id	sid	tid	token	lemma	pos
cana-4811	1	1	sample	sample	NOUN
cana-4811	1	2	chapter	chapter	NOUN
cana-4811	1	3	02.08.2022	02.08.2022	NOUN
cana-4811	1	4	communications	communication	NOUN
cana-4811	1	5	on	on	ADP
cana-4811	1	6	applied	apply	VERB
cana-4811	1	7	nonlinear	nonlinear	ADJ
cana-4811	1	8	analysis	analysis	NOUN
cana-4811	1	9	issn	issn	NOUN
cana-4811	1	10	:	:	PUNCT
cana-4811	1	11	1074	1074	NUM
cana-4811	1	12	-	-	PUNCT
cana-4811	1	13	133x	133x	NUM
cana-4811	1	14	vol	vol	NOUN
cana-4811	1	15	32	32	NUM
cana-4811	1	16	no	no	NOUN
cana-4811	1	17	.	.	PUNCT
cana-4811	2	1	01s	01s	PROPN
cana-4811	2	2	(	(	PUNCT
cana-4811	2	3	2025	2025	NUM
cana-4811	2	4	)	)	PUNCT
cana-4811	2	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-4811	2	6	a	a	DET
cana-4811	2	7	comparative	comparative	ADJ
cana-4811	2	8	study	study	NOUN
cana-4811	2	9	on	on	ADP
cana-4811	2	10	prediction	prediction	NOUN
cana-4811	2	11	efficiency	efficiency	NOUN
cana-4811	2	12	in	in	ADP
cana-4811	2	13	lung	lung	NOUN
cana-4811	2	14	cancer	cancer	NOUN
cana-4811	2	15	detection	detection	NOUN
cana-4811	2	16	using	use	VERB
cana-4811	2	17	machine	machine	NOUN
cana-4811	2	18	learning	learning	NOUN
cana-4811	2	19	models	model	NOUN
cana-4811	2	20	dr	dr	PROPN
cana-4811	2	21	.	.	PROPN
cana-4811	2	22	d.	d.	PROPN
cana-4811	2	23	kumaresan1	kumaresan1	PROPN
cana-4811	2	24	,	,	PUNCT
cana-4811	2	25	dr	dr	PROPN
cana-4811	2	26	.	.	PROPN
cana-4811	2	27	b.	b.	PROPN
cana-4811	2	28	santhosh	santhosh	PROPN
cana-4811	2	29	kumar2	kumar2	PROPN
cana-4811	3	1	*	*	PROPN
cana-4811	3	2	1department	1department	NUM
cana-4811	3	3	of	of	ADP
cana-4811	3	4	computer	computer	NOUN
cana-4811	3	5	and	and	CCONJ
cana-4811	3	6	information	information	NOUN
cana-4811	3	7	science	science	NOUN
cana-4811	3	8	,	,	PUNCT
cana-4811	3	9	faculty	faculty	NOUN
cana-4811	3	10	of	of	ADP
cana-4811	3	11	science	science	NOUN
cana-4811	3	12	,	,	PUNCT
cana-4811	3	13	annamalai	annamalai	PROPN
cana-4811	3	14	university	university	PROPN
cana-4811	3	15	,	,	PUNCT
cana-4811	3	16	annamalai	annamalai	PROPN
cana-4811	3	17	nagar	nagar	PROPN
cana-4811	3	18	,	,	PUNCT
cana-4811	3	19	tamil	tamil	PROPN
cana-4811	3	20	nadu	nadu	PROPN
cana-4811	3	21	,	,	PUNCT
cana-4811	3	22	india	india	PROPN
cana-4811	3	23	.	.	PUNCT
cana-4811	3	24	2*assistant	2*assistant	PROPN
cana-4811	3	25	professor	professor	NOUN
cana-4811	3	26	,	,	PUNCT
cana-4811	3	27	department	department	NOUN
cana-4811	3	28	of	of	ADP
cana-4811	3	29	computer	computer	NOUN
cana-4811	3	30	applications	application	NOUN
cana-4811	3	31	,	,	PUNCT
cana-4811	3	32	periyar	periyar	NOUN
cana-4811	3	33	arts	arts	PROPN
cana-4811	3	34	college	college	PROPN
cana-4811	3	35	,	,	PUNCT
cana-4811	3	36	cuddalore	cuddalore	PROPN
cana-4811	3	37	,	,	PUNCT
cana-4811	3	38	tamil	tamil	PROPN
cana-4811	3	39	nadu	nadu	PROPN
cana-4811	3	40	,	,	PUNCT
cana-4811	4	1	india	india	PROPN
cana-4811	4	2	.	.	PUNCT
cana-4811	4	3	email	email	NOUN
cana-4811	4	4	:	:	PUNCT
cana-4811	4	5	aucsedks@yahoo.co.in	aucsedks@yahoo.co.in	PUNCT
cana-4811	4	6	*	*	PUNCT
cana-4811	4	7	corresponding	correspond	VERB
cana-4811	4	8	email	email	NOUN
cana-4811	4	9	:	:	PUNCT
cana-4811	5	1	santhoshcdm@gmail.com	santhoshcdm@gmail.com	X
cana-4811	5	2	article	article	PROPN
cana-4811	5	3	history	history	NOUN
cana-4811	5	4	:	:	PUNCT
cana-4811	5	5	received	receive	VERB
cana-4811	5	6	:	:	PUNCT
cana-4811	5	7	15	15	NUM
cana-4811	5	8	-	-	SYM
cana-4811	5	9	10	10	NUM
cana-4811	5	10	-	-	PUNCT
cana-4811	5	11	2024	2024	NUM
cana-4811	5	12	revised	revise	VERB
cana-4811	5	13	:	:	PUNCT
cana-4811	5	14	10	10	NUM
cana-4811	5	15	-	-	SYM
cana-4811	5	16	11	11	NUM
cana-4811	5	17	-	-	PUNCT
cana-4811	5	18	2024	2024	NUM
cana-4811	5	19	accepted	accept	VERB
cana-4811	5	20	:	:	PUNCT
cana-4811	5	21	30	30	NUM
cana-4811	5	22	-	-	SYM
cana-4811	5	23	01	01	NUM
cana-4811	5	24	-	-	PUNCT
cana-4811	5	25	2025	2025	NUM
cana-4811	5	26	abstract	abstract	NOUN
cana-4811	5	27	:	:	PUNCT
cana-4811	5	28	machine	machine	NOUN
cana-4811	5	29	learning	learning	NOUN
cana-4811	5	30	can	can	AUX
cana-4811	5	31	be	be	AUX
cana-4811	5	32	utilized	utilize	VERB
cana-4811	5	33	to	to	PART
cana-4811	5	34	analyze	analyze	VERB
cana-4811	5	35	lung	lung	NOUN
cana-4811	5	36	cancer	cancer	NOUN
cana-4811	5	37	data	datum	NOUN
cana-4811	5	38	and	and	CCONJ
cana-4811	5	39	make	make	VERB
cana-4811	5	40	predictions	prediction	NOUN
cana-4811	5	41	using	use	VERB
cana-4811	5	42	models	model	NOUN
cana-4811	5	43	trained	train	VERB
cana-4811	5	44	on	on	ADP
cana-4811	5	45	datasets	dataset	NOUN
cana-4811	5	46	.	.	PUNCT
cana-4811	6	1	this	this	DET
cana-4811	6	2	analysis	analysis	NOUN
cana-4811	6	3	and	and	CCONJ
cana-4811	6	4	prediction	prediction	NOUN
cana-4811	6	5	can	can	AUX
cana-4811	6	6	help	help	VERB
cana-4811	6	7	clinicians	clinician	NOUN
cana-4811	6	8	and	and	CCONJ
cana-4811	6	9	patients	patient	NOUN
cana-4811	6	10	by	by	ADP
cana-4811	6	11	reducing	reduce	VERB
cana-4811	6	12	and	and	CCONJ
cana-4811	6	13	improving	improve	VERB
cana-4811	6	14	early	early	ADJ
cana-4811	6	15	detection	detection	NOUN
cana-4811	6	16	of	of	ADP
cana-4811	6	17	lung	lung	NOUN
cana-4811	6	18	cancer	cancer	NOUN
cana-4811	6	19	.	.	PUNCT
cana-4811	7	1	some	some	DET
cana-4811	7	2	methods	method	NOUN
cana-4811	7	3	for	for	ADP
cana-4811	7	4	using	use	VERB
cana-4811	7	5	machine	machine	NOUN
cana-4811	7	6	learning	learning	NOUN
cana-4811	7	7	to	to	PART
cana-4811	7	8	analyze	analyze	VERB
cana-4811	7	9	lung	lung	NOUN
cana-4811	7	10	cancer	cancer	NOUN
cana-4811	7	11	datasets	dataset	NOUN
cana-4811	7	12	namely	namely	ADV
cana-4811	7	13	logistic	logistic	ADJ
cana-4811	7	14	regression	regression	NOUN
cana-4811	7	15	,	,	PUNCT
cana-4811	7	16	multilayer	multilayer	PROPN
cana-4811	7	17	perceptron	perceptron	PROPN
cana-4811	7	18	,	,	PUNCT
cana-4811	7	19	smo	smo	PROPN
cana-4811	7	20	,	,	PUNCT
cana-4811	7	21	j48	j48	PROPN
cana-4811	7	22	,	,	PUNCT
cana-4811	7	23	random	random	ADJ
cana-4811	7	24	forest	forest	NOUN
cana-4811	7	25	,	,	PUNCT
cana-4811	7	26	and	and	CCONJ
cana-4811	7	27	rep	rep	PROPN
cana-4811	7	28	tree	tree	NOUN
cana-4811	7	29	.	.	PUNCT
cana-4811	8	1	machine	machine	NOUN
cana-4811	8	2	learning	learn	VERB
cana-4811	8	3	algorithms	algorithm	NOUN
cana-4811	8	4	employ	employ	VERB
cana-4811	8	5	computational	computational	ADJ
cana-4811	8	6	techniques	technique	NOUN
cana-4811	8	7	to	to	PART
cana-4811	8	8	extract	extract	VERB
cana-4811	8	9	information	information	NOUN
cana-4811	8	10	directly	directly	ADV
cana-4811	8	11	from	from	ADP
cana-4811	8	12	the	the	DET
cana-4811	8	13	dataset	dataset	NOUN
cana-4811	8	14	.	.	PUNCT
cana-4811	9	1	these	these	DET
cana-4811	9	2	algorithms	algorithm	NOUN
cana-4811	9	3	find	find	VERB
cana-4811	9	4	a	a	DET
cana-4811	9	5	suitable	suitable	ADJ
cana-4811	9	6	variable	variable	NOUN
cana-4811	9	7	for	for	ADP
cana-4811	9	8	the	the	DET
cana-4811	9	9	prediction	prediction	NOUN
cana-4811	9	10	of	of	ADP
cana-4811	9	11	lung	lung	NOUN
cana-4811	9	12	cancer	cancer	NOUN
cana-4811	9	13	using	use	VERB
cana-4811	9	14	different	different	ADJ
cana-4811	9	15	machine	machine	NOUN
cana-4811	9	16	-	-	PUNCT
cana-4811	9	17	learning	learn	VERB
cana-4811	9	18	approaches	approach	NOUN
cana-4811	9	19	and	and	CCONJ
cana-4811	9	20	performance	performance	NOUN
cana-4811	9	21	matrices	matrix	NOUN
cana-4811	9	22	.	.	PUNCT
cana-4811	10	1	this	this	DET
cana-4811	10	2	paper	paper	NOUN
cana-4811	10	3	considers	consider	VERB
cana-4811	10	4	a	a	DET
cana-4811	10	5	lung	lung	NOUN
cana-4811	10	6	cancer	cancer	NOUN
cana-4811	10	7	prediction	prediction	NOUN
cana-4811	10	8	dataset	dataset	VERB
cana-4811	10	9	with	with	ADP
cana-4811	10	10	25	25	NUM
cana-4811	10	11	parameters	parameter	NOUN
cana-4811	10	12	.	.	PUNCT
cana-4811	11	1	numerical	numerical	ADJ
cana-4811	11	2	illustrations	illustration	NOUN
cana-4811	11	3	are	be	AUX
cana-4811	11	4	provided	provide	VERB
cana-4811	11	5	to	to	PART
cana-4811	11	6	prove	prove	VERB
cana-4811	11	7	the	the	DET
cana-4811	11	8	proposed	propose	VERB
cana-4811	11	9	results	result	NOUN
cana-4811	11	10	with	with	ADP
cana-4811	11	11	accuracy	accuracy	NOUN
cana-4811	11	12	parameters	parameter	NOUN
cana-4811	11	13	.	.	PUNCT
cana-4811	12	1	keywords	keyword	NOUN
cana-4811	12	2	:	:	PUNCT
cana-4811	12	3	lung	lung	NOUN
cana-4811	12	4	cancer	cancer	NOUN
cana-4811	12	5	detection	detection	NOUN
cana-4811	12	6	,	,	PUNCT
cana-4811	12	7	machine	machine	NOUN
cana-4811	12	8	learning	learning	NOUN
cana-4811	12	9	,	,	PUNCT
cana-4811	12	10	prediction	prediction	NOUN
cana-4811	12	11	,	,	PUNCT
cana-4811	12	12	and	and	CCONJ
cana-4811	12	13	accuracy	accuracy	NOUN
cana-4811	12	14	parameters	parameter	NOUN
cana-4811	12	15	.	.	PUNCT
cana-4811	13	1	1	1	X
cana-4811	13	2	.	.	X
cana-4811	13	3	introduction	introduction	NOUN
cana-4811	13	4	improving	improve	VERB
cana-4811	13	5	survival	survival	NOUN
cana-4811	13	6	rates	rate	NOUN
cana-4811	13	7	is	be	AUX
cana-4811	13	8	largely	largely	ADV
cana-4811	13	9	dependent	dependent	ADJ
cana-4811	13	10	on	on	ADP
cana-4811	13	11	early	early	ADJ
cana-4811	13	12	detection	detection	NOUN
cana-4811	13	13	of	of	ADP
cana-4811	13	14	lung	lung	NOUN
cana-4811	13	15	cancer	cancer	NOUN
cana-4811	13	16	,	,	PUNCT
cana-4811	13	17	which	which	PRON
cana-4811	13	18	continues	continue	VERB
cana-4811	13	19	to	to	PART
cana-4811	13	20	be	be	AUX
cana-4811	13	21	the	the	DET
cana-4811	13	22	primary	primary	ADJ
cana-4811	13	23	cause	cause	NOUN
cana-4811	13	24	of	of	ADP
cana-4811	13	25	death	death	NOUN
cana-4811	13	26	globally	globally	ADV
cana-4811	13	27	related	relate	VERB
cana-4811	13	28	to	to	ADP
cana-4811	13	29	lung	lung	NOUN
cana-4811	13	30	cancer	cancer	NOUN
cana-4811	13	31	.	.	PUNCT
cana-4811	14	1	accurate	accurate	ADJ
cana-4811	14	2	lung	lung	NOUN
cana-4811	14	3	cancer	cancer	NOUN
cana-4811	14	4	prediction	prediction	NOUN
cana-4811	14	5	and	and	CCONJ
cana-4811	14	6	classification	classification	NOUN
cana-4811	14	7	are	be	AUX
cana-4811	14	8	essential	essential	ADJ
cana-4811	14	9	to	to	ADP
cana-4811	14	10	improving	improve	VERB
cana-4811	14	11	clinical	clinical	ADJ
cana-4811	14	12	decision	decision	NOUN
cana-4811	14	13	-	-	PUNCT
cana-4811	14	14	making	make	VERB
cana-4811	14	15	and	and	CCONJ
cana-4811	14	16	treatment	treatment	NOUN
cana-4811	14	17	outcomes	outcome	NOUN
cana-4811	14	18	.	.	PUNCT
cana-4811	15	1	ml	ml	X
cana-4811	15	2	approaches	approach	NOUN
cana-4811	15	3	as	as	ADP
cana-4811	15	4	a	a	DET
cana-4811	15	5	valuable	valuable	ADJ
cana-4811	15	6	platform	platform	NOUN
cana-4811	15	7	for	for	ADP
cana-4811	15	8	medical	medical	ADJ
cana-4811	15	9	diagnostics	diagnostic	NOUN
cana-4811	15	10	,	,	PUNCT
cana-4811	15	11	offering	offer	VERB
cana-4811	15	12	the	the	DET
cana-4811	15	13	data	data	NOUN
cana-4811	15	14	analysis	analysis	NOUN
cana-4811	15	15	and	and	CCONJ
cana-4811	15	16	prediction	prediction	NOUN
cana-4811	15	17	of	of	ADP
cana-4811	15	18	large	large	ADJ
cana-4811	15	19	-	-	PUNCT
cana-4811	15	20	scale	scale	NOUN
cana-4811	15	21	clinical	clinical	NOUN
cana-4811	15	22	,	,	PUNCT
cana-4811	15	23	imaging	imaging	NOUN
cana-4811	15	24	,	,	PUNCT
cana-4811	15	25	and	and	CCONJ
cana-4811	15	26	genomic	genomic	ADJ
cana-4811	15	27	data	datum	NOUN
cana-4811	15	28	,	,	PUNCT
cana-4811	15	29	identifying	identify	VERB
cana-4811	15	30	patterns	pattern	NOUN
cana-4811	15	31	,	,	PUNCT
cana-4811	15	32	and	and	CCONJ
cana-4811	15	33	predicting	predict	VERB
cana-4811	15	34	disease	disease	NOUN
cana-4811	15	35	progression	progression	NOUN
cana-4811	15	36	.	.	PUNCT
cana-4811	16	1	a	a	DET
cana-4811	16	2	key	key	ADJ
cana-4811	16	3	challenge	challenge	NOUN
cana-4811	16	4	in	in	ADP
cana-4811	16	5	applying	apply	VERB
cana-4811	16	6	ml	ml	NOUN
cana-4811	16	7	to	to	ADP
cana-4811	16	8	lung	lung	NOUN
cana-4811	16	9	cancer	cancer	NOUN
cana-4811	16	10	detection	detection	NOUN
cana-4811	16	11	lies	lie	VERB
cana-4811	16	12	in	in	ADP
cana-4811	16	13	variable	variable	ADJ
cana-4811	16	14	selection	selection	NOUN
cana-4811	16	15	.	.	PUNCT
cana-4811	17	1	identifying	identify	VERB
cana-4811	17	2	the	the	DET
cana-4811	17	3	most	most	ADV
cana-4811	17	4	relevant	relevant	ADJ
cana-4811	17	5	features	feature	NOUN
cana-4811	17	6	–	–	PUNCT
cana-4811	17	7	from	from	ADP
cana-4811	17	8	clinical	clinical	ADJ
cana-4811	17	9	to	to	ADP
cana-4811	17	10	radiological	radiological	ADJ
cana-4811	17	11	,	,	PUNCT
cana-4811	17	12	can	can	AUX
cana-4811	17	13	significantly	significantly	ADV
cana-4811	17	14	improve	improve	VERB
cana-4811	17	15	the	the	DET
cana-4811	17	16	accuracy	accuracy	NOUN
cana-4811	17	17	and	and	CCONJ
cana-4811	17	18	effective	effective	ADJ
cana-4811	17	19	predictive	predictive	ADJ
cana-4811	17	20	models	model	NOUN
cana-4811	17	21	.	.	PUNCT
cana-4811	18	1	redundant	redundant	ADJ
cana-4811	18	2	variables	variable	NOUN
cana-4811	18	3	can	can	AUX
cana-4811	18	4	lead	lead	VERB
cana-4811	18	5	to	to	ADP
cana-4811	18	6	overfitting	overfitte	VERB
cana-4811	18	7	,	,	PUNCT
cana-4811	18	8	hinder	hinder	NOUN
cana-4811	18	9	model	model	NOUN
cana-4811	18	10	generalization	generalization	NOUN
cana-4811	18	11	,	,	PUNCT
cana-4811	18	12	and	and	CCONJ
cana-4811	18	13	increase	increase	VERB
cana-4811	18	14	computational	computational	ADJ
cana-4811	18	15	complexity	complexity	NOUN
cana-4811	18	16	.	.	PUNCT
cana-4811	19	1	therefore	therefore	ADV
cana-4811	19	2	,	,	PUNCT
cana-4811	19	3	effective	effective	ADJ
cana-4811	19	4	variable	variable	ADJ
cana-4811	19	5	selection	selection	NOUN
cana-4811	19	6	is	be	AUX
cana-4811	19	7	crucial	crucial	ADJ
cana-4811	19	8	for	for	ADP
cana-4811	19	9	optimizing	optimize	VERB
cana-4811	19	10	model	model	NOUN
cana-4811	19	11	performance	performance	NOUN
cana-4811	19	12	.	.	PUNCT
cana-4811	20	1	examines	examine	VERB
cana-4811	20	2	different	different	ADJ
cana-4811	20	3	machine	machine	NOUN
cana-4811	20	4	learning	learn	VERB
cana-4811	20	5	approaches	approach	NOUN
cana-4811	20	6	to	to	ADP
cana-4811	20	7	lung	lung	NOUN
cana-4811	20	8	632	632	NUM
cana-4811	20	9	mailto:aucsedks@yahoo.co.in	mailto:aucsedks@yahoo.co.in	NOUN
cana-4811	20	10	mailto:santhoshcdm@gmail.com	mailto:santhoshcdm@gmail.com	X
cana-4811	20	11	communications	communication	NOUN
cana-4811	20	12	on	on	ADP
cana-4811	20	13	applied	apply	VERB
cana-4811	20	14	nonlinear	nonlinear	ADJ
cana-4811	20	15	analysis	analysis	NOUN
cana-4811	20	16	issn	issn	NOUN
cana-4811	20	17	:	:	PUNCT
cana-4811	20	18	1074	1074	NUM
cana-4811	20	19	-	-	PUNCT
cana-4811	20	20	133x	133x	NUM
cana-4811	20	21	vol	vol	NOUN
cana-4811	20	22	32	32	NUM
cana-4811	20	23	no	no	NOUN
cana-4811	20	24	.	.	PUNCT
cana-4811	21	1	01s	01s	PROPN
cana-4811	21	2	(	(	PUNCT
cana-4811	21	3	2025	2025	NUM
cana-4811	21	4	)	)	PUNCT
cana-4811	21	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-4811	21	6	cancer	cancer	NOUN
cana-4811	21	7	detection	detection	NOUN
cana-4811	21	8	,	,	PUNCT
cana-4811	21	9	with	with	ADP
cana-4811	21	10	a	a	DET
cana-4811	21	11	focus	focus	NOUN
cana-4811	21	12	on	on	ADP
cana-4811	21	13	how	how	SCONJ
cana-4811	21	14	different	different	ADJ
cana-4811	21	15	variable	variable	ADJ
cana-4811	21	16	selection	selection	NOUN
cana-4811	21	17	techniques	technique	NOUN
cana-4811	21	18	.	.	PUNCT
cana-4811	22	1	by	by	ADP
cana-4811	22	2	examining	examine	VERB
cana-4811	22	3	the	the	DET
cana-4811	22	4	impact	impact	NOUN
cana-4811	22	5	of	of	ADP
cana-4811	22	6	feature	feature	NOUN
cana-4811	22	7	selection	selection	NOUN
cana-4811	22	8	on	on	ADP
cana-4811	22	9	prediction	prediction	NOUN
cana-4811	22	10	accuracy	accuracy	NOUN
cana-4811	22	11	,	,	PUNCT
cana-4811	22	12	we	we	PRON
cana-4811	22	13	aim	aim	VERB
cana-4811	22	14	to	to	PART
cana-4811	22	15	provide	provide	VERB
cana-4811	22	16	valuable	valuable	ADJ
cana-4811	22	17	insights	insight	NOUN
cana-4811	22	18	into	into	ADP
cana-4811	22	19	best	good	ADJ
cana-4811	22	20	practices	practice	NOUN
cana-4811	22	21	for	for	ADP
cana-4811	22	22	optimizing	optimize	VERB
cana-4811	22	23	model	model	NOUN
cana-4811	22	24	performance	performance	NOUN
cana-4811	22	25	in	in	ADP
cana-4811	22	26	lung	lung	NOUN
cana-4811	22	27	cancer	cancer	NOUN
cana-4811	22	28	diagnosis	diagnosis	NOUN
cana-4811	22	29	.	.	PUNCT
cana-4811	23	1	accurate	accurate	ADJ
cana-4811	23	2	identification	identification	NOUN
cana-4811	23	3	of	of	ADP
cana-4811	23	4	this	this	DET
cana-4811	23	5	disease	disease	NOUN
cana-4811	23	6	significantly	significantly	ADV
cana-4811	23	7	increases	increase	VERB
cana-4811	23	8	cancer	cancer	NOUN
cana-4811	23	9	survival	survival	NOUN
cana-4811	23	10	rates	rate	NOUN
cana-4811	23	11	.	.	PUNCT
cana-4811	24	1	a	a	DET
cana-4811	24	2	powerful	powerful	ADJ
cana-4811	24	3	lung	lung	NOUN
cana-4811	24	4	cancer	cancer	NOUN
cana-4811	24	5	detection	detection	NOUN
cana-4811	24	6	system	system	NOUN
cana-4811	24	7	should	should	AUX
cana-4811	24	8	precisely	precisely	ADV
cana-4811	24	9	locate	locate	ADJ
cana-4811	24	10	tumors	tumor	NOUN
cana-4811	24	11	,	,	PUNCT
cana-4811	24	12	often	often	ADV
cana-4811	24	13	using	use	VERB
cana-4811	24	14	computed	computed	ADJ
cana-4811	24	15	tomography	tomography	NOUN
cana-4811	24	16	(	(	PUNCT
cana-4811	24	17	ct	ct	PROPN
cana-4811	24	18	)	)	PUNCT
cana-4811	24	19	.	.	PUNCT
cana-4811	25	1	the	the	DET
cana-4811	25	2	phases	phase	NOUN
cana-4811	25	3	of	of	ADP
cana-4811	25	4	lung	lung	NOUN
cana-4811	25	5	cancer	cancer	NOUN
cana-4811	25	6	identification	identification	NOUN
cana-4811	25	7	and	and	CCONJ
cana-4811	25	8	detection	detection	NOUN
cana-4811	25	9	using	use	VERB
cana-4811	25	10	different	different	ADJ
cana-4811	25	11	techniques	technique	NOUN
cana-4811	25	12	discussed	discuss	VERB
cana-4811	25	13	(	(	PUNCT
cana-4811	25	14	ignatious	ignatious	ADJ
cana-4811	25	15	and	and	CCONJ
cana-4811	25	16	joseph	joseph	PROPN
cana-4811	25	17	,	,	PUNCT
cana-4811	25	18	2015	2015	NUM
cana-4811	25	19	)	)	PUNCT
cana-4811	25	20	.	.	PUNCT
cana-4811	26	1	numerous	numerous	ADJ
cana-4811	26	2	studies	study	NOUN
cana-4811	26	3	have	have	AUX
cana-4811	26	4	emphasized	emphasize	VERB
cana-4811	26	5	how	how	SCONJ
cana-4811	26	6	crucial	crucial	ADJ
cana-4811	26	7	feature	feature	NOUN
cana-4811	26	8	selection	selection	NOUN
cana-4811	26	9	is	be	AUX
cana-4811	26	10	to	to	PART
cana-4811	26	11	raise	raise	VERB
cana-4811	26	12	the	the	DET
cana-4811	26	13	precision	precision	NOUN
cana-4811	26	14	of	of	ADP
cana-4811	26	15	machine	machine	NOUN
cana-4811	26	16	learning	learning	NOUN
cana-4811	26	17	models	model	NOUN
cana-4811	26	18	used	use	VERB
cana-4811	26	19	to	to	PART
cana-4811	26	20	detect	detect	VERB
cana-4811	26	21	lung	lung	NOUN
cana-4811	26	22	cancer	cancer	NOUN
cana-4811	26	23	.	.	PUNCT
cana-4811	27	1	selected	select	VERB
cana-4811	27	2	the	the	DET
cana-4811	27	3	most	most	ADV
cana-4811	27	4	informative	informative	ADJ
cana-4811	27	5	radiomic	radiomic	ADJ
cana-4811	27	6	features	feature	NOUN
cana-4811	27	7	by	by	ADP
cana-4811	27	8	combining	combine	VERB
cana-4811	27	9	svm	svm	NOUN
cana-4811	27	10	and	and	CCONJ
cana-4811	27	11	rfe	rfe	NOUN
cana-4811	27	12	.	.	PUNCT
cana-4811	28	1	the	the	DET
cana-4811	28	2	findings	finding	NOUN
cana-4811	28	3	showed	show	VERB
cana-4811	28	4	that	that	SCONJ
cana-4811	28	5	decreasing	decrease	VERB
cana-4811	28	6	the	the	DET
cana-4811	28	7	dimensionality	dimensionality	NOUN
cana-4811	28	8	of	of	ADP
cana-4811	28	9	the	the	DET
cana-4811	28	10	input	input	NOUN
cana-4811	28	11	dataset	dataset	VERB
cana-4811	28	12	reduced	reduce	VERB
cana-4811	28	13	overfitting	overfitting	NOUN
cana-4811	28	14	and	and	CCONJ
cana-4811	28	15	increased	increase	VERB
cana-4811	28	16	svm	svm	PROPN
cana-4811	28	17	's	's	PART
cana-4811	28	18	classification	classification	NOUN
cana-4811	28	19	accuracy	accuracy	NOUN
cana-4811	28	20	for	for	ADP
cana-4811	28	21	lung	lung	NOUN
cana-4811	28	22	cancer	cancer	NOUN
cana-4811	28	23	(	(	PUNCT
cana-4811	28	24	zhang	zhang	PROPN
cana-4811	28	25	et	et	PROPN
cana-4811	28	26	al	al	PROPN
cana-4811	28	27	.	.	PROPN
cana-4811	28	28	,	,	PUNCT
cana-4811	28	29	2019	2019	NUM
cana-4811	28	30	)	)	PUNCT
cana-4811	28	31	.	.	PUNCT
cana-4811	29	1	integrated	integrate	VERB
cana-4811	29	2	clinical	clinical	ADJ
cana-4811	29	3	(	(	PUNCT
cana-4811	29	4	yang	yang	PROPN
cana-4811	29	5	,	,	PUNCT
cana-4811	29	6	2020	2020	NUM
cana-4811	29	7	)	)	PUNCT
cana-4811	29	8	and	and	CCONJ
cana-4811	29	9	radiomic	radiomic	ADJ
cana-4811	29	10	features	feature	NOUN
cana-4811	29	11	in	in	ADP
cana-4811	29	12	developing	develop	VERB
cana-4811	29	13	ml	ml	NOUN
cana-4811	29	14	models	model	NOUN
cana-4811	29	15	for	for	ADP
cana-4811	29	16	early	early	ADJ
cana-4811	29	17	-	-	PUNCT
cana-4811	29	18	stage	stage	NOUN
cana-4811	29	19	lung	lung	NOUN
cana-4811	29	20	cancer	cancer	NOUN
cana-4811	29	21	prediction	prediction	NOUN
cana-4811	29	22	.	.	PUNCT
cana-4811	30	1	using	use	VERB
cana-4811	30	2	lasso	lasso	NOUN
cana-4811	30	3	regression	regression	NOUN
cana-4811	30	4	for	for	ADP
cana-4811	30	5	feature	feature	NOUN
cana-4811	30	6	selection	selection	NOUN
cana-4811	30	7	,	,	PUNCT
cana-4811	30	8	the	the	DET
cana-4811	30	9	researchers	researcher	NOUN
cana-4811	30	10	identified	identify	VERB
cana-4811	30	11	the	the	DET
cana-4811	30	12	most	most	ADV
cana-4811	30	13	relevant	relevant	ADJ
cana-4811	30	14	variables	variable	NOUN
cana-4811	30	15	that	that	PRON
cana-4811	30	16	significantly	significantly	ADV
cana-4811	30	17	contributed	contribute	VERB
cana-4811	30	18	to	to	ADP
cana-4811	30	19	improving	improve	VERB
cana-4811	30	20	the	the	DET
cana-4811	30	21	model	model	NOUN
cana-4811	30	22	's	's	PART
cana-4811	30	23	diagnostic	diagnostic	ADJ
cana-4811	30	24	accuracy	accuracy	NOUN
cana-4811	30	25	.	.	PUNCT
cana-4811	31	1	their	their	PRON
cana-4811	31	2	findings	finding	NOUN
cana-4811	31	3	suggested	suggest	VERB
cana-4811	31	4	that	that	SCONJ
cana-4811	31	5	combining	combine	VERB
cana-4811	31	6	clinical	clinical	ADJ
cana-4811	31	7	and	and	CCONJ
cana-4811	31	8	radiomic	radiomic	ADJ
cana-4811	31	9	features	feature	NOUN
cana-4811	31	10	,	,	PUNCT
cana-4811	31	11	followed	follow	VERB
cana-4811	31	12	by	by	ADP
cana-4811	31	13	efficient	efficient	ADJ
cana-4811	31	14	feature	feature	NOUN
cana-4811	31	15	selection	selection	NOUN
cana-4811	31	16	,	,	PUNCT
cana-4811	31	17	could	could	AUX
cana-4811	31	18	boost	boost	VERB
cana-4811	31	19	the	the	DET
cana-4811	31	20	robustness	robustness	NOUN
cana-4811	31	21	of	of	ADP
cana-4811	31	22	mlbased	mlbased	ADJ
cana-4811	31	23	lung	lung	NOUN
cana-4811	31	24	cancer	cancer	NOUN
cana-4811	31	25	detection	detection	NOUN
cana-4811	31	26	systems	system	NOUN
cana-4811	31	27	,	,	PUNCT
cana-4811	31	28	leading	lead	VERB
cana-4811	31	29	to	to	ADP
cana-4811	31	30	better	well	ADJ
cana-4811	31	31	prediction	prediction	NOUN
cana-4811	31	32	performance	performance	NOUN
cana-4811	31	33	and	and	CCONJ
cana-4811	31	34	reduced	reduce	VERB
cana-4811	31	35	computational	computational	ADJ
cana-4811	31	36	costs	cost	NOUN
cana-4811	31	37	.	.	PUNCT
cana-4811	32	1	pca	pca	NOUN
cana-4811	32	2	which	which	PRON
cana-4811	32	3	is	be	AUX
cana-4811	32	4	used	use	VERB
cana-4811	32	5	to	to	PART
cana-4811	32	6	fine	fine	VERB
cana-4811	32	7	the	the	DET
cana-4811	32	8	important	important	ADJ
cana-4811	32	9	features	feature	NOUN
cana-4811	32	10	and	and	CCONJ
cana-4811	32	11	reported	report	VERB
cana-4811	32	12	improved	improved	ADJ
cana-4811	32	13	model	model	NOUN
cana-4811	32	14	accuracy	accuracy	NOUN
cana-4811	32	15	and	and	CCONJ
cana-4811	32	16	faster	fast	ADJ
cana-4811	32	17	convergence	convergence	NOUN
cana-4811	32	18	rates	rate	NOUN
cana-4811	32	19	in	in	ADP
cana-4811	32	20	neural	neural	ADJ
cana-4811	32	21	networks	network	NOUN
cana-4811	32	22	.	.	PUNCT
cana-4811	33	1	their	their	PRON
cana-4811	33	2	study	study	NOUN
cana-4811	33	3	highlighted	highlight	VERB
cana-4811	33	4	that	that	SCONJ
cana-4811	33	5	feature	feature	NOUN
cana-4811	33	6	selection	selection	NOUN
cana-4811	33	7	not	not	PART
cana-4811	33	8	only	only	ADV
cana-4811	33	9	improves	improve	VERB
cana-4811	33	10	prediction	prediction	NOUN
cana-4811	33	11	performance	performance	NOUN
cana-4811	33	12	but	but	CCONJ
cana-4811	33	13	also	also	ADV
cana-4811	33	14	reduces	reduce	VERB
cana-4811	33	15	model	model	NOUN
cana-4811	33	16	complexity	complexity	NOUN
cana-4811	33	17	and	and	CCONJ
cana-4811	33	18	training	training	NOUN
cana-4811	33	19	time	time	NOUN
cana-4811	33	20	,	,	PUNCT
cana-4811	33	21	making	make	VERB
cana-4811	33	22	deep	deep	ADJ
cana-4811	33	23	learning	learning	NOUN
cana-4811	33	24	models	model	NOUN
cana-4811	33	25	more	more	ADV
cana-4811	33	26	efficient	efficient	ADJ
cana-4811	33	27	in	in	ADP
cana-4811	33	28	clinical	clinical	ADJ
cana-4811	33	29	practice	practice	NOUN
cana-4811	33	30	(	(	PUNCT
cana-4811	33	31	ali	ali	PROPN
cana-4811	33	32	et	et	PROPN
cana-4811	33	33	al	al	PROPN
cana-4811	33	34	.	.	PROPN
cana-4811	33	35	,	,	PUNCT
cana-4811	33	36	2021	2021	NUM
cana-4811	33	37	)	)	PUNCT
cana-4811	33	38	.	.	PUNCT
cana-4811	34	1	examined	examine	VERB
cana-4811	34	2	the	the	DET
cana-4811	34	3	use	use	NOUN
cana-4811	34	4	of	of	ADP
cana-4811	34	5	radiomic	radiomic	ADJ
cana-4811	34	6	features	feature	NOUN
cana-4811	34	7	in	in	ADP
cana-4811	34	8	lung	lung	NOUN
cana-4811	34	9	cancer	cancer	NOUN
cana-4811	34	10	prediction	prediction	NOUN
cana-4811	34	11	and	and	CCONJ
cana-4811	34	12	the	the	DET
cana-4811	34	13	importance	importance	NOUN
cana-4811	34	14	of	of	ADP
cana-4811	34	15	selecting	select	VERB
cana-4811	34	16	the	the	DET
cana-4811	34	17	most	most	ADV
cana-4811	34	18	relevant	relevant	ADJ
cana-4811	34	19	variables	variable	NOUN
cana-4811	34	20	.	.	PUNCT
cana-4811	35	1	they	they	PRON
cana-4811	35	2	compared	compare	VERB
cana-4811	35	3	several	several	ADJ
cana-4811	35	4	feature	feature	NOUN
cana-4811	35	5	selection	selection	NOUN
cana-4811	35	6	techniques	technique	NOUN
cana-4811	35	7	,	,	PUNCT
cana-4811	35	8	including	include	VERB
cana-4811	35	9	mutual	mutual	ADJ
cana-4811	35	10	information	information	NOUN
cana-4811	35	11	(	(	PUNCT
cana-4811	35	12	mi	mi	NOUN
cana-4811	35	13	)	)	PUNCT
cana-4811	35	14	,	,	PUNCT
cana-4811	35	15	and	and	CCONJ
cana-4811	35	16	showed	show	VERB
cana-4811	35	17	an	an	DET
cana-4811	35	18	appropriate	appropriate	ADJ
cana-4811	35	19	selection	selection	NOUN
cana-4811	35	20	of	of	ADP
cana-4811	35	21	radiomic	radiomic	ADJ
cana-4811	35	22	features	feature	NOUN
cana-4811	35	23	using	use	VERB
cana-4811	35	24	random	random	ADJ
cana-4811	35	25	forest	forest	NOUN
cana-4811	35	26	(	(	PUNCT
cana-4811	35	27	rf	rf	NOUN
cana-4811	35	28	)	)	PUNCT
cana-4811	35	29	and	and	CCONJ
cana-4811	35	30	svm	svm	PROPN
cana-4811	35	31	.	.	PUNCT
cana-4811	36	1	their	their	PRON
cana-4811	36	2	results	result	NOUN
cana-4811	36	3	showed	show	VERB
cana-4811	36	4	that	that	SCONJ
cana-4811	36	5	optimized	optimize	VERB
cana-4811	36	6	variable	variable	ADJ
cana-4811	36	7	selection	selection	NOUN
cana-4811	36	8	is	be	AUX
cana-4811	36	9	key	key	ADJ
cana-4811	36	10	to	to	ADP
cana-4811	36	11	improving	improve	VERB
cana-4811	36	12	the	the	DET
cana-4811	36	13	diagnostic	diagnostic	ADJ
cana-4811	36	14	ability	ability	NOUN
cana-4811	36	15	of	of	ADP
cana-4811	36	16	models	model	NOUN
cana-4811	36	17	based	base	VERB
cana-4811	36	18	on	on	ADP
cana-4811	36	19	medical	medical	ADJ
cana-4811	36	20	imaging	imaging	NOUN
cana-4811	36	21	data	datum	NOUN
cana-4811	36	22	(	(	PUNCT
cana-4811	36	23	parmar	parmar	PROPN
cana-4811	36	24	,	,	PUNCT
cana-4811	36	25	2018	2018	NUM
cana-4811	36	26	)	)	PUNCT
cana-4811	36	27	.	.	PUNCT
cana-4811	37	1	liu	liu	PROPN
cana-4811	37	2	et	et	PROPN
cana-4811	37	3	al	al	PROPN
cana-4811	37	4	.	.	PROPN
cana-4811	37	5	,	,	PUNCT
cana-4811	37	6	2018	2018	NUM
cana-4811	37	7	investigated	investigate	VERB
cana-4811	37	8	integrating	integrate	VERB
cana-4811	37	9	genomic	genomic	NOUN
cana-4811	37	10	and	and	CCONJ
cana-4811	37	11	imaging	imaging	NOUN
cana-4811	37	12	data	datum	NOUN
cana-4811	37	13	in	in	ADP
cana-4811	37	14	machinelearning	machinelearning	NOUN
cana-4811	37	15	models	model	NOUN
cana-4811	37	16	for	for	ADP
cana-4811	37	17	lung	lung	NOUN
cana-4811	37	18	cancer	cancer	NOUN
cana-4811	37	19	detection	detection	NOUN
cana-4811	37	20	.	.	PUNCT
cana-4811	38	1	variable	variable	ADJ
cana-4811	38	2	selection	selection	NOUN
cana-4811	38	3	,	,	PUNCT
cana-4811	38	4	combining	combine	VERB
cana-4811	38	5	genetic	genetic	ADJ
cana-4811	38	6	markers	marker	NOUN
cana-4811	38	7	with	with	ADP
cana-4811	38	8	radiomic	radiomic	ADJ
cana-4811	38	9	features	feature	NOUN
cana-4811	38	10	.	.	PUNCT
cana-4811	39	1	the	the	DET
cana-4811	39	2	study	study	NOUN
cana-4811	39	3	demonstrated	demonstrate	VERB
cana-4811	39	4	that	that	SCONJ
cana-4811	39	5	feature	feature	NOUN
cana-4811	39	6	selection	selection	NOUN
cana-4811	39	7	methods	method	NOUN
cana-4811	39	8	tailored	tailor	VERB
cana-4811	39	9	to	to	PART
cana-4811	39	10	multi	multi	ADJ
cana-4811	39	11	-	-	ADJ
cana-4811	39	12	modal	modal	ADJ
cana-4811	39	13	datasets	dataset	NOUN
cana-4811	39	14	could	could	AUX
cana-4811	39	15	lead	lead	VERB
cana-4811	39	16	to	to	ADP
cana-4811	39	17	substantial	substantial	ADJ
cana-4811	39	18	improvements	improvement	NOUN
cana-4811	39	19	in	in	ADP
cana-4811	39	20	model	model	NOUN
cana-4811	39	21	performance	performance	NOUN
cana-4811	39	22	.	.	PUNCT
cana-4811	40	1	elastic	elastic	ADJ
cana-4811	40	2	net	net	NOUN
cana-4811	40	3	effectively	effectively	ADV
cana-4811	40	4	minimized	minimize	VERB
cana-4811	40	5	the	the	DET
cana-4811	40	6	number	number	NOUN
cana-4811	40	7	of	of	ADP
cana-4811	40	8	irrelevant	irrelevant	ADJ
cana-4811	40	9	variables	variable	NOUN
cana-4811	40	10	,	,	PUNCT
cana-4811	40	11	resulting	result	VERB
cana-4811	40	12	in	in	ADP
cana-4811	40	13	a	a	DET
cana-4811	40	14	more	more	ADV
cana-4811	40	15	robust	robust	ADJ
cana-4811	40	16	prediction	prediction	NOUN
cana-4811	40	17	model	model	NOUN
cana-4811	40	18	with	with	ADP
cana-4811	40	19	higher	high	ADJ
cana-4811	40	20	accuracy	accuracy	NOUN
cana-4811	40	21	.	.	PUNCT
cana-4811	41	1	the	the	DET
cana-4811	41	2	data	data	NOUN
cana-4811	41	3	analysis	analysis	NOUN
cana-4811	41	4	and	and	CCONJ
cana-4811	41	5	prediction	prediction	NOUN
cana-4811	41	6	for	for	ADP
cana-4811	41	7	weather	weather	NOUN
cana-4811	41	8	datasets	dataset	NOUN
cana-4811	41	9	and	and	CCONJ
cana-4811	41	10	play	play	VERB
cana-4811	41	11	golf	golf	NOUN
cana-4811	41	12	class	class	NOUN
cana-4811	41	13	variables	variable	NOUN
cana-4811	41	14	and	and	CCONJ
cana-4811	41	15	to	to	ADP
cana-4811	41	16	performance	performance	NOUN
cana-4811	41	17	parameters	parameter	NOUN
cana-4811	41	18	and	and	CCONJ
cana-4811	41	19	its	its	PRON
cana-4811	41	20	conditions	condition	NOUN
cana-4811	41	21	to	to	PART
cana-4811	41	22	playing	play	VERB
cana-4811	41	23	golf	golf	NOUN
cana-4811	41	24	or	or	CCONJ
cana-4811	41	25	not	not	PART
cana-4811	41	26	using	use	VERB
cana-4811	41	27	different	different	ADJ
cana-4811	41	28	machine	machine	NOUN
cana-4811	41	29	learning	learning	NOUN
cana-4811	41	30	approaches	approach	NOUN
cana-4811	41	31	using	use	VERB
cana-4811	41	32	j48	j48	PROPN
cana-4811	41	33	,	,	PUNCT
cana-4811	41	34	rt	rt	PROPN
cana-4811	41	35	,	,	PUNCT
cana-4811	41	36	ds	ds	PROPN
cana-4811	41	37	,	,	PUNCT
cana-4811	41	38	lmt	lmt	PROPN
cana-4811	41	39	,	,	PUNCT
cana-4811	41	40	ht	ht	PROPN
cana-4811	41	41	,	,	PUNCT
cana-4811	41	42	rep	rep	PROPN
cana-4811	41	43	,	,	PUNCT
cana-4811	41	44	and	and	CCONJ
cana-4811	41	45	rf	rf	NOUN
cana-4811	41	46	.	.	PUNCT
cana-4811	42	1	the	the	DET
cana-4811	42	2	performance	performance	NOUN
cana-4811	42	3	results	result	NOUN
cana-4811	42	4	were	be	AUX
cana-4811	42	5	calculated	calculate	VERB
cana-4811	42	6	using	use	VERB
cana-4811	42	7	measure	measure	NOUN
cana-4811	42	8	accuracy	accuracy	NOUN
cana-4811	42	9	using	use	VERB
cana-4811	42	10	different	different	ADJ
cana-4811	42	11	test	test	NOUN
cana-4811	42	12	statistics	statistic	NOUN
cana-4811	42	13	.	.	PUNCT
cana-4811	43	1	out	out	ADP
cana-4811	43	2	of	of	ADP
cana-4811	43	3	seven	seven	NUM
cana-4811	43	4	machine	machine	NOUN
cana-4811	43	5	learning	learning	NOUN
cana-4811	43	6	approaches	approach	NOUN
cana-4811	43	7	,	,	PUNCT
cana-4811	43	8	the	the	DET
cana-4811	43	9	random	random	ADJ
cana-4811	43	10	tree	tree	NOUN
cana-4811	43	11	algorithm	algorithm	NOUN
cana-4811	43	12	returns	return	VERB
cana-4811	43	13	the	the	DET
cana-4811	43	14	best	good	ADJ
cana-4811	43	15	performance	performance	NOUN
cana-4811	43	16	(	(	PUNCT
cana-4811	43	17	rajesh	rajesh	NOUN
cana-4811	43	18	and	and	CCONJ
cana-4811	43	19	karthikeyan	karthikeyan	PROPN
cana-4811	43	20	,	,	PUNCT
cana-4811	43	21	2017	2017	NUM
cana-4811	43	22	)	)	PUNCT
cana-4811	43	23	.	.	PUNCT
cana-4811	44	1	detect	detect	VERB
cana-4811	44	2	lung	lung	NOUN
cana-4811	44	3	cancer	cancer	NOUN
cana-4811	44	4	within	within	ADP
cana-4811	44	5	various	various	ADJ
cana-4811	44	6	lung	lung	NOUN
cana-4811	44	7	images	image	NOUN
cana-4811	44	8	as	as	ADP
cana-4811	44	9	input	input	NOUN
cana-4811	44	10	and	and	CCONJ
cana-4811	44	11	classify	classify	VERB
cana-4811	44	12	different	different	ADJ
cana-4811	44	13	cancers	cancer	NOUN
cana-4811	44	14	using	use	VERB
cana-4811	44	15	ml	ml	X
cana-4811	45	1	and	and	CCONJ
cana-4811	45	2	dl	dl	PROPN
cana-4811	45	3	methods	method	NOUN
cana-4811	45	4	(	(	PUNCT
cana-4811	45	5	bhuvaneswari	bhuvaneswari	NOUN
cana-4811	45	6	and	and	CCONJ
cana-4811	45	7	therese	therese	NOUN
cana-4811	45	8	,	,	PUNCT
cana-4811	45	9	2015	2015	NUM
cana-4811	45	10	)	)	PUNCT
cana-4811	45	11	.	.	PUNCT
cana-4811	46	1	lung	lung	NOUN
cana-4811	46	2	cancer	cancer	NOUN
cana-4811	46	3	detection	detection	NOUN
cana-4811	46	4	using	use	VERB
cana-4811	46	5	tumors	tumor	NOUN
cana-4811	46	6	from	from	ADP
cana-4811	46	7	x	x	NOUN
cana-4811	46	8	-	-	NOUN
cana-4811	46	9	ray	ray	NOUN
cana-4811	46	10	,	,	PUNCT
cana-4811	46	11	ct	ct	PROPN
cana-4811	46	12	,	,	PUNCT
cana-4811	46	13	and	and	CCONJ
cana-4811	46	14	mri	mri	NOUN
cana-4811	46	15	images	image	NOUN
cana-4811	46	16	.	.	PUNCT
cana-4811	47	1	the	the	DET
cana-4811	47	2	detection	detection	NOUN
cana-4811	47	3	process	process	NOUN
cana-4811	47	4	was	be	AUX
cana-4811	47	5	completed	complete	VERB
cana-4811	47	6	using	use	VERB
cana-4811	47	7	image	image	NOUN
cana-4811	47	8	processing	processing	NOUN
cana-4811	47	9	techniques	technique	NOUN
cana-4811	47	10	and	and	CCONJ
cana-4811	47	11	methods	method	NOUN
cana-4811	47	12	.	.	PUNCT
cana-4811	48	1	mean	mean	VERB
cana-4811	48	2	filter	filter	NOUN
cana-4811	48	3	and	and	CCONJ
cana-4811	48	4	median	median	ADJ
cana-4811	48	5	filters	filter	NOUN
cana-4811	48	6	are	be	AUX
cana-4811	48	7	common	common	ADJ
cana-4811	48	8	pre	pre	ADJ
cana-4811	48	9	-	-	ADJ
cana-4811	48	10	processing	processing	ADJ
cana-4811	48	11	techniques	technique	NOUN
cana-4811	48	12	for	for	ADP
cana-4811	48	13	various	various	ADJ
cana-4811	48	14	stages	stage	NOUN
cana-4811	48	15	.	.	PUNCT
cana-4811	49	1	the	the	DET
cana-4811	49	2	accuracy	accuracy	NOUN
cana-4811	49	3	parameters	parameter	NOUN
cana-4811	49	4	are	be	AUX
cana-4811	49	5	used	use	VERB
cana-4811	49	6	to	to	PART
cana-4811	49	7	prove	prove	VERB
cana-4811	49	8	the	the	DET
cana-4811	49	9	proposed	propose	VERB
cana-4811	49	10	research	research	NOUN
cana-4811	49	11	namely	namely	ADV
cana-4811	49	12	snr	snr	PROPN
cana-4811	49	13	,	,	PUNCT
cana-4811	49	14	mse	mse	PROPN
cana-4811	49	15	,	,	PUNCT
cana-4811	49	16	and	and	CCONJ
cana-4811	49	17	psnr	psnr	NOUN
cana-4811	49	18	utilized	utilize	VERB
cana-4811	49	19	(	(	PUNCT
cana-4811	49	20	asuntha	asuntha	PROPN
cana-4811	49	21	and	and	CCONJ
cana-4811	49	22	srinivasan	srinivasan	NOUN
cana-4811	49	23	,	,	PUNCT
cana-4811	49	24	2020	2020	NUM
cana-4811	49	25	)	)	PUNCT
cana-4811	49	26	.	.	PUNCT
cana-4811	50	1	data	datum	NOUN
cana-4811	50	2	mining	mining	NOUN
cana-4811	50	3	and	and	CCONJ
cana-4811	50	4	machine	machine	NOUN
cana-4811	50	5	learning	learning	NOUN
cana-4811	50	6	approaches	approach	NOUN
cana-4811	50	7	are	be	AUX
cana-4811	50	8	used	use	VERB
cana-4811	50	9	to	to	ADP
cana-4811	50	10	633	633	NUM
cana-4811	50	11	communications	communication	NOUN
cana-4811	50	12	on	on	ADP
cana-4811	50	13	applied	apply	VERB
cana-4811	50	14	nonlinear	nonlinear	ADJ
cana-4811	50	15	analysis	analysis	NOUN
cana-4811	50	16	issn	issn	NOUN
cana-4811	50	17	:	:	PUNCT
cana-4811	50	18	1074	1074	NUM
cana-4811	50	19	-	-	PUNCT
cana-4811	50	20	133x	133x	NUM
cana-4811	50	21	vol	vol	NOUN
cana-4811	50	22	32	32	NUM
cana-4811	50	23	no	no	NOUN
cana-4811	50	24	.	.	PUNCT
cana-4811	51	1	01s	01s	PROPN
cana-4811	51	2	(	(	PUNCT
cana-4811	51	3	2025	2025	NUM
cana-4811	51	4	)	)	PUNCT
cana-4811	52	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-4811	52	2	retrieve	retrieve	VERB
cana-4811	52	3	actionable	actionable	ADJ
cana-4811	52	4	results	result	NOUN
cana-4811	52	5	through	through	ADP
cana-4811	52	6	teaching	teaching	NOUN
cana-4811	52	7	and	and	CCONJ
cana-4811	52	8	testing	testing	NOUN
cana-4811	52	9	using	use	VERB
cana-4811	52	10	cross	cross	NOUN
cana-4811	52	11	-	-	NOUN
cana-4811	52	12	validations	validation	NOUN
cana-4811	52	13	.	.	PUNCT
cana-4811	53	1	the	the	DET
cana-4811	53	2	data	data	NOUN
cana-4811	53	3	analysis	analysis	NOUN
cana-4811	53	4	and	and	CCONJ
cana-4811	53	5	prediction	prediction	NOUN
cana-4811	53	6	related	relate	VERB
cana-4811	53	7	to	to	ADP
cana-4811	53	8	the	the	DET
cana-4811	53	9	corresponding	corresponding	ADJ
cana-4811	53	10	datasets	dataset	NOUN
cana-4811	53	11	using	use	VERB
cana-4811	53	12	different	different	ADJ
cana-4811	53	13	dm	dm	NOUN
cana-4811	53	14	and	and	CCONJ
cana-4811	53	15	ml	ml	ADP
cana-4811	53	16	algorithms	algorithm	NOUN
cana-4811	53	17	used	use	VERB
cana-4811	53	18	to	to	PART
cana-4811	53	19	retrieve	retrieve	VERB
cana-4811	53	20	the	the	DET
cana-4811	53	21	actionable	actionable	ADJ
cana-4811	53	22	results	result	NOUN
cana-4811	53	23	(	(	PUNCT
cana-4811	53	24	kannan	kannan	PROPN
cana-4811	53	25	and	and	CCONJ
cana-4811	53	26	naveen	naveen	PROPN
cana-4811	53	27	,	,	PUNCT
cana-4811	53	28	2020	2020	NUM
cana-4811	53	29	;	;	PUNCT
cana-4811	53	30	rajesh	rajesh	PROPN
cana-4811	53	31	et	et	PROPN
cana-4811	53	32	al	al	PROPN
cana-4811	53	33	.	.	PROPN
cana-4811	53	34	,	,	PUNCT
cana-4811	53	35	2019	2019	NUM
cana-4811	53	36	;	;	PUNCT
cana-4811	53	37	rajesh	rajesh	PROPN
cana-4811	53	38	and	and	CCONJ
cana-4811	53	39	karthikeyan	karthikeyan	PROPN
cana-4811	53	40	,	,	PUNCT
cana-4811	53	41	2019	2019	NUM
cana-4811	53	42	;	;	PUNCT
cana-4811	53	43	rajesh	rajesh	PROPN
cana-4811	53	44	et	et	PROPN
cana-4811	53	45	al	al	PROPN
cana-4811	53	46	.	.	PROPN
cana-4811	53	47	,	,	PUNCT
cana-4811	53	48	2019	2019	NUM
cana-4811	53	49	)	)	PUNCT
cana-4811	53	50	2	2	NUM
cana-4811	53	51	.	.	X
cana-4811	53	52	backgrounds	background	NOUN
cana-4811	53	53	and	and	CCONJ
cana-4811	53	54	methodologies	methodology	NOUN
cana-4811	53	55	logistic	logistic	ADJ
cana-4811	53	56	regression	regression	NOUN
cana-4811	53	57	binary	binary	NOUN
cana-4811	53	58	outcomes	outcome	NOUN
cana-4811	53	59	,	,	PUNCT
cana-4811	53	60	such	such	ADJ
cana-4811	53	61	as	as	ADP
cana-4811	53	62	true	true	ADJ
cana-4811	53	63	or	or	CCONJ
cana-4811	53	64	false	false	ADJ
cana-4811	53	65	,	,	PUNCT
cana-4811	53	66	can	can	AUX
cana-4811	53	67	be	be	AUX
cana-4811	53	68	predicted	predict	VERB
cana-4811	53	69	using	use	VERB
cana-4811	53	70	statistical	statistical	ADJ
cana-4811	53	71	modeling	modeling	NOUN
cana-4811	53	72	techniques	technique	NOUN
cana-4811	53	73	like	like	ADP
cana-4811	53	74	logistic	logistic	ADJ
cana-4811	53	75	regression	regression	NOUN
cana-4811	53	76	.	.	PUNCT
cana-4811	54	1	because	because	SCONJ
cana-4811	54	2	it	it	PRON
cana-4811	54	3	's	be	AUX
cana-4811	54	4	easy	easy	ADJ
cana-4811	54	5	to	to	PART
cana-4811	54	6	understand	understand	VERB
cana-4811	54	7	and	and	CCONJ
cana-4811	54	8	straightforward	straightforward	ADJ
cana-4811	54	9	,	,	PUNCT
cana-4811	54	10	it	it	PRON
cana-4811	54	11	's	be	AUX
cana-4811	54	12	a	a	DET
cana-4811	54	13	popular	popular	ADJ
cana-4811	54	14	option	option	NOUN
cana-4811	54	15	in	in	ADP
cana-4811	54	16	machine	machine	NOUN
cana-4811	54	17	learning	learning	NOUN
cana-4811	54	18	(	(	PUNCT
cana-4811	54	19	kohavi	kohavi	PROPN
cana-4811	54	20	and	and	CCONJ
cana-4811	54	21	sahami	sahami	NOUN
cana-4811	54	22	,	,	PUNCT
cana-4811	54	23	1996	1996	NUM
cana-4811	54	24	)	)	PUNCT
cana-4811	54	25	.	.	PUNCT
cana-4811	55	1	step	step	NOUN
cana-4811	55	2	1	1	NUM
cana-4811	55	3	.	.	PUNCT
cana-4811	55	4	follow	follow	VERB
cana-4811	55	5	the	the	DET
cana-4811	55	6	primary	primary	ADJ
cana-4811	55	7	steps	step	NOUN
cana-4811	55	8	of	of	ADP
cana-4811	55	9	data	datum	NOUN
cana-4811	55	10	preparation	preparation	NOUN
cana-4811	55	11	.	.	PUNCT
cana-4811	56	1	step	step	NOUN
cana-4811	56	2	2	2	NUM
cana-4811	56	3	.	.	PUNCT
cana-4811	56	4	based	base	VERB
cana-4811	56	5	on	on	ADP
cana-4811	56	6	the	the	DET
cana-4811	56	7	dataset	dataset	NOUN
cana-4811	56	8	and	and	CCONJ
cana-4811	56	9	problems	problem	NOUN
cana-4811	56	10	to	to	PART
cana-4811	56	11	perform	perform	VERB
cana-4811	56	12	the	the	DET
cana-4811	56	13	model	model	NOUN
cana-4811	56	14	.	.	PUNCT
cana-4811	57	1	step	step	NOUN
cana-4811	57	2	3	3	NUM
cana-4811	57	3	.	.	PUNCT
cana-4811	57	4	training	training	NOUN
cana-4811	57	5	and	and	CCONJ
cana-4811	57	6	testing	test	VERB
cana-4811	57	7	the	the	DET
cana-4811	57	8	data	datum	NOUN
cana-4811	57	9	until	until	ADP
cana-4811	57	10	convergence	convergence	NOUN
cana-4811	57	11	.	.	PUNCT
cana-4811	58	1	step	step	NOUN
cana-4811	58	2	4	4	NUM
cana-4811	58	3	.	.	PUNCT
cana-4811	58	4	prediction	prediction	NOUN
cana-4811	58	5	based	base	VERB
cana-4811	58	6	on	on	ADP
cana-4811	58	7	new	new	ADJ
cana-4811	58	8	features	feature	NOUN
cana-4811	58	9	.	.	PUNCT
cana-4811	59	1	step	step	NOUN
cana-4811	59	2	5	5	NUM
cana-4811	59	3	.	.	PUNCT
cana-4811	59	4	evaluation	evaluation	NOUN
cana-4811	59	5	using	use	VERB
cana-4811	59	6	a	a	DET
cana-4811	59	7	confusion	confusion	NOUN
cana-4811	59	8	matrix	matrix	NOUN
cana-4811	59	9	with	with	ADP
cana-4811	59	10	different	different	ADJ
cana-4811	59	11	performance	performance	NOUN
cana-4811	59	12	metrics	metric	NOUN
cana-4811	59	13	.	.	PUNCT
cana-4811	60	1	2.1	2.1	NUM
cana-4811	60	2	multilayer	multilayer	ADJ
cana-4811	60	3	perception	perception	NOUN
cana-4811	60	4	this	this	DET
cana-4811	60	5	core	core	NOUN
cana-4811	60	6	architecture	architecture	NOUN
cana-4811	60	7	of	of	ADP
cana-4811	60	8	deep	deep	ADJ
cana-4811	60	9	learning	learning	NOUN
cana-4811	60	10	is	be	AUX
cana-4811	60	11	applied	apply	VERB
cana-4811	60	12	to	to	ADP
cana-4811	60	13	a	a	DET
cana-4811	60	14	number	number	NOUN
cana-4811	60	15	of	of	ADP
cana-4811	60	16	tasks	task	NOUN
cana-4811	60	17	,	,	PUNCT
cana-4811	60	18	such	such	ADJ
cana-4811	60	19	as	as	ADP
cana-4811	60	20	regression	regression	NOUN
cana-4811	60	21	and	and	CCONJ
cana-4811	60	22	classification	classification	NOUN
cana-4811	60	23	.	.	PUNCT
cana-4811	61	1	three	three	NUM
cana-4811	61	2	different	different	ADJ
cana-4811	61	3	kinds	kind	NOUN
cana-4811	61	4	of	of	ADP
cana-4811	61	5	layers	layer	NOUN
cana-4811	61	6	are	be	AUX
cana-4811	61	7	commonly	commonly	ADV
cana-4811	61	8	found	find	VERB
cana-4811	61	9	in	in	ADP
cana-4811	61	10	an	an	DET
cana-4811	61	11	mlp	mlp	NOUN
cana-4811	61	12	's	's	PART
cana-4811	61	13	architecture	architecture	NOUN
cana-4811	61	14	.	.	PUNCT
cana-4811	62	1	input	input	NOUN
cana-4811	62	2	layer	layer	NOUN
cana-4811	62	3	step	step	NOUN
cana-4811	62	4	1	1	NUM
cana-4811	62	5	.	.	PUNCT
cana-4811	63	1	hidden	hide	VERB
cana-4811	63	2	layers	layer	NOUN
cana-4811	63	3	step	step	VERB
cana-4811	63	4	2	2	NUM
cana-4811	63	5	.	.	PUNCT
cana-4811	63	6	output	output	NOUN
cana-4811	63	7	layer	layer	NOUN
cana-4811	63	8	2.2	2.2	NUM
cana-4811	63	9	smo	smo	PROPN
cana-4811	63	10	svm	svm	ADJ
cana-4811	63	11	training	training	NOUN
cana-4811	63	12	is	be	AUX
cana-4811	63	13	done	do	VERB
cana-4811	63	14	through	through	ADP
cana-4811	63	15	sequential	sequential	ADJ
cana-4811	63	16	minimal	minimal	ADJ
cana-4811	63	17	optimization	optimization	NOUN
cana-4811	63	18	.	.	PUNCT
cana-4811	64	1	classification	classification	NOUN
cana-4811	64	2	and	and	CCONJ
cana-4811	64	3	regression	regression	NOUN
cana-4811	64	4	are	be	AUX
cana-4811	64	5	the	the	DET
cana-4811	64	6	two	two	NUM
cana-4811	64	7	distinct	distinct	ADJ
cana-4811	64	8	processes	process	NOUN
cana-4811	64	9	for	for	ADP
cana-4811	64	10	which	which	DET
cana-4811	64	11	machine	machine	NOUN
cana-4811	64	12	learning	learning	NOUN
cana-4811	64	13	techniques	technique	NOUN
cana-4811	64	14	are	be	AUX
cana-4811	64	15	frequently	frequently	ADV
cana-4811	64	16	employed	employ	VERB
cana-4811	64	17	.	.	PUNCT
cana-4811	65	1	optimizing	optimize	VERB
cana-4811	65	2	quadratic	quadratic	ADJ
cana-4811	65	3	programming	programming	NOUN
cana-4811	65	4	is	be	AUX
cana-4811	65	5	the	the	DET
cana-4811	65	6	goal	goal	NOUN
cana-4811	65	7	of	of	ADP
cana-4811	65	8	this	this	DET
cana-4811	65	9	machine	machine	NOUN
cana-4811	65	10	learning	learning	NOUN
cana-4811	65	11	technique	technique	NOUN
cana-4811	65	12	.	.	PUNCT
cana-4811	66	1	step	step	NOUN
cana-4811	66	2	1	1	NUM
cana-4811	66	3	.	.	PUNCT
cana-4811	66	4	initialization	initialization	NOUN
cana-4811	66	5	using	use	VERB
cana-4811	66	6	svm	svm	PROPN
cana-4811	66	7	.	.	PROPN
cana-4811	67	1	step	step	NOUN
cana-4811	67	2	2	2	NUM
cana-4811	67	3	.	.	PUNCT
cana-4811	67	4	select	select	VERB
cana-4811	67	5	two	two	NUM
cana-4811	67	6	different	different	ADJ
cana-4811	67	7	lagrange	lagrange	NOUN
cana-4811	67	8	multipliers	multiplier	NOUN
cana-4811	67	9	step	step	VERB
cana-4811	67	10	3	3	NUM
cana-4811	67	11	.	.	PUNCT
cana-4811	68	1	optimization	optimization	NOUN
cana-4811	68	2	to	to	ADP
cana-4811	68	3	the	the	DET
cana-4811	68	4	pair	pair	NOUN
cana-4811	68	5	step	step	NOUN
cana-4811	68	6	4	4	NUM
cana-4811	68	7	.	.	PUNCT
cana-4811	69	1	update	update	NOUN
cana-4811	69	2	corresponding	corresponding	ADJ
cana-4811	69	3	model	model	NOUN
cana-4811	69	4	performance	performance	NOUN
cana-4811	69	5	step	step	NOUN
cana-4811	69	6	5	5	NUM
cana-4811	69	7	.	.	PUNCT
cana-4811	70	1	checking	check	VERB
cana-4811	70	2	the	the	DET
cana-4811	70	3	corresponding	correspond	VERB
cana-4811	70	4	convergence	convergence	NOUN
cana-4811	70	5	:	:	PUNCT
cana-4811	70	6	step	step	NOUN
cana-4811	70	7	6	6	NUM
cana-4811	70	8	.	.	PUNCT
cana-4811	70	9	repeat	repeat	VERB
cana-4811	70	10	the	the	DET
cana-4811	70	11	process	process	NOUN
cana-4811	70	12	and	and	CCONJ
cana-4811	70	13	complete	complete	VERB
cana-4811	70	14	through	through	ADP
cana-4811	70	15	2	2	NUM
cana-4811	70	16	to	to	ADP
cana-4811	70	17	5	5	NUM
cana-4811	70	18	and	and	CCONJ
cana-4811	70	19	consolidate	consolidate	ADJ
cana-4811	70	20	.	.	PUNCT
cana-4811	71	1	2.3	2.3	NUM
cana-4811	71	2	j48	j48	PROPN
cana-4811	71	3	j48	j48	NOUN
cana-4811	71	4	,	,	PUNCT
cana-4811	71	5	adopted	adopt	VERB
cana-4811	71	6	by	by	ADP
cana-4811	71	7	using	use	VERB
cana-4811	71	8	the	the	DET
cana-4811	71	9	c4.5	c4.5	PROPN
cana-4811	71	10	algorithm	algorithm	NOUN
cana-4811	71	11	,	,	PUNCT
cana-4811	71	12	is	be	AUX
cana-4811	71	13	a	a	DET
cana-4811	71	14	problem	problem	NOUN
cana-4811	71	15	-	-	PUNCT
cana-4811	71	16	solving	solve	VERB
cana-4811	71	17	tool	tool	NOUN
cana-4811	71	18	that	that	PRON
cana-4811	71	19	fully	fully	ADV
cana-4811	71	20	classifies	classify	VERB
cana-4811	71	21	the	the	DET
cana-4811	71	22	results	result	NOUN
cana-4811	71	23	when	when	SCONJ
cana-4811	71	24	applying	apply	VERB
cana-4811	71	25	decision	decision	NOUN
cana-4811	71	26	tree	tree	NOUN
cana-4811	71	27	approaches	approach	NOUN
cana-4811	71	28	.	.	PUNCT
cana-4811	72	1	since	since	SCONJ
cana-4811	72	2	j48	j48	PROPN
cana-4811	72	3	solves	solve	NOUN
cana-4811	72	4	problems	problem	NOUN
cana-4811	72	5	using	use	VERB
cana-4811	72	6	both	both	CCONJ
cana-4811	72	7	numerical	numerical	ADJ
cana-4811	72	8	and	and	CCONJ
cana-4811	72	9	categorical	categorical	ADJ
cana-4811	72	10	features	feature	NOUN
cana-4811	72	11	,	,	PUNCT
cana-4811	72	12	it	it	PRON
cana-4811	72	13	is	be	AUX
cana-4811	72	14	an	an	DET
cana-4811	72	15	appropriate	appropriate	ADJ
cana-4811	72	16	method	method	NOUN
cana-4811	72	17	that	that	PRON
cana-4811	72	18	is	be	AUX
cana-4811	72	19	regularly	regularly	ADV
cana-4811	72	20	used	use	VERB
cana-4811	72	21	worldwide	worldwide	ADV
cana-4811	72	22	.	.	PUNCT
cana-4811	73	1	634	634	NUM
cana-4811	73	2	communications	communication	NOUN
cana-4811	73	3	on	on	ADP
cana-4811	73	4	applied	apply	VERB
cana-4811	73	5	nonlinear	nonlinear	ADJ
cana-4811	73	6	analysis	analysis	NOUN
cana-4811	73	7	issn	issn	NOUN
cana-4811	73	8	:	:	PUNCT
cana-4811	73	9	1074	1074	NUM
cana-4811	73	10	-	-	PUNCT
cana-4811	73	11	133x	133x	NUM
cana-4811	73	12	vol	vol	NOUN
cana-4811	73	13	32	32	NUM
cana-4811	73	14	no	no	NOUN
cana-4811	73	15	.	.	PUNCT
cana-4811	74	1	01s	01s	PROPN
cana-4811	74	2	(	(	PUNCT
cana-4811	74	3	2025	2025	NUM
cana-4811	74	4	)	)	PUNCT
cana-4811	74	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-4811	74	6	step	step	NOUN
cana-4811	74	7	1	1	NUM
cana-4811	74	8	.	.	PUNCT
cana-4811	74	9	select	select	VERB
cana-4811	74	10	a	a	DET
cana-4811	74	11	suitable	suitable	ADJ
cana-4811	74	12	attribute	attribute	NOUN
cana-4811	74	13	step	step	NOUN
cana-4811	74	14	2	2	NUM
cana-4811	74	15	.	.	PUNCT
cana-4811	75	1	different	different	ADJ
cana-4811	75	2	nodes	node	NOUN
cana-4811	75	3	are	be	AUX
cana-4811	75	4	found	find	VERB
cana-4811	75	5	then	then	ADV
cana-4811	75	6	splitting	split	VERB
cana-4811	75	7	the	the	DET
cana-4811	75	8	nodes	node	NOUN
cana-4811	75	9	step	step	VERB
cana-4811	75	10	3	3	NUM
cana-4811	75	11	.	.	PUNCT
cana-4811	75	12	adopt	adopt	VERB
cana-4811	75	13	the	the	DET
cana-4811	75	14	recursion	recursion	NOUN
cana-4811	75	15	approach	approach	NOUN
cana-4811	75	16	step	step	NOUN
cana-4811	75	17	4	4	NUM
cana-4811	75	18	.	.	PUNCT
cana-4811	76	1	pruning	prune	VERB
cana-4811	76	2	using	use	VERB
cana-4811	76	3	j48	j48	NOUN
cana-4811	76	4	with	with	ADP
cana-4811	76	5	accuracy	accuracy	NOUN
cana-4811	76	6	step	step	NOUN
cana-4811	76	7	5	5	NUM
cana-4811	76	8	.	.	PUNCT
cana-4811	76	9	finding	find	VERB
cana-4811	76	10	the	the	DET
cana-4811	76	11	missing	miss	VERB
cana-4811	76	12	values	value	NOUN
cana-4811	76	13	step	step	VERB
cana-4811	76	14	6	6	NUM
cana-4811	76	15	.	.	PUNCT
cana-4811	77	1	method	method	NOUN
cana-4811	77	2	of	of	ADP
cana-4811	77	3	post	post	NOUN
cana-4811	77	4	-	-	ADJ
cana-4811	77	5	pruning	prune	VERB
cana-4811	77	6	with	with	ADP
cana-4811	77	7	fully	fully	ADV
cana-4811	77	8	constructed	construct	VERB
cana-4811	77	9	step	step	NOUN
cana-4811	77	10	7	7	NUM
cana-4811	77	11	.	.	PUNCT
cana-4811	77	12	predict	predict	VERB
cana-4811	77	13	the	the	DET
cana-4811	77	14	leaf	leaf	NOUN
cana-4811	77	15	node	node	NOUN
cana-4811	77	16	based	base	VERB
cana-4811	77	17	on	on	ADP
cana-4811	77	18	different	different	ADJ
cana-4811	77	19	conditions	condition	NOUN
cana-4811	77	20	2.4	2.4	NUM
cana-4811	77	21	random	random	ADJ
cana-4811	77	22	forest	forest	NOUN
cana-4811	77	23	rf	rf	NOUN
cana-4811	77	24	is	be	AUX
cana-4811	77	25	the	the	DET
cana-4811	77	26	most	most	ADV
cana-4811	77	27	widely	widely	ADV
cana-4811	77	28	used	use	VERB
cana-4811	77	29	decision	decision	NOUN
cana-4811	77	30	tree	tree	NOUN
cana-4811	77	31	approach	approach	NOUN
cana-4811	77	32	in	in	ADP
cana-4811	77	33	ensemble	ensemble	ADJ
cana-4811	77	34	learning	learning	NOUN
cana-4811	77	35	that	that	PRON
cana-4811	77	36	uses	use	VERB
cana-4811	77	37	bagging	bag	VERB
cana-4811	77	38	to	to	PART
cana-4811	77	39	determine	determine	VERB
cana-4811	77	40	classification	classification	NOUN
cana-4811	77	41	and	and	CCONJ
cana-4811	77	42	regression	regression	NOUN
cana-4811	77	43	tasks	task	NOUN
cana-4811	77	44	.	.	PUNCT
cana-4811	78	1	step	step	NOUN
cana-4811	78	2	1	1	NUM
cana-4811	78	3	.	.	PUNCT
cana-4811	78	4	bootstrapped	bootstrappe	VERB
cana-4811	78	5	the	the	DET
cana-4811	78	6	sampling	sampling	NOUN
cana-4811	78	7	approaches	approach	NOUN
cana-4811	78	8	step	step	VERB
cana-4811	78	9	2	2	NUM
cana-4811	78	10	.	.	PUNCT
cana-4811	79	1	feature	feature	NOUN
cana-4811	79	2	selection	selection	NOUN
cana-4811	79	3	works	work	VERB
cana-4811	79	4	with	with	ADP
cana-4811	79	5	a	a	DET
cana-4811	79	6	random	random	ADJ
cana-4811	79	7	approach	approach	NOUN
cana-4811	79	8	step	step	NOUN
cana-4811	79	9	3	3	NUM
cana-4811	79	10	.	.	PUNCT
cana-4811	79	11	construct	construct	VERB
cana-4811	79	12	the	the	DET
cana-4811	79	13	dt	dt	NOUN
cana-4811	79	14	based	base	VERB
cana-4811	79	15	on	on	ADP
cana-4811	79	16	various	various	ADJ
cana-4811	79	17	conditions	condition	NOUN
cana-4811	79	18	step	step	VERB
cana-4811	79	19	4	4	NUM
cana-4811	79	20	.	.	PUNCT
cana-4811	80	1	the	the	DET
cana-4811	80	2	voting	voting	NOUN
cana-4811	80	3	system	system	NOUN
cana-4811	80	4	followed	follow	VERB
cana-4811	80	5	by	by	ADP
cana-4811	80	6	regression	regression	NOUN
cana-4811	80	7	tasks	task	NOUN
cana-4811	80	8	2.5	2.5	NUM
cana-4811	80	9	rep	rep	NOUN
cana-4811	80	10	tree	tree	NOUN
cana-4811	80	11	the	the	DET
cana-4811	80	12	reduced	reduce	VERB
cana-4811	80	13	error	error	NOUN
cana-4811	80	14	pruning	pruning	NOUN
cana-4811	80	15	(	(	PUNCT
cana-4811	80	16	rep	rep	NOUN
cana-4811	80	17	)	)	PUNCT
cana-4811	80	18	tree	tree	NOUN
cana-4811	80	19	is	be	AUX
cana-4811	80	20	a	a	DET
cana-4811	80	21	popular	popular	ADJ
cana-4811	80	22	dt	dt	NOUN
cana-4811	80	23	method	method	NOUN
cana-4811	80	24	for	for	ADP
cana-4811	80	25	machine	machine	NOUN
cana-4811	80	26	learning	learning	NOUN
cana-4811	80	27	problems	problem	NOUN
cana-4811	80	28	involving	involve	VERB
cana-4811	80	29	classification	classification	NOUN
cana-4811	80	30	.	.	PUNCT
cana-4811	81	1	in	in	ADP
cana-4811	81	2	order	order	NOUN
cana-4811	81	3	to	to	PART
cana-4811	81	4	prevent	prevent	VERB
cana-4811	81	5	overfitting	overfitting	NOUN
cana-4811	81	6	,	,	PUNCT
cana-4811	81	7	the	the	DET
cana-4811	81	8	dt	dt	NOUN
cana-4811	81	9	is	be	AUX
cana-4811	81	10	constructed	construct	VERB
cana-4811	81	11	using	use	VERB
cana-4811	81	12	a	a	DET
cana-4811	81	13	reduced	reduce	VERB
cana-4811	81	14	-	-	PUNCT
cana-4811	81	15	error	error	NOUN
cana-4811	81	16	pruning	pruning	NOUN
cana-4811	81	17	technique	technique	NOUN
cana-4811	81	18	.	.	PUNCT
cana-4811	82	1	step	step	NOUN
cana-4811	82	2	1	1	NUM
cana-4811	82	3	.	.	PUNCT
cana-4811	83	1	construction	construction	NOUN
cana-4811	83	2	is	be	AUX
cana-4811	83	3	based	base	VERB
cana-4811	83	4	on	on	ADP
cana-4811	83	5	the	the	DET
cana-4811	83	6	decision	decision	NOUN
cana-4811	83	7	step	step	NOUN
cana-4811	83	8	2	2	NUM
cana-4811	83	9	.	.	PUNCT
cana-4811	83	10	follow	follow	VERB
cana-4811	83	11	the	the	DET
cana-4811	83	12	splitting	splitting	NOUN
cana-4811	83	13	criteria	criterion	NOUN
cana-4811	83	14	recursively	recursively	ADV
cana-4811	83	15	step	step	VERB
cana-4811	83	16	3	3	NUM
cana-4811	83	17	.	.	PUNCT
cana-4811	83	18	reduced	reduce	VERB
cana-4811	83	19	error	error	NOUN
cana-4811	83	20	pruning	prune	VERB
cana-4811	83	21	using	use	VERB
cana-4811	83	22	eliminating	eliminate	VERB
cana-4811	83	23	branches	branch	NOUN
cana-4811	83	24	step	step	VERB
cana-4811	83	25	4	4	NUM
cana-4811	83	26	.	.	PUNCT
cana-4811	84	1	the	the	DET
cana-4811	84	2	final	final	ADJ
cana-4811	84	3	dt	dt	NOUN
cana-4811	84	4	is	be	AUX
cana-4811	84	5	constructed	construct	VERB
cana-4811	84	6	with	with	ADP
cana-4811	84	7	prediction	prediction	NOUN
cana-4811	84	8	2.6	2.6	NUM
cana-4811	84	9	common	common	ADJ
cana-4811	84	10	evaluation	evaluation	NOUN
cana-4811	84	11	metrics	metric	NOUN
cana-4811	84	12	in	in	ADP
cana-4811	84	13	machine	machine	NOUN
cana-4811	84	14	learning	learn	VERB
cana-4811	84	15	various	various	ADJ
cana-4811	84	16	performance	performance	NOUN
cana-4811	84	17	metrics	metric	NOUN
cana-4811	84	18	are	be	AUX
cana-4811	84	19	widely	widely	ADV
cana-4811	84	20	used	use	VERB
cana-4811	84	21	around	around	ADP
cana-4811	84	22	the	the	DET
cana-4811	84	23	world	world	NOUN
cana-4811	84	24	to	to	PART
cana-4811	84	25	determine	determine	VERB
cana-4811	84	26	the	the	DET
cana-4811	84	27	accurate	accurate	ADJ
cana-4811	84	28	performance	performance	NOUN
cana-4811	84	29	of	of	ADP
cana-4811	84	30	regression	regression	NOUN
cana-4811	84	31	models	model	NOUN
cana-4811	84	32	.	.	PUNCT
cana-4811	85	1	kappa	kappa	PROPN
cana-4811	85	2	(	(	PUNCT
cana-4811	85	3	cohen	cohen	PROPN
cana-4811	85	4	's	's	PART
cana-4811	85	5	kappa	kappa	ADJ
cana-4811	85	6	):	):	PUNCT
cana-4811	85	7	measures	measure	NOUN
cana-4811	85	8	the	the	DET
cana-4811	85	9	agreement	agreement	NOUN
cana-4811	85	10	between	between	ADP
cana-4811	85	11	two	two	NUM
cana-4811	85	12	observations	observation	NOUN
cana-4811	85	13	on	on	ADP
cana-4811	85	14	a	a	DET
cana-4811	85	15	categorical	categorical	ADJ
cana-4811	85	16	dataset	dataset	NOUN
cana-4811	85	17	.	.	PUNCT
cana-4811	86	1	κappa	κappa	PROPN
cana-4811	86	2	=	=	PUNCT
cana-4811	86	3	(	(	PUNCT
cana-4811	86	4	y(a	y(a	X
cana-4811	86	5	)	)	PUNCT
cana-4811	86	6	y(e	y(e	NOUN
cana-4811	86	7	)	)	PUNCT
cana-4811	86	8	)	)	PUNCT
cana-4811	86	9	/	/	PUNCT
cana-4811	87	1	(	(	PUNCT
cana-4811	87	2	1	1	NUM
cana-4811	87	3	y(e	y(e	NOUN
cana-4811	87	4	)	)	PUNCT
cana-4811	87	5	)	)	PUNCT
cana-4811	87	6	where	where	SCONJ
cana-4811	87	7	:	:	PUNCT
cana-4811	87	8	y(a	y(a	X
cana-4811	87	9	)	)	PUNCT
cana-4811	87	10	called	call	VERB
cana-4811	87	11	as	as	ADP
cana-4811	87	12	observed	observe	VERB
cana-4811	87	13	agreement	agreement	NOUN
cana-4811	87	14	,	,	PUNCT
cana-4811	87	15	and	and	CCONJ
cana-4811	87	16	y(e	y(e	PROPN
cana-4811	87	17	)	)	PUNCT
cana-4811	87	18	called	call	VERB
cana-4811	87	19	as	as	ADP
cana-4811	87	20	expected	expect	VERB
cana-4811	87	21	agreement	agreement	NOUN
cana-4811	87	22	.	.	PUNCT
cana-4811	88	1	mae	mae	PROPN
cana-4811	88	2	:	:	PUNCT
cana-4811	88	3	utilized	utilize	VERB
cana-4811	88	4	for	for	ADP
cana-4811	88	5	calculating	calculate	VERB
cana-4811	88	6	the	the	DET
cana-4811	88	7	absolute	absolute	ADJ
cana-4811	88	8	mean	mean	ADJ
cana-4811	88	9	difference	difference	NOUN
cana-4811	88	10	between	between	ADP
cana-4811	88	11	the	the	DET
cana-4811	88	12	actual	actual	ADJ
cana-4811	88	13	and	and	CCONJ
cana-4811	88	14	predicted	predict	VERB
cana-4811	88	15	observations	observation	NOUN
cana-4811	88	16	.	.	PUNCT
cana-4811	89	1	(	(	PUNCT
cana-4811	89	2	akusok	akusok	NOUN
cana-4811	89	3	,	,	PUNCT
cana-4811	89	4	2020	2020	NUM
cana-4811	89	5	)	)	PUNCT
cana-4811	89	6	.	.	PUNCT
cana-4811	90	1	mean	mean	VERB
cana-4811	90	2	absolute	absolute	ADJ
cana-4811	90	3	error	error	NOUN
cana-4811	90	4	=	=	PUNCT
cana-4811	90	5	σ|yi	σ|yi	PROPN
cana-4811	91	1	ŷi|	ŷi|	PROPN
cana-4811	91	2	*	*	PUNCT
cana-4811	91	3	1	1	NUM
cana-4811	91	4	/	/	SYM
cana-4811	91	5	n	n	NUM
cana-4811	91	6	where	where	SCONJ
cana-4811	91	7	:	:	PUNCT
cana-4811	91	8	n	n	CCONJ
cana-4811	91	9	:	:	PUNCT
cana-4811	91	10	how	how	SCONJ
cana-4811	91	11	many	many	ADJ
cana-4811	91	12	iterations	iteration	NOUN
cana-4811	91	13	,	,	PUNCT
cana-4811	91	14	y_i	y_i	NUM
cana-4811	91	15	:	:	PUNCT
cana-4811	91	16	actual	actual	ADJ
cana-4811	91	17	value	value	NOUN
cana-4811	91	18	,	,	PUNCT
cana-4811	91	19	ŷ_i	ŷ_i	PROPN
cana-4811	91	20	:	:	PUNCT
cana-4811	91	21	predicted	predict	VERB
cana-4811	91	22	value	value	NOUN
cana-4811	91	23	rmse	rmse	NOUN
cana-4811	91	24	:	:	PUNCT
cana-4811	91	25	the	the	DET
cana-4811	91	26	average	average	ADJ
cana-4811	91	27	square	square	ADJ
cana-4811	91	28	root	root	NOUN
cana-4811	91	29	of	of	ADP
cana-4811	91	30	the	the	DET
cana-4811	91	31	differences	difference	NOUN
cana-4811	91	32	between	between	ADP
cana-4811	91	33	the	the	DET
cana-4811	91	34	actual	actual	ADJ
cana-4811	91	35	and	and	CCONJ
cana-4811	91	36	predicted	predict	VERB
cana-4811	91	37	observations	observation	NOUN
cana-4811	91	38	(	(	PUNCT
cana-4811	91	39	hosseini	hosseini	PROPN
cana-4811	91	40	,	,	PUNCT
cana-4811	91	41	2019	2019	NUM
cana-4811	91	42	)	)	PUNCT
cana-4811	91	43	.	.	PUNCT
cana-4811	92	1	root	root	NOUN
cana-4811	92	2	mean	mean	VERB
cana-4811	92	3	squared	square	VERB
cana-4811	92	4	error	error	NOUN
cana-4811	92	5	=	=	SYM
cana-4811	92	6	√	√	NUM
cana-4811	92	7	(	(	PUNCT
cana-4811	92	8	1	1	NUM
cana-4811	92	9	/	/	SYM
cana-4811	92	10	n	n	CCONJ
cana-4811	92	11	*	*	PUNCT
cana-4811	92	12	σ(yi	σ(yi	ADJ
cana-4811	92	13	ŷi)^2	ŷi)^2	PROPN
cana-4811	92	14	)	)	PUNCT
cana-4811	92	15	rae	rae	PROPN
cana-4811	92	16	:	:	PUNCT
cana-4811	92	17	find	find	VERB
cana-4811	92	18	the	the	DET
cana-4811	92	19	difference	difference	NOUN
cana-4811	92	20	between	between	ADP
cana-4811	92	21	the	the	DET
cana-4811	92	22	relative	relative	ADJ
cana-4811	92	23	error	error	NOUN
cana-4811	92	24	for	for	ADP
cana-4811	92	25	actual	actual	ADJ
cana-4811	92	26	and	and	CCONJ
cana-4811	92	27	predicted	predict	VERB
cana-4811	92	28	values	value	NOUN
cana-4811	92	29	of	of	ADP
cana-4811	92	30	observations	observation	NOUN
cana-4811	92	31	.	.	PUNCT
cana-4811	93	1	(	(	PUNCT
cana-4811	93	2	chi	chi	NOUN
cana-4811	93	3	,	,	PUNCT
cana-4811	93	4	2020	2020	NUM
cana-4811	93	5	)	)	PUNCT
cana-4811	93	6	.	.	PUNCT
cana-4811	94	1	relative	relative	ADJ
cana-4811	94	2	absolute	absolute	ADJ
cana-4811	94	3	error	error	NOUN
cana-4811	94	4	=	=	SYM
cana-4811	94	5	(	(	PUNCT
cana-4811	94	6	σ|yi	σ|yi	X
cana-4811	94	7	ŷi|	ŷi|	X
cana-4811	94	8	)	)	PUNCT
cana-4811	94	9	/	/	SYM
cana-4811	94	10	(	(	PUNCT
cana-4811	94	11	σ|yi	σ|yi	NUM
cana-4811	94	12	ȳ|	ȳ|	PROPN
cana-4811	94	13	)	)	PUNCT
cana-4811	94	14	rrse	rrse	NOUN
cana-4811	94	15	:	:	PUNCT
cana-4811	94	16	similar	similar	ADJ
cana-4811	94	17	approaches	approach	NOUN
cana-4811	94	18	are	be	AUX
cana-4811	94	19	based	base	VERB
cana-4811	94	20	on	on	ADP
cana-4811	94	21	the	the	DET
cana-4811	94	22	method	method	NOUN
cana-4811	94	23	of	of	ADP
cana-4811	94	24	rae	rae	PROPN
cana-4811	94	25	but	but	CCONJ
cana-4811	94	26	use	use	VERB
cana-4811	94	27	rmse	rmse	NOUN
cana-4811	94	28	instead	instead	ADV
cana-4811	94	29	of	of	ADP
cana-4811	95	1	mae	mae	PROPN
cana-4811	95	2	.	.	PROPN
cana-4811	95	3	root	root	PROPN
cana-4811	95	4	relative	relative	ADJ
cana-4811	95	5	squared	square	VERB
cana-4811	95	6	error	error	NOUN
cana-4811	95	7	=	=	SYM
cana-4811	95	8	√	√	NUM
cana-4811	95	9	(	(	PUNCT
cana-4811	95	10	σ	σ	PROPN
cana-4811	95	11	(	(	PUNCT
cana-4811	95	12	yi	yi	PROPN
cana-4811	95	13	ŷi	ŷi	ADJ
cana-4811	95	14	)	)	PUNCT
cana-4811	95	15	2	2	NUM
cana-4811	95	16	)	)	PUNCT
cana-4811	95	17	/	/	SYM
cana-4811	96	1	√	√	PROPN
cana-4811	96	2	(	(	PUNCT
cana-4811	96	3	σ	σ	PROPN
cana-4811	96	4	(	(	PUNCT
cana-4811	96	5	yi	yi	PROPN
cana-4811	96	6	ȳ)2	ȳ)2	PROPN
cana-4811	96	7	)	)	PUNCT
cana-4811	96	8	tpr	tpr	NOUN
cana-4811	96	9	or	or	CCONJ
cana-4811	96	10	recall	recall	NOUN
cana-4811	96	11	:	:	PUNCT
cana-4811	96	12	find	find	VERB
cana-4811	96	13	the	the	DET
cana-4811	96	14	positive	positive	ADJ
cana-4811	96	15	results	result	NOUN
cana-4811	96	16	with	with	ADP
cana-4811	96	17	a	a	DET
cana-4811	96	18	correctly	correctly	ADV
cana-4811	96	19	predicted	predict	VERB
cana-4811	96	20	.	.	PUNCT
cana-4811	97	1	635	635	NUM
cana-4811	97	2	communications	communication	NOUN
cana-4811	97	3	on	on	ADP
cana-4811	97	4	applied	apply	VERB
cana-4811	97	5	nonlinear	nonlinear	ADJ
cana-4811	97	6	analysis	analysis	NOUN
cana-4811	97	7	issn	issn	NOUN
cana-4811	97	8	:	:	PUNCT
cana-4811	97	9	1074	1074	NUM
cana-4811	97	10	-	-	PUNCT
cana-4811	97	11	133x	133x	NUM
cana-4811	97	12	vol	vol	NOUN
cana-4811	97	13	32	32	NUM
cana-4811	97	14	no	no	NOUN
cana-4811	97	15	.	.	PUNCT
cana-4811	98	1	01s	01s	PROPN
cana-4811	98	2	(	(	PUNCT
cana-4811	98	3	2025	2025	NUM
cana-4811	98	4	)	)	PUNCT
cana-4811	99	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-4811	99	2	true	true	ADJ
cana-4811	99	3	positive	positive	ADJ
cana-4811	99	4	rate	rate	NOUN
cana-4811	99	5	=	=	PUNCT
cana-4811	99	6	true_positive	true_positive	PROPN
cana-4811	99	7	/	/	SYM
cana-4811	99	8	(	(	PUNCT
cana-4811	99	9	true_positive	true_positive	PROPN
cana-4811	99	10	+	+	CCONJ
cana-4811	99	11	false_negative	false_negative	X
cana-4811	99	12	)	)	PUNCT
cana-4811	99	13	fpr	fpr	NOUN
cana-4811	99	14	:	:	PUNCT
cana-4811	99	15	find	find	VERB
cana-4811	99	16	the	the	DET
cana-4811	99	17	actual	actual	ADJ
cana-4811	99	18	negative	negative	ADJ
cana-4811	99	19	results	result	NOUN
cana-4811	99	20	with	with	ADP
cana-4811	99	21	incorrectly	incorrectly	ADV
cana-4811	99	22	predicted	predict	VERB
cana-4811	99	23	as	as	ADP
cana-4811	99	24	positive	positive	ADJ
cana-4811	99	25	.	.	PUNCT
cana-4811	100	1	false	false	ADJ
cana-4811	100	2	positive	positive	ADJ
cana-4811	100	3	rate	rate	NOUN
cana-4811	100	4	=	=	SYM
cana-4811	100	5	false_positive	false_positive	NOUN
cana-4811	100	6	/	/	SYM
cana-4811	100	7	(	(	PUNCT
cana-4811	100	8	false_positive	false_positive	ADJ
cana-4811	100	9	+	+	CCONJ
cana-4811	100	10	true_negative	true_negative	ADJ
cana-4811	100	11	)	)	PUNCT
cana-4811	100	12	precision	precision	NOUN
cana-4811	100	13	:	:	PUNCT
cana-4811	100	14	find	find	VERB
cana-4811	100	15	the	the	DET
cana-4811	100	16	positive	positive	ADJ
cana-4811	100	17	predicted	predict	VERB
cana-4811	100	18	instances	instance	NOUN
cana-4811	100	19	precision	precision	VERB
cana-4811	100	20	=	=	PUNCT
cana-4811	100	21	true_positive	true_positive	PROPN
cana-4811	100	22	/	/	SYM
cana-4811	100	23	(	(	PUNCT
cana-4811	100	24	true_positive	true_positive	PROPN
cana-4811	100	25	+	+	CCONJ
cana-4811	100	26	false_positive	false_positive	ADJ
cana-4811	100	27	)	)	PUNCT
cana-4811	100	28	recall	recall	NOUN
cana-4811	100	29	:	:	PUNCT
cana-4811	100	30	similar	similar	ADJ
cana-4811	100	31	results	result	NOUN
cana-4811	100	32	reflected	reflect	VERB
cana-4811	100	33	in	in	ADP
cana-4811	100	34	tpr	tpr	NOUN
cana-4811	100	35	.	.	PUNCT
cana-4811	101	1	f	f	X
cana-4811	101	2	-	-	PUNCT
cana-4811	101	3	measure	measure	NOUN
cana-4811	101	4	:	:	PUNCT
cana-4811	101	5	the	the	DET
cana-4811	101	6	average	average	ADJ
cana-4811	101	7	values	value	NOUN
cana-4811	101	8	based	base	VERB
cana-4811	101	9	on	on	ADP
cana-4811	101	10	harmonic_mean	harmonic_mean	PROPN
cana-4811	101	11	between	between	ADP
cana-4811	101	12	precision	precision	NOUN
cana-4811	101	13	and	and	CCONJ
cana-4811	101	14	the	the	DET
cana-4811	101	15	recall	recall	NOUN
cana-4811	101	16	.	.	PUNCT
cana-4811	102	1	f_measure	f_measure	VERB
cana-4811	103	1	=	=	SYM
cana-4811	103	2	2	2	NUM
cana-4811	103	3	*	*	PUNCT
cana-4811	103	4	(	(	PUNCT
cana-4811	103	5	precision*recall	precision*recall	NOUN
cana-4811	103	6	)	)	PUNCT
cana-4811	103	7	/	/	PUNCT
cana-4811	103	8	(	(	PUNCT
cana-4811	103	9	precision+recall	precision+recall	PROPN
cana-4811	103	10	)	)	PUNCT
cana-4811	103	11	area	area	NOUN
cana-4811	103	12	under	under	ADP
cana-4811	103	13	the	the	DET
cana-4811	103	14	curve	curve	NOUN
cana-4811	103	15	(	(	PUNCT
cana-4811	103	16	auc	auc	NOUN
cana-4811	103	17	)	)	PUNCT
cana-4811	103	18	roc	roc	NOUN
cana-4811	103	19	auc	auc	NOUN
cana-4811	103	20	:	:	PUNCT
cana-4811	103	21	determine	determine	VERB
cana-4811	103	22	the	the	DET
cana-4811	103	23	area	area	NOUN
cana-4811	103	24	under	under	ADP
cana-4811	103	25	the	the	DET
cana-4811	103	26	receiver	receiver	NOUN
cana-4811	103	27	operating	operate	VERB
cana-4811	103	28	characteristic	characteristic	ADJ
cana-4811	103	29	curve	curve	NOUN
cana-4811	103	30	using	use	VERB
cana-4811	103	31	the	the	DET
cana-4811	103	32	model	model	NOUN
cana-4811	103	33	performance	performance	NOUN
cana-4811	103	34	to	to	PART
cana-4811	103	35	analyze	analyze	VERB
cana-4811	103	36	both	both	CCONJ
cana-4811	103	37	positive	positive	ADJ
cana-4811	103	38	and	and	CCONJ
cana-4811	103	39	negative	negative	ADJ
cana-4811	103	40	approaches	approach	NOUN
cana-4811	103	41	.	.	PUNCT
cana-4811	104	1	better	well	ADJ
cana-4811	104	2	performance	performance	NOUN
cana-4811	104	3	in	in	ADP
cana-4811	104	4	this	this	DET
cana-4811	104	5	case	case	NOUN
cana-4811	104	6	was	be	AUX
cana-4811	104	7	mentioned	mention	VERB
cana-4811	104	8	by	by	ADP
cana-4811	104	9	the	the	DET
cana-4811	104	10	strongly	strongly	ADV
cana-4811	104	11	positive	positive	ADJ
cana-4811	104	12	auc	auc	NOUN
cana-4811	104	13	.	.	PUNCT
cana-4811	105	1	use	use	VERB
cana-4811	105	2	prc	prc	PROPN
cana-4811	105	3	auc	auc	NOUN
cana-4811	105	4	to	to	PART
cana-4811	105	5	determine	determine	VERB
cana-4811	105	6	the	the	DET
cana-4811	105	7	area	area	NOUN
cana-4811	105	8	under	under	ADP
cana-4811	105	9	the	the	DET
cana-4811	105	10	behaviors	behavior	NOUN
cana-4811	105	11	of	of	ADP
cana-4811	105	12	precision	precision	NOUN
cana-4811	105	13	values	value	NOUN
cana-4811	105	14	and	and	CCONJ
cana-4811	105	15	recall	recall	NOUN
cana-4811	105	16	values	value	NOUN
cana-4811	105	17	reflected	reflect	VERB
cana-4811	105	18	in	in	ADP
cana-4811	105	19	the	the	DET
cana-4811	105	20	curve	curve	NOUN
cana-4811	105	21	when	when	SCONJ
cana-4811	105	22	the	the	DET
cana-4811	105	23	distribution	distribution	NOUN
cana-4811	105	24	is	be	AUX
cana-4811	105	25	unbalanced	unbalanced	ADJ
cana-4811	105	26	.	.	PUNCT
cana-4811	106	1	larger	large	ADJ
cana-4811	106	2	values	value	NOUN
cana-4811	106	3	indicate	indicate	VERB
cana-4811	106	4	that	that	SCONJ
cana-4811	106	5	prc	prc	PROPN
cana-4811	106	6	-	-	PUNCT
cana-4811	106	7	auc	auc	NOUN
cana-4811	106	8	performs	perform	VERB
cana-4811	106	9	better	well	ADV
cana-4811	106	10	when	when	SCONJ
cana-4811	106	11	precision	precision	NOUN
cana-4811	106	12	is	be	AUX
cana-4811	106	13	more	more	ADV
cana-4811	106	14	important	important	ADJ
cana-4811	106	15	than	than	ADP
cana-4811	106	16	recall	recall	NOUN
cana-4811	106	17	.	.	PUNCT
cana-4811	107	1	3.0	3.0	NUM
cana-4811	107	2	experimental	experimental	ADJ
cana-4811	107	3	outcomes	outcome	NOUN
cana-4811	107	4	kaggle	kaggle	VERB
cana-4811	107	5	website	website	NOUN
cana-4811	107	6	data	datum	NOUN
cana-4811	107	7	repository	repository	NOUN
cana-4811	107	8	which	which	PRON
cana-4811	107	9	is	be	AUX
cana-4811	107	10	used	use	VERB
cana-4811	107	11	to	to	PART
cana-4811	107	12	download	download	VERB
cana-4811	107	13	the	the	DET
cana-4811	107	14	related	relate	VERB
cana-4811	107	15	dataset	dataset	NOUN
cana-4811	107	16	.	.	PUNCT
cana-4811	108	1	this	this	DET
cana-4811	108	2	research	research	NOUN
cana-4811	108	3	is	be	AUX
cana-4811	108	4	based	base	VERB
cana-4811	108	5	on	on	ADP
cana-4811	108	6	a	a	DET
cana-4811	108	7	lung	lung	NOUN
cana-4811	108	8	cancer	cancer	NOUN
cana-4811	108	9	prediction	prediction	NOUN
cana-4811	108	10	dataset	dataset	NOUN
cana-4811	108	11	that	that	PRON
cana-4811	108	12	contains	contain	VERB
cana-4811	108	13	25	25	NUM
cana-4811	108	14	features	feature	NOUN
cana-4811	108	15	or	or	CCONJ
cana-4811	108	16	parameters	parameter	NOUN
cana-4811	108	17	with	with	ADP
cana-4811	108	18	different	different	ADJ
cana-4811	108	19	categories	category	NOUN
cana-4811	108	20	listed	list	VERB
cana-4811	108	21	in	in	ADP
cana-4811	108	22	table	table	NOUN
cana-4811	108	23	1	1	NUM
cana-4811	108	24	(	(	PUNCT
cana-4811	108	25	kaggle	kaggle	VERB
cana-4811	108	26	2018	2018	NUM
cana-4811	108	27	)	)	PUNCT
cana-4811	108	28	.	.	PUNCT
cana-4811	109	1	table	table	NOUN
cana-4811	109	2	1	1	NUM
cana-4811	109	3	.	.	PUNCT
cana-4811	110	1	sample	sample	NOUN
cana-4811	110	2	dataset	dataset	NOUN
cana-4811	110	3	*	*	PUNCT
cana-4811	110	4	l	l	NOUN
cana-4811	110	5	-	-	ADJ
cana-4811	110	6	low	low	ADJ
cana-4811	110	7	,	,	PUNCT
cana-4811	110	8	m	m	NOUN
cana-4811	110	9	-	-	ADJ
cana-4811	110	10	medium	medium	ADJ
cana-4811	110	11	,	,	PUNCT
cana-4811	110	12	h	h	NOUN
cana-4811	110	13	-	-	PUNCT
cana-4811	110	14	high	high	ADJ
cana-4811	110	15	table	table	NOUN
cana-4811	110	16	2	2	NUM
cana-4811	110	17	.	.	PUNCT
cana-4811	110	18	machine	machine	NOUN
cana-4811	110	19	learning	learning	NOUN
cana-4811	110	20	models	model	NOUN
cana-4811	110	21	with	with	ADP
cana-4811	110	22	correctly	correctly	ADV
cana-4811	110	23	and	and	CCONJ
cana-4811	110	24	incorrectly	incorrectly	ADV
cana-4811	110	25	classification	classification	NOUN
cana-4811	110	26	(	(	PUNCT
cana-4811	110	27	%	%	INTJ
cana-4811	110	28	)	)	PUNCT
cana-4811	110	29	ml	ml	ADP
cana-4811	110	30	approaches	approach	NOUN
cana-4811	110	31	correctly	correctly	ADV
cana-4811	110	32	classified	classify	VERB
cana-4811	110	33	incorrectly	incorrectly	ADV
cana-4811	110	34	classified	classify	VERB
cana-4811	110	35	logistic	logistic	ADJ
cana-4811	110	36	99	99	NUM
cana-4811	110	37	1	1	NUM
cana-4811	110	38	636	636	NUM
cana-4811	110	39	communications	communication	NOUN
cana-4811	110	40	on	on	ADP
cana-4811	110	41	applied	apply	VERB
cana-4811	110	42	nonlinear	nonlinear	ADJ
cana-4811	110	43	analysis	analysis	NOUN
cana-4811	110	44	issn	issn	NOUN
cana-4811	110	45	:	:	PUNCT
cana-4811	110	46	1074	1074	NUM
cana-4811	110	47	-	-	PUNCT
cana-4811	110	48	133x	133x	NUM
cana-4811	110	49	vol	vol	NOUN
cana-4811	110	50	32	32	NUM
cana-4811	110	51	no	no	NOUN
cana-4811	110	52	.	.	PUNCT
cana-4811	111	1	01s	01s	PROPN
cana-4811	111	2	(	(	PUNCT
cana-4811	111	3	2025	2025	NUM
cana-4811	111	4	)	)	PUNCT
cana-4811	111	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-4811	111	6	multilayer	multilayer	NOUN
cana-4811	111	7	perceptron	perceptron	PROPN
cana-4811	111	8	100	100	NUM
cana-4811	111	9	0	0	NUM
cana-4811	111	10	smo	smo	PROPN
cana-4811	111	11	98	98	NUM
cana-4811	111	12	2	2	NUM
cana-4811	111	13	j48	j48	NOUN
cana-4811	111	14	100	100	NUM
cana-4811	111	15	0	0	NUM
cana-4811	111	16	random	random	ADJ
cana-4811	111	17	forest	forest	NOUN
cana-4811	111	18	100	100	NUM
cana-4811	111	19	0	0	NUM
cana-4811	111	20	rep	rep	NOUN
cana-4811	111	21	tree	tree	NOUN
cana-4811	111	22	99	99	NUM
cana-4811	111	23	1	1	NUM
cana-4811	111	24	figure	figure	NOUN
cana-4811	111	25	1	1	NUM
cana-4811	111	26	.	.	PUNCT
cana-4811	111	27	comparison	comparison	NOUN
cana-4811	111	28	of	of	ADP
cana-4811	111	29	correctly	correctly	ADV
cana-4811	111	30	&	&	CCONJ
cana-4811	111	31	incorrectly	incorrectly	ADV
cana-4811	111	32	classification	classification	NOUN
cana-4811	111	33	table	table	NOUN
cana-4811	111	34	3	3	NUM
cana-4811	111	35	.	.	PUNCT
cana-4811	111	36	kappa	kappa	PROPN
cana-4811	111	37	statistic	statistic	PROPN
cana-4811	111	38	ml	ml	PROPN
cana-4811	111	39	models	model	NOUN
cana-4811	111	40	kappa	kappa	VERB
cana-4811	111	41	statistic	statistic	PROPN
cana-4811	111	42	logistic_regression	logistic_regression	NOUN
cana-4811	111	43	0.9900	0.9900	NUM
cana-4811	111	44	multilayer	multilayer	NOUN
cana-4811	111	45	perceptron	perceptron	PROPN
cana-4811	111	46	1.0000	1.0000	NUM
cana-4811	111	47	smo	smo	PROPN
cana-4811	111	48	0.9821	0.9821	NUM
cana-4811	111	49	j48	j48	NOUN
cana-4811	111	50	1.0000	1.0000	NUM
cana-4811	111	51	lmt	lmt	PROPN
cana-4811	111	52	1.0000	1.0000	NUM
cana-4811	111	53	random	random	ADJ
cana-4811	111	54	forest	forest	NOUN
cana-4811	111	55	1.0000	1.0000	NUM
cana-4811	111	56	rep	rep	NOUN
cana-4811	111	57	tree	tree	NOUN
cana-4811	111	58	0.9987	0.9987	NUM
cana-4811	111	59	figure	figure	NOUN
cana-4811	111	60	2	2	NUM
cana-4811	111	61	.	.	PUNCT
cana-4811	111	62	kappa	kappa	PROPN
cana-4811	111	63	statistics	statistic	NOUN
cana-4811	111	64	for	for	ADP
cana-4811	111	65	each	each	DET
cana-4811	111	66	model	model	NOUN
cana-4811	111	67	0	0	NUM
cana-4811	111	68	10	10	NUM
cana-4811	111	69	20	20	NUM
cana-4811	111	70	30	30	NUM
cana-4811	111	71	40	40	NUM
cana-4811	111	72	50	50	NUM
cana-4811	111	73	60	60	NUM
cana-4811	111	74	70	70	NUM
cana-4811	111	75	80	80	NUM
cana-4811	111	76	90	90	NUM
cana-4811	111	77	100	100	NUM
cana-4811	111	78	correctly	correctly	ADV
cana-4811	111	79	classified	classified	ADJ
cana-4811	111	80	instances	instance	NOUN
cana-4811	111	81	(	(	PUNCT
cana-4811	111	82	%	%	INTJ
cana-4811	111	83	)	)	PUNCT
cana-4811	111	84	incorrectly	incorrectly	ADV
cana-4811	111	85	classified	classified	ADJ
cana-4811	111	86	instances	instance	NOUN
cana-4811	111	87	(	(	PUNCT
cana-4811	111	88	%	%	INTJ
cana-4811	111	89	)	)	PUNCT
cana-4811	111	90	c	c	NOUN
cana-4811	112	1	o	o	PROPN
cana-4811	112	2	rr	rr	PROPN
cana-4811	112	3	ec	ec	PROPN
cana-4811	112	4	tl	tl	PROPN
cana-4811	112	5	y	y	PROPN
cana-4811	112	6	&	&	CCONJ
cana-4811	112	7	in	in	ADP
cana-4811	112	8	co	co	PROPN
cana-4811	112	9	rr	rr	PROPN
cana-4811	112	10	ec	ec	PROPN
cana-4811	112	11	tl	tl	PROPN
cana-4811	112	12	y	y	PROPN
cana-4811	112	13	c	c	PROPN
cana-4811	112	14	la	la	INTJ
cana-4811	112	15	ss	ss	PROPN
cana-4811	113	1	if	if	SCONJ
cana-4811	113	2	ie	ie	PROPN
cana-4811	113	3	d	d	PROPN
cana-4811	113	4	in	in	ADP
cana-4811	113	5	%	%	NOUN
cana-4811	113	6	ml	ml	ADP
cana-4811	113	7	approachs	approach	NOUN
cana-4811	113	8	logistic	logistic	ADJ
cana-4811	113	9	multilayer	multilayer	NOUN
cana-4811	113	10	perceptron	perceptron	PROPN
cana-4811	113	11	smo	smo	PROPN
cana-4811	113	12	j48	j48	PROPN
cana-4811	113	13	random	random	PROPN
cana-4811	113	14	forest	forest	NOUN
cana-4811	113	15	rep	rep	NOUN
cana-4811	113	16	tree	tree	NOUN
cana-4811	113	17	0.97	0.97	NUM
cana-4811	113	18	0.975	0.975	NUM
cana-4811	113	19	0.98	0.98	NUM
cana-4811	113	20	0.985	0.985	NUM
cana-4811	113	21	0.99	0.99	NUM
cana-4811	113	22	0.995	0.995	NUM
cana-4811	113	23	1	1	NUM
cana-4811	113	24	kappa	kappa	PROPN
cana-4811	113	25	statistic	statistic	PROPN
cana-4811	113	26	k	k	PROPN
cana-4811	113	27	ap	ap	PROPN
cana-4811	114	1	p	p	PROPN
cana-4811	114	2	a	a	DET
cana-4811	114	3	st	st	NOUN
cana-4811	114	4	at	at	ADP
cana-4811	114	5	is	be	AUX
cana-4811	114	6	ti	ti	X
cana-4811	114	7	c	c	NOUN
cana-4811	114	8	ml	ml	AUX
cana-4811	114	9	approachs	approach	VERB
cana-4811	114	10	logistic	logistic	ADJ
cana-4811	114	11	multilayer	multilayer	NOUN
cana-4811	114	12	perceptron	perceptron	PROPN
cana-4811	114	13	smo	smo	PROPN
cana-4811	114	14	j48	j48	PROPN
cana-4811	114	15	lmt	lmt	PROPN
cana-4811	114	16	random	random	PROPN
cana-4811	114	17	forest	forest	NOUN
cana-4811	114	18	rep	rep	NOUN
cana-4811	114	19	tree	tree	NOUN
cana-4811	114	20	637	637	NUM
cana-4811	114	21	communications	communication	NOUN
cana-4811	114	22	on	on	ADP
cana-4811	114	23	applied	apply	VERB
cana-4811	114	24	nonlinear	nonlinear	ADJ
cana-4811	114	25	analysis	analysis	NOUN
cana-4811	114	26	issn	issn	NOUN
cana-4811	114	27	:	:	PUNCT
cana-4811	114	28	1074	1074	NUM
cana-4811	114	29	-	-	PUNCT
cana-4811	114	30	133x	133x	NUM
cana-4811	114	31	vol	vol	NOUN
cana-4811	114	32	32	32	NUM
cana-4811	114	33	no	no	NOUN
cana-4811	114	34	.	.	PUNCT
cana-4811	115	1	01s	01s	PROPN
cana-4811	115	2	(	(	PUNCT
cana-4811	115	3	2025	2025	NUM
cana-4811	115	4	)	)	PUNCT
cana-4811	115	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-4811	115	6	table	table	NOUN
cana-4811	115	7	4	4	NUM
cana-4811	115	8	.	.	PUNCT
cana-4811	115	9	machine	machine	NOUN
cana-4811	115	10	learning	learning	NOUN
cana-4811	115	11	models	model	NOUN
cana-4811	115	12	with	with	ADP
cana-4811	115	13	mae	mae	PROPN
cana-4811	115	14	and	and	CCONJ
cana-4811	115	15	rmse	rmse	PROPN
cana-4811	115	16	ml	ml	NOUN
cana-4811	115	17	models	model	NOUN
cana-4811	115	18	mae	mae	PROPN
cana-4811	115	19	rmse	rmse	PROPN
cana-4811	115	20	logistic_regression	logistic_regression	PROPN
cana-4811	115	21	0.0010	0.0010	NUM
cana-4811	115	22	0.0003	0.0003	NUM
cana-4811	115	23	multilayer	multilayer	ADJ
cana-4811	115	24	perceptron	perceptron	PROPN
cana-4811	115	25	0.0018	0.0018	NUM
cana-4811	115	26	0.0035	0.0035	NUM
cana-4811	115	27	smo	smo	PROPN
cana-4811	115	28	0.2222	0.2222	NUM
cana-4811	115	29	0.2722	0.2722	NUM
cana-4811	115	30	j48	j48	NOUN
cana-4811	115	31	0.0000	0.0000	NUM
cana-4811	115	32	0.0000	0.0000	NUM
cana-4811	115	33	random_forest	random_forest	NOUN
cana-4811	115	34	0.0001	0.0001	NUM
cana-4811	115	35	0.0013	0.0013	NUM
cana-4811	115	36	rep_tree	rep_tree	NUM
cana-4811	115	37	0.0120	0.0120	NUM
cana-4811	115	38	0.0024	0.0024	NUM
cana-4811	115	39	figure	figure	NOUN
cana-4811	115	40	3	3	NUM
cana-4811	115	41	.	.	PUNCT
cana-4811	115	42	presents	present	VERB
cana-4811	115	43	the	the	DET
cana-4811	115	44	mae	mae	PROPN
cana-4811	115	45	and	and	CCONJ
cana-4811	115	46	rmse	rmse	ADJ
cana-4811	115	47	values	value	NOUN
cana-4811	115	48	for	for	ADP
cana-4811	115	49	each	each	DET
cana-4811	115	50	model	model	NOUN
cana-4811	115	51	table	table	NOUN
cana-4811	115	52	5	5	NUM
cana-4811	115	53	.	.	PUNCT
cana-4811	116	1	rae	rae	PROPN
cana-4811	116	2	(	(	PUNCT
cana-4811	116	3	%	%	INTJ
cana-4811	116	4	)	)	PUNCT
cana-4811	116	5	and	and	CCONJ
cana-4811	116	6	rrse	rrse	NOUN
cana-4811	116	7	(	(	PUNCT
cana-4811	116	8	%	%	NOUN
cana-4811	116	9	)	)	PUNCT
cana-4811	116	10	ml	ml	PROPN
cana-4811	116	11	models	model	NOUN
cana-4811	116	12	rae	rae	PROPN
cana-4811	116	13	rrse	rrse	PROPN
cana-4811	116	14	logistic_regression	logistic_regression	NOUN
cana-4811	116	15	0.0019	0.0019	NUM
cana-4811	116	16	0.0670	0.0670	NUM
cana-4811	116	17	multilayer	multilayer	ADJ
cana-4811	116	18	perceptron	perceptron	NOUN
cana-4811	116	19	0.4174	0.4174	NUM
cana-4811	116	20	0.7331	0.7331	NUM
cana-4811	116	21	smo	smo	PROPN
cana-4811	116	22	50.1438	50.1438	NUM
cana-4811	116	23	57.8179	57.8179	NUM
cana-4811	116	24	j48	j48	NOUN
cana-4811	116	25	0.0000	0.0000	NUM
cana-4811	116	26	0.0000	0.0000	NUM
cana-4811	116	27	random	random	ADJ
cana-4811	116	28	forest	forest	NOUN
cana-4811	116	29	0.0196	0.0196	NUM
cana-4811	116	30	0.2687	0.2687	NUM
cana-4811	116	31	rep	rep	NOUN
cana-4811	116	32	tree	tree	NOUN
cana-4811	116	33	0.0015	0.0015	NUM
cana-4811	116	34	0.0378	0.0378	NUM
cana-4811	116	35	figure	figure	NOUN
cana-4811	116	36	4	4	NUM
cana-4811	116	37	.	.	PUNCT
cana-4811	116	38	visualizes	visualize	VERB
cana-4811	116	39	the	the	DET
cana-4811	116	40	rae	rae	NOUN
cana-4811	116	41	and	and	CCONJ
cana-4811	116	42	rrse	rrse	NOUN
cana-4811	116	43	values	value	NOUN
cana-4811	116	44	for	for	ADP
cana-4811	116	45	each	each	DET
cana-4811	116	46	model	model	NOUN
cana-4811	116	47	0	0	NUM
cana-4811	116	48	0.05	0.05	NUM
cana-4811	116	49	0.1	0.1	NUM
cana-4811	116	50	0.15	0.15	NUM
cana-4811	116	51	0.2	0.2	NUM
cana-4811	116	52	0.25	0.25	NUM
cana-4811	116	53	0.3	0.3	NUM
cana-4811	116	54	mae	mae	PROPN
cana-4811	116	55	rmse	rmse	PROPN
cana-4811	116	56	m	m	VERB
cana-4811	116	57	a	a	DET
cana-4811	116	58	e	e	NOUN
cana-4811	116	59	an	an	DET
cana-4811	116	60	d	d	NOUN
cana-4811	116	61	r	r	NOUN
cana-4811	116	62	m	m	NOUN
cana-4811	116	63	se	se	X
cana-4811	116	64	ml	ml	AUX
cana-4811	116	65	approachs	approach	VERB
cana-4811	116	66	logistic	logistic	ADJ
cana-4811	116	67	multilayer	multilayer	NOUN
cana-4811	116	68	perceptron	perceptron	PROPN
cana-4811	116	69	smo	smo	PROPN
cana-4811	116	70	j48	j48	PROPN
cana-4811	116	71	random	random	PROPN
cana-4811	116	72	forest	forest	NOUN
cana-4811	116	73	rep	rep	NOUN
cana-4811	116	74	tree	tree	NOUN
cana-4811	116	75	0	0	NUM
cana-4811	117	1	10	10	NUM
cana-4811	117	2	20	20	NUM
cana-4811	117	3	30	30	NUM
cana-4811	117	4	40	40	NUM
cana-4811	117	5	50	50	NUM
cana-4811	117	6	60	60	NUM
cana-4811	117	7	70	70	NUM
cana-4811	117	8	rae	rae	NOUN
cana-4811	117	9	(	(	PUNCT
cana-4811	117	10	%	%	INTJ
cana-4811	117	11	)	)	PUNCT
cana-4811	117	12	rrse	rrse	NOUN
cana-4811	117	13	(	(	PUNCT
cana-4811	117	14	%	%	NOUN
cana-4811	117	15	)	)	PUNCT
cana-4811	117	16	r	r	NOUN
cana-4811	117	17	a	a	DET
cana-4811	117	18	e	e	NOUN
cana-4811	117	19	an	an	DET
cana-4811	117	20	d	d	NOUN
cana-4811	117	21	r	r	NOUN
cana-4811	117	22	r	r	NOUN
cana-4811	117	23	se	se	ADV
cana-4811	117	24	in	in	ADP
cana-4811	117	25	%	%	NOUN
cana-4811	117	26	ml	ml	ADP
cana-4811	117	27	approachs	approach	NOUN
cana-4811	117	28	logistic	logistic	ADJ
cana-4811	117	29	multilayer	multilayer	NOUN
cana-4811	117	30	perceptron	perceptron	PROPN
cana-4811	117	31	smo	smo	PROPN
cana-4811	117	32	j48	j48	PROPN
cana-4811	117	33	random	random	PROPN
cana-4811	117	34	forest	forest	NOUN
cana-4811	117	35	rep	rep	NOUN
cana-4811	117	36	tree	tree	NOUN
cana-4811	117	37	638	638	NUM
cana-4811	117	38	communications	communication	NOUN
cana-4811	117	39	on	on	ADP
cana-4811	117	40	applied	apply	VERB
cana-4811	117	41	nonlinear	nonlinear	ADJ
cana-4811	117	42	analysis	analysis	NOUN
cana-4811	117	43	issn	issn	NOUN
cana-4811	117	44	:	:	PUNCT
cana-4811	117	45	1074	1074	NUM
cana-4811	117	46	-	-	PUNCT
cana-4811	117	47	133x	133x	NUM
cana-4811	117	48	vol	vol	NOUN
cana-4811	117	49	32	32	NUM
cana-4811	117	50	no	no	NOUN
cana-4811	117	51	.	.	PUNCT
cana-4811	118	1	01s	01s	PROPN
cana-4811	118	2	(	(	PUNCT
cana-4811	118	3	2025	2025	NUM
cana-4811	118	4	)	)	PUNCT
cana-4811	118	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-4811	118	6	table	table	NOUN
cana-4811	118	7	6	6	NUM
cana-4811	118	8	.	.	PUNCT
cana-4811	118	9	time	time	NOUN
cana-4811	118	10	spent	spend	VERB
cana-4811	118	11	on	on	ADP
cana-4811	118	12	model	model	NOUN
cana-4811	118	13	creation	creation	NOUN
cana-4811	118	14	(	(	PUNCT
cana-4811	118	15	seconds	second	NOUN
cana-4811	118	16	)	)	PUNCT
cana-4811	118	17	ml	ml	NOUN
cana-4811	118	18	models	model	NOUN
cana-4811	118	19	time	time	NOUN
cana-4811	119	1	logistic_regression	logistic_regression	NOUN
cana-4811	119	2	0.6300	0.6300	NUM
cana-4811	119	3	multilayer_perceptron	multilayer_perceptron	NOUN
cana-4811	119	4	4.6600	4.6600	NUM
cana-4811	119	5	smo	smo	PROPN
cana-4811	119	6	0.4100	0.4100	NUM
cana-4811	119	7	j48	j48	NOUN
cana-4811	119	8	0.1400	0.1400	NUM
cana-4811	119	9	random_forest	random_for	ADJ
cana-4811	119	10	0.4900	0.4900	NUM
cana-4811	119	11	rep_tree	rep_tree	NUM
cana-4811	119	12	0.0700	0.0700	NUM
cana-4811	119	13	figure	figure	NOUN
cana-4811	119	14	5	5	NUM
cana-4811	119	15	.	.	PUNCT
cana-4811	119	16	depicts	depict	VERB
cana-4811	119	17	the	the	DET
cana-4811	119	18	time	time	NOUN
cana-4811	119	19	taken	take	VERB
cana-4811	119	20	to	to	PART
cana-4811	119	21	build	build	VERB
cana-4811	119	22	each	each	DET
cana-4811	119	23	model	model	NOUN
cana-4811	119	24	table	table	NOUN
cana-4811	119	25	7	7	NUM
cana-4811	119	26	.	.	PUNCT
cana-4811	119	27	common	common	ADJ
cana-4811	119	28	evaluation	evaluation	NOUN
cana-4811	119	29	metrics	metric	NOUN
cana-4811	119	30	in	in	ADP
cana-4811	119	31	machine	machine	NOUN
cana-4811	119	32	learning	learn	VERB
cana-4811	119	33	ml	ml	AUX
cana-4811	119	34	approaches	approach	VERB
cana-4811	119	35	logistic	logistic	ADJ
cana-4811	119	36	multilayer	multilayer	ADJ
cana-4811	119	37	perceptron	perceptron	PROPN
cana-4811	119	38	smo	smo	PROPN
cana-4811	119	39	j48	j48	PROPN
cana-4811	119	40	random	random	PROPN
cana-4811	119	41	forest	forest	NOUN
cana-4811	119	42	rep	rep	NOUN
cana-4811	119	43	tree	tree	NOUN
cana-4811	119	44	tp	tp	PART
cana-4811	119	45	rate	rate	VERB
cana-4811	119	46	0.9000	0.9000	NUM
cana-4811	119	47	1.0000	1.0000	NUM
cana-4811	119	48	0.8000	0.8000	NUM
cana-4811	119	49	1.0000	1.0000	NUM
cana-4811	119	50	1.0000	1.0000	NUM
cana-4811	119	51	0.9000	0.9000	NUM
cana-4811	119	52	fp	fp	NOUN
cana-4811	119	53	rate	rate	NOUN
cana-4811	119	54	0.1000	0.1000	NUM
cana-4811	119	55	0.0000	0.0000	NUM
cana-4811	119	56	0.2000	0.2000	NUM
cana-4811	119	57	0.0000	0.0000	NUM
cana-4811	119	58	0.0000	0.0000	NUM
cana-4811	119	59	0.1000	0.1000	NUM
cana-4811	119	60	precision	precision	NOUN
cana-4811	119	61	0.9000	0.9000	NUM
cana-4811	119	62	1.0000	1.0000	NUM
cana-4811	119	63	0.8000	0.8000	NUM
cana-4811	119	64	1.0000	1.0000	NUM
cana-4811	119	65	1.0000	1.0000	NUM
cana-4811	119	66	0.9000	0.9000	NUM
cana-4811	119	67	recall	recall	NOUN
cana-4811	119	68	0.9000	0.9000	NUM
cana-4811	119	69	1.0000	1.0000	NUM
cana-4811	119	70	0.8000	0.8000	NUM
cana-4811	119	71	1.0000	1.0000	NUM
cana-4811	119	72	1.0000	1.0000	NUM
cana-4811	119	73	0.9000	0.9000	NUM
cana-4811	119	74	f	f	NOUN
cana-4811	119	75	-	-	PUNCT
cana-4811	119	76	measure	measure	NOUN
cana-4811	119	77	0.9000	0.9000	NUM
cana-4811	119	78	1.0000	1.0000	NUM
cana-4811	119	79	0.8000	0.8000	NUM
cana-4811	119	80	1.0000	1.0000	NUM
cana-4811	119	81	1.0000	1.0000	NUM
cana-4811	119	82	0.9000	0.9000	NUM
cana-4811	119	83	mcc	mcc	NOUN
cana-4811	119	84	0.9000	0.9000	NUM
cana-4811	119	85	1.0000	1.0000	NUM
cana-4811	119	86	0.8000	0.8000	NUM
cana-4811	119	87	1.0000	1.0000	NUM
cana-4811	119	88	1.0000	1.0000	NUM
cana-4811	119	89	0.9000	0.9000	NUM
cana-4811	119	90	roc	roc	PROPN
cana-4811	119	91	area	area	NOUN
cana-4811	119	92	0.9000	0.9000	NUM
cana-4811	119	93	1.0000	1.0000	NUM
cana-4811	119	94	0.8000	0.8000	NUM
cana-4811	119	95	1.0000	1.0000	NUM
cana-4811	119	96	1.0000	1.0000	NUM
cana-4811	119	97	0.9000	0.9000	NUM
cana-4811	119	98	prc	prc	PROPN
cana-4811	119	99	area	area	NOUN
cana-4811	119	100	0.9000	0.9000	NUM
cana-4811	119	101	1.0000	1.0000	NUM
cana-4811	119	102	0.8000	0.8000	NUM
cana-4811	119	103	1.0000	1.0000	NUM
cana-4811	119	104	1.0000	1.0000	NUM
cana-4811	119	105	0.9000	0.9000	NUM
cana-4811	119	106	figure	figure	NOUN
cana-4811	119	107	6	6	NUM
cana-4811	119	108	.	.	PUNCT
cana-4811	119	109	common	common	ADJ
cana-4811	119	110	evaluation	evaluation	NOUN
cana-4811	119	111	metrics	metric	NOUN
cana-4811	119	112	in	in	ADP
cana-4811	119	113	machine	machine	NOUN
cana-4811	119	114	learning	learn	VERB
cana-4811	119	115	0.6300	0.6300	NUM
cana-4811	120	1	4.6600	4.6600	NUM
cana-4811	120	2	0.4100	0.4100	NUM
cana-4811	120	3	0.1400	0.1400	NUM
cana-4811	120	4	0.4900	0.4900	NUM
cana-4811	120	5	0.0700	0.0700	NUM
cana-4811	120	6	0.0000	0.0000	NUM
cana-4811	120	7	1.0000	1.0000	NUM
cana-4811	120	8	2.0000	2.0000	NUM
cana-4811	120	9	3.0000	3.0000	NUM
cana-4811	120	10	4.0000	4.0000	NUM
cana-4811	120	11	5.0000	5.0000	NUM
cana-4811	120	12	time	time	NOUN
cana-4811	120	13	taken	take	VERB
cana-4811	120	14	(	(	PUNCT
cana-4811	120	15	seconds)ti	seconds)ti	PROPN
cana-4811	120	16	m	m	PROPN
cana-4811	120	17	e	e	NOUN
cana-4811	120	18	ta	ta	ADP
cana-4811	120	19	ke	ke	PROPN
cana-4811	120	20	n	n	PROPN
cana-4811	120	21	in	in	ADP
cana-4811	120	22	s	s	PRON
cana-4811	120	23	ec	ec	NOUN
cana-4811	120	24	o	o	NOUN
cana-4811	121	1	n	n	PROPN
cana-4811	121	2	d	d	PROPN
cana-4811	121	3	s	s	ADP
cana-4811	121	4	ml	ml	X
cana-4811	121	5	approachs	approach	NOUN
cana-4811	121	6	logistic	logistic	ADJ
cana-4811	121	7	multilayer	multilayer	NOUN
cana-4811	121	8	perceptron	perceptron	PROPN
cana-4811	121	9	smo	smo	PROPN
cana-4811	121	10	j48	j48	PROPN
cana-4811	121	11	random	random	PROPN
cana-4811	121	12	forest	forest	NOUN
cana-4811	121	13	rep	rep	NOUN
cana-4811	121	14	tree	tree	NOUN
cana-4811	121	15	0.0000	0.0000	NUM
cana-4811	121	16	0.2000	0.2000	NUM
cana-4811	122	1	0.4000	0.4000	NUM
cana-4811	122	2	0.6000	0.6000	NUM
cana-4811	122	3	0.8000	0.8000	NUM
cana-4811	122	4	1.0000	1.0000	NUM
cana-4811	122	5	lo	lo	PROPN
cana-4811	122	6	w	w	PROPN
cana-4811	122	7	accuracy	accuracy	NOUN
cana-4811	122	8	performance	performance	NOUN
cana-4811	122	9	logistic	logistic	ADJ
cana-4811	122	10	multilayer	multilayer	NOUN
cana-4811	122	11	perceptron	perceptron	PROPN
cana-4811	122	12	smo	smo	PROPN
cana-4811	122	13	j48	j48	PROPN
cana-4811	122	14	random	random	PROPN
cana-4811	122	15	forest	forest	NOUN
cana-4811	122	16	rep	rep	NOUN
cana-4811	122	17	tree	tree	NOUN
cana-4811	122	18	639	639	NUM
cana-4811	122	19	communications	communication	NOUN
cana-4811	122	20	on	on	ADP
cana-4811	122	21	applied	apply	VERB
cana-4811	122	22	nonlinear	nonlinear	ADJ
cana-4811	122	23	analysis	analysis	NOUN
cana-4811	122	24	issn	issn	NOUN
cana-4811	122	25	:	:	PUNCT
cana-4811	122	26	1074	1074	NUM
cana-4811	122	27	-	-	PUNCT
cana-4811	122	28	133x	133x	NUM
cana-4811	122	29	vol	vol	NOUN
cana-4811	122	30	32	32	NUM
cana-4811	122	31	no	no	NOUN
cana-4811	122	32	.	.	PUNCT
cana-4811	123	1	01s	01s	PROPN
cana-4811	123	2	(	(	PUNCT
cana-4811	123	3	2025	2025	NUM
cana-4811	123	4	)	)	PUNCT
cana-4811	123	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-4811	123	6	3	3	X
cana-4811	123	7	.	.	NOUN
cana-4811	123	8	result	result	NOUN
cana-4811	123	9	and	and	CCONJ
cana-4811	123	10	discussion	discussion	VERB
cana-4811	123	11	the	the	DET
cana-4811	123	12	analysis	analysis	NOUN
cana-4811	123	13	of	of	ADP
cana-4811	123	14	various	various	ADJ
cana-4811	123	15	ml	ml	NOUN
cana-4811	123	16	models	model	NOUN
cana-4811	123	17	for	for	ADP
cana-4811	123	18	lung	lung	NOUN
cana-4811	123	19	cancer	cancer	NOUN
cana-4811	123	20	identification	identification	NOUN
cana-4811	123	21	using	use	VERB
cana-4811	123	22	the	the	DET
cana-4811	123	23	dataset	dataset	NOUN
cana-4811	123	24	with	with	ADP
cana-4811	123	25	25	25	NUM
cana-4811	123	26	parameters	parameter	NOUN
cana-4811	123	27	was	be	AUX
cana-4811	123	28	conducted	conduct	VERB
cana-4811	123	29	.	.	PUNCT
cana-4811	124	1	the	the	DET
cana-4811	124	2	models	model	NOUN
cana-4811	124	3	evaluated	evaluate	VERB
cana-4811	124	4	include	include	VERB
cana-4811	124	5	logistic	logistic	ADJ
cana-4811	124	6	regression	regression	NOUN
cana-4811	124	7	,	,	PUNCT
cana-4811	124	8	multilayer	multilayer	PROPN
cana-4811	124	9	perceptron	perceptron	PROPN
cana-4811	124	10	,	,	PUNCT
cana-4811	124	11	smo	smo	PROPN
cana-4811	124	12	,	,	PUNCT
cana-4811	124	13	j48	j48	PROPN
cana-4811	124	14	,	,	PUNCT
cana-4811	124	15	random	random	ADJ
cana-4811	124	16	forest	forest	NOUN
cana-4811	124	17	,	,	PUNCT
cana-4811	124	18	and	and	CCONJ
cana-4811	124	19	rep	rep	PROPN
cana-4811	124	20	tree	tree	NOUN
cana-4811	124	21	.	.	PUNCT
cana-4811	125	1	the	the	DET
cana-4811	125	2	analysis	analysis	NOUN
cana-4811	125	3	covered	cover	VERB
cana-4811	125	4	key	key	ADJ
cana-4811	125	5	performance	performance	NOUN
cana-4811	125	6	metrics	metric	NOUN
cana-4811	125	7	such	such	ADJ
cana-4811	125	8	as	as	ADP
cana-4811	125	9	instances	instance	NOUN
cana-4811	125	10	of	of	ADP
cana-4811	125	11	correct	correct	ADJ
cana-4811	125	12	and	and	CCONJ
cana-4811	125	13	incorrect	incorrect	ADJ
cana-4811	125	14	classification	classification	NOUN
cana-4811	125	15	,	,	PUNCT
cana-4811	125	16	kappa	kappa	PROPN
cana-4811	125	17	statistics	statistics	PROPN
cana-4811	125	18	,	,	PUNCT
cana-4811	125	19	mae	mae	PROPN
cana-4811	125	20	,	,	PUNCT
cana-4811	125	21	rmse	rmse	NOUN
cana-4811	125	22	,	,	PUNCT
cana-4811	125	23	and	and	CCONJ
cana-4811	125	24	rae	rae	PROPN
cana-4811	125	25	,	,	PUNCT
cana-4811	125	26	among	among	ADP
cana-4811	125	27	others	other	NOUN
cana-4811	125	28	.	.	PUNCT
cana-4811	126	1	as	as	SCONJ
cana-4811	126	2	shown	show	VERB
cana-4811	126	3	in	in	ADP
cana-4811	126	4	table	table	NOUN
cana-4811	126	5	2	2	NUM
cana-4811	126	6	and	and	CCONJ
cana-4811	126	7	figure	figure	NOUN
cana-4811	126	8	1	1	NUM
cana-4811	126	9	,	,	PUNCT
cana-4811	126	10	the	the	DET
cana-4811	126	11	maximum	maximum	ADJ
cana-4811	126	12	classification	classification	NOUN
cana-4811	126	13	accuracy	accuracy	NOUN
cana-4811	126	14	(	(	PUNCT
cana-4811	126	15	100	100	NUM
cana-4811	126	16	%	%	NOUN
cana-4811	126	17	)	)	PUNCT
cana-4811	126	18	was	be	AUX
cana-4811	126	19	achieved	achieve	VERB
cana-4811	126	20	by	by	ADP
cana-4811	126	21	multilayer	multilayer	PROPN
cana-4811	126	22	perceptron	perceptron	PROPN
cana-4811	126	23	,	,	PUNCT
cana-4811	126	24	j48	j48	PROPN
cana-4811	126	25	,	,	PUNCT
cana-4811	126	26	and	and	CCONJ
cana-4811	126	27	random	random	ADJ
cana-4811	126	28	forest	forest	NOUN
cana-4811	126	29	.	.	PUNCT
cana-4811	127	1	logistic	logistic	ADJ
cana-4811	127	2	regression	regression	NOUN
cana-4811	127	3	and	and	CCONJ
cana-4811	127	4	rep	rep	NOUN
cana-4811	127	5	tree	tree	NOUN
cana-4811	127	6	both	both	PRON
cana-4811	127	7	achieved	achieve	VERB
cana-4811	127	8	99	99	NUM
cana-4811	127	9	%	%	NOUN
cana-4811	127	10	,	,	PUNCT
cana-4811	127	11	while	while	SCONJ
cana-4811	127	12	smo	smo	PROPN
cana-4811	127	13	had	have	VERB
cana-4811	127	14	98	98	NUM
cana-4811	127	15	%	%	NOUN
cana-4811	127	16	.	.	PUNCT
cana-4811	128	1	the	the	DET
cana-4811	128	2	kappa	kappa	PROPN
cana-4811	128	3	statistic	statistic	PROPN
cana-4811	128	4	was	be	AUX
cana-4811	128	5	used	use	VERB
cana-4811	128	6	to	to	PART
cana-4811	128	7	evaluate	evaluate	VERB
cana-4811	128	8	the	the	DET
cana-4811	128	9	inter	inter	ADJ
cana-4811	128	10	-	-	ADJ
cana-4811	128	11	rater	rater	ADJ
cana-4811	128	12	agreement	agreement	NOUN
cana-4811	128	13	.	.	PUNCT
cana-4811	129	1	multilayer	multilayer	PROPN
cana-4811	129	2	perceptron	perceptron	PROPN
cana-4811	129	3	,	,	PUNCT
cana-4811	129	4	j48	j48	PROPN
cana-4811	129	5	,	,	PUNCT
cana-4811	129	6	random	random	ADJ
cana-4811	129	7	forest	forest	NOUN
cana-4811	129	8	,	,	PUNCT
cana-4811	129	9	and	and	CCONJ
cana-4811	129	10	lmt	lmt	PROPN
cana-4811	129	11	achieved	achieve	VERB
cana-4811	129	12	a	a	DET
cana-4811	129	13	perfect	perfect	ADJ
cana-4811	129	14	score	score	NOUN
cana-4811	129	15	of	of	ADP
cana-4811	129	16	1.000	1.000	NUM
cana-4811	129	17	,	,	PUNCT
cana-4811	129	18	indicating	indicate	VERB
cana-4811	129	19	almost	almost	ADV
cana-4811	129	20	perfect	perfect	ADJ
cana-4811	129	21	agreement	agreement	NOUN
cana-4811	129	22	,	,	PUNCT
cana-4811	129	23	while	while	SCONJ
cana-4811	129	24	logistic	logistic	ADJ
cana-4811	129	25	regression	regression	NOUN
cana-4811	129	26	and	and	CCONJ
cana-4811	129	27	rep	rep	NOUN
cana-4811	129	28	tree	tree	NOUN
cana-4811	129	29	were	be	AUX
cana-4811	129	30	close	close	ADJ
cana-4811	129	31	with	with	ADP
cana-4811	129	32	values	value	NOUN
cana-4811	129	33	of	of	ADP
cana-4811	129	34	0.9900	0.9900	NUM
cana-4811	129	35	and	and	CCONJ
cana-4811	129	36	0.9987	0.9987	NUM
cana-4811	129	37	,	,	PUNCT
cana-4811	129	38	respectively	respectively	ADV
cana-4811	129	39	(	(	PUNCT
cana-4811	129	40	table	table	NOUN
cana-4811	129	41	3	3	NUM
cana-4811	129	42	and	and	CCONJ
cana-4811	129	43	figure	figure	NOUN
cana-4811	129	44	2	2	NUM
cana-4811	129	45	)	)	PUNCT
cana-4811	129	46	.	.	PUNCT
cana-4811	130	1	in	in	ADP
cana-4811	130	2	terms	term	NOUN
cana-4811	130	3	of	of	ADP
cana-4811	130	4	error	error	NOUN
cana-4811	130	5	metrics	metric	NOUN
cana-4811	130	6	,	,	PUNCT
cana-4811	130	7	the	the	DET
cana-4811	130	8	j48	j48	PROPN
cana-4811	130	9	model	model	NOUN
cana-4811	130	10	outperformed	outperform	VERB
cana-4811	130	11	the	the	DET
cana-4811	130	12	others	other	NOUN
cana-4811	130	13	with	with	ADP
cana-4811	130	14	an	an	DET
cana-4811	130	15	mae	mae	PROPN
cana-4811	130	16	and	and	CCONJ
cana-4811	130	17	rmse	rmse	ADJ
cana-4811	130	18	returns	return	NOUN
cana-4811	130	19	of	of	ADP
cana-4811	130	20	0	0	NUM
cana-4811	130	21	,	,	PUNCT
cana-4811	130	22	for	for	ADP
cana-4811	130	23	using	use	VERB
cana-4811	130	24	random	random	ADJ
cana-4811	130	25	forest	forest	NOUN
cana-4811	130	26	with	with	ADP
cana-4811	130	27	an	an	DET
cana-4811	130	28	mae=0.0001	mae=0.0001	NOUN
cana-4811	130	29	and	and	CCONJ
cana-4811	130	30	rmse=0.0013	rmse=0.0013	PROPN
cana-4811	130	31	.	.	PROPN
cana-4811	130	32	smo	smo	PROPN
cana-4811	130	33	showed	show	VERB
cana-4811	130	34	the	the	DET
cana-4811	130	35	highest	high	ADJ
cana-4811	130	36	mae	mae	PROPN
cana-4811	130	37	and	and	CCONJ
cana-4811	130	38	rmse	rmse	ADJ
cana-4811	130	39	values	value	NOUN
cana-4811	130	40	,	,	PUNCT
cana-4811	130	41	indicating	indicate	VERB
cana-4811	130	42	more	more	ADV
cana-4811	130	43	significant	significant	ADJ
cana-4811	130	44	prediction	prediction	NOUN
cana-4811	130	45	errors	error	NOUN
cana-4811	130	46	(	(	PUNCT
cana-4811	130	47	table	table	NOUN
cana-4811	130	48	4	4	NUM
cana-4811	130	49	and	and	CCONJ
cana-4811	130	50	figure	figure	VERB
cana-4811	130	51	3	3	NUM
cana-4811	130	52	)	)	PUNCT
cana-4811	130	53	.	.	PUNCT
cana-4811	131	1	the	the	DET
cana-4811	131	2	lowest	low	ADJ
cana-4811	131	3	relative	relative	ADJ
cana-4811	131	4	errors	error	NOUN
cana-4811	131	5	,	,	PUNCT
cana-4811	131	6	in	in	ADP
cana-4811	131	7	both	both	DET
cana-4811	131	8	rae	rae	PROPN
cana-4811	131	9	and	and	CCONJ
cana-4811	131	10	rrse	rrse	PROPN
cana-4811	131	11	,	,	PUNCT
cana-4811	131	12	were	be	AUX
cana-4811	131	13	achieved	achieve	VERB
cana-4811	131	14	by	by	ADP
cana-4811	131	15	j48	j48	PROPN
cana-4811	131	16	,	,	PUNCT
cana-4811	131	17	followed	follow	VERB
cana-4811	131	18	by	by	ADP
cana-4811	131	19	random	random	ADJ
cana-4811	131	20	forest	forest	NOUN
cana-4811	131	21	.	.	PUNCT
cana-4811	132	1	in	in	ADP
cana-4811	132	2	contrast	contrast	NOUN
cana-4811	132	3	,	,	PUNCT
cana-4811	132	4	smo	smo	PROPN
cana-4811	132	5	exhibited	exhibit	VERB
cana-4811	132	6	the	the	DET
cana-4811	132	7	highest	high	ADJ
cana-4811	132	8	relative	relative	ADJ
cana-4811	132	9	errors	error	NOUN
cana-4811	132	10	,	,	PUNCT
cana-4811	132	11	indicating	indicate	VERB
cana-4811	132	12	a	a	DET
cana-4811	132	13	lower	low	ADJ
cana-4811	132	14	prediction	prediction	NOUN
cana-4811	132	15	performance	performance	NOUN
cana-4811	132	16	(	(	PUNCT
cana-4811	132	17	table	table	NOUN
cana-4811	132	18	5	5	NUM
cana-4811	132	19	and	and	CCONJ
cana-4811	132	20	figure	figure	VERB
cana-4811	132	21	4	4	NUM
cana-4811	132	22	)	)	PUNCT
cana-4811	132	23	.	.	PUNCT
cana-4811	133	1	regarding	regard	VERB
cana-4811	133	2	training	training	NOUN
cana-4811	133	3	time	time	NOUN
cana-4811	133	4	,	,	PUNCT
cana-4811	133	5	rep	rep	NOUN
cana-4811	133	6	tree	tree	NOUN
cana-4811	133	7	was	be	AUX
cana-4811	133	8	the	the	DET
cana-4811	133	9	fastest	fast	ADJ
cana-4811	133	10	,	,	PUNCT
cana-4811	133	11	taking	take	VERB
cana-4811	133	12	only	only	ADV
cana-4811	133	13	0.07	0.07	NUM
cana-4811	133	14	seconds	second	NOUN
cana-4811	133	15	,	,	PUNCT
cana-4811	133	16	followed	follow	VERB
cana-4811	133	17	by	by	ADP
cana-4811	133	18	j48	j48	NOUN
cana-4811	133	19	with	with	ADP
cana-4811	133	20	0.14	0.14	NUM
cana-4811	133	21	seconds	second	NOUN
cana-4811	133	22	.	.	PUNCT
cana-4811	134	1	the	the	DET
cana-4811	134	2	multilayer	multilayer	PROPN
cana-4811	134	3	perceptron	perceptron	PROPN
cana-4811	134	4	took	take	VERB
cana-4811	134	5	the	the	DET
cana-4811	134	6	longest	long	ADJ
cana-4811	134	7	time	time	NOUN
cana-4811	134	8	(	(	PUNCT
cana-4811	134	9	4.66	4.66	NUM
cana-4811	134	10	seconds	second	NOUN
cana-4811	134	11	)	)	PUNCT
cana-4811	134	12	to	to	PART
cana-4811	134	13	train	train	VERB
cana-4811	134	14	,	,	PUNCT
cana-4811	134	15	but	but	CCONJ
cana-4811	134	16	this	this	PRON
cana-4811	134	17	was	be	AUX
cana-4811	134	18	offset	offset	VERB
cana-4811	134	19	by	by	ADP
cana-4811	134	20	its	its	PRON
cana-4811	134	21	perfect	perfect	ADJ
cana-4811	134	22	performance	performance	NOUN
cana-4811	134	23	in	in	ADP
cana-4811	134	24	classification	classification	NOUN
cana-4811	134	25	accuracy	accuracy	NOUN
cana-4811	134	26	(	(	PUNCT
cana-4811	134	27	table	table	NOUN
cana-4811	134	28	6	6	NUM
cana-4811	134	29	and	and	CCONJ
cana-4811	134	30	figure	figure	VERB
cana-4811	134	31	5	5	NUM
cana-4811	134	32	)	)	PUNCT
cana-4811	134	33	.	.	PUNCT
cana-4811	135	1	all	all	DET
cana-4811	135	2	models	model	NOUN
cana-4811	135	3	,	,	PUNCT
cana-4811	135	4	except	except	SCONJ
cana-4811	135	5	smo	smo	PROPN
cana-4811	135	6	,	,	PUNCT
cana-4811	135	7	achieved	achieve	VERB
cana-4811	135	8	a	a	DET
cana-4811	135	9	tp	tp	NOUN
cana-4811	135	10	rate	rate	NOUN
cana-4811	135	11	,	,	PUNCT
cana-4811	135	12	precision	precision	NOUN
cana-4811	135	13	,	,	PUNCT
cana-4811	135	14	recall	recall	NOUN
cana-4811	135	15	,	,	PUNCT
cana-4811	135	16	and	and	CCONJ
cana-4811	135	17	f	f	X
cana-4811	135	18	-	-	PUNCT
cana-4811	135	19	measure	measure	NOUN
cana-4811	135	20	of	of	ADP
cana-4811	135	21	1.000	1.000	NUM
cana-4811	135	22	,	,	PUNCT
cana-4811	135	23	with	with	ADP
cana-4811	135	24	the	the	DET
cana-4811	135	25	smo	smo	PROPN
cana-4811	135	26	model	model	NOUN
cana-4811	135	27	trailing	trail	VERB
cana-4811	135	28	slightly	slightly	ADV
cana-4811	135	29	behind	behind	ADV
cana-4811	135	30	at	at	ADP
cana-4811	135	31	0.800	0.800	NUM
cana-4811	135	32	for	for	ADP
cana-4811	135	33	all	all	DET
cana-4811	135	34	metrics	metric	NOUN
cana-4811	135	35	(	(	PUNCT
cana-4811	135	36	table	table	NOUN
cana-4811	135	37	7	7	NUM
cana-4811	135	38	and	and	CCONJ
cana-4811	135	39	figure	figure	VERB
cana-4811	135	40	6	6	NUM
cana-4811	135	41	)	)	PUNCT
cana-4811	135	42	.	.	PUNCT
cana-4811	136	1	this	this	DET
cana-4811	136	2	analysis	analysis	NOUN
cana-4811	136	3	highlights	highlight	NOUN
cana-4811	136	4	that	that	PRON
cana-4811	136	5	j48	j48	ADJ
cana-4811	136	6	and	and	CCONJ
cana-4811	136	7	random	random	ADJ
cana-4811	136	8	forest	forest	NOUN
cana-4811	136	9	are	be	AUX
cana-4811	136	10	the	the	DET
cana-4811	136	11	most	most	ADV
cana-4811	136	12	efficient	efficient	ADJ
cana-4811	136	13	models	model	NOUN
cana-4811	136	14	for	for	ADP
cana-4811	136	15	lung	lung	NOUN
cana-4811	136	16	cancer	cancer	NOUN
cana-4811	136	17	prediction	prediction	NOUN
cana-4811	136	18	,	,	PUNCT
cana-4811	136	19	delivering	deliver	VERB
cana-4811	136	20	the	the	DET
cana-4811	136	21	highest	high	ADJ
cana-4811	136	22	accuracy	accuracy	NOUN
cana-4811	136	23	and	and	CCONJ
cana-4811	136	24	lowest	low	ADJ
cana-4811	136	25	error	error	NOUN
cana-4811	136	26	metrics	metric	NOUN
cana-4811	136	27	with	with	ADP
cana-4811	136	28	minimal	minimal	ADJ
cana-4811	136	29	training	training	NOUN
cana-4811	136	30	time	time	NOUN
cana-4811	136	31	.	.	PUNCT
cana-4811	137	1	the	the	DET
cana-4811	137	2	multilayer	multilayer	PROPN
cana-4811	137	3	perceptron	perceptron	PROPN
cana-4811	137	4	also	also	ADV
cana-4811	137	5	performed	perform	VERB
cana-4811	137	6	exceptionally	exceptionally	ADV
cana-4811	137	7	well	well	ADV
cana-4811	137	8	,	,	PUNCT
cana-4811	137	9	but	but	CCONJ
cana-4811	137	10	with	with	ADP
cana-4811	137	11	a	a	DET
cana-4811	137	12	longer	long	ADJ
cana-4811	137	13	training	training	NOUN
cana-4811	137	14	duration	duration	NOUN
cana-4811	137	15	.	.	PUNCT
cana-4811	138	1	smo	smo	PROPN
cana-4811	138	2	,	,	PUNCT
cana-4811	138	3	while	while	SCONJ
cana-4811	138	4	still	still	ADV
cana-4811	138	5	performing	perform	VERB
cana-4811	138	6	reasonably	reasonably	ADV
cana-4811	138	7	,	,	PUNCT
cana-4811	138	8	had	have	VERB
cana-4811	138	9	the	the	DET
cana-4811	138	10	lowest	low	ADJ
cana-4811	138	11	accuracy	accuracy	NOUN
cana-4811	138	12	and	and	CCONJ
cana-4811	138	13	highest	high	ADJ
cana-4811	138	14	error	error	NOUN
cana-4811	138	15	metrics	metric	NOUN
cana-4811	138	16	,	,	PUNCT
cana-4811	138	17	indicating	indicate	VERB
cana-4811	138	18	it	it	PRON
cana-4811	138	19	may	may	AUX
cana-4811	138	20	not	not	PART
cana-4811	138	21	be	be	AUX
cana-4811	138	22	the	the	DET
cana-4811	138	23	best	good	ADJ
cana-4811	138	24	choice	choice	NOUN
cana-4811	138	25	for	for	ADP
cana-4811	138	26	this	this	DET
cana-4811	138	27	dataset	dataset	NOUN
cana-4811	138	28	.	.	PUNCT
cana-4811	139	1	4	4	X
cana-4811	139	2	.	.	X
cana-4811	139	3	conclusion	conclusion	NOUN
cana-4811	139	4	and	and	CCONJ
cana-4811	139	5	further	further	ADJ
cana-4811	139	6	research	research	NOUN
cana-4811	139	7	the	the	DET
cana-4811	139	8	study	study	NOUN
cana-4811	139	9	demonstrates	demonstrate	VERB
cana-4811	139	10	using	use	VERB
cana-4811	139	11	different	different	ADJ
cana-4811	139	12	ml	ml	NOUN
cana-4811	139	13	models	model	NOUN
cana-4811	139	14	for	for	ADP
cana-4811	139	15	detecting	detect	VERB
cana-4811	139	16	and	and	CCONJ
cana-4811	139	17	classifying	classify	VERB
cana-4811	139	18	the	the	DET
cana-4811	139	19	lung	lung	NOUN
cana-4811	139	20	cancer	cancer	NOUN
cana-4811	139	21	using	use	VERB
cana-4811	139	22	25	25	NUM
cana-4811	139	23	parameters	parameter	NOUN
cana-4811	139	24	.	.	PUNCT
cana-4811	140	1	among	among	ADP
cana-4811	140	2	the	the	DET
cana-4811	140	3	models	model	NOUN
cana-4811	140	4	evaluated	evaluate	VERB
cana-4811	140	5	,	,	PUNCT
cana-4811	140	6	j48	j48	PROPN
cana-4811	140	7	,	,	PUNCT
cana-4811	140	8	random	random	ADJ
cana-4811	140	9	forest	forest	NOUN
cana-4811	140	10	,	,	PUNCT
cana-4811	140	11	and	and	CCONJ
cana-4811	140	12	multilayer	multilayer	PROPN
cana-4811	140	13	perceptron	perceptron	PROPN
cana-4811	140	14	exhibited	exhibit	VERB
cana-4811	140	15	perfect	perfect	ADJ
cana-4811	140	16	classification	classification	NOUN
cana-4811	140	17	performance	performance	NOUN
cana-4811	140	18	with	with	ADP
cana-4811	140	19	100	100	NUM
cana-4811	140	20	%	%	NOUN
cana-4811	140	21	accuracy	accuracy	NOUN
cana-4811	140	22	.	.	PUNCT
cana-4811	141	1	these	these	DET
cana-4811	141	2	models	model	NOUN
cana-4811	141	3	also	also	ADV
cana-4811	141	4	scored	score	VERB
cana-4811	141	5	highly	highly	ADV
cana-4811	141	6	in	in	ADP
cana-4811	141	7	terms	term	NOUN
cana-4811	141	8	of	of	ADP
cana-4811	141	9	kappa	kappa	PROPN
cana-4811	141	10	statistic	statistic	PROPN
cana-4811	141	11	,	,	PUNCT
cana-4811	141	12	low	low	ADJ
cana-4811	141	13	error	error	NOUN
cana-4811	141	14	metrics	metric	NOUN
cana-4811	141	15	(	(	PUNCT
cana-4811	141	16	mae	mae	PROPN
cana-4811	141	17	,	,	PUNCT
cana-4811	141	18	rmse	rmse	PROPN
cana-4811	141	19	,	,	PUNCT
cana-4811	141	20	rae	rae	PROPN
cana-4811	141	21	,	,	PUNCT
cana-4811	141	22	and	and	CCONJ
cana-4811	141	23	rrse	rrse	NOUN
cana-4811	141	24	)	)	PUNCT
cana-4811	141	25	,	,	PUNCT
cana-4811	141	26	and	and	CCONJ
cana-4811	141	27	efficient	efficient	ADJ
cana-4811	141	28	training	training	NOUN
cana-4811	141	29	time	time	NOUN
cana-4811	141	30	,	,	PUNCT
cana-4811	141	31	particularly	particularly	ADV
cana-4811	141	32	j48	j48	ADJ
cana-4811	141	33	and	and	CCONJ
cana-4811	141	34	random	random	ADJ
cana-4811	141	35	forest	forest	NOUN
cana-4811	141	36	,	,	PUNCT
cana-4811	141	37	which	which	PRON
cana-4811	141	38	balanced	balance	VERB
cana-4811	141	39	high	high	ADJ
cana-4811	141	40	accuracy	accuracy	NOUN
cana-4811	141	41	with	with	ADP
cana-4811	141	42	minimal	minimal	ADJ
cana-4811	141	43	computational	computational	ADJ
cana-4811	141	44	cost	cost	NOUN
cana-4811	141	45	.	.	PUNCT
cana-4811	142	1	the	the	DET
cana-4811	142	2	smo	smo	PROPN
cana-4811	142	3	model	model	NOUN
cana-4811	142	4	,	,	PUNCT
cana-4811	142	5	while	while	SCONJ
cana-4811	142	6	performing	perform	VERB
cana-4811	142	7	reasonably	reasonably	ADV
cana-4811	142	8	,	,	PUNCT
cana-4811	142	9	showed	show	VERB
cana-4811	142	10	lower	low	ADJ
cana-4811	142	11	accuracy	accuracy	NOUN
cana-4811	142	12	and	and	CCONJ
cana-4811	142	13	higher	high	ADJ
cana-4811	142	14	error	error	NOUN
cana-4811	142	15	rates	rate	NOUN
cana-4811	142	16	compared	compare	VERB
cana-4811	142	17	to	to	ADP
cana-4811	142	18	the	the	DET
cana-4811	142	19	other	other	ADJ
cana-4811	142	20	methods	method	NOUN
cana-4811	142	21	.	.	PUNCT
cana-4811	143	1	the	the	DET
cana-4811	143	2	superior	superior	ADJ
cana-4811	143	3	performance	performance	NOUN
cana-4811	143	4	of	of	ADP
cana-4811	143	5	random	random	ADJ
cana-4811	143	6	forest	forest	NOUN
cana-4811	143	7	and	and	CCONJ
cana-4811	143	8	j48	j48	PROPN
cana-4811	143	9	demonstrates	demonstrate	VERB
cana-4811	143	10	the	the	DET
cana-4811	143	11	robustness	robustness	NOUN
cana-4811	143	12	and	and	CCONJ
cana-4811	143	13	reliability	reliability	NOUN
cana-4811	143	14	of	of	ADP
cana-4811	143	15	ensemble	ensemble	ADJ
cana-4811	143	16	and	and	CCONJ
cana-4811	143	17	decision	decision	NOUN
cana-4811	143	18	-	-	PUNCT
cana-4811	143	19	tree	tree	NOUN
cana-4811	143	20	-	-	PUNCT
cana-4811	143	21	based	base	VERB
cana-4811	143	22	models	model	NOUN
cana-4811	143	23	for	for	ADP
cana-4811	143	24	lung	lung	NOUN
cana-4811	143	25	cancer	cancer	NOUN
cana-4811	143	26	prediction	prediction	NOUN
cana-4811	143	27	.	.	PUNCT
cana-4811	144	1	in	in	ADP
cana-4811	144	2	conclusion	conclusion	NOUN
cana-4811	144	3	,	,	PUNCT
cana-4811	144	4	decision	decision	NOUN
cana-4811	144	5	-	-	PUNCT
cana-4811	144	6	tree	tree	NOUN
cana-4811	144	7	-	-	PUNCT
cana-4811	144	8	based	base	VERB
cana-4811	144	9	models	model	NOUN
cana-4811	144	10	such	such	ADJ
cana-4811	144	11	as	as	ADP
cana-4811	144	12	j48	j48	ADJ
cana-4811	144	13	and	and	CCONJ
cana-4811	144	14	ensemble	ensemble	ADJ
cana-4811	144	15	methods	method	NOUN
cana-4811	144	16	like	like	ADP
cana-4811	144	17	random	random	ADJ
cana-4811	144	18	forest	forest	NOUN
cana-4811	144	19	are	be	AUX
cana-4811	144	20	recommended	recommend	VERB
cana-4811	144	21	for	for	ADP
cana-4811	144	22	lung	lung	NOUN
cana-4811	144	23	cancer	cancer	NOUN
cana-4811	144	24	detection	detection	NOUN
cana-4811	144	25	tasks	task	NOUN
cana-4811	144	26	due	due	ADP
cana-4811	144	27	to	to	ADP
cana-4811	144	28	their	their	PRON
cana-4811	144	29	high	high	ADJ
cana-4811	144	30	accuracy	accuracy	NOUN
cana-4811	144	31	,	,	PUNCT
cana-4811	144	32	low	low	ADJ
cana-4811	144	33	error	error	NOUN
cana-4811	144	34	metrics	metric	NOUN
cana-4811	144	35	,	,	PUNCT
cana-4811	144	36	and	and	CCONJ
cana-4811	144	37	efficient	efficient	ADJ
cana-4811	144	38	training	training	NOUN
cana-4811	144	39	times	time	NOUN
cana-4811	144	40	.	.	PUNCT
cana-4811	145	1	these	these	DET
cana-4811	145	2	models	model	NOUN
cana-4811	145	3	provide	provide	VERB
cana-4811	145	4	clinicians	clinician	NOUN
cana-4811	145	5	with	with	ADP
cana-4811	145	6	valuable	valuable	ADJ
cana-4811	145	7	640	640	NUM
cana-4811	145	8	communications	communication	NOUN
cana-4811	145	9	on	on	ADP
cana-4811	145	10	applied	apply	VERB
cana-4811	145	11	nonlinear	nonlinear	ADJ
cana-4811	145	12	analysis	analysis	NOUN
cana-4811	145	13	issn	issn	NOUN
cana-4811	145	14	:	:	PUNCT
cana-4811	145	15	1074	1074	NUM
cana-4811	145	16	-	-	PUNCT
cana-4811	145	17	133x	133x	NUM
cana-4811	145	18	vol	vol	NOUN
cana-4811	145	19	32	32	NUM
cana-4811	145	20	no	no	NOUN
cana-4811	145	21	.	.	PUNCT
cana-4811	146	1	01s	01s	PROPN
cana-4811	146	2	(	(	PUNCT
cana-4811	146	3	2025	2025	NUM
cana-4811	146	4	)	)	PUNCT
cana-4811	146	5	https://internationalpubls.com	https://internationalpubls.com	X
cana-4811	146	6	tools	tool	NOUN
cana-4811	146	7	for	for	ADP
cana-4811	146	8	early	early	ADJ
cana-4811	146	9	detection	detection	NOUN
cana-4811	146	10	and	and	CCONJ
cana-4811	146	11	diagnosis	diagnosis	NOUN
cana-4811	146	12	,	,	PUNCT
cana-4811	146	13	ultimately	ultimately	ADV
cana-4811	146	14	contributing	contribute	VERB
cana-4811	146	15	to	to	ADP
cana-4811	146	16	improved	improve	VERB
cana-4811	146	17	treatment	treatment	NOUN
cana-4811	146	18	outcomes	outcome	NOUN
cana-4811	146	19	for	for	ADP
cana-4811	146	20	patients	patient	NOUN
cana-4811	146	21	.	.	PUNCT
cana-4811	147	1	further	further	ADJ
cana-4811	147	2	research	research	VERB
cana-4811	147	3	this	this	DET
cana-4811	147	4	study	study	NOUN
cana-4811	147	5	includes	include	VERB
cana-4811	147	6	multilayer	multilayer	ADJ
cana-4811	147	7	perceptron	perceptron	PROPN
cana-4811	147	8	,	,	PUNCT
cana-4811	147	9	further	further	ADJ
cana-4811	147	10	exploration	exploration	NOUN
cana-4811	147	11	of	of	ADP
cana-4811	147	12	deep	deep	ADJ
cana-4811	147	13	learning	learning	NOUN
cana-4811	147	14	approaches	approach	NOUN
cana-4811	147	15	with	with	ADP
cana-4811	147	16	different	different	ADJ
cana-4811	147	17	optimization	optimization	NOUN
cana-4811	147	18	techniques	technique	NOUN
cana-4811	147	19	like	like	ADP
cana-4811	147	20	cnns	cnn	NOUN
cana-4811	147	21	and	and	CCONJ
cana-4811	147	22	rnns	rnn	NOUN
cana-4811	147	23	can	can	AUX
cana-4811	147	24	be	be	AUX
cana-4811	147	25	conducted	conduct	VERB
cana-4811	147	26	to	to	PART
cana-4811	147	27	assess	assess	VERB
cana-4811	147	28	their	their	PRON
cana-4811	147	29	efficacy	efficacy	NOUN
cana-4811	147	30	,	,	PUNCT
cana-4811	147	31	particularly	particularly	ADV
cana-4811	147	32	with	with	ADP
cana-4811	147	33	larger	large	ADJ
cana-4811	147	34	and	and	CCONJ
cana-4811	147	35	more	more	ADV
cana-4811	147	36	complex	complex	ADJ
cana-4811	147	37	datasets	dataset	NOUN
cana-4811	147	38	such	such	ADJ
cana-4811	147	39	as	as	ADP
cana-4811	147	40	imaging	image	VERB
cana-4811	147	41	data	datum	NOUN
cana-4811	147	42	from	from	ADP
cana-4811	147	43	ct	ct	NUM
cana-4811	147	44	or	or	CCONJ
cana-4811	147	45	mri	mri	NOUN
cana-4811	147	46	scans	scan	NOUN
cana-4811	147	47	.	.	PUNCT
cana-4811	148	1	to	to	PART
cana-4811	148	2	transition	transition	VERB
cana-4811	148	3	these	these	DET
cana-4811	148	4	machine	machine	NOUN
cana-4811	148	5	learning	learning	NOUN
cana-4811	148	6	models	model	NOUN
cana-4811	148	7	into	into	ADP
cana-4811	148	8	practical	practical	ADJ
cana-4811	148	9	clinical	clinical	ADJ
cana-4811	148	10	use	use	NOUN
cana-4811	148	11	,	,	PUNCT
cana-4811	148	12	further	further	ADJ
cana-4811	148	13	validation	validation	NOUN
cana-4811	148	14	is	be	AUX
cana-4811	148	15	necessary	necessary	ADJ
cana-4811	148	16	through	through	ADP
cana-4811	148	17	real	real	ADJ
cana-4811	148	18	-	-	PUNCT
cana-4811	148	19	time	time	NOUN
cana-4811	148	20	applications	application	NOUN
cana-4811	148	21	and	and	CCONJ
cana-4811	148	22	clinical	clinical	ADJ
cana-4811	148	23	trials	trial	NOUN
cana-4811	148	24	.	.	PUNCT
cana-4811	149	1	investigating	investigate	VERB
cana-4811	149	2	the	the	DET
cana-4811	149	3	performance	performance	NOUN
cana-4811	149	4	of	of	ADP
cana-4811	149	5	these	these	DET
cana-4811	149	6	models	model	NOUN
cana-4811	149	7	on	on	ADP
cana-4811	149	8	real	real	ADJ
cana-4811	149	9	-	-	PUNCT
cana-4811	149	10	world	world	NOUN
cana-4811	149	11	patient	patient	NOUN
cana-4811	149	12	data	datum	NOUN
cana-4811	149	13	will	will	AUX
cana-4811	149	14	provide	provide	VERB
cana-4811	149	15	insights	insight	NOUN
cana-4811	149	16	into	into	ADP
cana-4811	149	17	their	their	PRON
cana-4811	149	18	applicability	applicability	NOUN
cana-4811	149	19	in	in	ADP
cana-4811	149	20	clinical	clinical	ADJ
cana-4811	149	21	environments	environment	NOUN
cana-4811	149	22	.	.	PUNCT
cana-4811	150	1	by	by	ADP
cana-4811	150	2	addressing	address	VERB
cana-4811	150	3	these	these	DET
cana-4811	150	4	areas	area	NOUN
cana-4811	150	5	,	,	PUNCT
cana-4811	150	6	future	future	ADJ
cana-4811	150	7	research	research	NOUN
cana-4811	150	8	can	can	AUX
cana-4811	150	9	enhance	enhance	VERB
cana-4811	150	10	the	the	DET
cana-4811	150	11	accuracy	accuracy	NOUN
cana-4811	150	12	,	,	PUNCT
cana-4811	150	13	scalability	scalability	NOUN
cana-4811	150	14	,	,	PUNCT
cana-4811	150	15	and	and	CCONJ
cana-4811	150	16	clinical	clinical	ADJ
cana-4811	150	17	relevance	relevance	NOUN
cana-4811	150	18	of	of	ADP
cana-4811	150	19	ml	ml	ADP
cana-4811	150	20	algorithms	algorithm	NOUN
cana-4811	150	21	in	in	ADP
cana-4811	150	22	lung	lung	NOUN
cana-4811	150	23	cancer	cancer	NOUN
cana-4811	150	24	detection	detection	NOUN
cana-4811	150	25	,	,	PUNCT
cana-4811	150	26	paving	pave	VERB
cana-4811	150	27	the	the	DET
cana-4811	150	28	way	way	NOUN
cana-4811	150	29	for	for	ADP
cana-4811	150	30	more	more	ADV
cana-4811	150	31	sophisticated	sophisticated	ADJ
cana-4811	150	32	and	and	CCONJ
cana-4811	150	33	personalized	personalized	ADJ
cana-4811	150	34	diagnostic	diagnostic	ADJ
cana-4811	150	35	tools	tool	NOUN
cana-4811	150	36	.	.	PUNCT
cana-4811	151	1	references	reference	NOUN
cana-4811	151	2	[	[	X
cana-4811	151	3	1	1	NUM
cana-4811	151	4	]	]	PUNCT
cana-4811	151	5	akusok	akusok	NOUN
cana-4811	151	6	,	,	PUNCT
cana-4811	151	7	a.	a.	NOUN
cana-4811	151	8	what	what	PRON
cana-4811	151	9	is	be	AUX
cana-4811	151	10	mean	mean	VERB
cana-4811	151	11	absolute	absolute	ADJ
cana-4811	151	12	error	error	NOUN
cana-4811	151	13	(	(	PUNCT
cana-4811	151	14	mae	mae	PROPN
cana-4811	151	15	)	)	PUNCT
cana-4811	151	16	?	?	PUNCT
cana-4811	152	1	retrieved	retrieve	VERB
cana-4811	152	2	from	from	ADP
cana-4811	152	3	https://machinelearningmastery.com/mean-absolute-error-mae-for-machine-learning/	https://machinelearningmastery.com/mean-absolute-error-mae-for-machine-learning/	PROPN
cana-4811	152	4	(	(	PUNCT
cana-4811	152	5	2020	2020	NUM
cana-4811	152	6	)	)	PUNCT
cana-4811	153	1	[	[	X
cana-4811	153	2	2	2	NUM
cana-4811	153	3	]	]	X
cana-4811	153	4	ali	ali	PROPN
cana-4811	153	5	,	,	PUNCT
cana-4811	153	6	m.	m.	NOUN
cana-4811	153	7	,	,	PUNCT
cana-4811	153	8	et	et	PROPN
cana-4811	153	9	al	al	PROPN
cana-4811	153	10	.	.	PUNCT
cana-4811	154	1	the	the	DET
cana-4811	154	2	impact	impact	NOUN
cana-4811	154	3	of	of	ADP
cana-4811	154	4	feature	feature	NOUN
cana-4811	154	5	selection	selection	NOUN
cana-4811	154	6	on	on	ADP
cana-4811	154	7	neural	neural	ADJ
cana-4811	154	8	networks	network	NOUN
cana-4811	154	9	for	for	ADP
cana-4811	154	10	lung	lung	NOUN
cana-4811	154	11	cancer	cancer	NOUN
cana-4811	154	12	prediction	prediction	NOUN
cana-4811	154	13	.	.	PUNCT
cana-4811	155	1	journal	journal	PROPN
cana-4811	155	2	of	of	ADP
cana-4811	155	3	biomedical	biomedical	ADJ
cana-4811	155	4	informatics	informatic	NOUN
cana-4811	155	5	(	(	PUNCT
cana-4811	155	6	2021	2021	NUM
cana-4811	155	7	)	)	PUNCT
cana-4811	155	8	114	114	NUM
cana-4811	155	9	:	:	PUNCT
cana-4811	155	10	103684	103684	NUM
cana-4811	155	11	.	.	PUNCT
cana-4811	156	1	[	[	X
cana-4811	156	2	3	3	NUM
cana-4811	156	3	]	]	PUNCT
cana-4811	156	4	asuntha	asuntha	PROPN
cana-4811	156	5	,	,	PUNCT
cana-4811	156	6	a.	a.	NOUN
cana-4811	156	7	,	,	PUNCT
cana-4811	156	8	srinivasan	srinivasan	NOUN
cana-4811	156	9	,	,	PUNCT
cana-4811	156	10	a.	a.	NOUN
cana-4811	156	11	deep	deep	ADJ
cana-4811	156	12	learning	learn	VERB
cana-4811	156	13	for	for	ADP
cana-4811	156	14	lung	lung	NOUN
cana-4811	156	15	cancer	cancer	NOUN
cana-4811	156	16	detection	detection	NOUN
cana-4811	156	17	and	and	CCONJ
cana-4811	156	18	classification	classification	NOUN
cana-4811	156	19	.	.	PUNCT
cana-4811	157	1	multimedia	multimedia	NOUN
cana-4811	157	2	tools	tool	NOUN
cana-4811	157	3	and	and	CCONJ
cana-4811	157	4	applications	application	NOUN
cana-4811	157	5	(	(	PUNCT
cana-4811	157	6	2020	2020	NUM
cana-4811	157	7	)	)	PUNCT
cana-4811	157	8	79(11	79(11	NUM
cana-4811	157	9	):	):	PUNCT
cana-4811	157	10	7731	7731	NUM
cana-4811	157	11	-	-	SYM
cana-4811	157	12	7762	7762	NUM
cana-4811	157	13	.	.	PUNCT
cana-4811	158	1	[	[	X
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cana-4811	158	3	]	]	X
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cana-4811	158	5	,	,	PUNCT
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cana-4811	158	9	,	,	PUNCT
cana-4811	158	10	a.	a.	PROPN
cana-4811	158	11	b.	b.	PROPN
cana-4811	158	12	detection	detection	NOUN
cana-4811	158	13	of	of	ADP
cana-4811	158	14	cancer	cancer	NOUN
cana-4811	158	15	in	in	ADP
cana-4811	158	16	the	the	DET
cana-4811	158	17	lung	lung	NOUN
cana-4811	158	18	with	with	ADP
cana-4811	158	19	k	k	PROPN
cana-4811	158	20	-	-	PUNCT
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cana-4811	158	25	algorithm	algorithm	NOUN
cana-4811	158	26	.	.	PUNCT
cana-4811	159	1	procedia	procedia	NOUN
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cana-4811	159	5	2015	2015	NUM
cana-4811	159	6	)	)	PUNCT
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cana-4811	159	8	:	:	SYM
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cana-4811	159	10	-	-	SYM
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cana-4811	160	5	,	,	PUNCT
cana-4811	160	6	w.	w.	PROPN
cana-4811	160	7	relative	relative	ADJ
cana-4811	160	8	absolute	absolute	ADJ
cana-4811	160	9	error	error	NOUN
cana-4811	160	10	(	(	PUNCT
cana-4811	160	11	rae	rae	NOUN
cana-4811	160	12	)	)	PUNCT
cana-4811	160	13	–	–	PUNCT
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cana-4811	160	15	and	and	CCONJ
cana-4811	160	16	examples	example	NOUN
cana-4811	160	17	.	.	PUNCT
cana-4811	161	1	medium	medium	ADJ
cana-4811	161	2	.	.	PUNCT
cana-4811	161	3	https://medium.com/@wchi/relative-absolute-error-rae-definition-and-examplese37a24c1b566	https://medium.com/@wchi/relative-absolute-error-rae-definition-and-examplese37a24c1b566	PROPN
cana-4811	161	4	(	(	PUNCT
cana-4811	161	5	2020	2020	NUM
cana-4811	161	6	)	)	PUNCT
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cana-4811	162	22	error	error	NOUN
cana-4811	162	23	(	(	PUNCT
cana-4811	162	24	rmse	rmse	NOUN
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cana-4811	162	26	a	a	DET
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cana-4811	163	2	journal	journal	PROPN
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cana-4811	163	4	applied	apply	VERB
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cana-4811	163	6	and	and	CCONJ
cana-4811	163	7	statistics	statistic	NOUN
cana-4811	163	8	,	,	PUNCT
cana-4811	163	9	(	(	PUNCT
cana-4811	163	10	2019	2019	NUM
cana-4811	163	11	):	):	PUNCT
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cana-4811	163	13	):	):	PUNCT
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cana-4811	163	15	.	.	PUNCT
cana-4811	164	1	[	[	X
cana-4811	164	2	7	7	NUM
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cana-4811	164	5	,	,	PUNCT
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cana-4811	164	17	.	.	PUNCT
cana-4811	165	1	in	in	ADP
cana-4811	165	2	2015	2015	NUM
cana-4811	165	3	global	global	ADJ
cana-4811	165	4	conference	conference	NOUN
cana-4811	165	5	on	on	ADP
cana-4811	165	6	communication	communication	NOUN
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cana-4811	165	8	(	(	PUNCT
cana-4811	165	9	2015	2015	NUM
cana-4811	165	10	)	)	PUNCT
cana-4811	165	11	555	555	NUM
cana-4811	165	12	-	-	SYM
cana-4811	165	13	558	558	NUM
cana-4811	165	14	.	.	PUNCT
cana-4811	166	1	[	[	X
cana-4811	166	2	8	8	NUM
cana-4811	166	3	]	]	X
cana-4811	166	4	kaggle	kaggle	PROPN
cana-4811	166	5	,	,	PUNCT
cana-4811	166	6	https://www.kaggle.com/datasets/thedevastator/cancer-patients-and-airpollution-a-new-link/data	https://www.kaggle.com/datasets/thedevastator/cancer-patients-and-airpollution-a-new-link/data	NOUN
cana-4811	166	7	(	(	PUNCT
cana-4811	166	8	2018	2018	NUM
cana-4811	166	9	)	)	PUNCT
cana-4811	167	1	[	[	X
cana-4811	167	2	9	9	NUM
cana-4811	167	3	]	]	X
cana-4811	167	4	kannan	kannan	PROPN
cana-4811	167	5	,	,	PUNCT
cana-4811	167	6	v.	v.	PROPN
cana-4811	167	7	,	,	PUNCT
cana-4811	167	8	naveen	naveen	PROPN
cana-4811	167	9	,	,	PUNCT
cana-4811	167	10	v.	v.	PROPN
cana-4811	167	11	j.	j.	PROPN
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cana-4811	167	15	cancer	cancer	NOUN
cana-4811	167	16	using	use	VERB
cana-4811	167	17	image	image	NOUN
cana-4811	167	18	segmentation	segmentation	NOUN
cana-4811	167	19	.	.	PUNCT
cana-4811	168	1	international	international	ADJ
cana-4811	168	2	journal	journal	NOUN
cana-4811	168	3	of	of	ADP
cana-4811	168	4	electrical	electrical	ADJ
cana-4811	168	5	engineering	engineering	NOUN
cana-4811	168	6	&	&	CCONJ
cana-4811	168	7	technology	technology	PROPN
cana-4811	168	8	,	,	PUNCT
cana-4811	168	9	(	(	PUNCT
cana-4811	168	10	2020	2020	NUM
cana-4811	168	11	)	)	PUNCT
cana-4811	168	12	2(11	2(11	NUM
cana-4811	168	13	):	):	PUNCT
cana-4811	168	14	7	7	NUM
cana-4811	168	15	-	-	SYM
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cana-4811	168	17	.	.	PUNCT
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cana-4811	169	2	10	10	NUM
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cana-4811	169	9	,	,	PUNCT
cana-4811	169	10	m.	m.	NOUN
cana-4811	169	11	error	error	NOUN
cana-4811	169	12	-	-	PUNCT
cana-4811	169	13	based	base	VERB
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cana-4811	169	18	.	.	PUNCT
cana-4811	170	1	in	in	ADP
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cana-4811	170	3	conference	conference	NOUN
cana-4811	170	4	on	on	ADP
cana-4811	170	5	machine	machine	NOUN
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cana-4811	170	8	1996	1996	NUM
cana-4811	170	9	):	):	PUNCT
cana-4811	170	10	pp	pp	ADJ
cana-4811	170	11	.	.	PUNCT
cana-4811	170	12	278	278	NUM
cana-4811	170	13	-	-	SYM
cana-4811	170	14	286	286	NUM
cana-4811	170	15	.	.	PUNCT
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cana-4811	171	2	11	11	NUM
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cana-4811	171	5	,	,	PUNCT
cana-4811	171	6	y.	y.	PROPN
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cana-4811	172	1	ieee	ieee	NOUN
cana-4811	172	2	transactions	transaction	NOUN
cana-4811	172	3	on	on	ADP
cana-4811	172	4	medical	medical	ADJ
cana-4811	172	5	imaging	imaging	NOUN
cana-4811	172	6	(	(	PUNCT
cana-4811	172	7	2022	2022	NUM
cana-4811	172	8	)	)	PUNCT
cana-4811	172	9	41(5	41(5	NUM
cana-4811	172	10	):	):	PUNCT
cana-4811	172	11	1352	1352	NUM
cana-4811	172	12	-	-	SYM
cana-4811	172	13	1363	1363	NUM
cana-4811	172	14	.	.	PUNCT
cana-4811	173	1	[	[	X
cana-4811	173	2	12	12	NUM
cana-4811	173	3	]	]	X
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cana-4811	173	5	,	,	PUNCT
cana-4811	173	6	c.	c.	PROPN
cana-4811	173	7	,	,	PUNCT
cana-4811	173	8	et	et	PROPN
cana-4811	173	9	al	al	PROPN
cana-4811	173	10	.	.	PROPN
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cana-4811	173	13	selection	selection	NOUN
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cana-4811	173	15	lung	lung	NOUN
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cana-4811	173	17	:	:	PUNCT
cana-4811	173	18	an	an	DET
cana-4811	173	19	empirical	empirical	ADJ
cana-4811	173	20	study	study	NOUN
cana-4811	173	21	.	.	PUNCT
cana-4811	174	1	medical	medical	ADJ
cana-4811	174	2	physics	physics	PROPN
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cana-4811	174	5	)	)	PUNCT
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cana-4811	174	7	):	):	PUNCT
cana-4811	174	8	5472	5472	NUM
cana-4811	174	9	-	-	SYM
cana-4811	174	10	5481	5481	NUM
cana-4811	174	11	.	.	PUNCT
cana-4811	175	1	[	[	X
cana-4811	175	2	13	13	NUM
cana-4811	175	3	]	]	X
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cana-4811	175	5	,	,	PUNCT
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cana-4811	175	17	algorithms	algorithm	NOUN
cana-4811	175	18	for	for	ADP
cana-4811	175	19	decision	decision	NOUN
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cana-4811	176	3	natural	natural	ADJ
cana-4811	176	4	and	and	CCONJ
cana-4811	176	5	applied	applied	ADJ
cana-4811	176	6	sciences	science	NOUN
cana-4811	176	7	(	(	PUNCT
cana-4811	176	8	2017	2017	NUM
cana-4811	176	9	)	)	PUNCT
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cana-4811	176	11	):	):	PUNCT
cana-4811	176	12	230	230	NUM
cana-4811	176	13	-	-	SYM
cana-4811	176	14	243	243	NUM
cana-4811	176	15	.	.	PUNCT
cana-4811	177	1	641	641	NUM
cana-4811	177	2	https://machinelearningmastery.com/mean-absolute-error-mae-for-machine-learning/	https://machinelearningmastery.com/mean-absolute-error-mae-for-machine-learning/	VERB
cana-4811	177	3	https://medium.com/@wchi/relative-absolute-error-rae-definition-and-examples-e37a24c1b566	https://medium.com/@wchi/relative-absolute-error-rae-definition-and-examples-e37a24c1b566	X
cana-4811	178	1	https://medium.com/@wchi/relative-absolute-error-rae-definition-and-examples-e37a24c1b566	https://medium.com/@wchi/relative-absolute-error-rae-definition-and-examples-e37a24c1b566	X
cana-4811	179	1	https://www.kaggle.com/datasets/thedevastator/cancer-patients-and-air-pollution-a-new-link/data	https://www.kaggle.com/datasets/thedevastator/cancer-patients-and-air-pollution-a-new-link/data	ADP
cana-4811	179	2	https://www.kaggle.com/datasets/thedevastator/cancer-patients-and-air-pollution-a-new-link/data	https://www.kaggle.com/datasets/thedevastator/cancer-patients-and-air-pollution-a-new-link/data	ADV
cana-4811	179	3	communications	communication	NOUN
cana-4811	179	4	on	on	ADP
cana-4811	179	5	applied	apply	VERB
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cana-4811	179	8	issn	issn	NOUN
cana-4811	179	9	:	:	PUNCT
cana-4811	179	10	1074	1074	NUM
cana-4811	179	11	-	-	PUNCT
cana-4811	179	12	133x	133x	NUM
cana-4811	179	13	vol	vol	NOUN
cana-4811	179	14	32	32	NUM
cana-4811	179	15	no	no	NOUN
cana-4811	179	16	.	.	PUNCT
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cana-4811	180	4	)	)	PUNCT
cana-4811	181	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-4811	182	1	[	[	X
cana-4811	182	2	14	14	NUM
cana-4811	182	3	]	]	X
cana-4811	182	4	rajesh	rajesh	PROPN
cana-4811	182	5	,	,	PUNCT
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cana-4811	182	14	to	to	PART
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cana-4811	182	18	that	that	PRON
cana-4811	182	19	affect	affect	VERB
cana-4811	182	20	agriculture	agriculture	NOUN
cana-4811	182	21	growth	growth	NOUN
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cana-4811	182	23	stochastic	stochastic	ADJ
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cana-4811	182	25	.	.	PUNCT
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cana-4811	183	5	sciences	science	NOUN
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cana-4811	183	8	(	(	PUNCT
cana-4811	183	9	2019	2019	NUM
cana-4811	183	10	)	)	PUNCT
cana-4811	183	11	7(4	7(4	NUM
cana-4811	183	12	):	):	PUNCT
cana-4811	183	13	18	18	NUM
cana-4811	183	14	-	-	SYM
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cana-4811	183	16	.	.	PUNCT
cana-4811	184	1	[	[	X
cana-4811	184	2	15	15	NUM
cana-4811	184	3	]	]	X
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cana-4811	184	5	,	,	PUNCT
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cana-4811	184	17	approaches	approach	NOUN
cana-4811	184	18	to	to	PART
cana-4811	184	19	predict	predict	VERB
cana-4811	184	20	the	the	DET
cana-4811	184	21	factors	factor	NOUN
cana-4811	184	22	that	that	PRON
cana-4811	184	23	affect	affect	VERB
cana-4811	184	24	the	the	DET
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cana-4811	184	28	a	a	DET
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cana-4811	184	30	model	model	NOUN
cana-4811	184	31	.	.	PUNCT
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cana-4811	185	2	aip	aip	PROPN
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cana-4811	185	5	(	(	PUNCT
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cana-4811	185	7	)	)	PUNCT
cana-4811	185	8	2177(1	2177(1	NUM
cana-4811	185	9	):	):	PUNCT
cana-4811	185	10	1	1	NUM
cana-4811	185	11	[	[	SYM
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cana-4811	185	13	]	]	X
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cana-4811	185	15	,	,	PUNCT
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cana-4811	185	17	,	,	PUNCT
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cana-4811	185	19	,	,	PUNCT
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cana-4811	185	21	,	,	PUNCT
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cana-4811	185	24	,	,	PUNCT
cana-4811	185	25	b.	b.	PROPN
cana-4811	185	26	,	,	PUNCT
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cana-4811	185	29	,	,	PUNCT
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cana-4811	185	31	y.	y.	PROPN
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cana-4811	185	33	study	study	NOUN
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cana-4811	185	40	mining	mining	NOUN
cana-4811	185	41	using	use	VERB
cana-4811	185	42	chronic	chronic	ADJ
cana-4811	185	43	disease	disease	NOUN
cana-4811	185	44	indicators	indicator	NOUN
cana-4811	185	45	(	(	PUNCT
cana-4811	185	46	cdi	cdi	PROPN
cana-4811	185	47	)	)	PUNCT
cana-4811	185	48	data	data	PROPN
cana-4811	185	49	.	.	PUNCT
cana-4811	186	1	journal	journal	PROPN
cana-4811	186	2	of	of	ADP
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cana-4811	186	4	and	and	CCONJ
cana-4811	186	5	theoretical	theoretical	ADJ
cana-4811	186	6	nanoscience	nanoscience	NOUN
cana-4811	186	7	(	(	PUNCT
cana-4811	186	8	2019	2019	NUM
cana-4811	186	9	)	)	PUNCT
cana-4811	186	10	16(4	16(4	NUM
cana-4811	186	11	):	):	PUNCT
cana-4811	186	12	1472	1472	NUM
cana-4811	186	13	-	-	SYM
cana-4811	186	14	1477	1477	NUM
cana-4811	186	15	.	.	PUNCT
cana-4811	187	1	[	[	X
cana-4811	187	2	17	17	NUM
cana-4811	187	3	]	]	X
cana-4811	187	4	yang	yang	PROPN
cana-4811	187	5	,	,	PUNCT
cana-4811	187	6	x.	x.	PROPN
cana-4811	187	7	,	,	PUNCT
cana-4811	187	8	et	et	PROPN
cana-4811	187	9	al	al	PROPN
cana-4811	187	10	.	.	PROPN
cana-4811	188	1	clinical	clinical	ADJ
cana-4811	188	2	and	and	CCONJ
cana-4811	188	3	radiomic	radiomic	ADJ
cana-4811	188	4	feature	feature	NOUN
cana-4811	188	5	selection	selection	NOUN
cana-4811	188	6	for	for	ADP
cana-4811	188	7	lung	lung	NOUN
cana-4811	188	8	cancer	cancer	NOUN
cana-4811	188	9	risk	risk	NOUN
cana-4811	188	10	prediction	prediction	NOUN
cana-4811	188	11	:	:	PUNCT
cana-4811	188	12	a	a	DET
cana-4811	188	13	lasso	lasso	NOUN
cana-4811	188	14	-	-	PUNCT
cana-4811	188	15	based	base	VERB
cana-4811	188	16	approach	approach	NOUN
cana-4811	188	17	.	.	PUNCT
cana-4811	189	1	medical	medical	ADJ
cana-4811	189	2	physics	physics	PROPN
cana-4811	189	3	(	(	PUNCT
cana-4811	189	4	2020	2020	NUM
cana-4811	189	5	)	)	PUNCT
cana-4811	189	6	47(8	47(8	NOUN
cana-4811	189	7	):	):	PUNCT
cana-4811	189	8	3757	3757	NUM
cana-4811	189	9	-	-	SYM
cana-4811	189	10	3768	3768	NUM
cana-4811	189	11	.	.	PUNCT
cana-4811	190	1	[	[	X
cana-4811	190	2	18	18	NUM
cana-4811	190	3	]	]	X
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cana-4811	190	5	,	,	PUNCT
cana-4811	190	6	l.	l.	PROPN
cana-4811	190	7	,	,	PUNCT
cana-4811	190	8	et	et	PROPN
cana-4811	190	9	al	al	PROPN
cana-4811	190	10	.	.	PROPN
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cana-4811	190	12	selection	selection	NOUN
cana-4811	190	13	and	and	CCONJ
cana-4811	190	14	machine	machine	NOUN
cana-4811	190	15	learning	learning	NOUN
cana-4811	190	16	-	-	PUNCT
cana-4811	190	17	based	base	VERB
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cana-4811	190	21	using	use	VERB
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cana-4811	190	23	.	.	PUNCT
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cana-4811	191	2	in	in	ADP
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cana-4811	191	4	and	and	CCONJ
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cana-4811	191	6	(	(	PUNCT
cana-4811	191	7	2019	2019	NUM
cana-4811	191	8	)	)	PUNCT
cana-4811	191	9	107	107	NUM
cana-4811	191	10	:	:	PUNCT
cana-4811	191	11	41	41	NUM
cana-4811	191	12	-	-	SYM
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cana-4811	191	14	.	.	PUNCT
cana-4811	191	15	642	642	NUM