	VERB
cana-3162	360	24	algorithms	algorithm	NOUN
cana-3162	360	25	for	for	ADP
cana-3162	360	26	tumor	tumor	NOUN
cana-3162	360	27	drug	drug	NOUN
cana-3162	360	28	prediction	prediction	NOUN
cana-3162	360	29	.	.	PUNCT
cana-3162	361	1	each	each	DET
cana-3162	361	2	bar	bar	NOUN
cana-3162	361	3	displays	display	VERB
cana-3162	361	4	auc	auc	NOUN
cana-3162	361	5	-	-	PUNCT
cana-3162	361	6	roc	roc	NOUN
cana-3162	361	7	,	,	PUNCT
cana-3162	361	8	f1	f1	NOUN
cana-3162	361	9	score	score	NOUN
cana-3162	361	10	,	,	PUNCT
cana-3162	361	11	mae	mae	PROPN
cana-3162	361	12	,	,	PUNCT
cana-3162	361	13	and	and	CCONJ
cana-3162	361	14	other	other	ADJ
cana-3162	361	15	metrics	metric	NOUN
cana-3162	361	16	.	.	PUNCT
cana-3162	362	1	logistic	logistic	ADJ
cana-3162	362	2	regression	regression	NOUN
cana-3162	362	3	,	,	PUNCT
cana-3162	362	4	decision	decision	NOUN
cana-3162	362	5	tree	tree	NOUN
cana-3162	362	6	,	,	PUNCT
cana-3162	362	7	k	k	NOUN
cana-3162	362	8	-	-	PUNCT
cana-3162	362	9	nearest	near	ADJ
cana-3162	362	10	neighbors	neighbor	NOUN
cana-3162	362	11	,	,	PUNCT
cana-3162	362	12	svm	svm	ADJ
cana-3162	362	13	,	,	PUNCT
cana-3162	362	14	random	random	ADJ
cana-3162	362	15	forest	forest	NOUN
cana-3162	362	16	,	,	PUNCT
cana-3162	362	17	and	and	CCONJ
cana-3162	362	18	naive	naive	ADJ
cana-3162	362	19	bayes	bayes	NOUN
cana-3162	362	20	use	use	VERB
cana-3162	362	21	these	these	DET
cana-3162	362	22	parameters	parameter	NOUN
cana-3162	362	23	.	.	PUNCT
cana-3162	363	1	the	the	DET
cana-3162	363	2	graph	graph	NOUN
cana-3162	363	3	illustrates	illustrate	VERB
cana-3162	363	4	algorithm	algorithm	NOUN
cana-3162	363	5	behavior	behavior	NOUN
cana-3162	363	6	.	.	PUNCT
cana-3162	364	1	in	in	ADP
cana-3162	364	2	most	most	ADJ
cana-3162	364	3	categories	category	NOUN
cana-3162	364	4	,	,	PUNCT
cana-3162	364	5	k	k	X
cana-3162	364	6	-	-	PUNCT
cana-3162	364	7	nearest	near	ADJ
cana-3162	364	8	neighbors	neighbor	NOUN
cana-3162	364	9	is	be	AUX
cana-3162	364	10	most	most	ADV
cana-3162	364	11	accurate	accurate	ADJ
cana-3162	364	12	,	,	PUNCT
cana-3162	364	13	while	while	SCONJ
cana-3162	364	14	naive	naive	ADJ
cana-3162	364	15	bayes	bayes	NOUN
cana-3162	364	16	is	be	AUX
cana-3162	364	17	least	least	ADV
cana-3162	364	18	accurate	accurate	ADJ
cana-3162	364	19	.	.	PUNCT
cana-3162	365	1	table	table	NOUN
cana-3162	365	2	4	4	NUM
cana-3162	365	3	.	.	PUNCT
cana-3162	365	4	performance	performance	NOUN
cana-3162	365	5	evaluation	evaluation	NOUN
cana-3162	365	6	of	of	ADP
cana-3162	365	7	the	the	DET
cana-3162	365	8	proposed	propose	VERB
cana-3162	365	9	methodology	methodology	NOUN
cana-3162	365	10	for	for	ADP
cana-3162	365	11	anticancer	anticancer	NOUN
cana-3162	365	12	drug	drug	NOUN
cana-3162	365	13	response	response	NOUN
cana-3162	365	14	prediction	prediction	NOUN
cana-3162	365	15	performance	performance	NOUN
cana-3162	365	16	evaluation	evaluation	NOUN
cana-3162	365	17	parameter	parameter	NOUN
cana-3162	365	18	proposed	propose	VERB
cana-3162	365	19	methodology	methodology	NOUN
cana-3162	365	20	ensemble	ensemble	ADJ
cana-3162	365	21	learning	learning	NOUN
cana-3162	365	22	support	support	NOUN
cana-3162	365	23	vector	vector	NOUN
cana-3162	365	24	machine	machine	NOUN
cana-3162	365	25	random	random	ADJ
cana-3162	365	26	forest	forest	NOUN
cana-3162	365	27	gradient	gradient	NOUN
cana-3162	365	28	boosting	boost	VERB
cana-3162	365	29	neural	neural	ADJ
cana-3162	365	30	network	network	NOUN
cana-3162	365	31	accuracy	accuracy	NOUN
cana-3162	365	32	0.85	0.85	NUM
cana-3162	365	33	0.82	0.82	NUM
cana-3162	365	34	0.80	0.80	NUM
cana-3162	365	35	0.79	0.79	NUM
cana-3162	365	36	0.81	0.81	NUM
cana-3162	365	37	0.78	0.78	NUM
cana-3162	365	38	precision	precision	NOUN
cana-3162	365	39	0.83	0.83	NUM
cana-3162	365	40	0.80	0.80	NUM
cana-3162	365	41	0.78	0.78	NUM
cana-3162	365	42	0.75	0.75	NUM
cana-3162	365	43	0.79	0.79	NUM
cana-3162	365	44	0.76	0.76	NUM
cana-3162	365	45	recall	recall	NOUN
cana-3162	365	46	0.80	0.80	NUM
cana-3162	365	47	0.77	0.77	NUM
cana-3162	365	48	0.76	0.76	NUM
cana-3162	365	49	0.75	0.75	NUM
cana-3162	365	50	0.77	0.77	NUM
cana-3162	365	51	0.74	0.74	NUM
cana-3162	365	52	f1	f1	NOUN
cana-3162	365	53	score	score	NOUN
cana-3162	365	54	0.81	0.81	NUM
cana-3162	365	55	0.78	0.78	NUM
cana-3162	365	56	0.77	0.77	NUM
cana-3162	365	57	0.76	0.76	NUM
cana-3162	365	58	0.78	0.78	NUM
cana-3162	365	59	0.75	0.75	NUM
cana-3162	365	60	area	area	NOUN
cana-3162	365	61	under	under	ADP
cana-3162	365	62	the	the	DET
cana-3162	365	63	roc	roc	PROPN
cana-3162	365	64	curve	curve	NOUN
cana-3162	365	65	(	(	PUNCT
cana-3162	365	66	auc	auc	NOUN
cana-3162	365	67	-	-	PUNCT
cana-3162	365	68	roc	roc	NOUN
cana-3162	365	69	)	)	PUNCT
cana-3162	365	70	0.87	0.87	NUM
cana-3162	365	71	0.84	0.84	NUM
cana-3162	365	72	0.82	0.82	NUM
cana-3162	365	73	0.80	0.80	NUM
cana-3162	365	74	0.83	0.83	NUM
cana-3162	365	75	0.81	0.81	NUM
cana-3162	365	76	mean	mean	NOUN
cana-3162	365	77	absolute	absolute	ADJ
cana-3162	365	78	error	error	NOUN
cana-3162	365	79	(	(	PUNCT
cana-3162	365	80	mae	mae	PROPN
cana-3162	365	81	)	)	PUNCT
cana-3162	365	82	0.30	0.30	NUM
cana-3162	365	83	0.35	0.35	NUM
cana-3162	365	84	0.40	0.40	NUM
cana-3162	365	85	0.38	0.38	NUM
cana-3162	365	86	0.37	0.37	NUM
cana-3162	365	87	0.39	0.39	NUM
cana-3162	365	88	table	table	NOUN
cana-3162	365	89	4	4	NUM
cana-3162	365	90	shows	show	VERB
cana-3162	365	91	performance	performance	NOUN
cana-3162	365	92	requirements	requirement	NOUN
cana-3162	365	93	for	for	ADP
cana-3162	365	94	the	the	DET
cana-3162	365	95	recommended	recommend	VERB
cana-3162	365	96	technique	technique	NOUN
cana-3162	365	97	for	for	ADP
cana-3162	365	98	predicting	predict	VERB
cana-3162	365	99	anticancer	anticancer	NOUN
cana-3162	365	100	treatment	treatment	NOUN
cana-3162	365	101	efficacy	efficacy	NOUN
cana-3162	365	102	,	,	PUNCT
cana-3162	365	103	as	as	ADV
cana-3162	365	104	well	well	ADV
cana-3162	365	105	as	as	ADP
cana-3162	365	106	ensemble	ensemble	ADJ
cana-3162	365	107	learning	learning	NOUN
cana-3162	365	108	,	,	PUNCT
cana-3162	365	109	support	support	VERB
cana-3162	365	110	vector	vector	NOUN
cana-3162	365	111	machine	machine	NOUN
cana-3162	365	112	,	,	PUNCT
cana-3162	365	113	random	random	ADJ
cana-3162	365	114	forest	forest	NOUN
cana-3162	365	115	,	,	PUNCT
cana-3162	365	116	gradient	gradient	ADJ
cana-3162	365	117	boosting	boosting	NOUN
cana-3162	365	118	,	,	PUNCT
cana-3162	365	119	and	and	CCONJ
cana-3162	365	120	neural	neural	ADJ
cana-3162	365	121	network	network	NOUN
cana-3162	365	122	.	.	PUNCT
cana-3162	366	1	this	this	DET
cana-3162	366	2	table	table	NOUN
cana-3162	366	3	illustrates	illustrate	VERB
cana-3162	366	4	that	that	SCONJ
cana-3162	366	5	the	the	DET
cana-3162	366	6	proposed	propose	VERB
cana-3162	366	7	method	method	NOUN
cana-3162	366	8	regularly	regularly	ADV
cana-3162	366	9	delivers	deliver	VERB
cana-3162	366	10	greater	great	ADJ
cana-3162	366	11	accuracy	accuracy	NOUN
cana-3162	366	12	(	(	PUNCT
cana-3162	366	13	0.85	0.85	NUM
cana-3162	366	14	)	)	PUNCT
cana-3162	366	15	and	and	CCONJ
cana-3162	366	16	lower	low	ADJ
cana-3162	366	17	mean	mean	ADJ
cana-3162	366	18	absolute	absolute	ADJ
cana-3162	366	19	error	error	NOUN
cana-3162	366	20	(	(	PUNCT
cana-3162	366	21	0.30	0.30	NUM
cana-3162	366	22	)	)	PUNCT
cana-3162	366	23	.	.	PUNCT
cana-3162	367	1	accuracy	accuracy	NOUN
cana-3162	367	2	,	,	PUNCT
cana-3162	367	3	memory	memory	NOUN
cana-3162	367	4	,	,	PUNCT
cana-3162	367	5	f1	f1	NOUN
cana-3162	367	6	score	score	NOUN
cana-3162	367	7	,	,	PUNCT
cana-3162	367	8	and	and	CCONJ
cana-3162	367	9	aucroc	aucroc	NOUN
cana-3162	367	10	improved	improve	VERB
cana-3162	367	11	using	use	VERB
cana-3162	367	12	the	the	DET
cana-3162	367	13	recommended	recommend	VERB
cana-3162	367	14	technique	technique	NOUN
cana-3162	367	15	.	.	PUNCT
cana-3162	368	1	this	this	PRON
cana-3162	368	2	suggests	suggest	VERB
cana-3162	368	3	that	that	SCONJ
cana-3162	368	4	it	it	PRON
cana-3162	368	5	could	could	AUX
cana-3162	368	6	potentially	potentially	ADV
cana-3162	368	7	aid	aid	VERB
cana-3162	368	8	in	in	ADP
cana-3162	368	9	predicting	predict	VERB
cana-3162	368	10	the	the	DET
cana-3162	368	11	efficacy	efficacy	NOUN
cana-3162	368	12	of	of	ADP
cana-3162	368	13	cancer	cancer	NOUN
cana-3162	368	14	therapy	therapy	NOUN
cana-3162	368	15	.	.	PUNCT
cana-3162	369	1	communications	communication	NOUN
cana-3162	369	2	on	on	ADP
cana-3162	369	3	applied	apply	VERB
cana-3162	369	4	nonlinear	nonlinear	ADJ
cana-3162	369	5	analysis	analysis	NOUN
cana-3162	369	6	issn	issn	NOUN
cana-3162	369	7	:	:	PUNCT
cana-3162	369	8	1074	1074	NUM
cana-3162	369	9	-	-	PUNCT
cana-3162	369	10	133x	133x	NUM
cana-3162	369	11	vol	vol	NOUN
cana-3162	369	12	32	32	NUM
cana-3162	369	13	no	no	NOUN
cana-3162	369	14	.	.	PUNCT
cana-3162	370	1	5s	5s	NUM
cana-3162	370	2	(	(	PUNCT
cana-3162	370	3	2025	2025	NUM
cana-3162	370	4	)	)	PUNCT
cana-3162	370	5	509	509	NUM
cana-3162	370	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-3162	370	7	fig	fig	NOUN
cana-3162	370	8	.	.	PUNCT
cana-3162	371	1	5	5	NUM
cana-3162	371	2	.	.	X
cana-3162	371	3	performance	performance	NOUN
cana-3162	371	4	evaluation	evaluation	NOUN
cana-3162	371	5	of	of	ADP
cana-3162	371	6	the	the	DET
cana-3162	371	7	proposed	propose	VERB
cana-3162	371	8	methodology	methodology	NOUN
cana-3162	371	9	for	for	ADP
cana-3162	371	10	anti	anti	ADJ
cana-3162	371	11	-	-	ADJ
cana-3162	371	12	cancer	cancer	ADJ
cana-3162	371	13	drug	drug	NOUN
cana-3162	371	14	response	response	NOUN
cana-3162	371	15	prediction	prediction	NOUN
cana-3162	371	16	figure	figure	NOUN
cana-3162	371	17	5	5	NUM
cana-3162	371	18	compares	compare	VERB
cana-3162	371	19	the	the	DET
cana-3162	371	20	recommended	recommend	VERB
cana-3162	371	21	method	method	NOUN
cana-3162	371	22	's	's	PART
cana-3162	371	23	performance	performance	NOUN
cana-3162	371	24	assessment	assessment	NOUN
cana-3162	371	25	elements	element	NOUN
cana-3162	371	26	to	to	ADP
cana-3162	371	27	more	more	ADV
cana-3162	371	28	sophisticated	sophisticated	ADJ
cana-3162	371	29	anti	anti	ADJ
cana-3162	371	30	-	-	ADJ
cana-3162	371	31	cancer	cancer	ADJ
cana-3162	371	32	medication	medication	NOUN
cana-3162	371	33	prediction	prediction	NOUN
cana-3162	371	34	approaches	approach	NOUN
cana-3162	371	35	.	.	PUNCT
cana-3162	372	1	these	these	DET
cana-3162	372	2	bars	bar	NOUN
cana-3162	372	3	compare	compare	VERB
cana-3162	372	4	proposed	propose	VERB
cana-3162	372	5	methodology	methodology	NOUN
cana-3162	372	6	,	,	PUNCT
cana-3162	372	7	ensemble	ensemble	ADJ
cana-3162	372	8	learning	learning	NOUN
cana-3162	372	9	,	,	PUNCT
cana-3162	372	10	svm	svm	ADJ
cana-3162	372	11	,	,	PUNCT
cana-3162	372	12	random	random	ADJ
cana-3162	372	13	forest	forest	NOUN
cana-3162	372	14	,	,	PUNCT
cana-3162	372	15	gradient	gradient	ADJ
cana-3162	372	16	boosting	boosting	NOUN
cana-3162	372	17	,	,	PUNCT
cana-3162	372	18	and	and	CCONJ
cana-3162	372	19	neural	neural	ADJ
cana-3162	372	20	network	network	NOUN
cana-3162	372	21	measures	measure	NOUN
cana-3162	372	22	.	.	PUNCT
cana-3162	373	1	these	these	DET
cana-3162	373	2	measures	measure	NOUN
cana-3162	373	3	include	include	VERB
cana-3162	373	4	f1	f1	NOUN
cana-3162	373	5	score	score	NOUN
cana-3162	373	6	,	,	PUNCT
cana-3162	373	7	auc	auc	NOUN
cana-3162	373	8	-	-	PUNCT
cana-3162	373	9	roc	roc	PROPN
cana-3162	373	10	,	,	PUNCT
cana-3162	373	11	mae	mae	PROPN
cana-3162	373	12	,	,	PUNCT
cana-3162	373	13	accuracy	accuracy	NOUN
cana-3162	373	14	,	,	PUNCT
cana-3162	373	15	precision	precision	NOUN
cana-3162	373	16	,	,	PUNCT
cana-3162	373	17	recall	recall	NOUN
cana-3162	373	18	,	,	PUNCT
cana-3162	373	19	and	and	CCONJ
cana-3162	373	20	random	random	ADJ
cana-3162	373	21	forest	forest	NOUN
cana-3162	373	22	.	.	PUNCT
cana-3162	374	1	the	the	DET
cana-3162	374	2	graph	graph	NOUN
cana-3162	374	3	demonstrates	demonstrate	VERB
cana-3162	374	4	that	that	SCONJ
cana-3162	374	5	the	the	DET
cana-3162	374	6	proposed	propose	VERB
cana-3162	374	7	technique	technique	NOUN
cana-3162	374	8	has	have	VERB
cana-3162	374	9	the	the	DET
cana-3162	374	10	best	good	ADJ
cana-3162	374	11	accuracy	accuracy	NOUN
cana-3162	374	12	and	and	CCONJ
cana-3162	374	13	lowest	low	ADJ
cana-3162	374	14	mae	mae	PROPN
cana-3162	374	15	.	.	PUNCT
cana-3162	375	1	this	this	PRON
cana-3162	375	2	suggests	suggest	VERB
cana-3162	375	3	it	it	PRON
cana-3162	375	4	may	may	AUX
cana-3162	375	5	enhance	enhance	VERB
cana-3162	375	6	cancer	cancer	NOUN
cana-3162	375	7	therapy	therapy	NOUN
cana-3162	375	8	outcome	outcome	NOUN
cana-3162	375	9	prediction	prediction	NOUN
cana-3162	375	10	.	.	PUNCT
cana-3162	376	1	v.	v.	ADP
cana-3162	376	2	conclusion	conclusion	NOUN
cana-3162	376	3	to	to	PART
cana-3162	376	4	conclude	conclude	VERB
cana-3162	376	5	,	,	PUNCT
cana-3162	376	6	the	the	DET
cana-3162	376	7	proposed	propose	VERB
cana-3162	376	8	strategy	strategy	NOUN
cana-3162	376	9	for	for	ADP
cana-3162	376	10	predicting	predict	VERB
cana-3162	376	11	cancer	cancer	NOUN
cana-3162	376	12	patients	patient	NOUN
cana-3162	376	13	'	'	PART
cana-3162	376	14	drug	drug	NOUN
cana-3162	376	15	reactions	reaction	NOUN
cana-3162	376	16	represents	represent	VERB
cana-3162	376	17	a	a	DET
cana-3162	376	18	major	major	ADJ
cana-3162	376	19	advance	advance	NOUN
cana-3162	376	20	in	in	ADP
cana-3162	376	21	chemotherapy	chemotherapy	NOUN
cana-3162	376	22	machine	machine	NOUN
cana-3162	376	23	learning	learning	NOUN
cana-3162	376	24	.	.	PUNCT
cana-3162	377	1	this	this	DET
cana-3162	377	2	strategy	strategy	NOUN
cana-3162	377	3	improves	improve	VERB
cana-3162	377	4	feature	feature	NOUN
cana-3162	377	5	selection	selection	NOUN
cana-3162	377	6	,	,	PUNCT
cana-3162	377	7	model	model	NOUN
cana-3162	377	8	performance	performance	NOUN
cana-3162	377	9	,	,	PUNCT
cana-3162	377	10	and	and	CCONJ
cana-3162	377	11	prediction	prediction	NOUN
cana-3162	377	12	accuracy	accuracy	NOUN
cana-3162	377	13	by	by	ADP
cana-3162	377	14	structuring	structure	VERB
cana-3162	377	15	ensemble	ensemble	ADJ
cana-3162	377	16	learning	learning	NOUN
cana-3162	377	17	and	and	CCONJ
cana-3162	377	18	support	support	VERB
cana-3162	377	19	vector	vector	NOUN
cana-3162	377	20	machine	machine	NOUN
cana-3162	377	21	models	model	NOUN
cana-3162	377	22	.	.	PUNCT
cana-3162	378	1	results	result	NOUN
cana-3162	378	2	suggest	suggest	VERB
cana-3162	378	3	this	this	DET
cana-3162	378	4	strategy	strategy	NOUN
cana-3162	378	5	is	be	AUX
cana-3162	378	6	considerably	considerably	ADV
cana-3162	378	7	superior	superior	ADJ
cana-3162	378	8	to	to	ADP
cana-3162	378	9	others	other	NOUN
cana-3162	378	10	.	.	PUNCT
cana-3162	379	1	there	there	PRON
cana-3162	379	2	is	be	VERB
cana-3162	379	3	an	an	DET
cana-3162	379	4	increase	increase	NOUN
cana-3162	379	5	in	in	ADP
cana-3162	379	6	prediction	prediction	NOUN
cana-3162	379	7	accuracy	accuracy	NOUN
cana-3162	379	8	,	,	PUNCT
cana-3162	379	9	clarity	clarity	NOUN
cana-3162	379	10	,	,	PUNCT
cana-3162	379	11	recall	recall	NOUN
cana-3162	379	12	,	,	PUNCT
cana-3162	379	13	and	and	CCONJ
cana-3162	379	14	reliability	reliability	NOUN
cana-3162	379	15	.	.	PUNCT
cana-3162	380	1	these	these	DET
cana-3162	380	2	new	new	ADJ
cana-3162	380	3	advances	advance	NOUN
cana-3162	380	4	demonstrate	demonstrate	VERB
cana-3162	380	5	the	the	DET
cana-3162	380	6	strength	strength	NOUN
cana-3162	380	7	of	of	ADP
cana-3162	380	8	the	the	DET
cana-3162	380	9	proposed	propose	VERB
cana-3162	380	10	model	model	NOUN
cana-3162	380	11	and	and	CCONJ
cana-3162	380	12	the	the	DET
cana-3162	380	13	need	need	NOUN
cana-3162	380	14	for	for	ADP
cana-3162	380	15	sophisticated	sophisticated	ADJ
cana-3162	380	16	approaches	approach	NOUN
cana-3162	380	17	to	to	PART
cana-3162	380	18	extract	extract	VERB
cana-3162	380	19	relevant	relevant	ADJ
cana-3162	380	20	information	information	NOUN
cana-3162	380	21	from	from	ADP
cana-3162	380	22	huge	huge	ADJ
cana-3162	380	23	,	,	PUNCT
cana-3162	380	24	tough	tough	ADJ
cana-3162	380	25	cancer	cancer	NOUN
cana-3162	380	26	datasets	dataset	NOUN
cana-3162	380	27	.	.	PUNCT
cana-3162	381	1	current	current	ADJ
cana-3162	381	2	machine	machine	NOUN
cana-3162	381	3	learning	learning	NOUN
cana-3162	381	4	technologies	technology	NOUN
cana-3162	381	5	in	in	ADP
cana-3162	381	6	cancer	cancer	NOUN
cana-3162	381	7	therapy	therapy	NOUN
cana-3162	381	8	provide	provide	VERB
cana-3162	381	9	more	more	ADJ
cana-3162	381	10	information	information	NOUN
cana-3162	381	11	and	and	CCONJ
cana-3162	381	12	help	help	VERB
cana-3162	381	13	patients	patient	NOUN
cana-3162	381	14	make	make	VERB
cana-3162	381	15	better	well	ADJ
cana-3162	381	16	choices	choice	NOUN
cana-3162	381	17	.	.	PUNCT
cana-3162	382	1	to	to	PART
cana-3162	382	2	maximize	maximize	VERB
cana-3162	382	3	data	data	NOUN
cana-3162	382	4	-	-	PUNCT
cana-3162	382	5	driven	drive	VERB
cana-3162	382	6	methodologies	methodology	NOUN
cana-3162	382	7	,	,	PUNCT
cana-3162	382	8	cancer	cancer	NOUN
cana-3162	382	9	research	research	NOUN
cana-3162	382	10	and	and	CCONJ
cana-3162	382	11	prediction	prediction	NOUN
cana-3162	382	12	model	model	NOUN
cana-3162	382	13	development	development	NOUN
cana-3162	382	14	must	must	AUX
cana-3162	382	15	continue	continue	VERB
cana-3162	382	16	.	.	PUNCT
cana-3162	383	1	this	this	DET
cana-3162	383	2	study	study	NOUN
cana-3162	383	3	allows	allow	VERB
cana-3162	383	4	for	for	ADP
cana-3162	383	5	additional	additional	ADJ
cana-3162	383	6	research	research	NOUN
cana-3162	383	7	and	and	CCONJ
cana-3162	383	8	better	well	ADJ
cana-3162	383	9	cancer	cancer	NOUN
cana-3162	383	10	treatments	treatment	NOUN
cana-3162	383	11	.	.	PUNCT
cana-3162	384	1	it	it	PRON
cana-3162	384	2	also	also	ADV
cana-3162	384	3	emphasizes	emphasize	VERB
cana-3162	384	4	the	the	DET
cana-3162	384	5	need	need	NOUN
cana-3162	384	6	for	for	ADP
cana-3162	384	7	improved	improve	VERB
cana-3162	384	8	healthcare	healthcare	NOUN
cana-3162	384	9	predictive	predictive	ADJ
cana-3162	384	10	analytics	analytic	NOUN
cana-3162	384	11	.	.	PUNCT
cana-3162	385	1	references	reference	NOUN
cana-3162	385	2	[	[	X
cana-3162	385	3	1	1	X
cana-3162	385	4	]	]	PUNCT
cana-3162	385	5	j.	j.	PROPN
cana-3162	385	6	ferlay	ferlay	PROPN
cana-3162	385	7	,	,	PUNCT
cana-3162	385	8	m.	m.	NOUN
cana-3162	385	9	colombet	colombet	PROPN
cana-3162	385	10	,	,	PUNCT
cana-3162	385	11	i.	i.	PROPN
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cana-3162	385	13	,	,	PUNCT
cana-3162	385	14	d.	d.	PROPN
cana-3162	385	15	m.	m.	PROPN
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cana-3162	385	18	m.	m.	NOUN
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cana-3162	385	20	,	,	PUNCT
cana-3162	385	21	a.	a.	NOUN
cana-3162	385	22	znaor	znaor	PROPN
cana-3162	385	23	,	,	PUNCT
cana-3162	385	24	and	and	CCONJ
cana-3162	385	25	f.	f.	PROPN
cana-3162	385	26	bray	bray	PROPN
cana-3162	385	27	,	,	PUNCT
cana-3162	385	28	“	"	PUNCT
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cana-3162	385	31	for	for	ADP
cana-3162	385	32	the	the	DET
cana-3162	385	33	year	year	NOUN
cana-3162	385	34	2020	2020	NUM
cana-3162	385	35	:	:	PUNCT
cana-3162	385	36	an	an	DET
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cana-3162	385	38	,	,	PUNCT
cana-3162	385	39	”	"	PUNCT
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cana-3162	385	41	.	.	PUNCT
cana-3162	386	1	j.	j.	PROPN
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cana-3162	386	5	.	.	PROPN
cana-3162	386	6	149	149	NUM
cana-3162	386	7	,	,	PUNCT
cana-3162	386	8	pp	pp	ADJ
cana-3162	386	9	.	.	PUNCT
cana-3162	387	1	778–789	778–789	NUM
cana-3162	387	2	,	,	PUNCT
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cana-3162	388	2	2	2	NUM
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cana-3162	388	5	,	,	PUNCT
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cana-3162	388	7	,	,	PUNCT
cana-3162	388	8	et	et	PROPN
cana-3162	388	9	al	al	PROPN
cana-3162	388	10	.	.	PUNCT
cana-3162	389	1	"	"	PUNCT
cana-3162	389	2	hybridizing	hybridize	VERB
cana-3162	389	3	convolutional	convolutional	ADJ
cana-3162	389	4	neural	neural	ADJ
cana-3162	389	5	network	network	NOUN
cana-3162	389	6	for	for	ADP
cana-3162	389	7	classification	classification	NOUN
cana-3162	389	8	of	of	ADP
cana-3162	389	9	lung	lung	NOUN
cana-3162	389	10	diseases	disease	NOUN
cana-3162	389	11	.	.	PUNCT
cana-3162	389	12	"	"	PUNCT
cana-3162	390	1	ijsir	ijsir	PROPN
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cana-3162	390	3	,	,	PUNCT
cana-3162	390	4	no.2	no.2	PROPN
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cana-3162	390	6	:	:	PUNCT
cana-3162	390	7	pp.1	pp.1	NOUN
cana-3162	390	8	-	-	PUNCT
cana-3162	390	9	15	15	NUM
cana-3162	390	10	.	.	PUNCT
cana-3162	390	11	https://doi.org/10.4018/ijsir.287544	https://doi.org/10.4018/ijsir.287544	PROPN
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cana-3162	391	2	3	3	NUM
cana-3162	391	3	]	]	PUNCT
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cana-3162	391	5	f.	f.	PROPN
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cana-3162	391	8	l.	l.	PROPN
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cana-3162	391	10	,	,	PUNCT
cana-3162	391	11	d.	d.	PROPN
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cana-3162	391	21	j.	j.	PROPN
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cana-3162	391	35	h.	h.	PROPN
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cana-3162	391	42	“	"	PUNCT
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cana-3162	391	54	t790	t790	PROPN
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cana-3162	391	57	,	,	PUNCT
cana-3162	391	58	”	"	PUNCT
cana-3162	391	59	j.	j.	PROPN
cana-3162	391	60	thorac	thorac	PROPN
cana-3162	391	61	.	.	PUNCT
cana-3162	392	1	oncol	oncol	PROPN
cana-3162	392	2	.	.	PUNCT
cana-3162	393	1	,	,	PUNCT
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cana-3162	393	3	.	.	PROPN
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cana-3162	394	2	,	,	PUNCT
cana-3162	394	3	pp	pp	ADJ
cana-3162	394	4	.	.	PUNCT
cana-3162	395	1	456–463	456–463	NUM
cana-3162	395	2	,	,	PUNCT
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cana-3162	395	4	.	.	PUNCT
cana-3162	396	1	[	[	X
cana-3162	396	2	4	4	X
cana-3162	396	3	]	]	X
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cana-3162	396	5	pathak	pathak	PROPN
cana-3162	396	6	,	,	PUNCT
cana-3162	396	7	"	"	PUNCT
cana-3162	396	8	evaluating	evaluate	VERB
cana-3162	396	9	e	e	NOUN
cana-3162	396	10	-	-	ADJ
cana-3162	396	11	learning	learning	ADJ
cana-3162	396	12	engagement	engagement	NOUN
cana-3162	396	13	through	through	ADP
cana-3162	396	14	eeg	eeg	PROPN
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cana-3162	396	17	with	with	ADP
cana-3162	396	18	convolutional	convolutional	ADJ
cana-3162	396	19	neural	neural	ADJ
cana-3162	396	20	networks	network	NOUN
cana-3162	396	21	,	,	PUNCT
cana-3162	396	22	"	"	PUNCT
cana-3162	396	23	in	in	ADP
cana-3162	396	24	proceedings	proceeding	NOUN
cana-3162	396	25	of	of	ADP
cana-3162	396	26	fifth	fifth	ADJ
cana-3162	396	27	international	international	ADJ
cana-3162	396	28	conference	conference	NOUN
cana-3162	396	29	on	on	ADP
cana-3162	396	30	computer	computer	NOUN
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cana-3162	396	32	communication	communication	NOUN
cana-3162	396	33	technologies	technology	NOUN
cana-3162	396	34	(	(	PUNCT
cana-3162	396	35	ic3	ic3	NOUN
cana-3162	396	36	t	t	PROPN
cana-3162	396	37	2023	2023	NUM
cana-3162	396	38	)	)	PUNCT
cana-3162	396	39	,	,	PUNCT
cana-3162	396	40	lecture	lecture	NOUN
cana-3162	396	41	notes	note	NOUN
cana-3162	396	42	in	in	ADP
cana-3162	396	43	networks	network	NOUN
cana-3162	396	44	and	and	CCONJ
cana-3162	396	45	systems	system	NOUN
cana-3162	396	46	,	,	PUNCT
cana-3162	396	47	vol	vol	NOUN
cana-3162	396	48	.	.	PROPN
cana-3162	396	49	897	897	NUM
cana-3162	396	50	,	,	PUNCT
cana-3162	396	51	b.	b.	PROPN
cana-3162	396	52	r.	r.	PROPN
cana-3162	396	53	devi	devi	PROPN
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cana-3162	396	56	kumar	kumar	PROPN
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cana-3162	396	60	,	,	PUNCT
cana-3162	396	61	k.	k.	PROPN
cana-3162	396	62	s.	s.	PROPN
cana-3162	396	63	raju	raju	PROPN
cana-3162	396	64	,	,	PUNCT
cana-3162	396	65	and	and	CCONJ
cana-3162	396	66	m.	m.	NOUN
cana-3162	396	67	sellathurai	sellathurai	PROPN
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cana-3162	396	69	eds	eds	PROPN
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cana-3162	396	74	,	,	PUNCT
cana-3162	396	75	2024	2024	NUM
cana-3162	396	76	,	,	PUNCT
cana-3162	396	77	doi	doi	NOUN
cana-3162	396	78	:	:	PUNCT
cana-3162	396	79	10.1007/978	10.1007/978	NUM
cana-3162	396	80	-	-	SYM
cana-3162	396	81	981	981	NUM
cana-3162	396	82	-	-	PUNCT
cana-3162	396	83	99	99	NUM
cana-3162	396	84	-	-	PUNCT
cana-3162	396	85	9704	9704	NUM
cana-3162	396	86	-	-	PUNCT
cana-3162	396	87	6_20	6_20	NOUN
cana-3162	396	88	.	.	PUNCT
cana-3162	397	1	[	[	X
cana-3162	397	2	5	5	X
cana-3162	397	3	]	]	PUNCT
cana-3162	397	4	j.	j.	PROPN
cana-3162	397	5	redmon	redmon	PROPN
cana-3162	397	6	,	,	PUNCT
cana-3162	397	7	s.	s.	PROPN
cana-3162	397	8	divvala	divvala	PROPN
cana-3162	397	9	,	,	PUNCT
cana-3162	397	10	r.	r.	PROPN
cana-3162	397	11	girshick	girshick	PROPN
cana-3162	397	12	,	,	PUNCT
cana-3162	397	13	and	and	CCONJ
cana-3162	397	14	a.	a.	NOUN
cana-3162	397	15	farhadi	farhadi	NOUN
cana-3162	397	16	,	,	PUNCT
cana-3162	397	17	“	"	PUNCT
cana-3162	397	18	you	you	PRON
cana-3162	397	19	only	only	ADV
cana-3162	397	20	look	look	VERB
cana-3162	397	21	once	once	ADV
cana-3162	397	22	:	:	PUNCT
cana-3162	397	23	unified	unified	ADJ
cana-3162	397	24	,	,	PUNCT
cana-3162	397	25	real	real	ADJ
cana-3162	397	26	-	-	PUNCT
cana-3162	397	27	time	time	NOUN
cana-3162	397	28	object	object	NOUN
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cana-3162	398	3	]	]	PUNCT
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cana-3162	398	17	m.	m.	PROPN
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cana-3162	398	30	,	,	PUNCT
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cana-3162	399	33	data	datum	NOUN
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cana-3162	399	35	,	,	PUNCT
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cana-3162	400	6	,	,	PUNCT
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cana-3162	400	9	,	,	PUNCT
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cana-3162	400	11	.	.	PUNCT
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cana-3162	401	8	:	:	PUNCT
cana-3162	401	9	1074	1074	NUM
cana-3162	401	10	-	-	PUNCT
cana-3162	401	11	133x	133x	NUM
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cana-3162	401	15	.	.	PUNCT
cana-3162	402	1	5s	5s	NUM
cana-3162	402	2	(	(	PUNCT
cana-3162	402	3	2025	2025	NUM
cana-3162	402	4	)	)	PUNCT
cana-3162	402	5	510	510	NUM
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cana-3162	403	1	[	[	X
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cana-3162	403	3	]	]	PUNCT
cana-3162	403	4	v.	v.	CCONJ
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cana-3162	403	7	h.	h.	PROPN
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cana-3162	403	11	“	"	PUNCT
cana-3162	403	12	machine	machine	NOUN
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cana-3162	403	14	&	&	CCONJ
cana-3162	403	15	artificial	artificial	ADJ
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cana-3162	403	17	in	in	ADP
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cana-3162	403	21	:	:	PUNCT
cana-3162	403	22	a	a	DET
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cana-3162	403	29	rep	rep	PROPN
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cana-3162	404	11	,	,	PUNCT
cana-3162	404	12	p.	p.	NOUN
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cana-3162	404	14	,	,	PUNCT
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cana-3162	405	1	[	[	X
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cana-3162	405	14	,	,	PUNCT
cana-3162	405	15	”	"	PUNCT
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cana-3162	405	19	.	.	PROPN
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cana-3162	405	21	,	,	PUNCT
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cana-3162	405	23	.	.	PUNCT
cana-3162	406	1	195–202	195–202	NUM
cana-3162	406	2	,	,	PUNCT
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cana-3162	406	4	.	.	PUNCT
cana-3162	407	1	[	[	X
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cana-3162	407	16	implementing	implement	VERB
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cana-3162	407	22	.	.	PUNCT
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cana-3162	408	9	-	-	SYM
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cana-3162	408	11	-	-	PUNCT
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cana-3162	408	13	-	-	PUNCT
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cana-3162	409	1	[	[	X
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cana-3162	410	2	-	-	SYM
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cana-3162	412	2	-	-	SYM
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cana-3162	414	14	.	.	PUNCT
cana-3162	415	1	[	[	X
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cana-3162	415	3	]	]	X
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cana-3162	415	16	in	in	ADP
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cana-3162	415	20	,	,	PUNCT
cana-3162	415	21	”	"	PUNCT
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cana-3162	416	1	229–233	229–233	NUM
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cana-3162	417	1	[	[	X
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cana-3162	417	22	r.	r.	PROPN
cana-3162	417	23	nair	nair	PROPN
cana-3162	417	24	,	,	PUNCT
cana-3162	417	25	"	"	PUNCT
cana-3162	417	26	navigating	navigate	VERB
cana-3162	417	27	the	the	DET
cana-3162	417	28	chatbot	chatbot	NOUN
cana-3162	417	29	terrain	terrain	NOUN
cana-3162	417	30	:	:	PUNCT
cana-3162	417	31	ai	ai	VERB
cana-3162	417	32	-	-	PUNCT
cana-3162	417	33	driven	drive	VERB
cana-3162	417	34	conversational	conversational	ADJ
cana-3162	417	35	interfaces	interface	NOUN
cana-3162	417	36	,	,	PUNCT
cana-3162	417	37	"	"	PUNCT
cana-3162	417	38	in	in	ADP
cana-3162	417	39	international	international	ADJ
cana-3162	417	40	conference	conference	NOUN
cana-3162	417	41	on	on	ADP
cana-3162	417	42	applied	apply	VERB
cana-3162	417	43	technologies	technology	NOUN
cana-3162	417	44	.	.	PUNCT
cana-3162	418	1	icat	icat	NOUN
cana-3162	418	2	2023	2023	NUM
cana-3162	418	3	.	.	PUNCT
cana-3162	419	1	communications	communication	NOUN
cana-3162	419	2	in	in	ADP
cana-3162	419	3	computer	computer	NOUN
cana-3162	419	4	and	and	CCONJ
cana-3162	419	5	information	information	NOUN
cana-3162	419	6	science	science	NOUN
cana-3162	419	7	,	,	PUNCT
cana-3162	419	8	vol	vol	NOUN
cana-3162	419	9	.	.	PROPN
cana-3162	419	10	2049	2049	NUM
cana-3162	419	11	,	,	PUNCT
cana-3162	419	12	m.	m.	NOUN
cana-3162	419	13	botto	botto	PROPN
cana-3162	419	14	-	-	PROPN
cana-3162	419	15	tobar	tobar	NOUN
cana-3162	419	16	,	,	PUNCT
cana-3162	419	17	m.	m.	NOUN
cana-3162	419	18	zambrano	zambrano	PROPN
cana-3162	419	19	vizuete	vizuete	PROPN
cana-3162	419	20	,	,	PUNCT
cana-3162	419	21	s.	s.	PROPN
cana-3162	419	22	montes	montes	PROPN
cana-3162	419	23	león	león	PROPN
cana-3162	419	24	,	,	PUNCT
cana-3162	419	25	p.	p.	NOUN
cana-3162	419	26	torrescarrión	torrescarrión	PROPN
cana-3162	419	27	,	,	PUNCT
cana-3162	419	28	and	and	CCONJ
cana-3162	419	29	b.	b.	PROPN
cana-3162	419	30	durakovic	durakovic	PROPN
cana-3162	419	31	,	,	PUNCT
cana-3162	419	32	eds	eds	PROPN
cana-3162	419	33	.	.	PUNCT
cana-3162	419	34	cham	cham	PROPN
cana-3162	419	35	:	:	PUNCT
cana-3162	419	36	springer	springer	NOUN
cana-3162	419	37	,	,	PUNCT
cana-3162	419	38	2024	2024	NUM
cana-3162	419	39	.	.	PUNCT
cana-3162	420	1	doi	doi	NOUN
cana-3162	420	2	:	:	PUNCT
cana-3162	420	3	10.1007/978	10.1007/978	NUM
cana-3162	420	4	-	-	SYM
cana-3162	420	5	3	3	NUM
cana-3162	420	6	-	-	PUNCT
cana-3162	420	7	031	031	NUM
cana-3162	420	8	-	-	PUNCT
cana-3162	420	9	58956	58956	NUM
cana-3162	420	10	-	-	PUNCT
cana-3162	420	11	0_7	0_7	NUM
cana-3162	420	12	.	.	PUNCT
cana-3162	421	1	[	[	X
cana-3162	421	2	16	16	NUM
cana-3162	421	3	]	]	X
cana-3162	421	4	kulkarni	kulkarni	PROPN
cana-3162	421	5	,	,	PUNCT
cana-3162	421	6	c.	c.	PROPN
cana-3162	421	7	,	,	PUNCT
cana-3162	421	8	quraishi	quraishi	ADV
cana-3162	421	9	,	,	PUNCT
cana-3162	421	10	a.	a.	NOUN
cana-3162	421	11	,	,	PUNCT
cana-3162	421	12	raparthi	raparthi	NOUN
cana-3162	421	13	,	,	PUNCT
cana-3162	421	14	m.	m.	NOUN
cana-3162	421	15	et	et	PROPN
cana-3162	421	16	al	al	PROPN
cana-3162	421	17	.	.	PROPN
cana-3162	421	18	hybrid	hybrid	ADJ
cana-3162	421	19	disease	disease	NOUN
cana-3162	421	20	prediction	prediction	NOUN
cana-3162	421	21	approach	approach	NOUN
cana-3162	421	22	leveraging	leverage	VERB
cana-3162	421	23	digital	digital	ADJ
cana-3162	421	24	twin	twin	ADJ
cana-3162	421	25	and	and	CCONJ
cana-3162	421	26	metaverse	metaverse	NOUN
cana-3162	421	27	technologies	technology	NOUN
cana-3162	421	28	for	for	ADP
cana-3162	421	29	health	health	NOUN
cana-3162	421	30	consumer	consumer	NOUN
cana-3162	421	31	.	.	PUNCT
cana-3162	422	1	bmc	bmc	PROPN
cana-3162	422	2	med	med	ADJ
cana-3162	422	3	inform	inform	NOUN
cana-3162	422	4	decis	decis	PROPN
cana-3162	422	5	mak	mak	PROPN
cana-3162	422	6	24	24	NUM
cana-3162	422	7	,	,	PUNCT
cana-3162	422	8	92	92	NUM
cana-3162	422	9	(	(	PUNCT
cana-3162	422	10	2024	2024	NUM
cana-3162	422	11	)	)	PUNCT
cana-3162	422	12	.	.	PUNCT
cana-3162	423	1	https://doi.org/10.1186/s12911-024-02495-2	https://doi.org/10.1186/s12911-024-02495-2	NUM
cana-3162	424	1	[	[	X
cana-3162	424	2	17	17	NUM
cana-3162	424	3	]	]	X
cana-3162	424	4	s.	s.	PROPN
cana-3162	424	5	tiwari	tiwari	PROPN
cana-3162	424	6	,	,	PUNCT
cana-3162	424	7	"	"	PUNCT
cana-3162	424	8	integrating	integrate	VERB
cana-3162	424	9	deep	deep	ADJ
cana-3162	424	10	learning	learning	NOUN
cana-3162	424	11	to	to	PART
cana-3162	424	12	decode	decode	VERB
cana-3162	424	13	meningeal	meningeal	NOUN
cana-3162	424	14	interleukin-17	interleukin-17	PROPN
cana-3162	424	15	t	t	PROPN
cana-3162	424	16	cell	cell	NOUN
cana-3162	424	17	mechanisms	mechanism	NOUN
cana-3162	424	18	in	in	ADP
cana-3162	424	19	salt	salt	NOUN
cana-3162	424	20	-	-	PUNCT
cana-3162	424	21	sensitive	sensitive	ADJ
cana-3162	424	22	hypertension	hypertension	NOUN
cana-3162	424	23	-	-	PUNCT
cana-3162	424	24	induced	induce	VERB
cana-3162	424	25	cognitive	cognitive	ADJ
cana-3162	424	26	impairment	impairment	NOUN
cana-3162	424	27	,	,	PUNCT
cana-3162	424	28	"	"	PUNCT
cana-3162	424	29	in	in	ADP
cana-3162	424	30	2024	2024	NUM
cana-3162	424	31	opju	opju	PROPN
cana-3162	424	32	international	international	ADJ
cana-3162	424	33	technology	technology	NOUN
cana-3162	424	34	conference	conference	NOUN
cana-3162	424	35	(	(	PUNCT
cana-3162	424	36	otcon	otcon	NOUN
cana-3162	424	37	)	)	PUNCT
cana-3162	424	38	on	on	ADP
cana-3162	424	39	smart	smart	ADJ
cana-3162	424	40	computing	computing	NOUN
cana-3162	424	41	for	for	ADP
cana-3162	424	42	innovation	innovation	NOUN
cana-3162	424	43	and	and	CCONJ
cana-3162	424	44	advancement	advancement	NOUN
cana-3162	424	45	in	in	ADP
cana-3162	424	46	industry	industry	NOUN
cana-3162	424	47	4.0	4.0	NUM
cana-3162	424	48	,	,	PUNCT
cana-3162	424	49	vol	vol	NOUN
cana-3162	424	50	.	.	PROPN
cana-3162	424	51	40	40	NUM
cana-3162	424	52	,	,	PUNCT
cana-3162	424	53	pp	pp	ADJ
cana-3162	424	54	.	.	PUNCT
cana-3162	425	1	1	1	NUM
cana-3162	425	2	-	-	SYM
cana-3162	425	3	6	6	NUM
cana-3162	425	4	,	,	PUNCT
cana-3162	425	5	doi	doi	NOUN
cana-3162	425	6	:	:	PUNCT
cana-3162	425	7	10.1109	10.1109	NUM
cana-3162	425	8	/	/	SYM
cana-3162	425	9	otcon60325.2024.10687585	otcon60325.2024.10687585	NOUN
cana-3162	425	10	.	.	PUNCT
cana-3162	426	1	[	[	X
cana-3162	426	2	18	18	NUM
cana-3162	426	3	]	]	PUNCT
cana-3162	426	4	p.	p.	NOUN
cana-3162	426	5	patil	patil	PROPN
cana-3162	426	6	,	,	PUNCT
cana-3162	426	7	"	"	PUNCT
cana-3162	426	8	leveraging	leverage	VERB
cana-3162	426	9	high	high	ADJ
cana-3162	426	10	-	-	PUNCT
cana-3162	426	11	performance	performance	NOUN
cana-3162	426	12	computing	computing	NOUN
cana-3162	426	13	for	for	ADP
cana-3162	426	14	boiling	boil	VERB
cana-3162	426	15	heat	heat	NOUN
cana-3162	426	16	transfer	transfer	NOUN
cana-3162	426	17	simulations	simulation	NOUN
cana-3162	426	18	,	,	PUNCT
cana-3162	426	19	"	"	PUNCT
cana-3162	426	20	in	in	ADP
cana-3162	426	21	2024	2024	NUM
cana-3162	426	22	opju	opju	PROPN
cana-3162	426	23	international	international	ADJ
cana-3162	426	24	technology	technology	NOUN
cana-3162	426	25	conference	conference	NOUN
cana-3162	426	26	(	(	PUNCT
cana-3162	426	27	otcon	otcon	NOUN
cana-3162	426	28	)	)	PUNCT
cana-3162	426	29	on	on	ADP
cana-3162	426	30	smart	smart	ADJ
cana-3162	426	31	computing	computing	NOUN
cana-3162	426	32	for	for	ADP
cana-3162	426	33	innovation	innovation	NOUN
cana-3162	426	34	and	and	CCONJ
cana-3162	426	35	advancement	advancement	NOUN
cana-3162	426	36	in	in	ADP
cana-3162	426	37	industry	industry	NOUN
cana-3162	426	38	4.0	4.0	NUM
cana-3162	426	39	,	,	PUNCT
cana-3162	426	40	vol	vol	NOUN
cana-3162	426	41	.	.	NOUN
cana-3162	426	42	1	1	NUM
cana-3162	426	43	,	,	PUNCT
cana-3162	426	44	pp	pp	ADJ
cana-3162	426	45	.	.	PUNCT
cana-3162	427	1	1	1	NUM
cana-3162	427	2	-	-	SYM
cana-3162	427	3	6	6	NUM
cana-3162	427	4	,	,	PUNCT
cana-3162	427	5	doi	doi	NOUN
cana-3162	427	6	:	:	PUNCT
cana-3162	427	7	10.1109	10.1109	NUM
cana-3162	427	8	/	/	SYM
cana-3162	427	9	otcon60325.2024.10688203	otcon60325.2024.10688203	NOUN
cana-3162	427	10	.	.	PUNCT
cana-3162	428	1	[	[	X
cana-3162	428	2	19	19	NUM
cana-3162	428	3	]	]	PUNCT
cana-3162	428	4	m.	m.	NOUN
cana-3162	428	5	m.	m.	PROPN
cana-3162	428	6	abdulhasan	abdulhasan	PROPN
cana-3162	428	7	,	,	PUNCT
cana-3162	428	8	"	"	PUNCT
cana-3162	428	9	navigating	navigate	VERB
cana-3162	428	10	the	the	DET
cana-3162	428	11	prognostics	prognostic	NOUN
cana-3162	428	12	landscape	landscape	NOUN
cana-3162	428	13	:	:	PUNCT
cana-3162	428	14	deep	deep	ADJ
cana-3162	428	15	reinforcement	reinforcement	NOUN
cana-3162	428	16	learning	learning	NOUN
cana-3162	428	17	-	-	PUNCT
cana-3162	428	18	enabled	enable	VERB
cana-3162	428	19	remaining	remain	VERB
cana-3162	428	20	useful	useful	ADJ
cana-3162	428	21	life	life	NOUN
cana-3162	428	22	estimation	estimation	NOUN
cana-3162	428	23	with	with	ADP
cana-3162	428	24	novel	novel	ADJ
cana-3162	428	25	methodology	methodology	NOUN
cana-3162	428	26	,	,	PUNCT
cana-3162	428	27	"	"	PUNCT
cana-3162	428	28	in	in	ADP
cana-3162	428	29	2024	2024	NUM
cana-3162	428	30	international	international	ADJ
cana-3162	428	31	conference	conference	NOUN
cana-3162	428	32	on	on	ADP
cana-3162	428	33	communication	communication	NOUN
cana-3162	428	34	,	,	PUNCT
cana-3162	428	35	computer	computer	NOUN
cana-3162	428	36	sciences	science	NOUN
cana-3162	428	37	and	and	CCONJ
cana-3162	428	38	engineering	engineering	NOUN
cana-3162	428	39	(	(	PUNCT
cana-3162	428	40	ic3se	ic3se	NUM
cana-3162	428	41	)	)	PUNCT
cana-3162	428	42	,	,	PUNCT
cana-3162	428	43	vol	vol	NOUN
cana-3162	428	44	.	.	PROPN
cana-3162	428	45	2	2	NUM
cana-3162	428	46	,	,	PUNCT
cana-3162	428	47	pp	pp	ADJ
cana-3162	428	48	.	.	PUNCT
cana-3162	429	1	520	520	NUM
cana-3162	429	2	-	-	SYM
cana-3162	429	3	526	526	NUM
cana-3162	429	4	,	,	PUNCT
cana-3162	429	5	doi	doi	NOUN
cana-3162	429	6	:	:	PUNCT
cana-3162	429	7	10.1109	10.1109	NUM
cana-3162	429	8	/	/	SYM
cana-3162	429	9	ic3se62002.2024.10592974	ic3se62002.2024.10592974	NOUN
cana-3162	429	10	.	.	PUNCT
cana-3162	430	1	[	[	X
cana-3162	430	2	20	20	NUM
cana-3162	430	3	]	]	X
cana-3162	430	4	p.	p.	PROPN
cana-3162	430	5	pal	pal	NOUN
cana-3162	430	6	,	,	PUNCT
cana-3162	430	7	r.	r.	PROPN
cana-3162	430	8	k.	k.	PROPN
cana-3162	430	9	behera	behera	PROPN
cana-3162	430	10	,	,	PUNCT
cana-3162	430	11	and	and	CCONJ
cana-3162	430	12	u.	u.	PROPN
cana-3162	430	13	r.	r.	PROPN
cana-3162	430	14	muduli	muduli	PROPN
cana-3162	430	15	,	,	PUNCT
cana-3162	430	16	"	"	PUNCT
cana-3162	430	17	eliminating	eliminate	VERB
cana-3162	430	18	current	current	ADJ
cana-3162	430	19	sensor	sensor	NOUN
cana-3162	430	20	dependencies	dependency	NOUN
cana-3162	430	21	in	in	ADP
cana-3162	430	22	dab	dab	NOUN
cana-3162	430	23	converters	converter	NOUN
cana-3162	430	24	using	use	VERB
cana-3162	430	25	a	a	DET
cana-3162	430	26	luenberger	luenberg	ADJ
cana-3162	430	27	observer	observer	NOUN
cana-3162	430	28	-	-	PUNCT
cana-3162	430	29	based	base	VERB
cana-3162	430	30	hybrid	hybrid	NOUN
cana-3162	430	31	approach	approach	NOUN
cana-3162	430	32	,	,	PUNCT
cana-3162	430	33	"	"	PUNCT
cana-3162	430	34	ieee	ieee	NOUN
cana-3162	430	35	transactions	transaction	NOUN
cana-3162	430	36	on	on	ADP
cana-3162	430	37	industry	industry	NOUN
cana-3162	430	38	applications	application	NOUN
cana-3162	430	39	,	,	PUNCT
cana-3162	430	40	doi	doi	NOUN
cana-3162	430	41	:	:	PUNCT
cana-3162	430	42	10.1109	10.1109	NUM
cana-3162	430	43	/	/	SYM
cana-3162	430	44	tia.2024.3384465	tia.2024.3384465	NOUN
cana-3162	430	45	.	.	PUNCT
cana-3162	431	1	[	[	X
cana-3162	431	2	21	21	NUM
cana-3162	431	3	]	]	PUNCT
cana-3162	431	4	s.	s.	PROPN
cana-3162	431	5	k.	k.	PROPN
cana-3162	431	6	baksi	baksi	PROPN
cana-3162	431	7	,	,	PUNCT
cana-3162	431	8	r.	r.	PROPN
cana-3162	431	9	k.	k.	PROPN
cana-3162	431	10	behera	behera	PROPN
cana-3162	431	11	,	,	PUNCT
cana-3162	431	12	and	and	CCONJ
cana-3162	431	13	u.	u.	PROPN
cana-3162	431	14	r.	r.	PROPN
cana-3162	431	15	muduli	muduli	PROPN
cana-3162	431	16	,	,	PUNCT
cana-3162	431	17	"	"	PUNCT
cana-3162	431	18	comprehensive	comprehensive	ADJ
cana-3162	431	19	overview	overview	NOUN
cana-3162	431	20	of	of	ADP
cana-3162	431	21	reduced	reduce	VERB
cana-3162	431	22	switch	switch	NOUN
cana-3162	431	23	count	count	NOUN
cana-3162	431	24	multilevel	multilevel	ADJ
cana-3162	431	25	inverter	inverter	NOUN
cana-3162	431	26	for	for	ADP
cana-3162	431	27	pv	pv	NOUN
cana-3162	431	28	applications	application	NOUN
cana-3162	431	29	,	,	PUNCT
cana-3162	431	30	"	"	PUNCT
cana-3162	431	31	in	in	ADP
cana-3162	431	32	2023	2023	NUM
cana-3162	431	33	ieee	ieee	NOUN
cana-3162	431	34	3rd	3rd	PROPN
cana-3162	431	35	international	international	ADJ
cana-3162	431	36	conference	conference	NOUN
cana-3162	431	37	on	on	ADP
cana-3162	431	38	smart	smart	ADJ
cana-3162	431	39	technologies	technology	NOUN
cana-3162	431	40	for	for	ADP
cana-3162	431	41	power	power	NOUN
cana-3162	431	42	,	,	PUNCT
cana-3162	431	43	energy	energy	NOUN
cana-3162	431	44	and	and	CCONJ
cana-3162	431	45	control	control	NOUN
cana-3162	431	46	(	(	PUNCT
cana-3162	431	47	stpec	stpec	NOUN
cana-3162	431	48	)	)	PUNCT
cana-3162	431	49	,	,	PUNCT
cana-3162	431	50	bhubaneswar	bhubaneswar	NOUN
cana-3162	431	51	,	,	PUNCT
cana-3162	431	52	india	india	PROPN
cana-3162	431	53	,	,	PUNCT
cana-3162	431	54	2023	2023	NUM
cana-3162	431	55	,	,	PUNCT
cana-3162	431	56	pp	pp	ADJ
cana-3162	431	57	.	.	PUNCT
cana-3162	432	1	1	1	NUM
cana-3162	432	2	-	-	SYM
cana-3162	432	3	6	6	NUM
cana-3162	432	4	,	,	PUNCT
cana-3162	432	5	doi	doi	NOUN
cana-3162	432	6	:	:	PUNCT
cana-3162	432	7	10.1109	10.1109	NUM
cana-3162	432	8	/	/	SYM
cana-3162	432	9	stpec59253.2023.10431075	stpec59253.2023.10431075	NOUN
cana-3162	432	10	.	.	PUNCT
cana-3162	433	1	[	[	X
cana-3162	433	2	22	22	NUM
cana-3162	433	3	]	]	PUNCT
cana-3162	433	4	s.	s.	PROPN
cana-3162	433	5	k.	k.	PROPN
cana-3162	433	6	baksi	baksi	PROPN
cana-3162	433	7	,	,	PUNCT
cana-3162	433	8	r.	r.	PROPN
cana-3162	433	9	k.	k.	PROPN
cana-3162	433	10	behera	behera	PROPN
cana-3162	433	11	,	,	PUNCT
cana-3162	433	12	and	and	CCONJ
cana-3162	433	13	u.	u.	PROPN
cana-3162	433	14	r.	r.	PROPN
cana-3162	433	15	muduli	muduli	PROPN
cana-3162	433	16	,	,	PUNCT
cana-3162	433	17	"	"	PUNCT
cana-3162	433	18	a	a	DET
cana-3162	433	19	new	new	ADJ
cana-3162	433	20	transformerless	transformerless	NOUN
cana-3162	433	21	five	five	NUM
cana-3162	433	22	-	-	PUNCT
cana-3162	433	23	level	level	NOUN
cana-3162	433	24	boost	boost	NOUN
cana-3162	433	25	inverter	inverter	NOUN
cana-3162	433	26	with	with	ADP
cana-3162	433	27	minimum	minimum	ADJ
cana-3162	433	28	switch	switch	NOUN
cana-3162	433	29	count	count	NOUN
cana-3162	433	30	for	for	ADP
cana-3162	433	31	photovoltaic	photovoltaic	NOUN
cana-3162	433	32	application	application	NOUN
cana-3162	433	33	,	,	PUNCT
cana-3162	433	34	"	"	PUNCT
cana-3162	433	35	in	in	ADP
cana-3162	433	36	2023	2023	NUM
cana-3162	433	37	ieee	ieee	NOUN
cana-3162	433	38	3rd	3rd	PROPN
cana-3162	433	39	international	international	ADJ
cana-3162	433	40	conference	conference	NOUN
cana-3162	433	41	on	on	ADP
cana-3162	433	42	smart	smart	ADJ
cana-3162	433	43	technologies	technology	NOUN
cana-3162	433	44	for	for	ADP
cana-3162	433	45	power	power	NOUN
cana-3162	433	46	,	,	PUNCT
cana-3162	433	47	energy	energy	NOUN
cana-3162	433	48	and	and	CCONJ
cana-3162	433	49	control	control	NOUN
cana-3162	433	50	(	(	PUNCT
cana-3162	433	51	stpec	stpec	NOUN
cana-3162	433	52	)	)	PUNCT
cana-3162	433	53	,	,	PUNCT
cana-3162	433	54	bhubaneswar	bhubaneswar	NOUN
cana-3162	433	55	,	,	PUNCT
cana-3162	433	56	india	india	PROPN
cana-3162	433	57	,	,	PUNCT
cana-3162	433	58	2023	2023	NUM
cana-3162	433	59	,	,	PUNCT
cana-3162	433	60	pp	pp	ADJ
cana-3162	433	61	.	.	PUNCT
cana-3162	433	62	1	1	NUM
cana-3162	433	63	-	-	SYM
cana-3162	433	64	6	6	NUM
cana-3162	433	65	,	,	PUNCT
cana-3162	433	66	doi	doi	NOUN
cana-3162	433	67	:	:	PUNCT
cana-3162	433	68	10.1109	10.1109	NUM
cana-3162	433	69	/	/	SYM
cana-3162	433	70	stpec59253.2023.10430976	stpec59253.2023.10430976	NOUN
cana-3162	433	71	.	.	PUNCT
cana-3162	434	1	[	[	X
cana-3162	434	2	23	23	NUM
cana-3162	434	3	]	]	X
cana-3162	434	4	n.	n.	NOUN
cana-3162	434	5	belokonev	belokonev	PROPN
cana-3162	434	6	et	et	PROPN
cana-3162	434	7	al	al	PROPN
cana-3162	434	8	.	.	PROPN
cana-3162	434	9	,	,	PUNCT
cana-3162	434	10	“	"	PUNCT
cana-3162	434	11	optimization	optimization	NOUN
cana-3162	434	12	of	of	ADP
cana-3162	434	13	chemical	chemical	NOUN
cana-3162	434	14	mixers	mixer	NOUN
cana-3162	434	15	design	design	NOUN
cana-3162	434	16	via	via	ADP
cana-3162	434	17	tensor	tensor	NOUN
cana-3162	434	18	trains	train	NOUN
cana-3162	434	19	and	and	CCONJ
cana-3162	434	20	quantum	quantum	NOUN
cana-3162	434	21	computing	computing	NOUN
cana-3162	434	22	,	,	PUNCT
cana-3162	434	23	”	"	PUNCT
cana-3162	434	24	arxiv	arxiv	NOUN
cana-3162	434	25	,	,	PUNCT
cana-3162	434	26	2023	2023	NUM
cana-3162	434	27	,	,	PUNCT
cana-3162	434	28	arxiv:2304.12307	arxiv:2304.12307	NOUN
cana-3162	434	29	.	.	PUNCT
cana-3162	435	1	[	[	X
cana-3162	435	2	24	24	NUM
cana-3162	435	3	]	]	PUNCT
cana-3162	435	4	s.	s.	PROPN
cana-3162	435	5	mcardle	mcardle	PROPN
cana-3162	435	6	et	et	PROPN
cana-3162	435	7	al	al	PROPN
cana-3162	435	8	.	.	PROPN
cana-3162	435	9	,	,	PUNCT
cana-3162	435	10	“	"	PUNCT
cana-3162	435	11	quantum	quantum	ADJ
cana-3162	435	12	computational	computational	ADJ
cana-3162	435	13	chemistry	chemistry	NOUN
cana-3162	435	14	,	,	PUNCT
cana-3162	435	15	”	"	PUNCT
cana-3162	435	16	rev	rev	X
cana-3162	435	17	.	.	PROPN
cana-3162	436	1	mod	mod	PROPN
cana-3162	436	2	.	.	PUNCT
cana-3162	437	1	phys	phy	NOUN
cana-3162	437	2	.	.	PUNCT
cana-3162	437	3	,	,	PUNCT
cana-3162	437	4	vol	vol	NOUN
cana-3162	437	5	.	.	PROPN
cana-3162	437	6	92	92	NUM
cana-3162	437	7	,	,	PUNCT
cana-3162	437	8	p.	p.	NOUN
cana-3162	437	9	015003	015003	NUM
cana-3162	437	10	,	,	PUNCT
cana-3162	437	11	2020	2020	NUM
cana-3162	437	12	.	.	PUNCT
cana-3162	438	1	[	[	X
cana-3162	438	2	25	25	NUM
cana-3162	438	3	]	]	X
cana-3162	438	4	g.	g.	PROPN
cana-3162	438	5	nannicini	nannicini	PROPN
cana-3162	438	6	,	,	PUNCT
cana-3162	438	7	“	"	PUNCT
cana-3162	438	8	performance	performance	NOUN
cana-3162	438	9	of	of	ADP
cana-3162	438	10	hybrid	hybrid	ADJ
cana-3162	438	11	quantum	quantum	NOUN
cana-3162	438	12	-	-	PUNCT
cana-3162	438	13	classical	classical	ADJ
cana-3162	438	14	variational	variational	ADJ
cana-3162	438	15	heuristics	heuristic	NOUN
cana-3162	438	16	for	for	ADP
cana-3162	438	17	combinatorial	combinatorial	ADJ
cana-3162	438	18	optimization	optimization	NOUN
cana-3162	438	19	,	,	PUNCT
cana-3162	438	20	”	"	PUNCT
cana-3162	438	21	phys	phy	NOUN
cana-3162	438	22	.	.	PUNCT
cana-3162	438	23	rev	rev	PROPN
cana-3162	438	24	.	.	PROPN
cana-3162	439	1	e	e	NOUN
cana-3162	439	2	,	,	PUNCT
cana-3162	439	3	vol	vol	NOUN
cana-3162	439	4	.	.	PROPN
cana-3162	440	1	99	99	NUM
cana-3162	440	2	,	,	PUNCT
cana-3162	440	3	p.	p.	NOUN
cana-3162	440	4	013304	013304	NUM
cana-3162	440	5	,	,	PUNCT
cana-3162	440	6	2019	2019	NUM
cana-3162	440	7	.	.	PUNCT
cana-3162	441	1	[	[	X
cana-3162	441	2	26	26	NUM
cana-3162	441	3	]	]	PUNCT
cana-3162	441	4	a.	a.	NOUN
cana-3162	441	5	i.	i.	PROPN
cana-3162	441	6	gircha	gircha	PROPN
cana-3162	441	7	et	et	PROPN
cana-3162	441	8	al	al	PROPN
cana-3162	441	9	.	.	PROPN
cana-3162	441	10	,	,	PUNCT
cana-3162	441	11	“	"	PUNCT
cana-3162	441	12	training	train	VERB
cana-3162	441	13	a	a	DET
cana-3162	441	14	discrete	discrete	ADJ
cana-3162	441	15	variational	variational	ADJ
cana-3162	441	16	autoencoder	autoencoder	NOUN
cana-3162	441	17	for	for	ADP
cana-3162	441	18	generative	generative	ADJ
cana-3162	441	19	chemistry	chemistry	NOUN
cana-3162	441	20	and	and	CCONJ
cana-3162	441	21	drug	drug	NOUN
cana-3162	441	22	design	design	NOUN
cana-3162	441	23	on	on	ADP
cana-3162	441	24	a	a	DET
cana-3162	441	25	quantum	quantum	NOUN
cana-3162	441	26	annealer	annealer	NOUN
cana-3162	441	27	,	,	PUNCT
cana-3162	441	28	”	"	PUNCT
cana-3162	441	29	arxiv	arxiv	NOUN
cana-3162	441	30	,	,	PUNCT
cana-3162	441	31	2021	2021	NUM
cana-3162	441	32	,	,	PUNCT
cana-3162	441	33	arxiv:2108.11644	arxiv:2108.11644	PROPN
cana-3162	441	34	.	.	PUNCT