id	sid	tid	token	lemma	pos
ap-9918	1	1	acta	acta	PROPN
ap-9918	1	2	polytechnica	polytechnica	PROPN
ap-9918	1	3	https://doi.org/10.14311/ap.2025.65.0136	https://doi.org/10.14311/ap.2025.65.0136	PROPN
ap-9918	1	4	acta	acta	PROPN
ap-9918	1	5	polytechnica	polytechnica	PROPN
ap-9918	1	6	65(2):136–142	65(2):136–142	PROPN
ap-9918	1	7	,	,	PUNCT
ap-9918	1	8	2025	2025	NUM
ap-9918	1	9	©	©	ADP
ap-9918	1	10	2025	2025	NUM
ap-9918	1	11	the	the	DET
ap-9918	1	12	author(s	author(s	NOUN
ap-9918	1	13	)	)	PUNCT
ap-9918	1	14	.	.	PUNCT
ap-9918	2	1	licensed	license	VERB
ap-9918	2	2	under	under	ADP
ap-9918	2	3	a	a	DET
ap-9918	2	4	cc	cc	NOUN
ap-9918	2	5	-	-	PUNCT
ap-9918	2	6	by	by	ADP
ap-9918	2	7	4.0	4.0	NUM
ap-9918	2	8	licence	licence	NOUN
ap-9918	2	9	published	publish	VERB
ap-9918	2	10	by	by	ADP
ap-9918	2	11	the	the	DET
ap-9918	2	12	czech	czech	PROPN
ap-9918	2	13	technical	technical	PROPN
ap-9918	2	14	university	university	PROPN
ap-9918	2	15	in	in	ADP
ap-9918	2	16	prague	prague	PROPN
ap-9918	2	17	a	a	DET
ap-9918	2	18	comparative	comparative	ADJ
ap-9918	2	19	study	study	NOUN
ap-9918	2	20	of	of	ADP
ap-9918	2	21	breast	breast	NOUN
ap-9918	2	22	cancer	cancer	NOUN
ap-9918	2	23	detection	detection	NOUN
ap-9918	2	24	and	and	CCONJ
ap-9918	2	25	recurrence	recurrence	NOUN
ap-9918	2	26	prediction	prediction	NOUN
ap-9918	2	27	using	use	VERB
ap-9918	2	28	catboost	catboost	NOUN
ap-9918	2	29	classifier	classifier	PROPN
ap-9918	2	30	rana	rana	PROPN
ap-9918	2	31	dhia’a	dhia’a	PROPN
ap-9918	2	32	abdu	abdu	PROPN
ap-9918	2	33	-	-	PUNCT
ap-9918	2	34	aljabara,∗	aljabara,∗	PROPN
ap-9918	2	35	,	,	PUNCT
ap-9918	2	36	khansaa	khansaa	VERB
ap-9918	2	37	dheyaa	dheyaa	PROPN
ap-9918	2	38	aljafaara	aljafaara	PROPN
ap-9918	2	39	,	,	PUNCT
ap-9918	2	40	zinah	zinah	PROPN
ap-9918	2	41	jaffar	jaffar	PROPN
ap-9918	2	42	mohammed	mohammed	PROPN
ap-9918	2	43	ameenb	ameenb	PROPN
ap-9918	2	44	,	,	PUNCT
ap-9918	2	45	hala	hala	PROPN
ap-9918	2	46	a.	a.	PROPN
ap-9918	2	47	namanc	namanc	PROPN
ap-9918	2	48	a	a	DET
ap-9918	2	49	al	al	PROPN
ap-9918	2	50	-	-	PUNCT
ap-9918	2	51	nahrain	nahrain	PROPN
ap-9918	2	52	university	university	NOUN
ap-9918	2	53	,	,	PUNCT
ap-9918	2	54	information	information	NOUN
ap-9918	2	55	engineering	engineering	NOUN
ap-9918	2	56	college	college	PROPN
ap-9918	2	57	,	,	PUNCT
ap-9918	2	58	al	al	PROPN
ap-9918	2	59	-	-	PUNCT
ap-9918	2	60	jadryia	jadryia	PROPN
ap-9918	2	61	,	,	PUNCT
ap-9918	2	62	baghdad	baghdad	PROPN
ap-9918	2	63	,	,	PUNCT
ap-9918	2	64	iraq	iraq	PROPN
ap-9918	2	65	b	b	PROPN
ap-9918	2	66	university	university	PROPN
ap-9918	2	67	of	of	ADP
ap-9918	2	68	technology	technology	NOUN
ap-9918	2	69	,	,	PUNCT
ap-9918	2	70	computer	computer	NOUN
ap-9918	2	71	engineering	engineering	NOUN
ap-9918	2	72	department	department	PROPN
ap-9918	2	73	,	,	PUNCT
ap-9918	2	74	baghdad	baghdad	PROPN
ap-9918	2	75	,	,	PUNCT
ap-9918	2	76	alsinaea	alsinaea	ADJ
ap-9918	2	77	,	,	PUNCT
ap-9918	2	78	iraq	iraq	PROPN
ap-9918	2	79	c	c	PROPN
ap-9918	2	80	university	university	PROPN
ap-9918	2	81	of	of	ADP
ap-9918	2	82	wasit	wasit	PROPN
ap-9918	2	83	,	,	PUNCT
ap-9918	2	84	college	college	NOUN
ap-9918	2	85	of	of	ADP
ap-9918	2	86	engineering	engineering	PROPN
ap-9918	2	87	,	,	PUNCT
ap-9918	2	88	kut	kut	PROPN
ap-9918	2	89	,	,	PUNCT
ap-9918	2	90	wasit	wasit	PROPN
ap-9918	2	91	,	,	PUNCT
ap-9918	2	92	iraq	iraq	PROPN
ap-9918	2	93	∗	∗	VERB
ap-9918	2	94	corresponding	correspond	VERB
ap-9918	2	95	author	author	NOUN
ap-9918	2	96	:	:	PUNCT
ap-9918	2	97	ranadhiaa1@nahrainuniv.edu.iq	ranadhiaa1@nahrainuniv.edu.iq	NOUN
ap-9918	2	98	abstract	abstract	ADJ
ap-9918	2	99	.	.	PUNCT
ap-9918	3	1	in	in	ADP
ap-9918	3	2	2019	2019	NUM
ap-9918	3	3	,	,	PUNCT
ap-9918	3	4	breast	breast	NOUN
ap-9918	3	5	cancer	cancer	NOUN
ap-9918	3	6	accounted	account	VERB
ap-9918	3	7	for	for	ADP
ap-9918	3	8	over	over	ADP
ap-9918	3	9	one	one	NUM
ap-9918	3	10	-	-	PUNCT
ap-9918	3	11	third	third	NOUN
ap-9918	3	12	of	of	ADP
ap-9918	3	13	all	all	DET
ap-9918	3	14	cancer	cancer	NOUN
ap-9918	3	15	cases	case	NOUN
ap-9918	3	16	in	in	ADP
ap-9918	3	17	women	woman	NOUN
ap-9918	3	18	in	in	ADP
ap-9918	3	19	iraq	iraq	PROPN
ap-9918	3	20	.	.	PUNCT
ap-9918	4	1	it	it	PRON
ap-9918	4	2	affects	affect	VERB
ap-9918	4	3	both	both	DET
ap-9918	4	4	men	man	NOUN
ap-9918	4	5	and	and	CCONJ
ap-9918	4	6	women	woman	NOUN
ap-9918	4	7	,	,	PUNCT
ap-9918	4	8	though	though	SCONJ
ap-9918	4	9	it	it	PRON
ap-9918	4	10	is	be	AUX
ap-9918	4	11	more	more	ADV
ap-9918	4	12	common	common	ADJ
ap-9918	4	13	in	in	ADP
ap-9918	4	14	women	woman	NOUN
ap-9918	4	15	.	.	PUNCT
ap-9918	5	1	this	this	DET
ap-9918	5	2	study	study	NOUN
ap-9918	5	3	delves	delve	VERB
ap-9918	5	4	into	into	ADP
ap-9918	5	5	advanced	advanced	ADJ
ap-9918	5	6	machine	machine	NOUN
ap-9918	5	7	learning	learn	VERB
ap-9918	5	8	techniques	technique	NOUN
ap-9918	5	9	–	–	PUNCT
ap-9918	5	10	catboost	catboost	ADJ
ap-9918	5	11	,	,	PUNCT
ap-9918	5	12	xgboost	xgboost	ADV
ap-9918	5	13	,	,	PUNCT
ap-9918	5	14	random	random	ADJ
ap-9918	5	15	forest	forest	NOUN
ap-9918	5	16	,	,	PUNCT
ap-9918	5	17	svm	svm	PROPN
ap-9918	5	18	,	,	PUNCT
ap-9918	5	19	knn	knn	PROPN
ap-9918	5	20	,	,	PUNCT
ap-9918	5	21	and	and	CCONJ
ap-9918	5	22	naive	naive	ADJ
ap-9918	5	23	bayes	bayes	NOUN
ap-9918	5	24	–	–	PUNCT
ap-9918	5	25	to	to	PART
ap-9918	5	26	improve	improve	VERB
ap-9918	5	27	the	the	DET
ap-9918	5	28	detection	detection	NOUN
ap-9918	5	29	and	and	CCONJ
ap-9918	5	30	prediction	prediction	NOUN
ap-9918	5	31	of	of	ADP
ap-9918	5	32	breast	breast	NOUN
ap-9918	5	33	cancer	cancer	NOUN
ap-9918	5	34	recurrence	recurrence	NOUN
ap-9918	5	35	after	after	ADP
ap-9918	5	36	healing	healing	NOUN
ap-9918	5	37	.	.	PUNCT
ap-9918	6	1	the	the	DET
ap-9918	6	2	goal	goal	NOUN
ap-9918	6	3	is	be	AUX
ap-9918	6	4	to	to	PART
ap-9918	6	5	evaluate	evaluate	VERB
ap-9918	6	6	models	model	NOUN
ap-9918	6	7	using	use	VERB
ap-9918	6	8	key	key	ADJ
ap-9918	6	9	metrics	metric	NOUN
ap-9918	6	10	(	(	PUNCT
ap-9918	6	11	sensitivity	sensitivity	NOUN
ap-9918	6	12	,	,	PUNCT
ap-9918	6	13	specificity	specificity	NOUN
ap-9918	6	14	,	,	PUNCT
ap-9918	6	15	precision	precision	NOUN
ap-9918	6	16	,	,	PUNCT
ap-9918	6	17	f1	f1	NOUN
ap-9918	6	18	score	score	NOUN
ap-9918	6	19	,	,	PUNCT
ap-9918	6	20	accuracy	accuracy	NOUN
ap-9918	6	21	,	,	PUNCT
ap-9918	6	22	roc	roc	PROPN
ap-9918	6	23	,	,	PUNCT
ap-9918	6	24	and	and	CCONJ
ap-9918	6	25	auc	auc	NOUN
ap-9918	6	26	score	score	NOUN
ap-9918	6	27	)	)	PUNCT
ap-9918	6	28	.	.	PUNCT
ap-9918	7	1	among	among	ADP
ap-9918	7	2	all	all	DET
ap-9918	7	3	algorithms	algorithm	NOUN
ap-9918	7	4	examined	examine	VERB
ap-9918	7	5	,	,	PUNCT
ap-9918	7	6	catboost	catboost	PROPN
ap-9918	7	7	stood	stand	VERB
ap-9918	7	8	out	out	ADP
ap-9918	7	9	,	,	PUNCT
ap-9918	7	10	showcasing	showcase	VERB
ap-9918	7	11	auc	auc	ADJ
ap-9918	7	12	values	value	NOUN
ap-9918	7	13	above	above	ADP
ap-9918	7	14	98	98	NUM
ap-9918	7	15	%	%	NOUN
ap-9918	7	16	,	,	PUNCT
ap-9918	7	17	90	90	NUM
ap-9918	7	18	%	%	NOUN
ap-9918	7	19	,	,	PUNCT
ap-9918	7	20	and	and	CCONJ
ap-9918	7	21	83	83	NUM
ap-9918	7	22	%	%	NOUN
ap-9918	7	23	on	on	ADP
ap-9918	7	24	different	different	ADJ
ap-9918	7	25	datasets	dataset	NOUN
ap-9918	7	26	.	.	PUNCT
ap-9918	8	1	this	this	DET
ap-9918	8	2	research	research	NOUN
ap-9918	8	3	demonstrates	demonstrate	VERB
ap-9918	8	4	how	how	SCONJ
ap-9918	8	5	machine	machine	NOUN
ap-9918	8	6	learning	learn	VERB
ap-9918	8	7	techniques	technique	NOUN
ap-9918	8	8	can	can	AUX
ap-9918	8	9	significantly	significantly	ADV
ap-9918	8	10	improve	improve	VERB
ap-9918	8	11	the	the	DET
ap-9918	8	12	accuracy	accuracy	NOUN
ap-9918	8	13	of	of	ADP
ap-9918	8	14	breast	breast	NOUN
ap-9918	8	15	cancer	cancer	NOUN
ap-9918	8	16	detection	detection	NOUN
ap-9918	8	17	and	and	CCONJ
ap-9918	8	18	recurrence	recurrence	NOUN
ap-9918	8	19	prediction	prediction	NOUN
ap-9918	8	20	,	,	PUNCT
ap-9918	8	21	steering	steer	VERB
ap-9918	8	22	healthcare	healthcare	NOUN
ap-9918	8	23	providers	provider	NOUN
ap-9918	8	24	towards	towards	ADP
ap-9918	8	25	better	well	ADJ
ap-9918	8	26	patient	patient	ADJ
ap-9918	8	27	care	care	NOUN
ap-9918	8	28	outcomes	outcome	NOUN
ap-9918	8	29	and	and	CCONJ
ap-9918	8	30	more	more	ADV
ap-9918	8	31	effective	effective	ADJ
ap-9918	8	32	treatment	treatment	NOUN
ap-9918	8	33	plans	plan	NOUN
ap-9918	8	34	.	.	PUNCT
ap-9918	9	1	keywords	keyword	NOUN
ap-9918	9	2	:	:	PUNCT
ap-9918	9	3	catboost	catboost	PROPN
ap-9918	9	4	classifier	classifier	NOUN
ap-9918	9	5	,	,	PUNCT
ap-9918	9	6	breast	breast	NOUN
ap-9918	9	7	cancer	cancer	NOUN
ap-9918	9	8	,	,	PUNCT
ap-9918	9	9	machine	machine	NOUN
ap-9918	9	10	learning	learning	NOUN
ap-9918	9	11	,	,	PUNCT
ap-9918	9	12	bioinformatic	bioinformatic	ADJ
ap-9918	9	13	.	.	PUNCT
ap-9918	10	1	1	1	X
ap-9918	10	2	.	.	X
ap-9918	10	3	introduction	introduction	NOUN
ap-9918	10	4	the	the	DET
ap-9918	10	5	occurrence	occurrence	NOUN
ap-9918	10	6	rate	rate	NOUN
ap-9918	10	7	of	of	ADP
ap-9918	10	8	new	new	ADJ
ap-9918	10	9	cases	case	NOUN
ap-9918	10	10	of	of	ADP
ap-9918	10	11	cancer	cancer	NOUN
ap-9918	10	12	in	in	ADP
ap-9918	10	13	iraq	iraq	PROPN
ap-9918	10	14	grew	grow	VERB
ap-9918	10	15	between	between	ADP
ap-9918	10	16	2000	2000	NUM
ap-9918	10	17	and	and	CCONJ
ap-9918	10	18	2019	2019	NUM
ap-9918	10	19	(	(	PUNCT
ap-9918	10	20	from	from	ADP
ap-9918	10	21	52.00	52.00	NUM
ap-9918	10	22	to	to	ADP
ap-9918	10	23	91.66	91.66	NUM
ap-9918	10	24	incidences	incidence	NOUN
ap-9918	10	25	per	per	ADP
ap-9918	10	26	100	100	NUM
ap-9918	10	27	000	000	NUM
ap-9918	10	28	people	people	NOUN
ap-9918	10	29	)	)	PUNCT
ap-9918	10	30	.	.	PUNCT
ap-9918	11	1	in	in	ADP
ap-9918	11	2	2019	2019	NUM
ap-9918	11	3	,	,	PUNCT
ap-9918	11	4	breast	breast	NOUN
ap-9918	11	5	cancer	cancer	NOUN
ap-9918	11	6	ranked	rank	VERB
ap-9918	11	7	first	first	ADV
ap-9918	11	8	among	among	ADP
ap-9918	11	9	the	the	DET
ap-9918	11	10	top	top	ADJ
ap-9918	11	11	ten	ten	NUM
ap-9918	11	12	types	type	NOUN
ap-9918	11	13	cancers	cancer	NOUN
ap-9918	11	14	in	in	ADP
ap-9918	11	15	terms	term	NOUN
ap-9918	11	16	of	of	ADP
ap-9918	11	17	both	both	DET
ap-9918	11	18	percentage	percentage	NOUN
ap-9918	11	19	and	and	CCONJ
ap-9918	11	20	occurrence	occurrence	NOUN
ap-9918	11	21	(	(	PUNCT
ap-9918	11	22	34.08	34.08	NUM
ap-9918	11	23	%	%	NOUN
ap-9918	11	24	and	and	CCONJ
ap-9918	11	25	35.95/100	35.95/100	NUM
ap-9918	11	26	000	000	NUM
ap-9918	11	27	,	,	PUNCT
ap-9918	11	28	respectively	respectively	ADV
ap-9918	11	29	)	)	PUNCT
ap-9918	11	30	,	,	PUNCT
ap-9918	11	31	and	and	CCONJ
ap-9918	11	32	it	it	PRON
ap-9918	11	33	also	also	ADV
ap-9918	11	34	had	have	VERB
ap-9918	11	35	the	the	DET
ap-9918	11	36	highest	high	ADJ
ap-9918	11	37	fatality	fatality	NOUN
ap-9918	11	38	rate	rate	NOUN
ap-9918	11	39	(	(	PUNCT
ap-9918	11	40	22.58	22.58	NUM
ap-9918	11	41	%	%	NOUN
ap-9918	11	42	and	and	CCONJ
ap-9918	11	43	6.22/100	6.22/100	NUM
ap-9918	11	44	000	000	NUM
ap-9918	11	45	)	)	PUNCT
ap-9918	11	46	among	among	ADP
ap-9918	11	47	all	all	DET
ap-9918	11	48	types	type	NOUN
ap-9918	11	49	of	of	ADP
ap-9918	11	50	cancers	cancer	NOUN
ap-9918	11	51	[	[	X
ap-9918	11	52	1	1	NUM
ap-9918	11	53	]	]	PUNCT
ap-9918	11	54	.	.	PUNCT
ap-9918	12	1	due	due	ADP
ap-9918	12	2	to	to	ADP
ap-9918	12	3	developments	development	NOUN
ap-9918	12	4	in	in	ADP
ap-9918	12	5	breast	breast	NOUN
ap-9918	12	6	cancer	cancer	NOUN
ap-9918	12	7	screening	screening	NOUN
ap-9918	12	8	,	,	PUNCT
ap-9918	12	9	medical	medical	ADJ
ap-9918	12	10	professionals	professional	NOUN
ap-9918	12	11	can	can	AUX
ap-9918	12	12	now	now	ADV
ap-9918	12	13	detect	detect	VERB
ap-9918	12	14	breast	breast	NOUN
ap-9918	12	15	cancer	cancer	NOUN
ap-9918	12	16	at	at	ADP
ap-9918	12	17	an	an	DET
ap-9918	12	18	early	early	ADJ
ap-9918	12	19	stage	stage	NOUN
ap-9918	12	20	.	.	PUNCT
ap-9918	13	1	early	early	ADJ
ap-9918	13	2	detection	detection	NOUN
ap-9918	13	3	greatly	greatly	ADV
ap-9918	13	4	increases	increase	VERB
ap-9918	13	5	the	the	DET
ap-9918	13	6	likelihood	likelihood	NOUN
ap-9918	13	7	of	of	ADP
ap-9918	13	8	a	a	DET
ap-9918	13	9	successful	successful	ADJ
ap-9918	13	10	cancer	cancer	NOUN
ap-9918	13	11	cure	cure	NOUN
ap-9918	13	12	.	.	PUNCT
ap-9918	14	1	even	even	ADV
ap-9918	14	2	in	in	ADP
ap-9918	14	3	situations	situation	NOUN
ap-9918	14	4	where	where	SCONJ
ap-9918	14	5	breast	breast	NOUN
ap-9918	14	6	cancer	cancer	NOUN
ap-9918	14	7	is	be	AUX
ap-9918	14	8	incurable	incurable	ADJ
ap-9918	14	9	,	,	PUNCT
ap-9918	14	10	there	there	PRON
ap-9918	14	11	are	be	VERB
ap-9918	14	12	several	several	ADJ
ap-9918	14	13	strategies	strategy	NOUN
ap-9918	14	14	to	to	PART
ap-9918	14	15	extend	extend	VERB
ap-9918	14	16	the	the	DET
ap-9918	14	17	life	life	NOUN
ap-9918	14	18	of	of	ADP
ap-9918	14	19	the	the	DET
ap-9918	14	20	patient	patient	NOUN
ap-9918	14	21	.	.	PUNCT
ap-9918	15	1	research	research	NOUN
ap-9918	15	2	on	on	ADP
ap-9918	15	3	breast	breast	NOUN
ap-9918	15	4	cancer	cancer	NOUN
ap-9918	15	5	has	have	AUX
ap-9918	15	6	led	lead	VERB
ap-9918	15	7	to	to	ADP
ap-9918	15	8	discoveries	discovery	NOUN
ap-9918	15	9	that	that	PRON
ap-9918	15	10	are	be	AUX
ap-9918	15	11	helping	help	VERB
ap-9918	15	12	medical	medical	ADJ
ap-9918	15	13	practitioners	practitioner	NOUN
ap-9918	15	14	select	select	VERB
ap-9918	15	15	the	the	DET
ap-9918	15	16	best	good	ADJ
ap-9918	15	17	treatment	treatment	NOUN
ap-9918	15	18	plans	plan	NOUN
ap-9918	15	19	[	[	X
ap-9918	15	20	2	2	NUM
ap-9918	15	21	]	]	PUNCT
ap-9918	15	22	.	.	PUNCT
ap-9918	16	1	numerous	numerous	ADJ
ap-9918	16	2	papers	paper	NOUN
ap-9918	16	3	illustrate	illustrate	VERB
ap-9918	16	4	the	the	DET
ap-9918	16	5	continuous	continuous	ADJ
ap-9918	16	6	efforts	effort	NOUN
ap-9918	16	7	to	to	PART
ap-9918	16	8	use	use	VERB
ap-9918	16	9	machine	machine	NOUN
ap-9918	16	10	learning	learn	VERB
ap-9918	16	11	techniques	technique	NOUN
ap-9918	16	12	for	for	ADP
ap-9918	16	13	breast	breast	NOUN
ap-9918	16	14	cancer	cancer	NOUN
ap-9918	16	15	detection	detection	NOUN
ap-9918	16	16	,	,	PUNCT
ap-9918	16	17	diagnosis	diagnosis	NOUN
ap-9918	16	18	,	,	PUNCT
ap-9918	16	19	risk	risk	NOUN
ap-9918	16	20	assessment	assessment	NOUN
ap-9918	16	21	,	,	PUNCT
ap-9918	16	22	and	and	CCONJ
ap-9918	16	23	prognosis	prognosis	NOUN
ap-9918	16	24	prediction	prediction	NOUN
ap-9918	16	25	.	.	PUNCT
ap-9918	17	1	this	this	PRON
ap-9918	17	2	includes	include	VERB
ap-9918	17	3	different	different	ADJ
ap-9918	17	4	types	type	NOUN
ap-9918	17	5	of	of	ADP
ap-9918	17	6	medical	medical	ADJ
ap-9918	17	7	imaging	imaging	NOUN
ap-9918	17	8	data	datum	NOUN
ap-9918	17	9	and	and	CCONJ
ap-9918	17	10	clinical	clinical	ADJ
ap-9918	17	11	information	information	NOUN
ap-9918	17	12	.	.	PUNCT
ap-9918	18	1	deniz	deniz	PROPN
ap-9918	18	2	et	et	PROPN
ap-9918	18	3	al	al	PROPN
ap-9918	18	4	.	.	PROPN
ap-9918	19	1	(	(	PUNCT
ap-9918	19	2	2018	2018	NUM
ap-9918	19	3	)	)	PUNCT
ap-9918	19	4	delved	delve	VERB
ap-9918	19	5	into	into	ADP
ap-9918	19	6	the	the	DET
ap-9918	19	7	application	application	NOUN
ap-9918	19	8	of	of	ADP
ap-9918	19	9	convolutional	convolutional	ADJ
ap-9918	19	10	neural	neural	ADJ
ap-9918	19	11	networks	network	NOUN
ap-9918	19	12	(	(	PUNCT
ap-9918	19	13	cnns	cnns	PROPN
ap-9918	19	14	)	)	PUNCT
ap-9918	19	15	with	with	ADP
ap-9918	19	16	transfer	transfer	NOUN
ap-9918	19	17	learning	learn	VERB
ap-9918	19	18	for	for	ADP
ap-9918	19	19	automated	automate	VERB
ap-9918	19	20	classification	classification	NOUN
ap-9918	19	21	of	of	ADP
ap-9918	19	22	breast	breast	NOUN
ap-9918	19	23	cancer	cancer	NOUN
ap-9918	19	24	histopathological	histopathological	ADJ
ap-9918	19	25	images	image	NOUN
ap-9918	19	26	–	–	PUNCT
ap-9918	19	27	showcasing	showcase	VERB
ap-9918	19	28	a	a	DET
ap-9918	19	29	notable	notable	ADJ
ap-9918	19	30	high	high	ADJ
ap-9918	19	31	classification	classification	NOUN
ap-9918	19	32	accuracy	accuracy	NOUN
ap-9918	20	1	[	[	X
ap-9918	20	2	3	3	NUM
ap-9918	20	3	]	]	PUNCT
ap-9918	20	4	.	.	PUNCT
ap-9918	21	1	adam	adam	PROPN
ap-9918	21	2	yala	yala	PROPN
ap-9918	21	3	et	et	PROPN
ap-9918	21	4	al	al	PROPN
ap-9918	21	5	.	.	PROPN
ap-9918	22	1	(	(	PUNCT
ap-9918	22	2	2019	2019	NUM
ap-9918	22	3	)	)	PUNCT
ap-9918	22	4	studied	study	VERB
ap-9918	22	5	deep	deep	ADJ
ap-9918	22	6	learning	learning	NOUN
ap-9918	22	7	performance	performance	NOUN
ap-9918	22	8	in	in	ADP
ap-9918	22	9	interpreting	interpret	VERB
ap-9918	22	10	digital	digital	ADJ
ap-9918	22	11	mammography	mammography	NOUN
ap-9918	22	12	images	image	NOUN
ap-9918	22	13	specifically	specifically	ADV
ap-9918	22	14	for	for	ADP
ap-9918	22	15	breast	breast	NOUN
ap-9918	22	16	cancer	cancer	NOUN
ap-9918	22	17	screening	screening	NOUN
ap-9918	23	1	[	[	X
ap-9918	23	2	4	4	NUM
ap-9918	23	3	]	]	PUNCT
ap-9918	23	4	.	.	PUNCT
ap-9918	24	1	similarly	similarly	ADV
ap-9918	24	2	,	,	PUNCT
ap-9918	24	3	scott	scott	PROPN
ap-9918	24	4	m.	m.	PROPN
ap-9918	24	5	mckinney	mckinney	PROPN
ap-9918	24	6	et	et	PROPN
ap-9918	24	7	al	al	PROPN
ap-9918	24	8	.	.	PROPN
ap-9918	24	9	(	(	PUNCT
ap-9918	24	10	2020	2020	NUM
ap-9918	24	11	)	)	PUNCT
ap-9918	24	12	,	,	PUNCT
ap-9918	24	13	investigated	investigate	VERB
ap-9918	24	14	using	use	VERB
ap-9918	24	15	artificial	artificial	ADJ
ap-9918	24	16	intelligence	intelligence	NOUN
ap-9918	24	17	for	for	ADP
ap-9918	24	18	predicting	predict	VERB
ap-9918	24	19	breast	breast	NOUN
ap-9918	24	20	cancer	cancer	NOUN
ap-9918	24	21	risk	risk	NOUN
ap-9918	24	22	through	through	ADP
ap-9918	24	23	digital	digital	ADJ
ap-9918	24	24	mammography	mammography	NOUN
ap-9918	24	25	images	image	NOUN
ap-9918	24	26	showing	show	VERB
ap-9918	24	27	a	a	DET
ap-9918	24	28	promise	promise	NOUN
ap-9918	24	29	for	for	ADP
ap-9918	24	30	improved	improved	ADJ
ap-9918	24	31	risk	risk	NOUN
ap-9918	24	32	assessment	assessment	NOUN
ap-9918	25	1	[	[	X
ap-9918	25	2	5	5	NUM
ap-9918	25	3	]	]	PUNCT
ap-9918	25	4	.	.	PUNCT
ap-9918	26	1	a	a	DET
ap-9918	26	2	review	review	NOUN
ap-9918	26	3	paper	paper	NOUN
ap-9918	26	4	by	by	ADP
ap-9918	26	5	j.	j.	PROPN
ap-9918	26	6	kim	kim	PROPN
ap-9918	26	7	et	et	PROPN
ap-9918	26	8	al	al	PROPN
ap-9918	26	9	.	.	PROPN
ap-9918	26	10	(	(	PUNCT
ap-9918	26	11	2021	2021	NUM
ap-9918	26	12	)	)	PUNCT
ap-9918	26	13	,	,	PUNCT
ap-9918	26	14	summarised	summarise	VERB
ap-9918	26	15	new	new	ADJ
ap-9918	26	16	developments	development	NOUN
ap-9918	26	17	in	in	ADP
ap-9918	26	18	the	the	DET
ap-9918	26	19	use	use	NOUN
ap-9918	26	20	of	of	ADP
ap-9918	26	21	ultrasound	ultrasound	ADJ
ap-9918	26	22	imaging	imaging	NOUN
ap-9918	26	23	and	and	CCONJ
ap-9918	26	24	deep	deep	ADJ
ap-9918	26	25	learning	learning	NOUN
ap-9918	26	26	for	for	ADP
ap-9918	26	27	automated	automate	VERB
ap-9918	26	28	identification	identification	NOUN
ap-9918	26	29	and	and	CCONJ
ap-9918	26	30	categorisation	categorisation	NOUN
ap-9918	26	31	of	of	ADP
ap-9918	26	32	breast	breast	NOUN
ap-9918	26	33	cancer	cancer	NOUN
ap-9918	26	34	[	[	X
ap-9918	26	35	6	6	NUM
ap-9918	26	36	]	]	PUNCT
ap-9918	26	37	.	.	PUNCT
ap-9918	27	1	in	in	ADP
ap-9918	27	2	another	another	DET
ap-9918	27	3	paper	paper	NOUN
ap-9918	27	4	,	,	PUNCT
ap-9918	27	5	meenalochini	meenalochini	PROPN
ap-9918	27	6	et	et	PROPN
ap-9918	27	7	al	al	PROPN
ap-9918	27	8	.	.	PROPN
ap-9918	27	9	(	(	PUNCT
ap-9918	27	10	2021	2021	NUM
ap-9918	27	11	)	)	PUNCT
ap-9918	27	12	discussed	discuss	VERB
ap-9918	27	13	the	the	DET
ap-9918	27	14	results	result	NOUN
ap-9918	27	15	of	of	ADP
ap-9918	27	16	various	various	ADJ
ap-9918	27	17	machine	machine	NOUN
ap-9918	27	18	-	-	PUNCT
ap-9918	27	19	learning	learn	VERB
ap-9918	27	20	methods	method	NOUN
ap-9918	27	21	for	for	ADP
ap-9918	27	22	automating	automate	VERB
ap-9918	27	23	the	the	DET
ap-9918	27	24	classification	classification	NOUN
ap-9918	27	25	of	of	ADP
ap-9918	27	26	mammography	mammography	NOUN
ap-9918	27	27	images	image	NOUN
ap-9918	27	28	[	[	X
ap-9918	27	29	7	7	NUM
ap-9918	27	30	]	]	PUNCT
ap-9918	27	31	.	.	PUNCT
ap-9918	28	1	in	in	ADP
ap-9918	28	2	a	a	DET
ap-9918	28	3	different	different	ADJ
ap-9918	28	4	research	research	NOUN
ap-9918	28	5	,	,	PUNCT
ap-9918	28	6	s.	s.	PROPN
ap-9918	28	7	joo	joo	PROPN
ap-9918	28	8	et	et	PROPN
ap-9918	28	9	al	al	PROPN
ap-9918	28	10	.	.	PROPN
ap-9918	28	11	(	(	PUNCT
ap-9918	28	12	2021	2021	NUM
ap-9918	28	13	)	)	PUNCT
ap-9918	28	14	presented	present	VERB
ap-9918	28	15	a	a	DET
ap-9918	28	16	multimodal	multimodal	NOUN
ap-9918	28	17	deep	deep	ADJ
ap-9918	28	18	learning	learning	NOUN
ap-9918	28	19	model	model	NOUN
ap-9918	28	20	designed	design	VERB
ap-9918	28	21	to	to	PART
ap-9918	28	22	predict	predict	VERB
ap-9918	28	23	the	the	DET
ap-9918	28	24	pathologic	pathologic	ADJ
ap-9918	28	25	complete	complete	ADJ
ap-9918	28	26	response	response	NOUN
ap-9918	28	27	(	(	PUNCT
ap-9918	28	28	pcr	pcr	NOUN
ap-9918	28	29	)	)	PUNCT
ap-9918	28	30	to	to	ADP
ap-9918	28	31	neoadjuvant	neoadjuvant	ADJ
ap-9918	28	32	chemotherapy	chemotherapy	NOUN
ap-9918	28	33	(	(	PUNCT
ap-9918	28	34	nac	nac	PROPN
ap-9918	28	35	)	)	PUNCT
ap-9918	28	36	in	in	ADP
ap-9918	28	37	breast	breast	NOUN
ap-9918	28	38	cancer	cancer	NOUN
ap-9918	28	39	patients	patient	NOUN
ap-9918	28	40	by	by	ADP
ap-9918	28	41	integrating	integrate	VERB
ap-9918	28	42	clinical	clinical	ADJ
ap-9918	28	43	information	information	NOUN
ap-9918	28	44	and	and	CCONJ
ap-9918	28	45	pre	pre	ADJ
ap-9918	28	46	-	-	NOUN
ap-9918	28	47	treatment	treatment	ADJ
ap-9918	28	48	mr	mr	PROPN
ap-9918	28	49	images	image	VERB
ap-9918	28	50	[	[	X
ap-9918	28	51	8	8	NUM
ap-9918	28	52	]	]	PUNCT
ap-9918	28	53	.	.	PUNCT
ap-9918	29	1	similarly	similarly	ADV
ap-9918	29	2	,	,	PUNCT
ap-9918	29	3	treading	tread	VERB
ap-9918	29	4	the	the	DET
ap-9918	29	5	path	path	NOUN
ap-9918	29	6	of	of	ADP
ap-9918	29	7	innovation	innovation	NOUN
ap-9918	29	8	,	,	PUNCT
ap-9918	29	9	xiang	xiang	PROPN
ap-9918	29	10	li	li	PROPN
ap-9918	29	11	et	et	PROPN
ap-9918	29	12	al	al	PROPN
ap-9918	29	13	.	.	PROPN
ap-9918	30	1	(	(	PUNCT
ap-9918	30	2	2021	2021	NUM
ap-9918	30	3	)	)	PUNCT
ap-9918	30	4	investigated	investigate	VERB
ap-9918	30	5	a	a	DET
ap-9918	30	6	fascinating	fascinating	ADJ
ap-9918	30	7	direction	direction	NOUN
ap-9918	30	8	by	by	ADP
ap-9918	30	9	examining	examine	VERB
ap-9918	30	10	the	the	DET
ap-9918	30	11	data	datum	NOUN
ap-9918	30	12	taken	take	VERB
ap-9918	30	13	from	from	ADP
ap-9918	30	14	dynamic	dynamic	ADJ
ap-9918	30	15	contrastenhanced	contrastenhance	VERB
ap-9918	30	16	magnetic	magnetic	ADJ
ap-9918	30	17	resonance	resonance	NOUN
ap-9918	30	18	imaging	imaging	NOUN
ap-9918	30	19	(	(	PUNCT
ap-9918	30	20	dce	dce	PROPN
ap-9918	30	21	-	-	PUNCT
ap-9918	30	22	mri	mri	NOUN
ap-9918	30	23	)	)	PUNCT
ap-9918	30	24	to	to	PART
ap-9918	30	25	predict	predict	VERB
ap-9918	30	26	molecular	molecular	ADJ
ap-9918	30	27	subtypes	subtype	NOUN
ap-9918	30	28	of	of	ADP
ap-9918	30	29	breast	breast	NOUN
ap-9918	30	30	cancer	cancer	NOUN
ap-9918	30	31	using	use	VERB
ap-9918	30	32	machine	machine	NOUN
ap-9918	30	33	learning	learn	VERB
ap-9918	30	34	algorithms	algorithm	NOUN
ap-9918	31	1	[	[	X
ap-9918	31	2	9	9	NUM
ap-9918	31	3	]	]	PUNCT
ap-9918	31	4	.	.	PUNCT
ap-9918	32	1	j.	j.	PROPN
ap-9918	32	2	li	li	PROPN
ap-9918	32	3	et	et	PROPN
ap-9918	32	4	al	al	PROPN
ap-9918	32	5	.	.	PROPN
ap-9918	32	6	(	(	PUNCT
ap-9918	32	7	2021	2021	NUM
ap-9918	32	8	)	)	PUNCT
ap-9918	32	9	wrote	write	VERB
ap-9918	32	10	a	a	DET
ap-9918	32	11	review	review	NOUN
ap-9918	32	12	research	research	NOUN
ap-9918	32	13	to	to	PART
ap-9918	32	14	evaluate	evaluate	VERB
ap-9918	32	15	the	the	DET
ap-9918	32	16	present	present	ADJ
ap-9918	32	17	machine	machine	NOUN
ap-9918	32	18	learning	learning	NOUN
ap-9918	32	19	models	model	NOUN
ap-9918	32	20	and	and	CCONJ
ap-9918	32	21	studying	study	VERB
ap-9918	32	22	models	model	NOUN
ap-9918	32	23	for	for	ADP
ap-9918	32	24	predicting	predict	VERB
ap-9918	32	25	breast	breast	NOUN
ap-9918	32	26	cancer	cancer	NOUN
ap-9918	32	27	survival	survival	NOUN
ap-9918	32	28	,	,	PUNCT
ap-9918	32	29	highlighting	highlight	VERB
ap-9918	32	30	challenges	challenge	NOUN
ap-9918	32	31	and	and	CCONJ
ap-9918	32	32	opportunities	opportunity	NOUN
ap-9918	32	33	for	for	ADP
ap-9918	32	34	future	future	ADJ
ap-9918	32	35	research	research	NOUN
ap-9918	32	36	[	[	X
ap-9918	32	37	10	10	NUM
ap-9918	32	38	]	]	PUNCT
ap-9918	32	39	.	.	PUNCT
ap-9918	33	1	mahmood	mahmood	PROPN
ap-9918	33	2	,	,	PUNCT
ap-9918	33	3	ali	ali	PROPN
ap-9918	33	4	a.	a.	PROPN
ap-9918	33	5	et	et	PROPN
ap-9918	33	6	al	al	PROPN
ap-9918	33	7	.	.	PROPN
ap-9918	33	8	(	(	PUNCT
ap-9918	33	9	2023	2023	NUM
ap-9918	33	10	)	)	PUNCT
ap-9918	33	11	created	create	VERB
ap-9918	33	12	a	a	DET
ap-9918	33	13	model	model	NOUN
ap-9918	33	14	that	that	PRON
ap-9918	33	15	trains	train	VERB
ap-9918	33	16	largescale	largescale	NOUN
ap-9918	33	17	mwsis	mwsis	NOUN
ap-9918	33	18	using	use	VERB
ap-9918	33	19	the	the	DET
ap-9918	33	20	matlab	matlab	PROPN
ap-9918	33	21	platform	platform	NOUN
ap-9918	33	22	using	use	VERB
ap-9918	33	23	sample	sample	NOUN
ap-9918	33	24	-	-	PUNCT
ap-9918	33	25	based	base	VERB
ap-9918	33	26	processing	processing	NOUN
ap-9918	33	27	.	.	PUNCT
ap-9918	34	1	the	the	DET
ap-9918	34	2	model	model	NOUN
ap-9918	34	3	uses	use	VERB
ap-9918	34	4	transfer	transfer	NOUN
ap-9918	34	5	learning	learn	VERB
ap-9918	34	6	techniques	technique	NOUN
ap-9918	34	7	and	and	CCONJ
ap-9918	34	8	an	an	DET
ap-9918	34	9	inception	inception	NOUN
ap-9918	34	10	-	-	PUNCT
ap-9918	34	11	v3	v3	NOUN
ap-9918	34	12	-	-	PUNCT
ap-9918	34	13	based	base	VERB
ap-9918	34	14	architecture	architecture	NOUN
ap-9918	34	15	to	to	PART
ap-9918	34	16	detect	detect	VERB
ap-9918	34	17	cancer	cancer	NOUN
ap-9918	34	18	in	in	ADP
ap-9918	34	19	different	different	ADJ
ap-9918	34	20	samples	sample	NOUN
ap-9918	34	21	[	[	X
ap-9918	34	22	11	11	NUM
ap-9918	34	23	]	]	PUNCT
ap-9918	34	24	.	.	PUNCT
ap-9918	35	1	this	this	DET
ap-9918	35	2	study	study	NOUN
ap-9918	35	3	demonstrates	demonstrate	VERB
ap-9918	35	4	how	how	SCONJ
ap-9918	35	5	important	important	ADJ
ap-9918	35	6	it	it	PRON
ap-9918	35	7	is	be	AUX
ap-9918	35	8	to	to	PART
ap-9918	35	9	select	select	VERB
ap-9918	35	10	the	the	DET
ap-9918	35	11	most	most	ADV
ap-9918	35	12	suitable	suitable	ADJ
ap-9918	35	13	machine	machine	NOUN
ap-9918	35	14	learning	learning	NOUN
ap-9918	35	15	model	model	NOUN
ap-9918	35	16	for	for	ADP
ap-9918	35	17	medical	medical	ADJ
ap-9918	35	18	prediction	prediction	NOUN
ap-9918	35	19	tasks	task	NOUN
ap-9918	35	20	and	and	CCONJ
ap-9918	35	21	validates	validate	VERB
ap-9918	35	22	catboost	catboost	NOUN
ap-9918	35	23	’s	’s	PART
ap-9918	35	24	reliability	reliability	NOUN
ap-9918	35	25	.	.	PUNCT
ap-9918	36	1	136	136	NUM
ap-9918	36	2	https://doi.org/10.14311/ap.2025.65.0136	https://doi.org/10.14311/ap.2025.65.0136	PROPN
ap-9918	36	3	https://creativecommons.org/licenses/by/4.0/	https://creativecommons.org/licenses/by/4.0/	PROPN
ap-9918	36	4	https://www.cvut.cz/en	https://www.cvut.cz/en	PROPN
ap-9918	36	5	vol	vol	NOUN
ap-9918	36	6	.	.	PROPN
ap-9918	37	1	65	65	NUM
ap-9918	37	2	no	no	INTJ
ap-9918	37	3	.	.	PUNCT
ap-9918	38	1	2/2025	2/2025	PROPN
ap-9918	38	2	a	a	DET
ap-9918	38	3	comparative	comparative	ADJ
ap-9918	38	4	study	study	NOUN
ap-9918	38	5	of	of	ADP
ap-9918	38	6	breast	breast	NOUN
ap-9918	38	7	cancer	cancer	NOUN
ap-9918	38	8	detection	detection	NOUN
ap-9918	38	9	and	and	CCONJ
ap-9918	38	10	recurrence	recurrence	NOUN
ap-9918	38	11	.	.	PUNCT
ap-9918	38	12	.	.	PUNCT
ap-9918	39	1	.	.	PUNCT
ap-9918	40	1	2	2	X
ap-9918	40	2	.	.	X
ap-9918	40	3	dataset	dataset	ADJ
ap-9918	40	4	description	description	NOUN
ap-9918	40	5	this	this	DET
ap-9918	40	6	project	project	NOUN
ap-9918	40	7	used	use	VERB
ap-9918	40	8	three	three	NUM
ap-9918	40	9	different	different	ADJ
ap-9918	40	10	types	type	NOUN
ap-9918	40	11	of	of	ADP
ap-9918	40	12	datasets	dataset	NOUN
ap-9918	40	13	.	.	PUNCT
ap-9918	41	1	the	the	DET
ap-9918	41	2	first	first	ADJ
ap-9918	41	3	two	two	NUM
ap-9918	41	4	databases	database	NOUN
ap-9918	41	5	are	be	AUX
ap-9918	41	6	classified	classify	VERB
ap-9918	41	7	the	the	DET
ap-9918	41	8	patients	patient	NOUN
ap-9918	41	9	depending	depend	VERB
ap-9918	41	10	on	on	ADP
ap-9918	41	11	whether	whether	SCONJ
ap-9918	41	12	to	to	PART
ap-9918	41	13	detect	detect	VERB
ap-9918	41	14	breast	breast	NOUN
ap-9918	41	15	cancer	cancer	NOUN
ap-9918	41	16	or	or	CCONJ
ap-9918	41	17	not	not	PART
ap-9918	41	18	.	.	PUNCT
ap-9918	42	1	one	one	NUM
ap-9918	42	2	method	method	NOUN
ap-9918	42	3	relies	rely	VERB
ap-9918	42	4	on	on	ADP
ap-9918	42	5	data	datum	NOUN
ap-9918	42	6	from	from	ADP
ap-9918	42	7	digitised	digitise	VERB
ap-9918	42	8	images	image	NOUN
ap-9918	42	9	,	,	PUNCT
ap-9918	42	10	while	while	SCONJ
ap-9918	42	11	the	the	DET
ap-9918	42	12	other	other	ADJ
ap-9918	42	13	uses	use	VERB
ap-9918	42	14	microarray	microarray	NOUN
ap-9918	42	15	gene	gene	NOUN
ap-9918	42	16	expression	expression	NOUN
ap-9918	42	17	.	.	PUNCT
ap-9918	43	1	the	the	DET
ap-9918	43	2	third	third	ADJ
ap-9918	43	3	one	one	NOUN
ap-9918	43	4	classified	classify	VERB
ap-9918	43	5	the	the	DET
ap-9918	43	6	patients	patient	NOUN
ap-9918	43	7	depending	depend	VERB
ap-9918	43	8	on	on	ADP
ap-9918	43	9	the	the	DET
ap-9918	43	10	recurrence	recurrence	NOUN
ap-9918	43	11	of	of	ADP
ap-9918	43	12	the	the	DET
ap-9918	43	13	disease	disease	NOUN
ap-9918	43	14	after	after	ADP
ap-9918	43	15	the	the	DET
ap-9918	43	16	healing	healing	NOUN
ap-9918	43	17	.	.	PUNCT
ap-9918	44	1	the	the	DET
ap-9918	44	2	data	data	NOUN
ap-9918	44	3	is	be	AUX
ap-9918	44	4	computed	compute	VERB
ap-9918	44	5	from	from	ADP
ap-9918	44	6	clinical	clinical	ADJ
ap-9918	44	7	information	information	NOUN
ap-9918	44	8	.	.	PUNCT
ap-9918	45	1	the	the	DET
ap-9918	45	2	first	first	ADJ
ap-9918	45	3	dataset	dataset	NOUN
ap-9918	45	4	is	be	AUX
ap-9918	45	5	called	call	VERB
ap-9918	45	6	wdbc	wdbc	NOUN
ap-9918	45	7	(	(	PUNCT
ap-9918	45	8	wisconsin	wisconsin	PROPN
ap-9918	45	9	diagnostic	diagnostic	ADJ
ap-9918	45	10	breast	breast	NOUN
ap-9918	45	11	cancer	cancer	NOUN
ap-9918	45	12	)	)	PUNCT
ap-9918	45	13	,	,	PUNCT
ap-9918	45	14	which	which	PRON
ap-9918	45	15	is	be	AUX
ap-9918	45	16	taken	take	VERB
ap-9918	45	17	from	from	ADP
ap-9918	45	18	the	the	DET
ap-9918	45	19	uc	uc	PROPN
ap-9918	45	20	irvine	irvine	PROPN
ap-9918	45	21	machine	machine	NOUN
ap-9918	45	22	learning	learn	VERB
ap-9918	45	23	repository	repository	NOUN
ap-9918	46	1	[	[	X
ap-9918	46	2	12	12	NUM
ap-9918	46	3	]	]	PUNCT
ap-9918	46	4	.	.	PUNCT
ap-9918	47	1	32	32	NUM
ap-9918	47	2	features	feature	NOUN
ap-9918	47	3	are	be	AUX
ap-9918	47	4	computed	compute	VERB
ap-9918	47	5	from	from	ADP
ap-9918	47	6	a	a	DET
ap-9918	47	7	digital	digital	ADJ
ap-9918	47	8	image	image	NOUN
ap-9918	47	9	of	of	ADP
ap-9918	47	10	a	a	DET
ap-9918	47	11	fine	fine	ADJ
ap-9918	47	12	needle	needle	NOUN
ap-9918	47	13	aspirate	aspirate	NOUN
ap-9918	47	14	(	(	PUNCT
ap-9918	47	15	fna	fna	PROPN
ap-9918	47	16	)	)	PUNCT
ap-9918	47	17	of	of	ADP
ap-9918	47	18	a	a	DET
ap-9918	47	19	breast	breast	NOUN
ap-9918	47	20	tumour	tumour	NOUN
ap-9918	47	21	.	.	PUNCT
ap-9918	48	1	the	the	DET
ap-9918	48	2	explanation	explanation	NOUN
ap-9918	48	3	of	of	ADP
ap-9918	48	4	the	the	DET
ap-9918	48	5	features	feature	NOUN
ap-9918	48	6	of	of	ADP
ap-9918	48	7	the	the	DET
ap-9918	48	8	cell	cell	NOUN
ap-9918	48	9	nuclei	nucleus	NOUN
ap-9918	48	10	seen	see	VERB
ap-9918	48	11	in	in	ADP
ap-9918	48	12	the	the	DET
ap-9918	48	13	image	image	NOUN
ap-9918	48	14	is	be	AUX
ap-9918	48	15	as	as	SCONJ
ap-9918	48	16	follows	follow	VERB
ap-9918	48	17	:	:	PUNCT
ap-9918	48	18	the	the	DET
ap-9918	48	19	first	first	ADJ
ap-9918	48	20	two	two	NUM
ap-9918	48	21	features	feature	NOUN
ap-9918	48	22	are	be	AUX
ap-9918	48	23	the	the	DET
ap-9918	48	24	i	i	PROPN
ap-9918	48	25	d	d	PROPN
ap-9918	48	26	and	and	CCONJ
ap-9918	48	27	the	the	DET
ap-9918	48	28	diagnosis	diagnosis	NOUN
ap-9918	48	29	(	(	PUNCT
ap-9918	48	30	m	m	VERB
ap-9918	48	31	for	for	ADP
ap-9918	48	32	malignant	malignant	ADJ
ap-9918	48	33	,	,	PUNCT
ap-9918	48	34	b	b	NOUN
ap-9918	48	35	for	for	ADP
ap-9918	48	36	benign	benign	NOUN
ap-9918	48	37	)	)	PUNCT
ap-9918	48	38	,	,	PUNCT
ap-9918	48	39	while	while	SCONJ
ap-9918	48	40	the	the	DET
ap-9918	48	41	others	other	NOUN
ap-9918	48	42	are	be	AUX
ap-9918	48	43	input	input	NOUN
ap-9918	48	44	features	feature	NOUN
ap-9918	48	45	.	.	PUNCT
ap-9918	49	1	there	there	PRON
ap-9918	49	2	are	be	VERB
ap-9918	49	3	10	10	NUM
ap-9918	49	4	main	main	ADJ
ap-9918	49	5	real	real	ADV
ap-9918	49	6	-	-	PUNCT
ap-9918	49	7	valued	value	VERB
ap-9918	49	8	features	feature	NOUN
ap-9918	49	9	calculated	calculate	VERB
ap-9918	49	10	for	for	ADP
ap-9918	49	11	each	each	DET
ap-9918	49	12	cell	cell	NOUN
ap-9918	49	13	nucleus	nucleus	NOUN
ap-9918	49	14	:	:	PUNCT
ap-9918	50	1	radius	radius	NOUN
ap-9918	50	2	,	,	PUNCT
ap-9918	50	3	texture	texture	ADJ
ap-9918	50	4	,	,	PUNCT
ap-9918	50	5	smoothness	smoothness	PROPN
ap-9918	50	6	,	,	PUNCT
ap-9918	50	7	concavity	concavity	NOUN
ap-9918	50	8	,	,	PUNCT
ap-9918	50	9	concave	concave	ADJ
ap-9918	50	10	spots	spot	NOUN
ap-9918	50	11	,	,	PUNCT
ap-9918	50	12	compactness	compactness	NOUN
ap-9918	50	13	,	,	PUNCT
ap-9918	50	14	perimeter	perimeter	NOUN
ap-9918	50	15	,	,	PUNCT
ap-9918	50	16	area	area	NOUN
ap-9918	50	17	,	,	PUNCT
ap-9918	50	18	symmetry	symmetry	NOUN
ap-9918	50	19	,	,	PUNCT
ap-9918	50	20	and	and	CCONJ
ap-9918	50	21	fractal	fractal	ADJ
ap-9918	50	22	dimension	dimension	NOUN
ap-9918	50	23	.	.	PUNCT
ap-9918	51	1	each	each	DET
ap-9918	51	2	image	image	NOUN
ap-9918	51	3	has	have	VERB
ap-9918	51	4	30	30	NUM
ap-9918	51	5	features	feature	NOUN
ap-9918	51	6	calculated	calculate	VERB
ap-9918	51	7	from	from	ADP
ap-9918	51	8	the	the	DET
ap-9918	51	9	standard	standard	ADJ
ap-9918	51	10	error	error	NOUN
ap-9918	51	11	,	,	PUNCT
ap-9918	51	12	mean	mean	VERB
ap-9918	51	13	,	,	PUNCT
ap-9918	51	14	and	and	CCONJ
ap-9918	51	15	“	"	PUNCT
ap-9918	51	16	worst	bad	ADJ
ap-9918	51	17	”	"	PUNCT
ap-9918	51	18	(	(	PUNCT
ap-9918	51	19	the	the	DET
ap-9918	51	20	average	average	NOUN
ap-9918	51	21	of	of	ADP
ap-9918	51	22	the	the	DET
ap-9918	51	23	three	three	NUM
ap-9918	51	24	largest	large	ADJ
ap-9918	51	25	values	value	NOUN
ap-9918	51	26	)	)	PUNCT
ap-9918	51	27	of	of	ADP
ap-9918	51	28	the	the	DET
ap-9918	51	29	previous	previous	ADJ
ap-9918	51	30	10	10	NUM
ap-9918	51	31	features	feature	NOUN
ap-9918	51	32	.	.	PUNCT
ap-9918	52	1	the	the	DET
ap-9918	52	2	total	total	ADJ
ap-9918	52	3	number	number	NOUN
ap-9918	52	4	of	of	ADP
ap-9918	52	5	instances	instance	NOUN
ap-9918	52	6	is	be	AUX
ap-9918	52	7	569	569	NUM
ap-9918	52	8	(	(	PUNCT
ap-9918	52	9	357	357	NUM
ap-9918	52	10	benign	benign	ADJ
ap-9918	52	11	,	,	PUNCT
ap-9918	52	12	and	and	CCONJ
ap-9918	52	13	212	212	NUM
ap-9918	52	14	malignant	malignant	NOUN
ap-9918	52	15	)	)	PUNCT
ap-9918	52	16	.	.	PUNCT
ap-9918	53	1	in	in	ADP
ap-9918	53	2	the	the	DET
ap-9918	53	3	pre	pre	ADJ
ap-9918	53	4	-	-	ADJ
ap-9918	53	5	processing	processing	ADJ
ap-9918	53	6	stage	stage	NOUN
ap-9918	53	7	of	of	ADP
ap-9918	53	8	this	this	DET
ap-9918	53	9	dataset	dataset	NOUN
ap-9918	53	10	,	,	PUNCT
ap-9918	53	11	one	one	NUM
ap-9918	53	12	instance	instance	NOUN
ap-9918	53	13	was	be	AUX
ap-9918	53	14	deleted	delete	VERB
ap-9918	53	15	from	from	ADP
ap-9918	53	16	this	this	DET
ap-9918	53	17	dataset	dataset	NOUN
ap-9918	53	18	because	because	SCONJ
ap-9918	53	19	of	of	ADP
ap-9918	53	20	incomplete	incomplete	ADJ
ap-9918	53	21	information	information	NOUN
ap-9918	53	22	.	.	PUNCT
ap-9918	54	1	due	due	ADP
ap-9918	54	2	to	to	ADP
ap-9918	54	3	the	the	DET
ap-9918	54	4	imbalance	imbalance	NOUN
ap-9918	54	5	in	in	ADP
ap-9918	54	6	the	the	DET
ap-9918	54	7	dataset	dataset	NOUN
ap-9918	54	8	,	,	PUNCT
ap-9918	54	9	with	with	ADP
ap-9918	54	10	more	more	ADV
ap-9918	54	11	benign	benign	ADJ
ap-9918	54	12	than	than	ADP
ap-9918	54	13	malignant	malignant	ADJ
ap-9918	54	14	instances	instance	NOUN
ap-9918	54	15	,	,	PUNCT
ap-9918	54	16	the	the	DET
ap-9918	54	17	number	number	NOUN
ap-9918	54	18	of	of	ADP
ap-9918	54	19	malignant	malignant	ADJ
ap-9918	54	20	instances	instance	NOUN
ap-9918	54	21	is	be	AUX
ap-9918	54	22	increased	increase	VERB
ap-9918	54	23	to	to	PART
ap-9918	54	24	improve	improve	VERB
ap-9918	54	25	learning	learn	VERB
ap-9918	54	26	outcomes	outcome	NOUN
ap-9918	54	27	.	.	PUNCT
ap-9918	55	1	the	the	DET
ap-9918	55	2	second	second	ADJ
ap-9918	55	3	dataset	dataset	NOUN
ap-9918	55	4	is	be	AUX
ap-9918	55	5	called	call	VERB
ap-9918	55	6	gse42568	gse42568	NOUN
ap-9918	55	7	.	.	PUNCT
ap-9918	56	1	this	this	DET
ap-9918	56	2	dataset	dataset	NOUN
ap-9918	56	3	has	have	AUX
ap-9918	56	4	been	be	AUX
ap-9918	56	5	submitted	submit	VERB
ap-9918	56	6	to	to	ADP
ap-9918	56	7	the	the	DET
ap-9918	56	8	geo	geo	PROPN
ap-9918	56	9	(	(	PUNCT
ap-9918	56	10	gene	gene	NOUN
ap-9918	56	11	expression	expression	NOUN
ap-9918	56	12	omnibus	omnibus	NOUN
ap-9918	56	13	)	)	PUNCT
ap-9918	56	14	data	datum	NOUN
ap-9918	56	15	repository	repository	NOUN
ap-9918	56	16	represented	represent	VERB
ap-9918	56	17	by	by	ADP
ap-9918	56	18	gene	gene	NOUN
ap-9918	56	19	expressions	expression	NOUN
ap-9918	56	20	.	.	PUNCT
ap-9918	57	1	this	this	DET
ap-9918	57	2	dataset	dataset	NOUN
ap-9918	57	3	is	be	AUX
ap-9918	57	4	from	from	ADP
ap-9918	57	5	the	the	DET
ap-9918	57	6	type	type	NOUN
ap-9918	57	7	of	of	ADP
ap-9918	57	8	gene	gene	NOUN
ap-9918	57	9	expression	expression	NOUN
ap-9918	57	10	microarray	microarray	NOUN
ap-9918	57	11	with	with	ADP
ap-9918	57	12	its	its	PRON
ap-9918	57	13	clinical	clinical	ADJ
ap-9918	57	14	information	information	NOUN
ap-9918	57	15	.	.	PUNCT
ap-9918	58	1	there	there	PRON
ap-9918	58	2	are	be	VERB
ap-9918	58	3	17	17	NUM
ap-9918	58	4	normal	normal	ADJ
ap-9918	58	5	instances	instance	NOUN
ap-9918	58	6	and	and	CCONJ
ap-9918	58	7	104	104	NUM
ap-9918	58	8	cases	case	NOUN
ap-9918	58	9	of	of	ADP
ap-9918	58	10	breast	breast	NOUN
ap-9918	58	11	cancer	cancer	NOUN
ap-9918	58	12	samples	sample	NOUN
ap-9918	58	13	(	(	PUNCT
ap-9918	58	14	removed	remove	VERB
ap-9918	58	15	before	before	ADP
ap-9918	58	16	tamoxifen	tamoxifen	NOUN
ap-9918	58	17	or	or	CCONJ
ap-9918	58	18	chemotherapy	chemotherapy	NOUN
ap-9918	58	19	agent	agent	NOUN
ap-9918	58	20	treatment	treatment	NOUN
ap-9918	58	21	)	)	PUNCT
ap-9918	58	22	from	from	ADP
ap-9918	58	23	individuals	individual	NOUN
ap-9918	58	24	who	who	PRON
ap-9918	58	25	were	be	AUX
ap-9918	58	26	diagnosed	diagnose	VERB
ap-9918	58	27	between	between	ADP
ap-9918	58	28	the	the	DET
ap-9918	58	29	ages	age	NOUN
ap-9918	58	30	of	of	ADP
ap-9918	58	31	31	31	NUM
ap-9918	58	32	and	and	CCONJ
ap-9918	58	33	89	89	NUM
ap-9918	58	34	(	(	PUNCT
ap-9918	58	35	mean	mean	INTJ
ap-9918	58	36	age	age	NOUN
ap-9918	58	37	=	=	SYM
ap-9918	58	38	58	58	NUM
ap-9918	58	39	years	year	NOUN
ap-9918	58	40	)	)	PUNCT
ap-9918	58	41	.	.	PUNCT
ap-9918	59	1	at	at	ADP
ap-9918	59	2	the	the	DET
ap-9918	59	3	time	time	NOUN
ap-9918	59	4	of	of	ADP
ap-9918	59	5	diagnosis	diagnosis	NOUN
ap-9918	59	6	,	,	PUNCT
ap-9918	59	7	twenty	twenty	NUM
ap-9918	59	8	of	of	ADP
ap-9918	59	9	the	the	DET
ap-9918	59	10	women	woman	NOUN
ap-9918	59	11	were	be	AUX
ap-9918	59	12	under	under	ADP
ap-9918	59	13	fifty	fifty	NUM
ap-9918	59	14	and	and	CCONJ
ap-9918	59	15	seventy	seventy	NUM
ap-9918	59	16	-	-	PUNCT
ap-9918	59	17	seven	seven	NUM
ap-9918	59	18	women	woman	NOUN
ap-9918	59	19	were	be	AUX
ap-9918	59	20	exactly	exactly	ADV
ap-9918	59	21	or	or	CCONJ
ap-9918	59	22	over	over	ADP
ap-9918	59	23	fifty	fifty	NUM
ap-9918	59	24	years	year	NOUN
ap-9918	59	25	.	.	PUNCT
ap-9918	60	1	the	the	DET
ap-9918	60	2	size	size	NOUN
ap-9918	60	3	of	of	ADP
ap-9918	60	4	the	the	DET
ap-9918	60	5	tumours	tumours	PROPN
ap-9918	60	6	ranges	range	VERB
ap-9918	60	7	(	(	PUNCT
ap-9918	60	8	from	from	ADP
ap-9918	60	9	0.6	0.6	NUM
ap-9918	60	10	cm	cm	NOUN
ap-9918	60	11	to	to	ADP
ap-9918	60	12	8.0	8.0	NUM
ap-9918	60	13	cm	cm	NOUN
ap-9918	60	14	)	)	PUNCT
ap-9918	60	15	with	with	ADP
ap-9918	60	16	a	a	DET
ap-9918	60	17	mean	mean	NOUN
ap-9918	60	18	of	of	ADP
ap-9918	60	19	2.79	2.79	NUM
ap-9918	60	20	cm	cm	NOUN
ap-9918	60	21	.	.	PUNCT
ap-9918	61	1	eighteen	eighteen	NUM
ap-9918	61	2	tumours	tumour	NOUN
ap-9918	61	3	were	be	AUX
ap-9918	61	4	smaller	small	ADJ
ap-9918	61	5	than	than	ADP
ap-9918	61	6	2	2	NUM
ap-9918	61	7	cm	cm	NOUN
ap-9918	61	8	(	(	PUNCT
ap-9918	61	9	t1	t1	NOUN
ap-9918	61	10	)	)	PUNCT
ap-9918	61	11	in	in	ADP
ap-9918	61	12	the	the	DET
ap-9918	61	13	maximal	maximal	ADJ
ap-9918	61	14	dimension	dimension	NOUN
ap-9918	61	15	,	,	PUNCT
ap-9918	61	16	while	while	SCONJ
ap-9918	61	17	83	83	NUM
ap-9918	61	18	tumours	tumour	NOUN
ap-9918	61	19	were	be	AUX
ap-9918	61	20	between	between	ADP
ap-9918	61	21	2–5	2–5	PROPN
ap-9918	61	22	cm	cm	NOUN
ap-9918	61	23	(	(	PUNCT
ap-9918	61	24	t2	t2	PROPN
ap-9918	61	25	)	)	PUNCT
ap-9918	61	26	,	,	PUNCT
ap-9918	61	27	and	and	CCONJ
ap-9918	61	28	3	3	NUM
ap-9918	61	29	tumours	tumour	NOUN
ap-9918	61	30	were	be	AUX
ap-9918	61	31	larger	large	ADJ
ap-9918	61	32	than	than	ADP
ap-9918	61	33	5	5	NUM
ap-9918	61	34	cm	cm	NOUN
ap-9918	61	35	(	(	PUNCT
ap-9918	61	36	t3	t3	PROPN
ap-9918	61	37	)	)	PUNCT
ap-9918	61	38	.	.	PUNCT
ap-9918	62	1	the	the	DET
ap-9918	62	2	remaining	remain	VERB
ap-9918	62	3	54	54	NUM
ap-9918	62	4	666	666	NUM
ap-9918	62	5	features	feature	NOUN
ap-9918	62	6	are	be	AUX
ap-9918	62	7	dedicated	dedicate	VERB
ap-9918	62	8	to	to	PART
ap-9918	62	9	gene	gene	VERB
ap-9918	62	10	expression	expression	NOUN
ap-9918	62	11	[	[	X
ap-9918	62	12	13	13	NUM
ap-9918	62	13	]	]	PUNCT
ap-9918	62	14	.	.	PUNCT
ap-9918	63	1	the	the	DET
ap-9918	63	2	pre	pre	ADJ
ap-9918	63	3	-	-	ADJ
ap-9918	63	4	processing	processing	ADJ
ap-9918	63	5	step	step	NOUN
ap-9918	63	6	involves	involve	VERB
ap-9918	63	7	duplicating	duplicate	VERB
ap-9918	63	8	normal	normal	ADJ
ap-9918	63	9	instances	instance	NOUN
ap-9918	63	10	several	several	ADJ
ap-9918	63	11	times	time	NOUN
ap-9918	63	12	to	to	PART
ap-9918	63	13	reduce	reduce	VERB
ap-9918	63	14	the	the	DET
ap-9918	63	15	gap	gap	NOUN
ap-9918	63	16	between	between	ADP
ap-9918	63	17	normal	normal	ADJ
ap-9918	63	18	and	and	CCONJ
ap-9918	63	19	breast	breast	NOUN
ap-9918	63	20	cancer	cancer	NOUN
ap-9918	63	21	cases	case	NOUN
ap-9918	63	22	,	,	PUNCT
ap-9918	63	23	leading	lead	VERB
ap-9918	63	24	to	to	ADP
ap-9918	63	25	better	well	ADV
ap-9918	63	26	learning	learn	VERB
ap-9918	63	27	outcomes	outcome	NOUN
ap-9918	63	28	.	.	PUNCT
ap-9918	64	1	the	the	DET
ap-9918	64	2	third	third	ADJ
ap-9918	64	3	dataset	dataset	NOUN
ap-9918	64	4	is	be	AUX
ap-9918	64	5	called	call	VERB
ap-9918	64	6	bcrd	bcrd	NOUN
ap-9918	64	7	(	(	PUNCT
ap-9918	64	8	breast	breast	NOUN
ap-9918	64	9	cancer	cancer	NOUN
ap-9918	64	10	recurrence	recurrence	NOUN
ap-9918	64	11	data	datum	NOUN
ap-9918	64	12	)	)	PUNCT
ap-9918	65	1	[	[	X
ap-9918	65	2	14	14	NUM
ap-9918	65	3	]	]	PUNCT
ap-9918	65	4	,	,	PUNCT
ap-9918	65	5	which	which	PRON
ap-9918	65	6	was	be	AUX
ap-9918	65	7	obtained	obtain	VERB
ap-9918	65	8	from	from	ADP
ap-9918	65	9	the	the	DET
ap-9918	65	10	university	university	NOUN
ap-9918	65	11	medical	medical	ADJ
ap-9918	65	12	centre	centre	NOUN
ap-9918	65	13	,	,	PUNCT
ap-9918	65	14	institute	institute	NOUN
ap-9918	65	15	of	of	ADP
ap-9918	65	16	oncology	oncology	PROPN
ap-9918	65	17	,	,	PUNCT
ap-9918	65	18	ljubljana	ljubljana	PROPN
ap-9918	65	19	,	,	PUNCT
ap-9918	65	20	yugoslavia	yugoslavia	PROPN
ap-9918	65	21	.	.	PUNCT
ap-9918	66	1	there	there	PRON
ap-9918	66	2	are	be	VERB
ap-9918	66	3	286	286	NUM
ap-9918	66	4	cases	case	NOUN
ap-9918	66	5	total	total	ADJ
ap-9918	66	6	in	in	ADP
ap-9918	66	7	this	this	DET
ap-9918	66	8	dataset	dataset	NOUN
ap-9918	66	9	,	,	PUNCT
ap-9918	66	10	201	201	NUM
ap-9918	66	11	of	of	ADP
ap-9918	66	12	which	which	PRON
ap-9918	66	13	belong	belong	VERB
ap-9918	66	14	to	to	ADP
ap-9918	66	15	the	the	DET
ap-9918	66	16	no	no	DET
ap-9918	66	17	-	-	PUNCT
ap-9918	66	18	recurrence	recurrence	NOUN
ap-9918	66	19	class	class	NOUN
ap-9918	66	20	and	and	CCONJ
ap-9918	66	21	85	85	NUM
ap-9918	66	22	to	to	ADP
ap-9918	66	23	the	the	DET
ap-9918	66	24	recurrence	recurrence	NOUN
ap-9918	66	25	class	class	NOUN
ap-9918	66	26	.	.	PUNCT
ap-9918	67	1	the	the	DET
ap-9918	67	2	instances	instance	NOUN
ap-9918	67	3	are	be	AUX
ap-9918	67	4	described	describe	VERB
ap-9918	67	5	by	by	ADP
ap-9918	67	6	10	10	NUM
ap-9918	67	7	attributes	attribute	NOUN
ap-9918	67	8	.	.	PUNCT
ap-9918	68	1	attribute	attribute	NOUN
ap-9918	68	2	information	information	NOUN
ap-9918	68	3	:	:	PUNCT
ap-9918	68	4	the	the	DET
ap-9918	68	5	first	first	ADJ
ap-9918	68	6	attribute	attribute	NOUN
ap-9918	68	7	is	be	AUX
ap-9918	68	8	the	the	DET
ap-9918	68	9	class	class	NOUN
ap-9918	68	10	,	,	PUNCT
ap-9918	68	11	no	no	DET
ap-9918	68	12	-	-	PUNCT
ap-9918	68	13	recurrence	recurrence	NOUN
ap-9918	68	14	-	-	PUNCT
ap-9918	68	15	events	event	NOUN
ap-9918	68	16	or	or	CCONJ
ap-9918	68	17	recurrence	recurrence	NOUN
ap-9918	68	18	-	-	PUNCT
ap-9918	68	19	events	event	NOUN
ap-9918	68	20	,	,	PUNCT
ap-9918	68	21	and	and	CCONJ
ap-9918	68	22	the	the	DET
ap-9918	68	23	remaining	remain	VERB
ap-9918	68	24	9	9	NUM
ap-9918	68	25	attributes	attribute	NOUN
ap-9918	68	26	are	be	AUX
ap-9918	68	27	[	[	X
ap-9918	68	28	14	14	NUM
ap-9918	68	29	]	]	SYM
ap-9918	68	30	:	:	PUNCT
ap-9918	68	31	•	•	NUM
ap-9918	68	32	age	age	NOUN
ap-9918	68	33	:	:	PUNCT
ap-9918	68	34	10–19	10–19	NUM
ap-9918	68	35	,	,	PUNCT
ap-9918	68	36	20–29	20–29	NUM
ap-9918	68	37	,	,	PUNCT
ap-9918	68	38	30–39	30–39	NUM
ap-9918	68	39	,	,	PUNCT
ap-9918	68	40	40–49	40–49	NUM
ap-9918	68	41	,	,	PUNCT
ap-9918	68	42	50–59	50–59	NUM
ap-9918	68	43	,	,	PUNCT
ap-9918	68	44	60–69	60–69	NUM
ap-9918	68	45	,	,	PUNCT
ap-9918	68	46	70–79	70–79	ADV
ap-9918	68	47	,	,	PUNCT
ap-9918	68	48	80–89	80–89	NUM
ap-9918	68	49	,	,	PUNCT
ap-9918	68	50	90–99	90–99	NUM
ap-9918	68	51	.	.	NOUN
ap-9918	69	1	•	•	NUM
ap-9918	69	2	menopause	menopause	NOUN
ap-9918	69	3	(	(	PUNCT
ap-9918	69	4	pre	pre	ADJ
ap-9918	69	5	-	-	ADJ
ap-9918	69	6	or	or	CCONJ
ap-9918	69	7	postmenopausal	postmenopausal	ADJ
ap-9918	69	8	status	status	NOUN
ap-9918	69	9	at	at	ADP
ap-9918	69	10	the	the	DET
ap-9918	69	11	time	time	NOUN
ap-9918	69	12	of	of	ADP
ap-9918	69	13	diagnosis	diagnosis	NOUN
ap-9918	69	14	):	):	PUNCT
ap-9918	69	15	lt40	lt40	PROPN
ap-9918	69	16	,	,	PUNCT
ap-9918	69	17	ge40	ge40	PROPN
ap-9918	69	18	,	,	PUNCT
ap-9918	69	19	premeno	premeno	NOUN
ap-9918	69	20	.	.	PUNCT
ap-9918	70	1	•	•	NUM
ap-9918	70	2	tumour	tumour	NOUN
ap-9918	70	3	size	size	NOUN
ap-9918	70	4	:	:	PUNCT
ap-9918	70	5	every	every	DET
ap-9918	70	6	5	5	NUM
ap-9918	70	7	sequence	sequence	NOUN
ap-9918	70	8	units	unit	NOUN
ap-9918	70	9	represented	represent	VERB
ap-9918	70	10	a	a	DET
ap-9918	70	11	group	group	NOUN
ap-9918	70	12	.	.	PUNCT
ap-9918	71	1	for	for	ADP
ap-9918	71	2	example	example	NOUN
ap-9918	71	3	:	:	PUNCT
ap-9918	71	4	(	(	PUNCT
ap-9918	71	5	0–4	0–4	X
ap-9918	71	6	,	,	PUNCT
ap-9918	71	7	5–9	5–9	PROPN
ap-9918	71	8	,	,	PUNCT
ap-9918	71	9	.	.	PUNCT
ap-9918	71	10	.	.	PUNCT
ap-9918	71	11	.	.	PUNCT
ap-9918	72	1	,	,	PUNCT
ap-9918	72	2	55–59	55–59	NUM
ap-9918	72	3	)	)	PUNCT
ap-9918	72	4	•	•	NUM
ap-9918	72	5	inv	inv	NOUN
ap-9918	72	6	-	-	PUNCT
ap-9918	72	7	nodes	node	NOUN
ap-9918	72	8	(	(	PUNCT
ap-9918	72	9	axillary	axillary	ADJ
ap-9918	72	10	lymph	lymph	NOUN
ap-9918	72	11	nodes	node	NOUN
ap-9918	72	12	with	with	ADP
ap-9918	72	13	breast	breast	NOUN
ap-9918	72	14	cancer	cancer	NOUN
ap-9918	72	15	metastases	metastasis	NOUN
ap-9918	72	16	that	that	PRON
ap-9918	72	17	are	be	AUX
ap-9918	72	18	obvious	obvious	ADJ
ap-9918	72	19	):	):	PUNCT
ap-9918	72	20	every	every	DET
ap-9918	72	21	three	three	NUM
ap-9918	72	22	sequence	sequence	NOUN
ap-9918	72	23	numbers	number	NOUN
ap-9918	72	24	of	of	ADP
ap-9918	72	25	nodes	node	NOUN
ap-9918	72	26	represented	represent	VERB
ap-9918	72	27	one	one	NUM
ap-9918	72	28	group	group	NOUN
ap-9918	72	29	.	.	PUNCT
ap-9918	73	1	for	for	ADP
ap-9918	73	2	example	example	NOUN
ap-9918	73	3	:	:	PUNCT
ap-9918	73	4	(	(	PUNCT
ap-9918	73	5	0–2	0–2	NOUN
ap-9918	73	6	,	,	PUNCT
ap-9918	73	7	3–5	3–5	NOUN
ap-9918	73	8	,	,	PUNCT
ap-9918	73	9	.	.	PUNCT
ap-9918	73	10	.	.	PUNCT
ap-9918	73	11	.	.	PUNCT
ap-9918	74	1	,	,	PUNCT
ap-9918	74	2	36–39	36–39	NUM
ap-9918	74	3	)	)	PUNCT
ap-9918	74	4	•	•	NUM
ap-9918	74	5	node	node	NOUN
ap-9918	74	6	-	-	PUNCT
ap-9918	74	7	caps	cap	NOUN
ap-9918	74	8	:	:	PUNCT
ap-9918	75	1	yes	yes	INTJ
ap-9918	75	2	,	,	PUNCT
ap-9918	75	3	no	no	INTJ
ap-9918	75	4	.	.	NOUN
ap-9918	75	5	•	•	NOUN
ap-9918	75	6	degree	degree	NOUN
ap-9918	75	7	of	of	ADP
ap-9918	75	8	malign	malign	NOUN
ap-9918	75	9	:	:	PUNCT
ap-9918	75	10	1	1	NUM
ap-9918	75	11	,	,	PUNCT
ap-9918	75	12	2	2	NUM
ap-9918	75	13	,	,	PUNCT
ap-9918	75	14	3	3	NUM
ap-9918	75	15	.	.	NOUN
ap-9918	75	16	•	•	NUM
ap-9918	76	1	breast	breast	NOUN
ap-9918	76	2	:	:	PUNCT
ap-9918	76	3	right	right	ADJ
ap-9918	76	4	,	,	PUNCT
ap-9918	76	5	left	leave	VERB
ap-9918	76	6	.	.	PUNCT
ap-9918	77	1	•	•	NUM
ap-9918	77	2	breast	breast	NOUN
ap-9918	77	3	-	-	PUNCT
ap-9918	77	4	quad	quad	ADV
ap-9918	77	5	:	:	PUNCT
ap-9918	77	6	central	central	ADJ
ap-9918	77	7	,	,	PUNCT
ap-9918	77	8	left	leave	VERB
ap-9918	77	9	-	-	PUNCT
ap-9918	77	10	up	up	NOUN
ap-9918	77	11	,	,	PUNCT
ap-9918	77	12	left	left	ADJ
ap-9918	77	13	-	-	PUNCT
ap-9918	77	14	low	low	ADJ
ap-9918	77	15	,	,	PUNCT
ap-9918	77	16	right	right	ADJ
ap-9918	77	17	-	-	PUNCT
ap-9918	77	18	up	up	NOUN
ap-9918	77	19	,	,	PUNCT
ap-9918	77	20	right	right	ADJ
ap-9918	77	21	-	-	PUNCT
ap-9918	77	22	low	low	ADJ
ap-9918	77	23	.	.	PUNCT
ap-9918	78	1	•	•	NOUN
ap-9918	78	2	irradiance	irradiance	NOUN
ap-9918	78	3	:	:	PUNCT
ap-9918	78	4	no	no	INTJ
ap-9918	78	5	,	,	PUNCT
ap-9918	78	6	yes	yes	INTJ
ap-9918	78	7	.	.	PUNCT
ap-9918	79	1	during	during	ADP
ap-9918	79	2	pre	pre	ADJ
ap-9918	79	3	-	-	ADJ
ap-9918	79	4	processing	processing	NOUN
ap-9918	79	5	,	,	PUNCT
ap-9918	79	6	the	the	DET
ap-9918	79	7	dataset	dataset	NOUN
ap-9918	79	8	duplicates	duplicate	VERB
ap-9918	79	9	instances	instance	NOUN
ap-9918	79	10	of	of	ADP
ap-9918	79	11	the	the	DET
ap-9918	79	12	recurrence	recurrence	NOUN
ap-9918	79	13	class	class	NOUN
ap-9918	79	14	multiple	multiple	ADJ
ap-9918	79	15	times	time	NOUN
ap-9918	79	16	to	to	PART
ap-9918	79	17	balance	balance	VERB
ap-9918	79	18	it	it	PRON
ap-9918	79	19	with	with	ADP
ap-9918	79	20	the	the	DET
ap-9918	79	21	no	no	DET
ap-9918	79	22	-	-	PUNCT
ap-9918	79	23	recurrence	recurrence	NOUN
ap-9918	79	24	class	class	NOUN
ap-9918	79	25	,	,	PUNCT
ap-9918	79	26	improving	improve	VERB
ap-9918	79	27	learning	learn	VERB
ap-9918	79	28	outcomes	outcome	NOUN
ap-9918	79	29	.	.	PUNCT
ap-9918	80	1	this	this	DET
ap-9918	80	2	study	study	NOUN
ap-9918	80	3	used	use	VERB
ap-9918	80	4	three	three	NUM
ap-9918	80	5	datasets	dataset	NOUN
ap-9918	80	6	to	to	PART
ap-9918	80	7	offer	offer	VERB
ap-9918	80	8	various	various	ADJ
ap-9918	80	9	perspectives	perspective	NOUN
ap-9918	80	10	on	on	ADP
ap-9918	80	11	breast	breast	NOUN
ap-9918	80	12	cancer	cancer	NOUN
ap-9918	80	13	:	:	PUNCT
ap-9918	80	14	tumour	tumour	NOUN
ap-9918	80	15	cell	cell	NOUN
ap-9918	80	16	characteristics	characteristic	NOUN
ap-9918	80	17	(	(	PUNCT
ap-9918	80	18	wdbc	wdbc	PROPN
ap-9918	80	19	)	)	PUNCT
ap-9918	80	20	,	,	PUNCT
ap-9918	80	21	gene	gene	NOUN
ap-9918	80	22	-	-	PUNCT
ap-9918	80	23	level	level	NOUN
ap-9918	80	24	insights	insight	NOUN
ap-9918	80	25	(	(	PUNCT
ap-9918	80	26	gse42568	gse42568	NOUN
ap-9918	80	27	)	)	PUNCT
ap-9918	80	28	,	,	PUNCT
ap-9918	80	29	and	and	CCONJ
ap-9918	80	30	patient	patient	ADJ
ap-9918	80	31	recurrence	recurrence	NOUN
ap-9918	80	32	risk	risk	NOUN
ap-9918	80	33	factors	factor	NOUN
ap-9918	80	34	(	(	PUNCT
ap-9918	80	35	bcrd	bcrd	NOUN
ap-9918	80	36	)	)	PUNCT
ap-9918	80	37	.	.	PUNCT
ap-9918	81	1	they	they	PRON
ap-9918	81	2	help	help	VERB
ap-9918	81	3	create	create	VERB
ap-9918	81	4	strong	strong	ADJ
ap-9918	81	5	predictive	predictive	ADJ
ap-9918	81	6	models	model	NOUN
ap-9918	81	7	for	for	ADP
ap-9918	81	8	breast	breast	NOUN
ap-9918	81	9	cancer	cancer	NOUN
ap-9918	81	10	,	,	PUNCT
ap-9918	81	11	covering	cover	VERB
ap-9918	81	12	early	early	ADJ
ap-9918	81	13	diagnosis	diagnosis	NOUN
ap-9918	81	14	,	,	PUNCT
ap-9918	81	15	treatment	treatment	NOUN
ap-9918	81	16	planning	planning	NOUN
ap-9918	81	17	,	,	PUNCT
ap-9918	81	18	and	and	CCONJ
ap-9918	81	19	recurrence	recurrence	NOUN
ap-9918	81	20	prediction	prediction	NOUN
ap-9918	81	21	.	.	PUNCT
ap-9918	82	1	3	3	X
ap-9918	82	2	.	.	X
ap-9918	82	3	materials	material	NOUN
ap-9918	82	4	and	and	CCONJ
ap-9918	82	5	methods	method	NOUN
ap-9918	82	6	predicting	predict	VERB
ap-9918	82	7	breast	breast	NOUN
ap-9918	82	8	cancer	cancer	NOUN
ap-9918	82	9	recurrence	recurrence	NOUN
ap-9918	82	10	is	be	AUX
ap-9918	82	11	a	a	DET
ap-9918	82	12	critical	critical	ADJ
ap-9918	82	13	task	task	NOUN
ap-9918	82	14	in	in	ADP
ap-9918	82	15	medical	medical	ADJ
ap-9918	82	16	research	research	NOUN
ap-9918	82	17	,	,	PUNCT
ap-9918	82	18	with	with	ADP
ap-9918	82	19	the	the	DET
ap-9918	82	20	goal	goal	NOUN
ap-9918	82	21	of	of	ADP
ap-9918	82	22	accurately	accurately	ADV
ap-9918	82	23	identifying	identify	VERB
ap-9918	82	24	patients	patient	NOUN
ap-9918	82	25	at	at	ADP
ap-9918	82	26	risk	risk	NOUN
ap-9918	82	27	of	of	ADP
ap-9918	82	28	disease	disease	NOUN
ap-9918	82	29	recurrence	recurrence	NOUN
ap-9918	82	30	for	for	ADP
ap-9918	82	31	timely	timely	ADJ
ap-9918	82	32	intervention	intervention	NOUN
ap-9918	82	33	and	and	CCONJ
ap-9918	82	34	improved	improved	ADJ
ap-9918	82	35	prognosis	prognosis	NOUN
ap-9918	82	36	.	.	PUNCT
ap-9918	83	1	this	this	DET
ap-9918	83	2	comparative	comparative	ADJ
ap-9918	83	3	analysis	analysis	NOUN
ap-9918	83	4	study	study	NOUN
ap-9918	83	5	used	use	VERB
ap-9918	83	6	several	several	ADJ
ap-9918	83	7	state	state	NOUN
ap-9918	83	8	-	-	PUNCT
ap-9918	83	9	of	of	ADP
ap-9918	83	10	-	-	PUNCT
ap-9918	83	11	the	the	DET
ap-9918	83	12	-	-	PUNCT
ap-9918	83	13	art	art	NOUN
ap-9918	83	14	machine	machine	NOUN
ap-9918	83	15	learning	learn	VERB
ap-9918	83	16	algorithms	algorithm	NOUN
ap-9918	83	17	to	to	PART
ap-9918	83	18	explore	explore	VERB
ap-9918	83	19	their	their	PRON
ap-9918	83	20	effectiveness	effectiveness	NOUN
ap-9918	83	21	in	in	ADP
ap-9918	83	22	detecting	detect	VERB
ap-9918	83	23	and	and	CCONJ
ap-9918	83	24	predicting	predict	VERB
ap-9918	83	25	breast	breast	NOUN
ap-9918	83	26	cancer	cancer	NOUN
ap-9918	83	27	recurrence	recurrence	NOUN
ap-9918	83	28	.	.	PUNCT
ap-9918	84	1	it	it	PRON
ap-9918	84	2	used	use	VERB
ap-9918	84	3	catboost	catboost	PROPN
ap-9918	84	4	,	,	PUNCT
ap-9918	84	5	xgboost	xgboost	PROPN
ap-9918	84	6	,	,	PUNCT
ap-9918	84	7	knn	knn	PROPN
ap-9918	84	8	,	,	PUNCT
ap-9918	84	9	random	random	ADJ
ap-9918	84	10	forest	forest	NOUN
ap-9918	84	11	,	,	PUNCT
ap-9918	84	12	naive	naive	ADJ
ap-9918	84	13	bayes	bayes	NOUN
ap-9918	84	14	,	,	PUNCT
ap-9918	84	15	and	and	CCONJ
ap-9918	84	16	libsvm	libsvm	VERB
ap-9918	84	17	to	to	PART
ap-9918	84	18	ensure	ensure	VERB
ap-9918	84	19	a	a	DET
ap-9918	84	20	comprehensive	comprehensive	ADJ
ap-9918	84	21	comparison	comparison	NOUN
ap-9918	84	22	of	of	ADP
ap-9918	84	23	different	different	ADJ
ap-9918	84	24	machine	machine	NOUN
ap-9918	84	25	learning	learn	VERB
ap-9918	84	26	techniques	technique	NOUN
ap-9918	84	27	.	.	PUNCT
ap-9918	85	1	this	this	DET
ap-9918	85	2	diversity	diversity	NOUN
ap-9918	85	3	allows	allow	VERB
ap-9918	85	4	us	we	PRON
ap-9918	85	5	to	to	PART
ap-9918	85	6	identify	identify	VERB
ap-9918	85	7	the	the	DET
ap-9918	85	8	best	well	ADV
ap-9918	85	9	-	-	PUNCT
ap-9918	85	10	performing	perform	VERB
ap-9918	85	11	algorithm	algorithm	NOUN
ap-9918	85	12	for	for	ADP
ap-9918	85	13	breast	breast	NOUN
ap-9918	85	14	cancer	cancer	NOUN
ap-9918	85	15	classification	classification	NOUN
ap-9918	85	16	tasks	task	NOUN
ap-9918	85	17	and	and	CCONJ
ap-9918	85	18	better	well	ADV
ap-9918	85	19	understand	understand	VERB
ap-9918	85	20	how	how	SCONJ
ap-9918	85	21	different	different	ADJ
ap-9918	85	22	methods	method	NOUN
ap-9918	85	23	handle	handle	VERB
ap-9918	85	24	the	the	DET
ap-9918	85	25	nuances	nuance	NOUN
ap-9918	85	26	of	of	ADP
ap-9918	85	27	different	different	ADJ
ap-9918	85	28	datasets	dataset	NOUN
ap-9918	85	29	.	.	PUNCT
ap-9918	86	1	3.1	3.1	NUM
ap-9918	86	2	.	.	X
ap-9918	86	3	catboost	catboost	PROPN
ap-9918	86	4	catboost	catboost	PROPN
ap-9918	86	5	(	(	PUNCT
ap-9918	86	6	categorical	categorical	ADJ
ap-9918	86	7	boosting	boosting	NOUN
ap-9918	86	8	)	)	PUNCT
ap-9918	86	9	is	be	AUX
ap-9918	86	10	a	a	DET
ap-9918	86	11	gradient	gradient	ADJ
ap-9918	86	12	boosting	boost	VERB
ap-9918	86	13	algorithm	algorithm	NOUN
ap-9918	86	14	for	for	ADP
ap-9918	86	15	decision	decision	NOUN
ap-9918	86	16	trees	tree	NOUN
ap-9918	86	17	.	.	PUNCT
ap-9918	87	1	it	it	PRON
ap-9918	87	2	is	be	AUX
ap-9918	87	3	particularly	particularly	ADV
ap-9918	87	4	useful	useful	ADJ
ap-9918	87	5	for	for	ADP
ap-9918	87	6	categorical	categorical	ADJ
ap-9918	87	7	features	feature	NOUN
ap-9918	87	8	.	.	PUNCT
ap-9918	88	1	it	it	PRON
ap-9918	88	2	was	be	AUX
ap-9918	88	3	developed	develop	VERB
ap-9918	88	4	by	by	ADP
ap-9918	88	5	yandex	yandex	NOUN
ap-9918	88	6	researchers	researcher	NOUN
ap-9918	88	7	and	and	CCONJ
ap-9918	88	8	engineers	engineer	NOUN
ap-9918	88	9	[	[	X
ap-9918	88	10	15	15	NUM
ap-9918	88	11	]	]	PUNCT
ap-9918	88	12	.	.	PUNCT
ap-9918	89	1	thus	thus	ADV
ap-9918	89	2	,	,	PUNCT
ap-9918	89	3	datasets	dataset	NOUN
ap-9918	89	4	including	include	VERB
ap-9918	89	5	a	a	DET
ap-9918	89	6	range	range	NOUN
ap-9918	89	7	of	of	ADP
ap-9918	89	8	data	datum	NOUN
ap-9918	89	9	types	type	NOUN
ap-9918	89	10	can	can	AUX
ap-9918	89	11	benefit	benefit	VERB
ap-9918	89	12	from	from	ADP
ap-9918	89	13	it	it	PRON
ap-9918	89	14	.	.	PUNCT
ap-9918	90	1	it	it	PRON
ap-9918	90	2	makes	make	VERB
ap-9918	90	3	use	use	NOUN
ap-9918	90	4	of	of	ADP
ap-9918	90	5	an	an	DET
ap-9918	90	6	advanced	advanced	ADJ
ap-9918	90	7	technique	technique	NOUN
ap-9918	90	8	called	call	VERB
ap-9918	90	9	ordered	ordered	ADJ
ap-9918	90	10	boosting	boosting	NOUN
ap-9918	90	11	,	,	PUNCT
ap-9918	90	12	which	which	PRON
ap-9918	90	13	performs	perform	VERB
ap-9918	90	14	better	well	ADV
ap-9918	90	15	than	than	ADP
ap-9918	90	16	traditional	traditional	ADJ
ap-9918	90	17	137	137	NUM
ap-9918	90	18	r.	r.	PROPN
ap-9918	90	19	d.	d.	PROPN
ap-9918	90	20	abdu	abdu	PROPN
ap-9918	90	21	-	-	PUNCT
ap-9918	90	22	aljabar	aljabar	PROPN
ap-9918	90	23	,	,	PUNCT
ap-9918	90	24	k.	k.	PROPN
ap-9918	90	25	d.	d.	PROPN
ap-9918	90	26	aljafaar	aljafaar	PROPN
ap-9918	90	27	,	,	PUNCT
ap-9918	90	28	z.	z.	PROPN
ap-9918	90	29	j.	j.	PROPN
ap-9918	90	30	m.	m.	PROPN
ap-9918	90	31	ameen	ameen	PROPN
ap-9918	90	32	,	,	PUNCT
ap-9918	90	33	h.	h.	PROPN
ap-9918	90	34	a.	a.	PROPN
ap-9918	90	35	naman	naman	PROPN
ap-9918	90	36	acta	acta	PROPN
ap-9918	90	37	polytechnica	polytechnica	PROPN
ap-9918	90	38	gradient	gradient	NOUN
ap-9918	90	39	boosting	boost	VERB
ap-9918	90	40	algorithms	algorithm	NOUN
ap-9918	90	41	and	and	CCONJ
ap-9918	90	42	provides	provide	VERB
ap-9918	90	43	shorter	short	ADJ
ap-9918	90	44	training	training	NOUN
ap-9918	90	45	times	time	NOUN
ap-9918	91	1	[	[	X
ap-9918	91	2	16	16	NUM
ap-9918	91	3	]	]	PUNCT
ap-9918	91	4	.	.	PUNCT
ap-9918	92	1	with	with	ADP
ap-9918	92	2	its	its	PRON
ap-9918	92	3	ability	ability	NOUN
ap-9918	92	4	to	to	PART
ap-9918	92	5	reduce	reduce	VERB
ap-9918	92	6	overfitting	overfitte	VERB
ap-9918	92	7	and	and	CCONJ
ap-9918	92	8	limit	limit	VERB
ap-9918	92	9	the	the	DET
ap-9918	92	10	need	need	NOUN
ap-9918	92	11	for	for	ADP
ap-9918	92	12	hyperparameter	hyperparameter	NOUN
ap-9918	92	13	adjustments	adjustment	NOUN
ap-9918	92	14	,	,	PUNCT
ap-9918	92	15	catboost	catboost	PROPN
ap-9918	92	16	is	be	AUX
ap-9918	92	17	a	a	DET
ap-9918	92	18	desirable	desirable	ADJ
ap-9918	92	19	choice	choice	NOUN
ap-9918	92	20	for	for	ADP
ap-9918	92	21	practical	practical	ADJ
ap-9918	92	22	uses	use	NOUN
ap-9918	92	23	.	.	PUNCT
ap-9918	93	1	this	this	DET
ap-9918	93	2	method	method	NOUN
ap-9918	93	3	uses	use	VERB
ap-9918	93	4	the	the	DET
ap-9918	93	5	same	same	ADJ
ap-9918	93	6	framework	framework	NOUN
ap-9918	93	7	of	of	ADP
ap-9918	93	8	gradient	gradient	NOUN
ap-9918	93	9	boosting	boost	VERB
ap-9918	93	10	to	to	PART
ap-9918	93	11	increase	increase	VERB
ap-9918	93	12	the	the	DET
ap-9918	93	13	model	model	NOUN
ap-9918	93	14	accuracy	accuracy	NOUN
ap-9918	93	15	,	,	PUNCT
ap-9918	93	16	which	which	PRON
ap-9918	93	17	focuses	focus	VERB
ap-9918	93	18	on	on	ADP
ap-9918	93	19	poorly	poorly	ADV
ap-9918	93	20	classified	classify	VERB
ap-9918	93	21	samples	sample	NOUN
ap-9918	93	22	.	.	PUNCT
ap-9918	94	1	additionally	additionally	ADV
ap-9918	94	2	,	,	PUNCT
ap-9918	94	3	it	it	PRON
ap-9918	94	4	is	be	AUX
ap-9918	94	5	highly	highly	ADV
ap-9918	94	6	suitable	suitable	ADJ
ap-9918	94	7	for	for	ADP
ap-9918	94	8	real	real	ADJ
ap-9918	94	9	-	-	PUNCT
ap-9918	94	10	world	world	NOUN
ap-9918	94	11	data	datum	NOUN
ap-9918	94	12	as	as	SCONJ
ap-9918	94	13	it	it	PRON
ap-9918	94	14	handles	handle	VERB
ap-9918	94	15	categorical	categorical	ADJ
ap-9918	94	16	information	information	NOUN
ap-9918	94	17	without	without	ADP
ap-9918	94	18	requiring	require	VERB
ap-9918	94	19	extra	extra	ADJ
ap-9918	94	20	encoding	encoding	NOUN
ap-9918	94	21	or	or	CCONJ
ap-9918	94	22	pre	pre	NOUN
ap-9918	94	23	-	-	ADJ
ap-9918	94	24	processing	processing	NOUN
ap-9918	94	25	.	.	PUNCT
ap-9918	95	1	3.1.1	3.1.1	X
ap-9918	95	2	.	.	PUNCT
ap-9918	95	3	catboost	catboost	PROPN
ap-9918	95	4	work	work	VERB
ap-9918	95	5	the	the	DET
ap-9918	95	6	catboost	catboost	ADJ
ap-9918	95	7	approach	approach	NOUN
ap-9918	95	8	uses	use	VERB
ap-9918	95	9	gradient	gradient	ADJ
ap-9918	95	10	descent	descent	NOUN
ap-9918	95	11	to	to	PART
ap-9918	95	12	decrease	decrease	VERB
ap-9918	95	13	the	the	DET
ap-9918	95	14	loss	loss	NOUN
ap-9918	95	15	function	function	NOUN
ap-9918	95	16	,	,	PUNCT
ap-9918	95	17	iteratively	iteratively	ADV
ap-9918	95	18	constructing	construct	VERB
ap-9918	95	19	an	an	DET
ap-9918	95	20	ensemble	ensemble	NOUN
ap-9918	95	21	of	of	ADP
ap-9918	95	22	trees	tree	NOUN
ap-9918	95	23	.	.	PUNCT
ap-9918	96	1	each	each	DET
ap-9918	96	2	time	time	NOUN
ap-9918	96	3	,	,	PUNCT
ap-9918	96	4	a	a	DET
ap-9918	96	5	new	new	ADJ
ap-9918	96	6	tree	tree	NOUN
ap-9918	96	7	is	be	AUX
ap-9918	96	8	added	add	VERB
ap-9918	96	9	to	to	PART
ap-9918	96	10	minimise	minimise	VERB
ap-9918	96	11	the	the	DET
ap-9918	96	12	loss	loss	NOUN
ap-9918	96	13	function	function	NOUN
ap-9918	96	14	.	.	PUNCT
ap-9918	97	1	once	once	ADV
ap-9918	97	2	the	the	DET
ap-9918	97	3	negative	negative	ADJ
ap-9918	97	4	gradient	gradient	NOUN
ap-9918	97	5	of	of	ADP
ap-9918	97	6	the	the	DET
ap-9918	97	7	loss	loss	NOUN
ap-9918	97	8	function	function	NOUN
ap-9918	97	9	for	for	ADP
ap-9918	97	10	the	the	DET
ap-9918	97	11	current	current	ADJ
ap-9918	97	12	predictions	prediction	NOUN
ap-9918	97	13	has	have	AUX
ap-9918	97	14	been	be	AUX
ap-9918	97	15	found	find	VERB
ap-9918	97	16	,	,	PUNCT
ap-9918	97	17	a	a	DET
ap-9918	97	18	new	new	ADJ
ap-9918	97	19	tree	tree	NOUN
ap-9918	97	20	is	be	AUX
ap-9918	97	21	fitted	fit	VERB
ap-9918	97	22	to	to	ADP
ap-9918	97	23	the	the	DET
ap-9918	97	24	gradient	gradient	NOUN
ap-9918	97	25	.	.	PUNCT
ap-9918	98	1	the	the	DET
ap-9918	98	2	gradient	gradient	ADJ
ap-9918	98	3	descent	descent	NOUN
ap-9918	98	4	step	step	NOUN
ap-9918	98	5	size	size	NOUN
ap-9918	98	6	is	be	AUX
ap-9918	98	7	determined	determine	VERB
ap-9918	98	8	by	by	ADP
ap-9918	98	9	the	the	DET
ap-9918	98	10	learning	learning	NOUN
ap-9918	98	11	rate	rate	NOUN
ap-9918	98	12	.	.	PUNCT
ap-9918	99	1	the	the	DET
ap-9918	99	2	method	method	NOUN
ap-9918	99	3	is	be	AUX
ap-9918	99	4	repeated	repeat	VERB
ap-9918	99	5	until	until	SCONJ
ap-9918	99	6	the	the	DET
ap-9918	99	7	convergence	convergence	NOUN
ap-9918	99	8	conditions	condition	NOUN
ap-9918	99	9	are	be	AUX
ap-9918	99	10	met	meet	VERB
ap-9918	99	11	or	or	CCONJ
ap-9918	99	12	a	a	DET
ap-9918	99	13	predefined	predefine	VERB
ap-9918	99	14	number	number	NOUN
ap-9918	99	15	of	of	ADP
ap-9918	99	16	trees	tree	NOUN
ap-9918	99	17	has	have	AUX
ap-9918	99	18	been	be	AUX
ap-9918	99	19	added	add	VERB
ap-9918	99	20	.	.	PUNCT
ap-9918	100	1	catboost	catboost	PROPN
ap-9918	100	2	combines	combine	VERB
ap-9918	100	3	its	its	PRON
ap-9918	100	4	predictions	prediction	NOUN
ap-9918	100	5	from	from	ADP
ap-9918	100	6	every	every	DET
ap-9918	100	7	tree	tree	NOUN
ap-9918	100	8	in	in	ADP
ap-9918	100	9	the	the	DET
ap-9918	100	10	ensemble	ensemble	ADJ
ap-9918	100	11	to	to	PART
ap-9918	100	12	generate	generate	VERB
ap-9918	100	13	its	its	PRON
ap-9918	100	14	final	final	ADJ
ap-9918	100	15	output	output	NOUN
ap-9918	100	16	.	.	PUNCT
ap-9918	101	1	3.1.2	3.1.2	X
ap-9918	101	2	.	.	X
ap-9918	101	3	catboost	catboost	PROPN
ap-9918	101	4	math	math	NOUN
ap-9918	101	5	the	the	DET
ap-9918	101	6	following	follow	VERB
ap-9918	101	7	is	be	AUX
ap-9918	101	8	a	a	DET
ap-9918	101	9	representation	representation	NOUN
ap-9918	101	10	of	of	ADP
ap-9918	101	11	catboost	catboost	NOUN
ap-9918	101	12	:	:	PUNCT
ap-9918	101	13	catboost	catboost	VERB
ap-9918	101	14	attempts	attempt	NOUN
ap-9918	101	15	to	to	PART
ap-9918	101	16	learn	learn	VERB
ap-9918	101	17	an	an	DET
ap-9918	101	18	f	f	PROPN
ap-9918	101	19	(	(	PUNCT
ap-9918	101	20	x	x	NOUN
ap-9918	101	21	)	)	PUNCT
ap-9918	101	22	function	function	NOUN
ap-9918	101	23	that	that	PRON
ap-9918	101	24	predicts	predict	VERB
ap-9918	101	25	the	the	DET
ap-9918	101	26	target	target	NOUN
ap-9918	101	27	,	,	PUNCT
ap-9918	101	28	which	which	PRON
ap-9918	101	29	is	be	AUX
ap-9918	101	30	the	the	DET
ap-9918	101	31	y	y	PROPN
ap-9918	101	32	variable	variable	NOUN
ap-9918	101	33	,	,	PUNCT
ap-9918	101	34	from	from	ADP
ap-9918	101	35	a	a	DET
ap-9918	101	36	training	training	NOUN
ap-9918	101	37	dataset	dataset	NOUN
ap-9918	101	38	containing	contain	VERB
ap-9918	101	39	n	n	CCONJ
ap-9918	101	40	samples	sample	NOUN
ap-9918	101	41	and	and	CCONJ
ap-9918	101	42	m	m	NOUN
ap-9918	101	43	features	feature	NOUN
ap-9918	101	44	.	.	PUNCT
ap-9918	102	1	each	each	DET
ap-9918	102	2	sample	sample	NOUN
ap-9918	102	3	is	be	AUX
ap-9918	102	4	represented	represent	VERB
ap-9918	102	5	as	as	ADP
ap-9918	102	6	(	(	PUNCT
ap-9918	102	7	xi	xi	PROPN
ap-9918	102	8	,	,	PUNCT
ap-9918	102	9	yi	yi	PROPN
ap-9918	102	10	)	)	PUNCT
ap-9918	102	11	.	.	PUNCT
ap-9918	103	1	where	where	SCONJ
ap-9918	103	2	xi	xi	PROPN
ap-9918	103	3	is	be	AUX
ap-9918	103	4	the	the	DET
ap-9918	103	5	m	m	NOUN
ap-9918	103	6	features	feature	NOUN
ap-9918	103	7	vector	vector	NOUN
ap-9918	103	8	and	and	CCONJ
ap-9918	103	9	yi	yi	NOUN
ap-9918	103	10	is	be	AUX
ap-9918	103	11	its	its	PRON
ap-9918	103	12	target	target	NOUN
ap-9918	103	13	variable	variable	NOUN
ap-9918	103	14	.	.	PUNCT
ap-9918	104	1	f	f	X
ap-9918	104	2	(	(	PUNCT
ap-9918	104	3	x	x	X
ap-9918	104	4	)	)	PUNCT
ap-9918	104	5	=	=	SYM
ap-9918	104	6	f0(x	f0(x	NOUN
ap-9918	104	7	)	)	PUNCT
ap-9918	105	1	+	+	CCONJ
ap-9918	105	2	m∑	m∑	PROPN
ap-9918	105	3	m=1	m=1	PROPN
ap-9918	105	4	n∑	n∑	NOUN
ap-9918	105	5	i=1	i=1	PROPN
ap-9918	105	6	fm(xi	fm(xi	PROPN
ap-9918	105	7	)	)	PUNCT
ap-9918	105	8	,	,	PUNCT
ap-9918	105	9	(	(	PUNCT
ap-9918	105	10	1	1	X
ap-9918	105	11	)	)	PUNCT
ap-9918	105	12	where	where	SCONJ
ap-9918	105	13	f	f	PROPN
ap-9918	105	14	(	(	PUNCT
ap-9918	105	15	x	x	X
ap-9918	105	16	)	)	PUNCT
ap-9918	105	17	is	be	AUX
ap-9918	105	18	what	what	PRON
ap-9918	105	19	catboost	catboost	PROPN
ap-9918	105	20	aims	aim	VERB
ap-9918	105	21	to	to	PART
ap-9918	105	22	define	define	VERB
ap-9918	105	23	as	as	ADP
ap-9918	105	24	the	the	DET
ap-9918	105	25	general	general	ADJ
ap-9918	105	26	prediction	prediction	NOUN
ap-9918	105	27	function	function	NOUN
ap-9918	105	28	.	.	PUNCT
ap-9918	106	1	it	it	PRON
ap-9918	106	2	predicts	predict	VERB
ap-9918	106	3	the	the	DET
ap-9918	106	4	appropriate	appropriate	ADJ
ap-9918	106	5	target	target	NOUN
ap-9918	106	6	variable	variable	NOUN
ap-9918	106	7	,	,	PUNCT
ap-9918	106	8	y	y	PROPN
ap-9918	106	9	,	,	PUNCT
ap-9918	106	10	given	give	VERB
ap-9918	106	11	an	an	DET
ap-9918	106	12	input	input	NOUN
ap-9918	106	13	vector	vector	NOUN
ap-9918	106	14	,	,	PUNCT
ap-9918	106	15	x.	x.	NOUN
ap-9918	106	16	f0(x	f0(x	NOUN
ap-9918	106	17	)	)	PUNCT
ap-9918	106	18	is	be	AUX
ap-9918	106	19	the	the	DET
ap-9918	106	20	first	first	ADJ
ap-9918	106	21	estimation	estimation	NOUN
ap-9918	106	22	or	or	CCONJ
ap-9918	106	23	baseline	baseline	NOUN
ap-9918	106	24	forecast	forecast	NOUN
ap-9918	106	25	.	.	PUNCT
ap-9918	107	1	it	it	PRON
ap-9918	107	2	is	be	AUX
ap-9918	107	3	frequently	frequently	ADV
ap-9918	107	4	set	set	VERB
ap-9918	107	5	equal	equal	ADJ
ap-9918	107	6	to	to	ADP
ap-9918	107	7	the	the	DET
ap-9918	107	8	training	training	NOUN
ap-9918	107	9	dataset	dataset	NOUN
ap-9918	107	10	’s	’s	PART
ap-9918	107	11	target	target	PROPN
ap-9918	107	12	variable	variable	NOUN
ap-9918	107	13	’s	’s	PART
ap-9918	107	14	mean.∑m	mean.∑m	NOUN
ap-9918	107	15	m=1	m=1	PROPN
ap-9918	107	16	represents	represent	VERB
ap-9918	107	17	the	the	DET
ap-9918	107	18	total	total	ADJ
ap-9918	107	19	collection	collection	NOUN
ap-9918	107	20	of	of	ADP
ap-9918	107	21	trees	tree	NOUN
ap-9918	107	22	.	.	PUNCT
ap-9918	108	1	the	the	DET
ap-9918	108	2	ensemble	ensemble	ADJ
ap-9918	108	3	’s	’s	PART
ap-9918	108	4	total	total	ADJ
ap-9918	108	5	number	number	NOUN
ap-9918	108	6	of	of	ADP
ap-9918	108	7	trees	tree	NOUN
ap-9918	108	8	is	be	AUX
ap-9918	108	9	m	m	PROPN
ap-9918	108	10	.∑n	.∑n	NUM
ap-9918	108	11	i=1	i=1	PROPN
ap-9918	108	12	represents	represent	VERB
ap-9918	108	13	the	the	DET
ap-9918	108	14	total	total	ADJ
ap-9918	108	15	collection	collection	NOUN
ap-9918	108	16	of	of	ADP
ap-9918	108	17	training	training	NOUN
ap-9918	108	18	sets	set	NOUN
ap-9918	108	19	.	.	PUNCT
ap-9918	109	1	the	the	DET
ap-9918	109	2	ensemble	ensemble	ADJ
ap-9918	109	3	’s	’s	PART
ap-9918	109	4	total	total	ADJ
ap-9918	109	5	number	number	NOUN
ap-9918	109	6	of	of	ADP
ap-9918	109	7	training	training	NOUN
ap-9918	109	8	samples	sample	NOUN
ap-9918	109	9	is	be	AUX
ap-9918	109	10	n	n	PRON
ap-9918	109	11	.	.	PUNCT
ap-9918	110	1	fm(xi	fm(xi	NOUN
ap-9918	110	2	)	)	PUNCT
ap-9918	110	3	represents	represent	VERB
ap-9918	110	4	the	the	DET
ap-9918	110	5	mth	mth	NOUN
ap-9918	110	6	tree	tree	NOUN
ap-9918	110	7	’s	’s	PART
ap-9918	110	8	expected	expect	VERB
ap-9918	110	9	value	value	NOUN
ap-9918	110	10	for	for	ADP
ap-9918	110	11	the	the	DET
ap-9918	110	12	ith	ith	PROPN
ap-9918	110	13	training	training	NOUN
ap-9918	110	14	set	set	NOUN
ap-9918	110	15	.	.	PUNCT
ap-9918	111	1	every	every	DET
ap-9918	111	2	tree	tree	NOUN
ap-9918	111	3	in	in	ADP
ap-9918	111	4	the	the	DET
ap-9918	111	5	ensemble	ensemble	ADJ
ap-9918	111	6	contributes	contribute	VERB
ap-9918	111	7	its	its	PRON
ap-9918	111	8	own	own	ADJ
ap-9918	111	9	forecast	forecast	NOUN
ap-9918	111	10	to	to	ADP
ap-9918	111	11	the	the	DET
ap-9918	111	12	final	final	ADJ
ap-9918	111	13	prediction	prediction	NOUN
ap-9918	111	14	for	for	ADP
ap-9918	111	15	every	every	DET
ap-9918	111	16	training	training	NOUN
ap-9918	111	17	sample	sample	NOUN
ap-9918	111	18	.	.	PUNCT
ap-9918	112	1	the	the	DET
ap-9918	112	2	overall	overall	ADJ
ap-9918	112	3	prediction	prediction	NOUN
ap-9918	112	4	f	f	X
ap-9918	112	5	(	(	PUNCT
ap-9918	112	6	x	x	X
ap-9918	112	7	)	)	PUNCT
ap-9918	112	8	can	can	AUX
ap-9918	112	9	be	be	AUX
ap-9918	112	10	calculated	calculate	VERB
ap-9918	112	11	by	by	ADP
ap-9918	112	12	adding	add	VERB
ap-9918	112	13	the	the	DET
ap-9918	112	14	forecasts	forecast	NOUN
ap-9918	112	15	of	of	ADP
ap-9918	112	16	each	each	DET
ap-9918	112	17	tree	tree	NOUN
ap-9918	112	18	,	,	PUNCT
ap-9918	112	19	fm(xi	fm(xi	PROPN
ap-9918	112	20	)	)	PUNCT
ap-9918	112	21	,	,	PUNCT
ap-9918	112	22	for	for	ADP
ap-9918	112	23	each	each	DET
ap-9918	112	24	training	training	NOUN
ap-9918	112	25	sample	sample	NOUN
ap-9918	112	26	and	and	CCONJ
ap-9918	112	27	the	the	DET
ap-9918	112	28	initial	initial	ADJ
ap-9918	112	29	guess	guess	NOUN
ap-9918	112	30	f0(x	f0(x	NOUN
ap-9918	112	31	)	)	PUNCT
ap-9918	112	32	,	,	PUNCT
ap-9918	112	33	according	accord	VERB
ap-9918	112	34	to	to	ADP
ap-9918	112	35	the	the	DET
ap-9918	112	36	equation	equation	NOUN
ap-9918	112	37	.	.	PUNCT
ap-9918	113	1	every	every	DET
ap-9918	113	2	tree	tree	NOUN
ap-9918	113	3	(	(	PUNCT
ap-9918	113	4	m	m	NOUN
ap-9918	113	5	)	)	PUNCT
ap-9918	113	6	and	and	CCONJ
ap-9918	113	7	training	training	NOUN
ap-9918	113	8	sample	sample	NOUN
ap-9918	113	9	(	(	PUNCT
ap-9918	113	10	i	i	NOUN
ap-9918	113	11	)	)	PUNCT
ap-9918	113	12	passes	pass	VERB
ap-9918	113	13	through	through	ADP
ap-9918	113	14	the	the	DET
ap-9918	113	15	summing	sum	VERB
ap-9918	113	16	procedure	procedure	NOUN
ap-9918	113	17	[	[	X
ap-9918	113	18	16	16	NUM
ap-9918	113	19	]	]	PUNCT
ap-9918	113	20	.	.	PUNCT
ap-9918	114	1	3.2	3.2	NUM
ap-9918	114	2	.	.	PUNCT
ap-9918	115	1	k	k	X
ap-9918	115	2	-	-	PUNCT
ap-9918	115	3	nearest	near	ADJ
ap-9918	115	4	neighbours	neighbour	NOUN
ap-9918	115	5	(	(	PUNCT
ap-9918	115	6	knn	knn	PROPN
ap-9918	115	7	)	)	PUNCT
ap-9918	115	8	knn	knn	PROPN
ap-9918	115	9	is	be	AUX
ap-9918	115	10	a	a	DET
ap-9918	115	11	straightforward	straightforward	ADJ
ap-9918	115	12	,	,	PUNCT
ap-9918	115	13	yet	yet	CCONJ
ap-9918	115	14	effective	effective	ADJ
ap-9918	115	15	technique	technique	NOUN
ap-9918	115	16	that	that	PRON
ap-9918	115	17	can	can	AUX
ap-9918	115	18	be	be	AUX
ap-9918	115	19	applied	apply	VERB
ap-9918	115	20	to	to	ADP
ap-9918	115	21	regression	regression	NOUN
ap-9918	115	22	and	and	CCONJ
ap-9918	115	23	classification	classification	NOUN
ap-9918	115	24	problems	problem	NOUN
ap-9918	115	25	.	.	PUNCT
ap-9918	116	1	it	it	PRON
ap-9918	116	2	works	work	VERB
ap-9918	116	3	on	on	ADP
ap-9918	116	4	the	the	DET
ap-9918	116	5	principle	principle	NOUN
ap-9918	116	6	of	of	ADP
ap-9918	116	7	similarity	similarity	NOUN
ap-9918	116	8	,	,	PUNCT
ap-9918	116	9	where	where	SCONJ
ap-9918	116	10	the	the	DET
ap-9918	116	11	average	average	ADJ
ap-9918	116	12	value	value	NOUN
ap-9918	116	13	or	or	CCONJ
ap-9918	116	14	majority	majority	NOUN
ap-9918	116	15	class	class	NOUN
ap-9918	116	16	of	of	ADP
ap-9918	116	17	an	an	DET
ap-9918	116	18	instance	instance	NOUN
ap-9918	116	19	’s	’s	PART
ap-9918	116	20	closest	close	ADJ
ap-9918	116	21	neighbours	neighbour	NOUN
ap-9918	116	22	in	in	ADP
ap-9918	116	23	the	the	DET
ap-9918	116	24	feature	feature	NOUN
ap-9918	116	25	space	space	NOUN
ap-9918	116	26	is	be	AUX
ap-9918	116	27	used	use	VERB
ap-9918	116	28	to	to	PART
ap-9918	116	29	make	make	VERB
ap-9918	116	30	predictions	prediction	NOUN
ap-9918	116	31	for	for	ADP
ap-9918	116	32	that	that	DET
ap-9918	116	33	instance	instance	NOUN
ap-9918	116	34	.	.	PUNCT
ap-9918	117	1	knn	knn	PROPN
ap-9918	117	2	is	be	AUX
ap-9918	117	3	computationally	computationally	ADV
ap-9918	117	4	efficient	efficient	ADJ
ap-9918	117	5	for	for	ADP
ap-9918	117	6	small	small	ADJ
ap-9918	117	7	to	to	PART
ap-9918	117	8	medium	medium	ADJ
ap-9918	117	9	-	-	PUNCT
ap-9918	117	10	sized	sized	ADJ
ap-9918	117	11	datasets	dataset	NOUN
ap-9918	117	12	because	because	SCONJ
ap-9918	117	13	it	it	PRON
ap-9918	117	14	is	be	AUX
ap-9918	117	15	non	non	ADJ
ap-9918	117	16	-	-	ADJ
ap-9918	117	17	parametric	parametric	ADJ
ap-9918	117	18	and	and	CCONJ
ap-9918	117	19	does	do	AUX
ap-9918	117	20	n’t	not	PART
ap-9918	117	21	require	require	VERB
ap-9918	117	22	a	a	DET
ap-9918	117	23	training	training	NOUN
ap-9918	117	24	phase	phase	NOUN
ap-9918	117	25	[	[	X
ap-9918	117	26	17	17	NUM
ap-9918	117	27	]	]	SYM
ap-9918	117	28	.	.	PUNCT
ap-9918	118	1	3.3	3.3	NUM
ap-9918	118	2	.	.	PUNCT
ap-9918	119	1	xgboost	xgboost	X
ap-9918	120	1	the	the	DET
ap-9918	120	2	scalable	scalable	ADJ
ap-9918	120	3	and	and	CCONJ
ap-9918	120	4	high	high	ADJ
ap-9918	120	5	performance	performance	NOUN
ap-9918	120	6	gradient	gradient	NOUN
ap-9918	120	7	boosting	boost	VERB
ap-9918	120	8	system	system	NOUN
ap-9918	120	9	known	know	VERB
ap-9918	120	10	as	as	ADP
ap-9918	120	11	xgboost	xgboost	X
ap-9918	120	12	is	be	AUX
ap-9918	120	13	well	well	ADV
ap-9918	120	14	optimised	optimise	VERB
ap-9918	120	15	.	.	PUNCT
ap-9918	121	1	the	the	DET
ap-9918	121	2	goal	goal	NOUN
ap-9918	121	3	function	function	NOUN
ap-9918	121	4	incorporates	incorporate	VERB
ap-9918	121	5	a	a	DET
ap-9918	121	6	regularisation	regularisation	NOUN
ap-9918	121	7	term	term	NOUN
ap-9918	121	8	to	to	PART
ap-9918	121	9	manage	manage	VERB
ap-9918	121	10	model	model	NOUN
ap-9918	121	11	complexity	complexity	NOUN
ap-9918	121	12	and	and	CCONJ
ap-9918	121	13	avoid	avoid	VERB
ap-9918	121	14	overfitting	overfitte	VERB
ap-9918	121	15	.	.	PUNCT
ap-9918	122	1	with	with	ADP
ap-9918	122	2	its	its	PRON
ap-9918	122	3	many	many	ADJ
ap-9918	122	4	hyperparameters	hyperparameter	NOUN
ap-9918	122	5	,	,	PUNCT
ap-9918	122	6	xgboost	xgboost	PROPN
ap-9918	122	7	’s	’s	PART
ap-9918	122	8	great	great	ADJ
ap-9918	122	9	degree	degree	NOUN
ap-9918	122	10	of	of	ADP
ap-9918	122	11	customisation	customisation	NOUN
ap-9918	122	12	enables	enable	VERB
ap-9918	122	13	fine	fine	ADV
ap-9918	122	14	-	-	PUNCT
ap-9918	122	15	tuning	tuning	NOUN
ap-9918	122	16	for	for	ADP
ap-9918	122	17	specific	specific	ADJ
ap-9918	122	18	datasets	dataset	NOUN
ap-9918	122	19	and	and	CCONJ
ap-9918	122	20	goals	goal	NOUN
ap-9918	122	21	[	[	X
ap-9918	122	22	18	18	NUM
ap-9918	122	23	]	]	PUNCT
ap-9918	122	24	.	.	PUNCT
ap-9918	123	1	3.4	3.4	NUM
ap-9918	123	2	.	.	PUNCT
ap-9918	123	3	random	random	ADJ
ap-9918	123	4	forest	forest	NOUN
ap-9918	123	5	it	it	PRON
ap-9918	123	6	is	be	AUX
ap-9918	123	7	a	a	DET
ap-9918	123	8	classification	classification	NOUN
ap-9918	123	9	technique	technique	NOUN
ap-9918	123	10	based	base	VERB
ap-9918	123	11	on	on	ADP
ap-9918	123	12	an	an	DET
ap-9918	123	13	ensemble	ensemble	ADJ
ap-9918	123	14	learning	learning	NOUN
ap-9918	123	15	technique	technique	NOUN
ap-9918	123	16	that	that	PRON
ap-9918	123	17	builds	build	VERB
ap-9918	123	18	several	several	ADJ
ap-9918	123	19	decision	decision	NOUN
ap-9918	123	20	trees	tree	NOUN
ap-9918	123	21	during	during	ADP
ap-9918	123	22	training	training	NOUN
ap-9918	123	23	,	,	PUNCT
ap-9918	123	24	which	which	PRON
ap-9918	123	25	leads	lead	VERB
ap-9918	123	26	to	to	ADP
ap-9918	123	27	the	the	DET
ap-9918	123	28	prediction	prediction	NOUN
ap-9918	123	29	of	of	ADP
ap-9918	123	30	the	the	DET
ap-9918	123	31	class	class	NOUN
ap-9918	123	32	mode	mode	NOUN
ap-9918	123	33	for	for	ADP
ap-9918	123	34	each	each	DET
ap-9918	123	35	tree	tree	NOUN
ap-9918	123	36	.	.	PUNCT
ap-9918	124	1	using	use	VERB
ap-9918	124	2	feature	feature	NOUN
ap-9918	124	3	sampling	sampling	NOUN
ap-9918	124	4	and	and	CCONJ
ap-9918	124	5	bootstrap	bootstrap	NOUN
ap-9918	124	6	aggregating	aggregating	NOUN
ap-9918	124	7	(	(	PUNCT
ap-9918	124	8	bagging	bagging	NOUN
ap-9918	124	9	)	)	PUNCT
ap-9918	124	10	adds	add	VERB
ap-9918	124	11	unpredictability	unpredictability	NOUN
ap-9918	124	12	,	,	PUNCT
ap-9918	124	13	which	which	PRON
ap-9918	124	14	lessens	lessen	VERB
ap-9918	124	15	variance	variance	NOUN
ap-9918	124	16	and	and	CCONJ
ap-9918	124	17	improves	improve	VERB
ap-9918	124	18	generalisation	generalisation	NOUN
ap-9918	124	19	.	.	PUNCT
ap-9918	125	1	random	random	ADJ
ap-9918	125	2	forest	forest	NOUN
ap-9918	125	3	can	can	AUX
ap-9918	125	4	effectively	effectively	ADV
ap-9918	125	5	handle	handle	VERB
ap-9918	125	6	high	high	ADJ
ap-9918	125	7	-	-	PUNCT
ap-9918	125	8	dimensional	dimensional	ADJ
ap-9918	125	9	data	datum	NOUN
ap-9918	125	10	and	and	CCONJ
ap-9918	125	11	is	be	AUX
ap-9918	125	12	resistant	resistant	ADJ
ap-9918	125	13	to	to	PART
ap-9918	125	14	noise	noise	NOUN
ap-9918	125	15	and	and	CCONJ
ap-9918	125	16	outliers	outlier	NOUN
ap-9918	125	17	[	[	X
ap-9918	125	18	19	19	NUM
ap-9918	125	19	]	]	PUNCT
ap-9918	125	20	.	.	PUNCT
ap-9918	126	1	3.5	3.5	NUM
ap-9918	126	2	.	.	PUNCT
ap-9918	126	3	naive	naive	ADJ
ap-9918	126	4	bayes	baye	NOUN
ap-9918	126	5	based	base	VERB
ap-9918	126	6	on	on	ADP
ap-9918	126	7	the	the	DET
ap-9918	126	8	bayes	bayes	PROPN
ap-9918	126	9	theorem	theorem	VERB
ap-9918	126	10	and	and	CCONJ
ap-9918	126	11	the	the	DET
ap-9918	126	12	supposition	supposition	NOUN
ap-9918	126	13	of	of	ADP
ap-9918	126	14	conditional	conditional	ADJ
ap-9918	126	15	independence	independence	NOUN
ap-9918	126	16	of	of	ADP
ap-9918	126	17	features	feature	NOUN
ap-9918	126	18	given	give	VERB
ap-9918	126	19	the	the	DET
ap-9918	126	20	class	class	NOUN
ap-9918	126	21	label	label	NOUN
ap-9918	126	22	,	,	PUNCT
ap-9918	126	23	the	the	DET
ap-9918	126	24	naive	naive	ADJ
ap-9918	126	25	bayes	bayes	NOUN
ap-9918	126	26	classifier	classifier	NOUN
ap-9918	126	27	is	be	AUX
ap-9918	126	28	probabilistic	probabilistic	ADJ
ap-9918	126	29	.	.	PUNCT
ap-9918	127	1	in	in	ADP
ap-9918	127	2	fact	fact	NOUN
ap-9918	127	3	,	,	PUNCT
ap-9918	127	4	naive	naive	ADJ
ap-9918	127	5	bayes	bayes	NOUN
ap-9918	127	6	frequently	frequently	ADV
ap-9918	127	7	performs	perform	VERB
ap-9918	127	8	remarkably	remarkably	ADV
ap-9918	127	9	well	well	ADV
ap-9918	127	10	,	,	PUNCT
ap-9918	127	11	especially	especially	ADV
ap-9918	127	12	for	for	ADP
ap-9918	127	13	text	text	NOUN
ap-9918	127	14	classification	classification	NOUN
ap-9918	127	15	and	and	CCONJ
ap-9918	127	16	other	other	ADJ
ap-9918	127	17	highdimensional	highdimensional	ADJ
ap-9918	127	18	datasets	dataset	NOUN
ap-9918	127	19	,	,	PUNCT
ap-9918	127	20	despite	despite	SCONJ
ap-9918	127	21	its	its	PRON
ap-9918	127	22	seeming	seem	VERB
ap-9918	127	23	simplicity	simplicity	NOUN
ap-9918	127	24	and	and	CCONJ
ap-9918	127	25	naive	naive	ADJ
ap-9918	127	26	assumptions	assumption	NOUN
ap-9918	127	27	.	.	PUNCT
ap-9918	128	1	large	large	ADJ
ap-9918	128	2	-	-	PUNCT
ap-9918	128	3	scale	scale	NOUN
ap-9918	128	4	applications	application	NOUN
ap-9918	128	5	can	can	AUX
ap-9918	128	6	benefit	benefit	VERB
ap-9918	128	7	from	from	ADP
ap-9918	128	8	its	its	PRON
ap-9918	128	9	low	low	ADJ
ap-9918	128	10	training	training	NOUN
ap-9918	128	11	data	datum	NOUN
ap-9918	128	12	requirements	requirement	NOUN
ap-9918	128	13	and	and	CCONJ
ap-9918	128	14	processing	processing	NOUN
ap-9918	128	15	efficiency	efficiency	NOUN
ap-9918	128	16	[	[	X
ap-9918	128	17	20	20	NUM
ap-9918	128	18	]	]	PUNCT
ap-9918	128	19	.	.	PUNCT
ap-9918	129	1	3.6	3.6	NUM
ap-9918	129	2	.	.	PUNCT
ap-9918	129	3	libsvm	libsvm	VERB
ap-9918	129	4	a	a	DET
ap-9918	129	5	popular	popular	ADJ
ap-9918	129	6	library	library	NOUN
ap-9918	129	7	for	for	ADP
ap-9918	129	8	implementing	implement	VERB
ap-9918	129	9	support	support	NOUN
ap-9918	129	10	vector	vector	NOUN
ap-9918	129	11	machines	machine	NOUN
ap-9918	129	12	(	(	PUNCT
ap-9918	129	13	svms	svms	NOUN
ap-9918	129	14	)	)	PUNCT
ap-9918	129	15	,	,	PUNCT
ap-9918	129	16	libsvm	libsvm	NOUN
ap-9918	129	17	(	(	PUNCT
ap-9918	129	18	library	library	NOUN
ap-9918	129	19	for	for	ADP
ap-9918	129	20	support	support	NOUN
ap-9918	129	21	vector	vector	NOUN
ap-9918	129	22	machines	machine	NOUN
ap-9918	129	23	)	)	PUNCT
ap-9918	129	24	can	can	AUX
ap-9918	129	25	handle	handle	VERB
ap-9918	129	26	problems	problem	NOUN
ap-9918	129	27	involving	involve	VERB
ap-9918	129	28	both	both	CCONJ
ap-9918	129	29	regression	regression	NOUN
ap-9918	129	30	and	and	CCONJ
ap-9918	129	31	classification	classification	NOUN
ap-9918	129	32	.	.	PUNCT
ap-9918	130	1	in	in	ADP
ap-9918	130	2	order	order	NOUN
ap-9918	130	3	to	to	PART
ap-9918	130	4	achieve	achieve	VERB
ap-9918	130	5	strong	strong	ADJ
ap-9918	130	6	generalisation	generalisation	NOUN
ap-9918	130	7	performance	performance	NOUN
ap-9918	130	8	,	,	PUNCT
ap-9918	130	9	the	the	DET
ap-9918	130	10	svm	svm	NOUN
ap-9918	130	11	tries	try	VERB
ap-9918	130	12	to	to	PART
ap-9918	130	13	identify	identify	VERB
ap-9918	130	14	the	the	DET
ap-9918	130	15	ideal	ideal	ADJ
ap-9918	130	16	hyperplane	hyperplane	NOUN
ap-9918	130	17	that	that	PRON
ap-9918	130	18	separates	separate	VERB
ap-9918	130	19	multiple	multiple	ADJ
ap-9918	130	20	classes	class	NOUN
ap-9918	130	21	in	in	ADP
ap-9918	130	22	the	the	DET
ap-9918	130	23	feature	feature	NOUN
ap-9918	130	24	space	space	NOUN
ap-9918	130	25	with	with	ADP
ap-9918	130	26	the	the	DET
ap-9918	130	27	greatest	great	ADJ
ap-9918	130	28	margin	margin	NOUN
ap-9918	130	29	.	.	PUNCT
ap-9918	131	1	modelling	model	VERB
ap-9918	131	2	nonlinear	nonlinear	ADJ
ap-9918	131	3	interactions	interaction	NOUN
ap-9918	131	4	can	can	AUX
ap-9918	131	5	be	be	AUX
ap-9918	131	6	done	do	VERB
ap-9918	131	7	with	with	ADP
ap-9918	131	8	flexibility	flexibility	NOUN
ap-9918	131	9	because	because	SCONJ
ap-9918	131	10	of	of	ADP
ap-9918	131	11	the	the	DET
ap-9918	131	12	libsvm	libsvm	NOUN
ap-9918	131	13	’s	’s	PART
ap-9918	131	14	range	range	NOUN
ap-9918	131	15	of	of	ADP
ap-9918	131	16	kernel	kernel	PROPN
ap-9918	131	17	functions	function	NOUN
ap-9918	131	18	,	,	PUNCT
ap-9918	131	19	which	which	PRON
ap-9918	131	20	include	include	VERB
ap-9918	131	21	radial	radial	ADJ
ap-9918	131	22	basis	basis	NOUN
ap-9918	131	23	function	function	NOUN
ap-9918	131	24	(	(	PUNCT
ap-9918	131	25	rbf	rbf	PROPN
ap-9918	131	26	)	)	PUNCT
ap-9918	131	27	,	,	PUNCT
ap-9918	131	28	polynomial	polynomial	ADJ
ap-9918	131	29	,	,	PUNCT
ap-9918	131	30	and	and	CCONJ
ap-9918	131	31	linear	linear	ADJ
ap-9918	131	32	kernels	kernel	NOUN
ap-9918	131	33	[	[	X
ap-9918	131	34	21	21	NUM
ap-9918	131	35	]	]	PUNCT
ap-9918	131	36	.	.	PUNCT
ap-9918	132	1	in	in	ADP
ap-9918	132	2	order	order	NOUN
ap-9918	132	3	to	to	PART
ap-9918	132	4	determine	determine	VERB
ap-9918	132	5	the	the	DET
ap-9918	132	6	best	good	ADJ
ap-9918	132	7	strategy	strategy	NOUN
ap-9918	132	8	for	for	ADP
ap-9918	132	9	detecting	detect	VERB
ap-9918	132	10	and	and	CCONJ
ap-9918	132	11	predicting	predict	VERB
ap-9918	132	12	the	the	DET
ap-9918	132	13	recurrence	recurrence	NOUN
ap-9918	132	14	of	of	ADP
ap-9918	132	15	breast	breast	NOUN
ap-9918	132	16	cancer	cancer	NOUN
ap-9918	132	17	,	,	PUNCT
ap-9918	132	18	we	we	PRON
ap-9918	132	19	carefully	carefully	ADV
ap-9918	132	20	assessed	assess	VERB
ap-9918	132	21	and	and	CCONJ
ap-9918	132	22	analysed	analyse	VERB
ap-9918	132	23	the	the	DET
ap-9918	132	24	distinct	distinct	ADJ
ap-9918	132	25	qualities	quality	NOUN
ap-9918	132	26	and	and	CCONJ
ap-9918	132	27	trade	trade	NOUN
ap-9918	132	28	-	-	PUNCT
ap-9918	132	29	offs	off	NOUN
ap-9918	132	30	of	of	ADP
ap-9918	132	31	each	each	PRON
ap-9918	132	32	of	of	ADP
ap-9918	132	33	these	these	DET
ap-9918	132	34	algorithms	algorithm	NOUN
ap-9918	132	35	.	.	PUNCT
ap-9918	133	1	138	138	NUM
ap-9918	133	2	vol	vol	NOUN
ap-9918	133	3	.	.	PUNCT
ap-9918	134	1	65	65	NUM
ap-9918	134	2	no	no	INTJ
ap-9918	134	3	.	.	PUNCT
ap-9918	135	1	2/2025	2/2025	PROPN
ap-9918	135	2	a	a	DET
ap-9918	135	3	comparative	comparative	ADJ
ap-9918	135	4	study	study	NOUN
ap-9918	135	5	of	of	ADP
ap-9918	135	6	breast	breast	NOUN
ap-9918	135	7	cancer	cancer	NOUN
ap-9918	135	8	detection	detection	NOUN
ap-9918	135	9	and	and	CCONJ
ap-9918	135	10	recurrence	recurrence	NOUN
ap-9918	135	11	.	.	PUNCT
ap-9918	135	12	.	.	PUNCT
ap-9918	135	13	.	.	PUNCT
ap-9918	136	1	catboost	catboost	PROPN
ap-9918	136	2	xgboost	xgboost	PROPN
ap-9918	136	3	libsvm	libsvm	VERB
ap-9918	136	4	hyperparameter	hyperparameter	PROPN
ap-9918	136	5	value	value	PROPN
ap-9918	136	6	hyperparameter	hyperparameter	NOUN
ap-9918	136	7	value	value	NOUN
ap-9918	136	8	hyperparameter	hyperparameter	NOUN
ap-9918	136	9	value	value	NOUN
ap-9918	136	10	max_depth	max_depth	NOUN
ap-9918	136	11	6	6	NUM
ap-9918	136	12	max_depth	max_depth	NOUN
ap-9918	136	13	6	6	NUM
ap-9918	136	14	kernel	kernel	PROPN
ap-9918	136	15	rbf	rbf	PROPN
ap-9918	136	16	n_estimators	n_estimator	NOUN
ap-9918	136	17	(	(	PUNCT
ap-9918	136	18	trees	tree	NOUN
ap-9918	136	19	)	)	PUNCT
ap-9918	136	20	100	100	NUM
ap-9918	136	21	n_estimators	n_estimator	NOUN
ap-9918	136	22	(	(	PUNCT
ap-9918	136	23	trees	tree	NOUN
ap-9918	136	24	)	)	PUNCT
ap-9918	136	25	100	100	NUM
ap-9918	136	26	gamma	gamma	NOUN
ap-9918	136	27	1	1	NUM
ap-9918	136	28	learning	learning	NOUN
ap-9918	136	29	rate	rate	NOUN
ap-9918	136	30	0.3	0.3	NUM
ap-9918	136	31	learning	learning	NOUN
ap-9918	136	32	rate	rate	NOUN
ap-9918	136	33	0.3	0.3	NUM
ap-9918	136	34	tolerance	tolerance	NOUN
ap-9918	136	35	0.001	0.001	NUM
ap-9918	136	36	subsample	subsample	NOUN
ap-9918	136	37	0.7	0.7	NUM
ap-9918	136	38	subsample	subsample	NOUN
ap-9918	136	39	0.7	0.7	NUM
ap-9918	136	40	c	c	NOUN
ap-9918	136	41	1	1	NUM
ap-9918	136	42	grow_policy	grow_policy	NOUN
ap-9918	136	43	lossguide	lossguide	NOUN
ap-9918	136	44	random	random	PROPN
ap-9918	136	45	forest	forest	NOUN
ap-9918	136	46	knn	knn	PROPN
ap-9918	136	47	naive	naive	ADJ
ap-9918	136	48	bayes	bayes	PROPN
ap-9918	136	49	hyperparameter	hyperparameter	PROPN
ap-9918	136	50	value	value	PROPN
ap-9918	136	51	hyperparameter	hyperparameter	NOUN
ap-9918	136	52	value	value	NOUN
ap-9918	136	53	hyperparameter	hyperparameter	NOUN
ap-9918	136	54	value	value	NOUN
ap-9918	136	55	max_depth	max_depth	NOUN
ap-9918	136	56	6	6	NUM
ap-9918	136	57	n_neighbor	n_neighbor	NOUN
ap-9918	136	58	2	2	NUM
ap-9918	136	59	var_smoothing	var_smoothe	VERB
ap-9918	136	60	1e-9	1e-9	NUM
ap-9918	136	61	min_samples_leaf	min_samples_leaf	NOUN
ap-9918	136	62	1	1	NUM
ap-9918	136	63	weights	weight	NOUN
ap-9918	136	64	uniform	uniform	NOUN
ap-9918	136	65	sample_weight	sample_weight	NOUN
ap-9918	136	66	none	none	NOUN
ap-9918	136	67	n_trees	n_tree	NOUN
ap-9918	136	68	500	500	NUM
ap-9918	136	69	algorithm	algorithm	NOUN
ap-9918	136	70	auto	auto	NOUN
ap-9918	136	71	min_samples_split	min_samples_split	NOUN
ap-9918	136	72	0.05	0.05	NUM
ap-9918	136	73	leaf_size	leaf_size	NOUN
ap-9918	136	74	1	1	NUM
ap-9918	136	75	table	table	NOUN
ap-9918	136	76	1	1	NUM
ap-9918	136	77	.	.	PUNCT
ap-9918	137	1	hyperparameters	hyperparameter	NOUN
ap-9918	137	2	settings	setting	NOUN
ap-9918	137	3	of	of	ADP
ap-9918	137	4	the	the	DET
ap-9918	137	5	comparative	comparative	ADJ
ap-9918	137	6	models	model	NOUN
ap-9918	137	7	.	.	PUNCT
ap-9918	138	1	4	4	X
ap-9918	138	2	.	.	X
ap-9918	138	3	experimental	experimental	ADJ
ap-9918	138	4	setup	setup	NOUN
ap-9918	138	5	in	in	ADP
ap-9918	138	6	this	this	DET
ap-9918	138	7	experiment	experiment	NOUN
ap-9918	138	8	,	,	PUNCT
ap-9918	138	9	we	we	PRON
ap-9918	138	10	used	use	VERB
ap-9918	138	11	python	python	NOUN
ap-9918	138	12	3.7	3.7	NUM
ap-9918	138	13	for	for	ADP
ap-9918	138	14	programming	programming	NOUN
ap-9918	138	15	.	.	PUNCT
ap-9918	139	1	the	the	DET
ap-9918	139	2	hyperparameter	hyperparameter	NOUN
ap-9918	139	3	settings	setting	NOUN
ap-9918	139	4	of	of	ADP
ap-9918	139	5	all	all	DET
ap-9918	139	6	classification	classification	NOUN
ap-9918	139	7	systems	system	NOUN
ap-9918	139	8	presented	present	VERB
ap-9918	139	9	in	in	ADP
ap-9918	139	10	this	this	DET
ap-9918	139	11	article	article	NOUN
ap-9918	139	12	are	be	AUX
ap-9918	139	13	shown	show	VERB
ap-9918	139	14	in	in	ADP
ap-9918	139	15	table	table	NOUN
ap-9918	139	16	1	1	NUM
ap-9918	139	17	.	.	PUNCT
ap-9918	140	1	we	we	PRON
ap-9918	140	2	used	use	VERB
ap-9918	140	3	the	the	DET
ap-9918	140	4	sensitivity	sensitivity	NOUN
ap-9918	140	5	,	,	PUNCT
ap-9918	140	6	specificity	specificity	NOUN
ap-9918	140	7	,	,	PUNCT
ap-9918	140	8	f1	f1	NOUN
ap-9918	140	9	score	score	NOUN
ap-9918	140	10	,	,	PUNCT
ap-9918	140	11	accuracy	accuracy	NOUN
ap-9918	140	12	,	,	PUNCT
ap-9918	140	13	and	and	CCONJ
ap-9918	140	14	auc	auc	NOUN
ap-9918	140	15	matrices	matrix	NOUN
ap-9918	140	16	for	for	ADP
ap-9918	140	17	the	the	DET
ap-9918	140	18	comparison	comparison	NOUN
ap-9918	140	19	of	of	ADP
ap-9918	140	20	all	all	DET
ap-9918	140	21	classification	classification	NOUN
ap-9918	140	22	systems	system	NOUN
ap-9918	140	23	.	.	PUNCT
ap-9918	141	1	the	the	DET
ap-9918	141	2	train	train	NOUN
ap-9918	141	3	-	-	PUNCT
ap-9918	141	4	test	test	NOUN
ap-9918	141	5	split	split	NOUN
ap-9918	141	6	was	be	AUX
ap-9918	141	7	70	70	NUM
ap-9918	141	8	%	%	NOUN
ap-9918	141	9	for	for	ADP
ap-9918	141	10	training	training	NOUN
ap-9918	141	11	and	and	CCONJ
ap-9918	141	12	30	30	NUM
ap-9918	141	13	%	%	NOUN
ap-9918	141	14	for	for	ADP
ap-9918	141	15	testing	testing	NOUN
ap-9918	141	16	.	.	PUNCT
ap-9918	142	1	before	before	ADP
ap-9918	142	2	going	go	VERB
ap-9918	142	3	through	through	ADP
ap-9918	142	4	the	the	DET
ap-9918	142	5	results	result	NOUN
ap-9918	142	6	,	,	PUNCT
ap-9918	142	7	it	it	PRON
ap-9918	142	8	is	be	AUX
ap-9918	142	9	necessary	necessary	ADJ
ap-9918	142	10	to	to	PART
ap-9918	142	11	explain	explain	VERB
ap-9918	142	12	the	the	DET
ap-9918	142	13	meaning	meaning	NOUN
ap-9918	142	14	of	of	ADP
ap-9918	142	15	each	each	DET
ap-9918	142	16	metric	metric	NOUN
ap-9918	142	17	used	use	VERB
ap-9918	142	18	in	in	ADP
ap-9918	142	19	this	this	DET
ap-9918	142	20	study	study	NOUN
ap-9918	142	21	:	:	PUNCT
ap-9918	142	22	sensitivity	sensitivity	NOUN
ap-9918	142	23	measures	measure	VERB
ap-9918	142	24	the	the	DET
ap-9918	142	25	proportion	proportion	NOUN
ap-9918	142	26	of	of	ADP
ap-9918	142	27	actual	actual	ADJ
ap-9918	142	28	positive	positive	ADJ
ap-9918	142	29	cases	case	NOUN
ap-9918	142	30	that	that	SCONJ
ap-9918	142	31	the	the	DET
ap-9918	142	32	model	model	NOUN
ap-9918	142	33	correctly	correctly	ADV
ap-9918	142	34	identifies	identify	VERB
ap-9918	142	35	as	as	ADP
ap-9918	142	36	positive	positive	ADJ
ap-9918	142	37	.	.	PUNCT
ap-9918	143	1	high	high	ADJ
ap-9918	143	2	sensitivity	sensitivity	NOUN
ap-9918	143	3	indicates	indicate	VERB
ap-9918	143	4	that	that	SCONJ
ap-9918	143	5	the	the	DET
ap-9918	143	6	model	model	NOUN
ap-9918	143	7	is	be	AUX
ap-9918	143	8	effective	effective	ADJ
ap-9918	143	9	at	at	ADP
ap-9918	143	10	identifying	identify	VERB
ap-9918	143	11	positive	positive	ADJ
ap-9918	143	12	cases	case	NOUN
ap-9918	143	13	.	.	PUNCT
ap-9918	144	1	this	this	DET
ap-9918	144	2	metric	metric	NOUN
ap-9918	144	3	is	be	AUX
ap-9918	144	4	crucial	crucial	ADJ
ap-9918	144	5	in	in	ADP
ap-9918	144	6	situations	situation	NOUN
ap-9918	144	7	where	where	SCONJ
ap-9918	144	8	missing	miss	VERB
ap-9918	144	9	positive	positive	ADJ
ap-9918	144	10	cases	case	NOUN
ap-9918	144	11	(	(	PUNCT
ap-9918	144	12	false	false	ADJ
ap-9918	144	13	negatives	negative	NOUN
ap-9918	144	14	)	)	PUNCT
ap-9918	144	15	is	be	AUX
ap-9918	144	16	costly	costly	ADJ
ap-9918	144	17	,	,	PUNCT
ap-9918	144	18	such	such	ADJ
ap-9918	144	19	as	as	ADP
ap-9918	144	20	in	in	ADP
ap-9918	144	21	disease	disease	NOUN
ap-9918	144	22	detection	detection	NOUN
ap-9918	144	23	.	.	PUNCT
ap-9918	145	1	high	high	ADJ
ap-9918	145	2	specificity	specificity	NOUN
ap-9918	145	3	,	,	PUNCT
ap-9918	145	4	or	or	CCONJ
ap-9918	145	5	true	true	ADJ
ap-9918	145	6	negative	negative	ADJ
ap-9918	145	7	rate	rate	NOUN
ap-9918	145	8	,	,	PUNCT
ap-9918	145	9	demonstrates	demonstrate	VERB
ap-9918	145	10	the	the	DET
ap-9918	145	11	model	model	NOUN
ap-9918	145	12	’s	’s	PART
ap-9918	145	13	proficiency	proficiency	NOUN
ap-9918	145	14	in	in	ADP
ap-9918	145	15	accurately	accurately	ADV
ap-9918	145	16	identifying	identify	VERB
ap-9918	145	17	negative	negative	ADJ
ap-9918	145	18	cases	case	NOUN
ap-9918	145	19	.	.	PUNCT
ap-9918	146	1	it	it	PRON
ap-9918	146	2	’s	’	VERB
ap-9918	146	3	especially	especially	ADV
ap-9918	146	4	important	important	ADJ
ap-9918	146	5	when	when	SCONJ
ap-9918	146	6	falsely	falsely	ADV
ap-9918	146	7	identifying	identify	VERB
ap-9918	146	8	negatives	negative	NOUN
ap-9918	146	9	as	as	ADP
ap-9918	146	10	positives	positive	NOUN
ap-9918	146	11	(	(	PUNCT
ap-9918	146	12	false	false	ADJ
ap-9918	146	13	positives	positive	NOUN
ap-9918	146	14	)	)	PUNCT
ap-9918	146	15	has	have	VERB
ap-9918	146	16	significant	significant	ADJ
ap-9918	146	17	consequences	consequence	NOUN
ap-9918	146	18	.	.	PUNCT
ap-9918	147	1	precision	precision	NOUN
ap-9918	147	2	measures	measure	VERB
ap-9918	147	3	the	the	DET
ap-9918	147	4	ratio	ratio	NOUN
ap-9918	147	5	of	of	ADP
ap-9918	147	6	correct	correct	ADJ
ap-9918	147	7	positive	positive	ADJ
ap-9918	147	8	predictions	prediction	NOUN
ap-9918	147	9	to	to	ADP
ap-9918	147	10	the	the	DET
ap-9918	147	11	total	total	ADJ
ap-9918	147	12	positive	positive	ADJ
ap-9918	147	13	predictions	prediction	NOUN
ap-9918	147	14	made	make	VERB
ap-9918	147	15	.	.	PUNCT
ap-9918	148	1	high	high	ADJ
ap-9918	148	2	precision	precision	NOUN
ap-9918	148	3	indicates	indicate	VERB
ap-9918	148	4	that	that	SCONJ
ap-9918	148	5	the	the	DET
ap-9918	148	6	model	model	NOUN
ap-9918	148	7	generates	generate	VERB
ap-9918	148	8	fewer	few	ADJ
ap-9918	148	9	false	false	ADJ
ap-9918	148	10	positive	positive	ADJ
ap-9918	148	11	errors	error	NOUN
ap-9918	148	12	when	when	SCONJ
ap-9918	148	13	identifying	identify	VERB
ap-9918	148	14	positive	positive	ADJ
ap-9918	148	15	predictions	prediction	NOUN
ap-9918	148	16	.	.	PUNCT
ap-9918	149	1	this	this	DET
ap-9918	149	2	metric	metric	NOUN
ap-9918	149	3	is	be	AUX
ap-9918	149	4	particularly	particularly	ADV
ap-9918	149	5	important	important	ADJ
ap-9918	149	6	when	when	SCONJ
ap-9918	149	7	false	false	ADJ
ap-9918	149	8	positives	positive	NOUN
ap-9918	149	9	are	be	AUX
ap-9918	149	10	costly	costly	ADJ
ap-9918	149	11	or	or	CCONJ
ap-9918	149	12	undesirable	undesirable	ADJ
ap-9918	149	13	.	.	PUNCT
ap-9918	150	1	the	the	DET
ap-9918	150	2	f1	f1	PROPN
ap-9918	150	3	score	score	NOUN
ap-9918	150	4	metric	metric	ADJ
ap-9918	150	5	is	be	AUX
ap-9918	150	6	especially	especially	ADV
ap-9918	150	7	useful	useful	ADJ
ap-9918	150	8	when	when	SCONJ
ap-9918	150	9	there	there	PRON
ap-9918	150	10	’s	’	VERB
ap-9918	150	11	a	a	DET
ap-9918	150	12	class	class	NOUN
ap-9918	150	13	imbalance	imbalance	NOUN
ap-9918	150	14	,	,	PUNCT
ap-9918	150	15	and	and	CCONJ
ap-9918	150	16	it	it	PRON
ap-9918	150	17	needs	need	VERB
ap-9918	150	18	to	to	PART
ap-9918	150	19	balance	balance	VERB
ap-9918	150	20	both	both	CCONJ
ap-9918	150	21	recall	recall	NOUN
ap-9918	150	22	and	and	CCONJ
ap-9918	150	23	precision	precision	NOUN
ap-9918	150	24	.	.	PUNCT
ap-9918	151	1	a	a	DET
ap-9918	151	2	high	high	ADJ
ap-9918	151	3	f1	f1	NOUN
ap-9918	151	4	score	score	NOUN
ap-9918	151	5	indicates	indicate	VERB
ap-9918	151	6	a	a	DET
ap-9918	151	7	balance	balance	NOUN
ap-9918	151	8	between	between	ADP
ap-9918	151	9	correctly	correctly	ADV
ap-9918	151	10	identifying	identify	VERB
ap-9918	151	11	positive	positive	ADJ
ap-9918	151	12	cases	case	NOUN
ap-9918	151	13	and	and	CCONJ
ap-9918	151	14	limiting	limit	VERB
ap-9918	151	15	false	false	ADJ
ap-9918	151	16	positives	positive	NOUN
ap-9918	151	17	.	.	PUNCT
ap-9918	152	1	the	the	DET
ap-9918	152	2	auc	auc	NOUN
ap-9918	152	3	(	(	PUNCT
ap-9918	152	4	area	area	NOUN
ap-9918	152	5	under	under	ADP
ap-9918	152	6	the	the	DET
ap-9918	152	7	roc	roc	PROPN
ap-9918	152	8	curve	curve	PROPN
ap-9918	152	9	)	)	PUNCT
ap-9918	152	10	metric	metric	ADJ
ap-9918	152	11	measures	measure	NOUN
ap-9918	152	12	the	the	DET
ap-9918	152	13	area	area	NOUN
ap-9918	152	14	under	under	ADP
ap-9918	152	15	the	the	DET
ap-9918	152	16	receiver	receiver	NOUN
ap-9918	152	17	operating	operate	VERB
ap-9918	152	18	characteristic	characteristic	NOUN
ap-9918	152	19	(	(	PUNCT
ap-9918	152	20	roc	roc	PROPN
ap-9918	152	21	)	)	PUNCT
ap-9918	152	22	curve	curve	NOUN
ap-9918	152	23	,	,	PUNCT
ap-9918	152	24	which	which	PRON
ap-9918	152	25	plots	plot	VERB
ap-9918	152	26	sensitivity	sensitivity	NOUN
ap-9918	152	27	against	against	ADP
ap-9918	152	28	specificity	specificity	NOUN
ap-9918	152	29	.	.	PUNCT
ap-9918	153	1	a	a	DET
ap-9918	153	2	high	high	ADJ
ap-9918	153	3	auc	auc	NOUN
ap-9918	153	4	indicates	indicate	VERB
ap-9918	153	5	that	that	SCONJ
ap-9918	153	6	the	the	DET
ap-9918	153	7	model	model	NOUN
ap-9918	153	8	has	have	VERB
ap-9918	153	9	a	a	DET
ap-9918	153	10	good	good	ADJ
ap-9918	153	11	balance	balance	NOUN
ap-9918	153	12	between	between	ADP
ap-9918	153	13	sensitivity	sensitivity	NOUN
ap-9918	153	14	and	and	CCONJ
ap-9918	153	15	specificity	specificity	NOUN
ap-9918	153	16	across	across	ADP
ap-9918	153	17	all	all	DET
ap-9918	153	18	threshold	threshold	NOUN
ap-9918	153	19	levels	level	NOUN
ap-9918	153	20	.	.	PUNCT
ap-9918	154	1	it	it	PRON
ap-9918	154	2	is	be	AUX
ap-9918	154	3	a	a	DET
ap-9918	154	4	valuable	valuable	ADJ
ap-9918	154	5	measure	measure	NOUN
ap-9918	154	6	for	for	ADP
ap-9918	154	7	understanding	understand	VERB
ap-9918	154	8	the	the	DET
ap-9918	154	9	overall	overall	ADJ
ap-9918	154	10	model	model	NOUN
ap-9918	154	11	performance	performance	NOUN
ap-9918	154	12	,	,	PUNCT
ap-9918	154	13	especially	especially	ADV
ap-9918	154	14	for	for	ADP
ap-9918	154	15	datasets	dataset	NOUN
ap-9918	154	16	with	with	ADP
ap-9918	154	17	imbalanced	imbalanced	ADJ
ap-9918	154	18	classes	class	NOUN
ap-9918	154	19	.	.	PUNCT
ap-9918	155	1	the	the	DET
ap-9918	155	2	accuracy	accuracy	NOUN
ap-9918	155	3	metric	metric	ADJ
ap-9918	155	4	measures	measure	NOUN
ap-9918	155	5	the	the	DET
ap-9918	155	6	ratio	ratio	NOUN
ap-9918	155	7	of	of	ADP
ap-9918	155	8	correct	correct	ADJ
ap-9918	155	9	predictions	prediction	NOUN
ap-9918	155	10	to	to	ADP
ap-9918	155	11	total	total	ADJ
ap-9918	155	12	predictions	prediction	NOUN
ap-9918	155	13	.	.	PUNCT
ap-9918	156	1	high	high	ADJ
ap-9918	156	2	accuracy	accuracy	NOUN
ap-9918	156	3	may	may	AUX
ap-9918	156	4	not	not	PART
ap-9918	156	5	mean	mean	VERB
ap-9918	156	6	an	an	DET
ap-9918	156	7	effective	effective	ADJ
ap-9918	156	8	performance	performance	NOUN
ap-9918	156	9	if	if	SCONJ
ap-9918	156	10	one	one	NUM
ap-9918	156	11	class	class	NOUN
ap-9918	156	12	dominates	dominate	VERB
ap-9918	156	13	,	,	PUNCT
ap-9918	156	14	as	as	SCONJ
ap-9918	156	15	the	the	DET
ap-9918	156	16	model	model	NOUN
ap-9918	156	17	may	may	AUX
ap-9918	156	18	be	be	AUX
ap-9918	156	19	biased	bias	VERB
ap-9918	156	20	towards	towards	ADP
ap-9918	156	21	the	the	DET
ap-9918	156	22	majority	majority	NOUN
ap-9918	156	23	class	class	NOUN
ap-9918	156	24	.	.	PUNCT
ap-9918	157	1	5	5	X
ap-9918	157	2	.	.	NOUN
ap-9918	157	3	results	result	NOUN
ap-9918	157	4	and	and	CCONJ
ap-9918	157	5	discussion	discussion	NOUN
ap-9918	157	6	the	the	DET
ap-9918	157	7	research	research	NOUN
ap-9918	157	8	used	use	VERB
ap-9918	157	9	a	a	DET
ap-9918	157	10	variety	variety	NOUN
ap-9918	157	11	of	of	ADP
ap-9918	157	12	databases	database	NOUN
ap-9918	157	13	for	for	ADP
ap-9918	157	14	comparing	compare	VERB
ap-9918	157	15	the	the	DET
ap-9918	157	16	catboost	catboost	ADJ
ap-9918	157	17	model	model	NOUN
ap-9918	157	18	with	with	ADP
ap-9918	157	19	other	other	ADJ
ap-9918	157	20	machine	machine	NOUN
ap-9918	157	21	learning	learning	NOUN
ap-9918	157	22	models	model	NOUN
ap-9918	157	23	.	.	PUNCT
ap-9918	158	1	the	the	DET
ap-9918	158	2	results	result	NOUN
ap-9918	158	3	of	of	ADP
ap-9918	158	4	each	each	DET
ap-9918	158	5	machine	machine	NOUN
ap-9918	158	6	learning	learn	VERB
ap-9918	158	7	model	model	NOUN
ap-9918	158	8	applied	apply	VERB
ap-9918	158	9	to	to	ADP
ap-9918	158	10	each	each	DET
ap-9918	158	11	database	database	NOUN
ap-9918	158	12	are	be	AUX
ap-9918	158	13	illustrated	illustrate	VERB
ap-9918	158	14	in	in	ADP
ap-9918	158	15	table	table	NOUN
ap-9918	158	16	2	2	NUM
ap-9918	158	17	and	and	CCONJ
ap-9918	158	18	figures	figure	NOUN
ap-9918	158	19	1	1	NUM
ap-9918	158	20	,	,	PUNCT
ap-9918	158	21	2	2	NUM
ap-9918	158	22	,	,	PUNCT
ap-9918	158	23	and	and	CCONJ
ap-9918	159	1	3	3	X
ap-9918	159	2	.	.	X
ap-9918	159	3	the	the	DET
ap-9918	159	4	table	table	NOUN
ap-9918	159	5	and	and	CCONJ
ap-9918	159	6	figures	figure	NOUN
ap-9918	159	7	compare	compare	VERB
ap-9918	159	8	various	various	ADJ
ap-9918	159	9	machinelearning	machinelearning	NOUN
ap-9918	159	10	models	model	NOUN
ap-9918	159	11	on	on	ADP
ap-9918	159	12	the	the	DET
ap-9918	159	13	wdbc	wdbc	NOUN
ap-9918	159	14	,	,	PUNCT
ap-9918	159	15	gse42568	gse42568	NOUN
ap-9918	159	16	,	,	PUNCT
ap-9918	159	17	and	and	CCONJ
ap-9918	159	18	bcrd	bcrd	VERB
ap-9918	159	19	datasets	dataset	NOUN
ap-9918	159	20	.	.	PUNCT
ap-9918	160	1	the	the	DET
ap-9918	160	2	models	model	NOUN
ap-9918	160	3	evaluated	evaluate	VERB
ap-9918	160	4	are	be	AUX
ap-9918	160	5	catboost	catboost	ADJ
ap-9918	160	6	,	,	PUNCT
ap-9918	160	7	xgboost	xgboost	ADV
ap-9918	160	8	,	,	PUNCT
ap-9918	160	9	libsvm	libsvm	NOUN
ap-9918	160	10	,	,	PUNCT
ap-9918	160	11	random	random	ADJ
ap-9918	160	12	forest	forest	NOUN
ap-9918	160	13	,	,	PUNCT
ap-9918	160	14	knn	knn	PROPN
ap-9918	160	15	,	,	PUNCT
ap-9918	160	16	and	and	CCONJ
ap-9918	160	17	naive	naive	ADJ
ap-9918	160	18	bayes	baye	NOUN
ap-9918	160	19	.	.	PUNCT
ap-9918	161	1	the	the	DET
ap-9918	161	2	performance	performance	NOUN
ap-9918	161	3	metrics	metric	NOUN
ap-9918	161	4	used	use	VERB
ap-9918	161	5	include	include	VERB
ap-9918	161	6	sensitivity	sensitivity	NOUN
ap-9918	161	7	,	,	PUNCT
ap-9918	161	8	specificity	specificity	NOUN
ap-9918	161	9	,	,	PUNCT
ap-9918	161	10	precision	precision	NOUN
ap-9918	161	11	,	,	PUNCT
ap-9918	161	12	f1	f1	NOUN
ap-9918	161	13	score	score	NOUN
ap-9918	161	14	,	,	PUNCT
ap-9918	161	15	auc	auc	NOUN
ap-9918	161	16	,	,	PUNCT
ap-9918	161	17	and	and	CCONJ
ap-9918	161	18	accuracy	accuracy	NOUN
ap-9918	161	19	.	.	PUNCT
ap-9918	162	1	here	here	ADV
ap-9918	162	2	are	be	AUX
ap-9918	162	3	the	the	DET
ap-9918	162	4	key	key	ADJ
ap-9918	162	5	points	point	NOUN
ap-9918	162	6	:	:	PUNCT
ap-9918	162	7	catboost	catboost	PROPN
ap-9918	162	8	performed	perform	VERB
ap-9918	162	9	exceptionally	exceptionally	ADV
ap-9918	162	10	well	well	ADV
ap-9918	162	11	on	on	ADP
ap-9918	162	12	the	the	DET
ap-9918	162	13	wdbc	wdbc	NOUN
ap-9918	162	14	dataset	dataset	NOUN
ap-9918	162	15	,	,	PUNCT
ap-9918	162	16	achieving	achieve	VERB
ap-9918	162	17	the	the	DET
ap-9918	162	18	highest	high	ADJ
ap-9918	162	19	sensitivity	sensitivity	NOUN
ap-9918	162	20	(	(	PUNCT
ap-9918	162	21	0.95	0.95	NUM
ap-9918	162	22	)	)	PUNCT
ap-9918	162	23	,	,	PUNCT
ap-9918	162	24	perfect	perfect	ADJ
ap-9918	162	25	specificity	specificity	NOUN
ap-9918	162	26	(	(	PUNCT
ap-9918	162	27	1.00	1.00	NUM
ap-9918	162	28	)	)	PUNCT
ap-9918	162	29	,	,	PUNCT
ap-9918	162	30	precision	precision	NOUN
ap-9918	162	31	(	(	PUNCT
ap-9918	162	32	1.00	1.00	NUM
ap-9918	162	33	)	)	PUNCT
ap-9918	162	34	,	,	PUNCT
ap-9918	162	35	f1	f1	NOUN
ap-9918	162	36	score	score	NOUN
ap-9918	162	37	(	(	PUNCT
ap-9918	162	38	0.98	0.98	NUM
ap-9918	162	39	)	)	PUNCT
ap-9918	162	40	,	,	PUNCT
ap-9918	162	41	auc	auc	X
ap-9918	162	42	(	(	PUNCT
ap-9918	162	43	0.98	0.98	NUM
ap-9918	162	44	)	)	PUNCT
ap-9918	162	45	,	,	PUNCT
ap-9918	162	46	and	and	CCONJ
ap-9918	162	47	accuracy	accuracy	NOUN
ap-9918	162	48	(	(	PUNCT
ap-9918	162	49	0.98	0.98	NUM
ap-9918	162	50	)	)	PUNCT
ap-9918	162	51	.	.	PUNCT
ap-9918	163	1	xgboost	xgboost	PROPN
ap-9918	163	2	followed	follow	VERB
ap-9918	163	3	closely	closely	ADV
ap-9918	163	4	with	with	ADP
ap-9918	163	5	a	a	DET
ap-9918	163	6	similar	similar	ADJ
ap-9918	163	7	performance	performance	NOUN
ap-9918	163	8	,	,	PUNCT
ap-9918	163	9	but	but	CCONJ
ap-9918	163	10	slightly	slightly	ADV
ap-9918	163	11	lower	low	ADJ
ap-9918	163	12	sensitivity	sensitivity	NOUN
ap-9918	163	13	(	(	PUNCT
ap-9918	163	14	0.94	0.94	NUM
ap-9918	163	15	)	)	PUNCT
ap-9918	163	16	and	and	CCONJ
ap-9918	163	17	f1	f1	PROPN
ap-9918	163	18	score	score	NOUN
ap-9918	163	19	(	(	PUNCT
ap-9918	163	20	0.97	0.97	NUM
ap-9918	163	21	)	)	PUNCT
ap-9918	163	22	.	.	PUNCT
ap-9918	164	1	the	the	DET
ap-9918	164	2	other	other	ADJ
ap-9918	164	3	models	model	NOUN
ap-9918	164	4	,	,	PUNCT
ap-9918	164	5	libsvm	libsvm	NOUN
ap-9918	164	6	,	,	PUNCT
ap-9918	164	7	random	random	ADJ
ap-9918	164	8	forest	forest	NOUN
ap-9918	164	9	,	,	PUNCT
ap-9918	164	10	knn	knn	PROPN
ap-9918	164	11	,	,	PUNCT
ap-9918	164	12	and	and	CCONJ
ap-9918	164	13	naive	naive	ADJ
ap-9918	164	14	bayes	bayes	NOUN
ap-9918	164	15	,	,	PUNCT
ap-9918	164	16	performed	perform	VERB
ap-9918	164	17	competitively	competitively	ADV
ap-9918	164	18	but	but	CCONJ
ap-9918	164	19	were	be	AUX
ap-9918	164	20	slightly	slightly	ADV
ap-9918	164	21	behind	behind	ADP
ap-9918	164	22	catboost	catboost	NOUN
ap-9918	164	23	and	and	CCONJ
ap-9918	164	24	xgboost	xgboost	ADV
ap-9918	164	25	on	on	ADP
ap-9918	164	26	most	most	ADJ
ap-9918	164	27	metrics	metric	NOUN
ap-9918	164	28	.	.	PUNCT
ap-9918	165	1	in	in	ADP
ap-9918	165	2	the	the	DET
ap-9918	165	3	second	second	ADJ
ap-9918	165	4	dataset	dataset	NOUN
ap-9918	165	5	(	(	PUNCT
ap-9918	165	6	gse42568	gse42568	NOUN
ap-9918	165	7	dataset	dataset	PROPN
ap-9918	165	8	)	)	PUNCT
ap-9918	165	9	;	;	PUNCT
ap-9918	165	10	catboost	catboost	VERB
ap-9918	165	11	and	and	CCONJ
ap-9918	165	12	libsvm	libsvm	VERB
ap-9918	165	13	tied	tie	VERB
ap-9918	165	14	for	for	ADP
ap-9918	165	15	the	the	DET
ap-9918	165	16	best	good	ADJ
ap-9918	165	17	performance	performance	NOUN
ap-9918	165	18	in	in	ADP
ap-9918	165	19	several	several	ADJ
ap-9918	165	20	metrics	metric	NOUN
ap-9918	165	21	:	:	PUNCT
ap-9918	165	22	sensitivity	sensitivity	NOUN
ap-9918	165	23	(	(	PUNCT
ap-9918	165	24	1.00	1.00	NUM
ap-9918	165	25	)	)	PUNCT
ap-9918	165	26	,	,	PUNCT
ap-9918	165	27	precision	precision	NOUN
ap-9918	165	28	(	(	PUNCT
ap-9918	165	29	0.97	0.97	NUM
ap-9918	165	30	)	)	PUNCT
ap-9918	165	31	,	,	PUNCT
ap-9918	165	32	f1	f1	NOUN
ap-9918	165	33	score	score	NOUN
ap-9918	165	34	(	(	PUNCT
ap-9918	165	35	0.98	0.98	NUM
ap-9918	165	36	)	)	PUNCT
ap-9918	165	37	,	,	PUNCT
ap-9918	165	38	auc	auc	X
ap-9918	165	39	(	(	PUNCT
ap-9918	165	40	0.90	0.90	NUM
ap-9918	165	41	)	)	PUNCT
ap-9918	165	42	,	,	PUNCT
ap-9918	165	43	and	and	CCONJ
ap-9918	165	44	accuracy	accuracy	NOUN
ap-9918	165	45	(	(	PUNCT
ap-9918	165	46	0.97	0.97	NUM
ap-9918	165	47	)	)	PUNCT
ap-9918	165	48	.	.	PUNCT
ap-9918	166	1	xgboost	xgboost	PROPN
ap-9918	166	2	showed	show	VERB
ap-9918	166	3	a	a	DET
ap-9918	166	4	lower	low	ADJ
ap-9918	166	5	performance	performance	NOUN
ap-9918	166	6	,	,	PUNCT
ap-9918	166	7	particularly	particularly	ADV
ap-9918	166	8	in	in	ADP
ap-9918	166	9	specificity	specificity	NOUN
ap-9918	166	10	(	(	PUNCT
ap-9918	166	11	0.40	0.40	NUM
ap-9918	166	12	)	)	PUNCT
ap-9918	166	13	and	and	CCONJ
ap-9918	166	14	auc	auc	NOUN
ap-9918	166	15	(	(	PUNCT
ap-9918	166	16	0.67	0.67	NUM
ap-9918	166	17	)	)	PUNCT
ap-9918	166	18	,	,	PUNCT
ap-9918	166	19	indicating	indicate	VERB
ap-9918	166	20	that	that	SCONJ
ap-9918	166	21	it	it	PRON
ap-9918	166	22	struggled	struggle	VERB
ap-9918	166	23	with	with	ADP
ap-9918	166	24	this	this	DET
ap-9918	166	25	dataset	dataset	NOUN
ap-9918	166	26	.	.	PUNCT
ap-9918	167	1	naive	naive	ADJ
ap-9918	167	2	bayes	bayes	PROPN
ap-9918	167	3	performed	perform	VERB
ap-9918	167	4	poorly	poorly	ADV
ap-9918	167	5	in	in	ADP
ap-9918	167	6	specificity	specificity	NOUN
ap-9918	167	7	(	(	PUNCT
ap-9918	167	8	0.0	0.0	NUM
ap-9918	167	9	)	)	PUNCT
ap-9918	167	10	and	and	CCONJ
ap-9918	167	11	auc	auc	NOUN
ap-9918	167	12	(	(	PUNCT
ap-9918	167	13	0.50	0.50	NUM
ap-9918	167	14	)	)	PUNCT
ap-9918	167	15	,	,	PUNCT
ap-9918	167	16	suggesting	suggest	VERB
ap-9918	167	17	that	that	SCONJ
ap-9918	167	18	it	it	PRON
ap-9918	167	19	was	be	AUX
ap-9918	167	20	not	not	PART
ap-9918	167	21	suitable	suitable	ADJ
ap-9918	167	22	for	for	ADP
ap-9918	167	23	this	this	DET
ap-9918	167	24	dataset	dataset	NOUN
ap-9918	167	25	.	.	PUNCT
ap-9918	168	1	in	in	ADP
ap-9918	168	2	the	the	DET
ap-9918	168	3	bcrd	bcrd	NOUN
ap-9918	168	4	dataset	dataset	NOUN
ap-9918	168	5	,	,	PUNCT
ap-9918	168	6	catboost	catboost	PROPN
ap-9918	168	7	outperformed	outperform	VERB
ap-9918	168	8	other	other	ADJ
ap-9918	168	9	models	model	NOUN
ap-9918	168	10	with	with	ADP
ap-9918	168	11	sensitivity	sensitivity	NOUN
ap-9918	168	12	(	(	PUNCT
ap-9918	168	13	0.78	0.78	NUM
ap-9918	168	14	)	)	PUNCT
ap-9918	168	15	,	,	PUNCT
ap-9918	168	16	specificity	specificity	NOUN
ap-9918	168	17	(	(	PUNCT
ap-9918	168	18	0.88	0.88	NUM
ap-9918	168	19	)	)	PUNCT
ap-9918	168	20	,	,	PUNCT
ap-9918	168	21	precision	precision	NOUN
ap-9918	168	22	(	(	PUNCT
ap-9918	168	23	0.84	0.84	NUM
ap-9918	168	24	)	)	PUNCT
ap-9918	168	25	,	,	PUNCT
ap-9918	168	26	f1	f1	NOUN
ap-9918	168	27	score	score	NOUN
ap-9918	168	28	(	(	PUNCT
ap-9918	168	29	0.81	0.81	NUM
ap-9918	168	30	)	)	PUNCT
ap-9918	168	31	,	,	PUNCT
ap-9918	168	32	auc	auc	X
ap-9918	168	33	(	(	PUNCT
ap-9918	168	34	0.83	0.83	NUM
ap-9918	168	35	)	)	PUNCT
ap-9918	168	36	,	,	PUNCT
ap-9918	168	37	and	and	CCONJ
ap-9918	168	38	accuracy	accuracy	NOUN
ap-9918	168	39	(	(	PUNCT
ap-9918	168	40	0.83	0.83	NUM
ap-9918	168	41	)	)	PUNCT
ap-9918	168	42	.	.	PUNCT
ap-9918	169	1	xgboost	xgboost	ADV
ap-9918	169	2	and	and	CCONJ
ap-9918	169	3	random	random	ADJ
ap-9918	169	4	forest	forest	NOUN
ap-9918	169	5	demon139	demon139	PROPN
ap-9918	169	6	r.	r.	PROPN
ap-9918	169	7	d.	d.	PROPN
ap-9918	169	8	abdu	abdu	PROPN
ap-9918	169	9	-	-	PUNCT
ap-9918	169	10	aljabar	aljabar	PROPN
ap-9918	169	11	,	,	PUNCT
ap-9918	169	12	k.	k.	PROPN
ap-9918	169	13	d.	d.	PROPN
ap-9918	169	14	aljafaar	aljafaar	PROPN
ap-9918	169	15	,	,	PUNCT
ap-9918	169	16	z.	z.	PROPN
ap-9918	169	17	j.	j.	PROPN
ap-9918	169	18	m.	m.	PROPN
ap-9918	169	19	ameen	ameen	PROPN
ap-9918	169	20	,	,	PUNCT
ap-9918	169	21	h.	h.	PROPN
ap-9918	169	22	a.	a.	PROPN
ap-9918	169	23	naman	naman	PROPN
ap-9918	169	24	acta	acta	PROPN
ap-9918	169	25	polytechnica	polytechnica	PROPN
ap-9918	169	26	model	model	NOUN
ap-9918	169	27	name	name	NOUN
ap-9918	169	28	sensitivity	sensitivity	NOUN
ap-9918	169	29	specificity	specificity	NOUN
ap-9918	169	30	precision	precision	NOUN
ap-9918	169	31	f1	f1	NOUN
ap-9918	169	32	score	score	NOUN
ap-9918	169	33	auc	auc	NOUN
ap-9918	169	34	accuracy	accuracy	NOUN
ap-9918	169	35	wdbc	wdbc	VERB
ap-9918	169	36	dataset	dataset	VERB
ap-9918	169	37	catboost	catboost	VERB
ap-9918	169	38	0.95	0.95	NUM
ap-9918	169	39	1.00	1.00	NUM
ap-9918	169	40	1.00	1.00	NUM
ap-9918	169	41	0.98	0.98	NUM
ap-9918	169	42	0.98	0.98	NUM
ap-9918	169	43	0.98	0.98	NUM
ap-9918	169	44	xgboost	xgboost	ADP
ap-9918	169	45	0.94	0.94	NUM
ap-9918	169	46	1.00	1.00	NUM
ap-9918	169	47	1.00	1.00	NUM
ap-9918	169	48	0.97	0.97	NUM
ap-9918	169	49	0.97	0.97	NUM
ap-9918	169	50	0.98	0.98	NUM
ap-9918	169	51	libsvm	libsvm	NOUN
ap-9918	169	52	0.91	0.91	NUM
ap-9918	169	53	0.98	0.98	NUM
ap-9918	169	54	0.97	0.97	NUM
ap-9918	169	55	0.94	0.94	NUM
ap-9918	169	56	0.94	0.94	NUM
ap-9918	169	57	0.95	0.95	NUM
ap-9918	169	58	rforest	rforest	NOUN
ap-9918	169	59	0.92	0.92	NUM
ap-9918	169	60	0.99	0.99	NUM
ap-9918	169	61	0.98	0.98	NUM
ap-9918	169	62	0.95	0.95	NUM
ap-9918	169	63	0.96	0.96	NUM
ap-9918	169	64	0.96	0.96	NUM
ap-9918	169	65	knn	knn	NOUN
ap-9918	169	66	0.91	0.91	NUM
ap-9918	169	67	0.99	0.99	NUM
ap-9918	169	68	0.98	0.98	NUM
ap-9918	169	69	0.94	0.94	NUM
ap-9918	169	70	0.96	0.96	NUM
ap-9918	169	71	0.96	0.96	NUM
ap-9918	169	72	naive	naive	ADJ
ap-9918	169	73	bayes	bayes	NOUN
ap-9918	169	74	0.92	0.92	NUM
ap-9918	169	75	0.99	0.99	NUM
ap-9918	169	76	0.98	0.98	NUM
ap-9918	169	77	0.95	0.95	NUM
ap-9918	169	78	0.96	0.96	NUM
ap-9918	169	79	0.96	0.96	NUM
ap-9918	169	80	gse42568	gse42568	NOUN
ap-9918	169	81	dataset	dataset	PROPN
ap-9918	169	82	catboost	catboost	VERB
ap-9918	169	83	1.00	1.00	NUM
ap-9918	169	84	0.80	0.80	NUM
ap-9918	169	85	0.97	0.97	NUM
ap-9918	169	86	0.98	0.98	NUM
ap-9918	169	87	0.90	0.90	NUM
ap-9918	169	88	0.97	0.97	NUM
ap-9918	169	89	xgboost	xgboost	ADV
ap-9918	169	90	0.94	0.94	NUM
ap-9918	169	91	0.40	0.40	NUM
ap-9918	169	92	0.91	0.91	NUM
ap-9918	169	93	0.92	0.92	NUM
ap-9918	169	94	0.67	0.67	NUM
ap-9918	169	95	0.86	0.86	NUM
ap-9918	169	96	libsvm	libsvm	NOUN
ap-9918	169	97	1.00	1.00	NUM
ap-9918	169	98	0.80	0.80	NUM
ap-9918	169	99	0.97	0.97	NUM
ap-9918	169	100	0.98	0.98	NUM
ap-9918	169	101	0.90	0.90	NUM
ap-9918	169	102	0.97	0.97	NUM
ap-9918	169	103	rforest	rforest	NOUN
ap-9918	169	104	1.00	1.00	NUM
ap-9918	169	105	0.60	0.60	NUM
ap-9918	169	106	0.94	0.94	NUM
ap-9918	169	107	0.97	0.97	NUM
ap-9918	169	108	0.80	0.80	NUM
ap-9918	169	109	0.95	0.95	NUM
ap-9918	169	110	knn	knn	NOUN
ap-9918	169	111	0.97	0.97	NUM
ap-9918	169	112	0.80	0.80	NUM
ap-9918	169	113	0.97	0.97	NUM
ap-9918	169	114	0.97	0.97	NUM
ap-9918	169	115	0.88	0.88	NUM
ap-9918	169	116	0.95	0.95	NUM
ap-9918	169	117	naive	naive	ADJ
ap-9918	169	118	bayes	bayes	NOUN
ap-9918	169	119	1.00	1.00	NUM
ap-9918	169	120	0.0	0.0	NUM
ap-9918	169	121	0.86	0.86	NUM
ap-9918	169	122	0.93	0.93	NUM
ap-9918	169	123	0.50	0.50	NUM
ap-9918	169	124	0.86	0.86	NUM
ap-9918	169	125	bcrd	bcrd	NOUN
ap-9918	169	126	dataset	dataset	NOUN
ap-9918	169	127	catboost	catboost	NOUN
ap-9918	169	128	0.78	0.78	NUM
ap-9918	169	129	0.88	0.88	NUM
ap-9918	169	130	0.84	0.84	NUM
ap-9918	169	131	0.81	0.81	NUM
ap-9918	169	132	0.83	0.83	NUM
ap-9918	169	133	0.83	0.83	NUM
ap-9918	169	134	xgboost	xgboost	ADV
ap-9918	169	135	0.76	0.76	NUM
ap-9918	169	136	0.80	0.80	NUM
ap-9918	169	137	0.76	0.76	NUM
ap-9918	169	138	0.76	0.76	NUM
ap-9918	169	139	0.78	0.78	NUM
ap-9918	169	140	0.78	0.78	NUM
ap-9918	169	141	libsvm	libsvm	VERB
ap-9918	169	142	0.29	0.29	NUM
ap-9918	169	143	0.83	0.83	NUM
ap-9918	169	144	0.58	0.58	NUM
ap-9918	169	145	0.38	0.38	NUM
ap-9918	169	146	0.56	0.56	NUM
ap-9918	169	147	0.58	0.58	NUM
ap-9918	169	148	rforest	rforest	NOUN
ap-9918	169	149	0.53	0.53	NUM
ap-9918	169	150	0.80	0.80	NUM
ap-9918	169	151	0.68	0.68	NUM
ap-9918	169	152	0.60	0.60	NUM
ap-9918	169	153	0.66	0.66	NUM
ap-9918	169	154	0.68	0.68	NUM
ap-9918	169	155	knn	knn	NOUN
ap-9918	169	156	0.43	0.43	NUM
ap-9918	169	157	0.85	0.85	NUM
ap-9918	169	158	0.70	0.70	NUM
ap-9918	169	159	0.53	0.53	NUM
ap-9918	169	160	0.64	0.64	NUM
ap-9918	169	161	0.66	0.66	NUM
ap-9918	169	162	naive	naive	ADJ
ap-9918	169	163	bayes	bayes	NOUN
ap-9918	169	164	0.33	0.33	NUM
ap-9918	169	165	0.81	0.81	NUM
ap-9918	169	166	0.59	0.59	NUM
ap-9918	169	167	0.42	0.42	NUM
ap-9918	169	168	0.57	0.57	NUM
ap-9918	169	169	0.59	0.59	NUM
ap-9918	169	170	table	table	NOUN
ap-9918	169	171	2	2	NUM
ap-9918	169	172	.	.	PUNCT
ap-9918	169	173	comparison	comparison	NOUN
ap-9918	169	174	of	of	ADP
ap-9918	169	175	results	result	NOUN
ap-9918	169	176	for	for	ADP
ap-9918	169	177	breast	breast	NOUN
ap-9918	169	178	cancer	cancer	NOUN
ap-9918	169	179	detection	detection	NOUN
ap-9918	169	180	and	and	CCONJ
ap-9918	169	181	recurrence	recurrence	NOUN
ap-9918	169	182	prediction	prediction	NOUN
ap-9918	169	183	.	.	PUNCT
ap-9918	170	1	figure	figure	NOUN
ap-9918	170	2	1	1	NUM
ap-9918	170	3	.	.	PUNCT
ap-9918	171	1	the	the	DET
ap-9918	171	2	roc	roc	PROPN
ap-9918	171	3	and	and	CCONJ
ap-9918	171	4	auc	auc	NOUN
ap-9918	171	5	values	value	NOUN
ap-9918	171	6	of	of	ADP
ap-9918	171	7	the	the	DET
ap-9918	171	8	comparative	comparative	ADJ
ap-9918	171	9	models	model	NOUN
ap-9918	171	10	when	when	SCONJ
ap-9918	171	11	used	use	VERB
ap-9918	171	12	with	with	ADP
ap-9918	171	13	the	the	DET
ap-9918	171	14	wdbc	wdbc	NOUN
ap-9918	171	15	dataset	dataset	NOUN
ap-9918	171	16	.	.	PUNCT
ap-9918	172	1	strated	strate	VERB
ap-9918	172	2	moderate	moderate	ADJ
ap-9918	172	3	performance	performance	NOUN
ap-9918	172	4	,	,	PUNCT
ap-9918	172	5	they	they	PRON
ap-9918	172	6	fell	fall	VERB
ap-9918	172	7	short	short	ADV
ap-9918	172	8	in	in	ADP
ap-9918	172	9	effectiveness	effectiveness	NOUN
ap-9918	172	10	when	when	SCONJ
ap-9918	172	11	compared	compare	VERB
ap-9918	172	12	to	to	ADP
ap-9918	172	13	catboost	catboost	NOUN
ap-9918	172	14	.	.	PUNCT
ap-9918	173	1	ibsvm	ibsvm	PROPN
ap-9918	173	2	and	and	CCONJ
ap-9918	173	3	naive	naive	ADJ
ap-9918	173	4	bayes	bayes	NOUN
ap-9918	173	5	had	have	VERB
ap-9918	173	6	the	the	DET
ap-9918	173	7	lowest	low	ADJ
ap-9918	173	8	sensitivity	sensitivity	NOUN
ap-9918	173	9	at	at	ADP
ap-9918	173	10	0.29	0.29	NUM
ap-9918	173	11	and	and	CCONJ
ap-9918	173	12	0.33	0.33	NUM
ap-9918	173	13	,	,	PUNCT
ap-9918	173	14	respectively	respectively	ADV
ap-9918	173	15	,	,	PUNCT
ap-9918	173	16	making	make	VERB
ap-9918	173	17	them	they	PRON
ap-9918	173	18	less	less	ADV
ap-9918	173	19	effective	effective	ADJ
ap-9918	173	20	at	at	ADP
ap-9918	173	21	predicting	predict	VERB
ap-9918	173	22	outcomes	outcome	NOUN
ap-9918	173	23	for	for	ADP
ap-9918	173	24	this	this	DET
ap-9918	173	25	dataset	dataset	NOUN
ap-9918	173	26	.	.	PUNCT
ap-9918	174	1	the	the	DET
ap-9918	174	2	results	result	NOUN
ap-9918	174	3	show	show	VERB
ap-9918	174	4	that	that	SCONJ
ap-9918	174	5	catboost	catboost	VERB
ap-9918	174	6	excels	excel	NOUN
ap-9918	174	7	in	in	ADP
ap-9918	174	8	its	its	PRON
ap-9918	174	9	performance	performance	NOUN
ap-9918	174	10	across	across	ADP
ap-9918	174	11	multiple	multiple	ADJ
ap-9918	174	12	datasets	dataset	NOUN
ap-9918	174	13	,	,	PUNCT
ap-9918	174	14	particularly	particularly	ADV
ap-9918	174	15	in	in	ADP
ap-9918	174	16	wdbc	wdbc	NOUN
ap-9918	174	17	and	and	CCONJ
ap-9918	174	18	gse42568	gse42568	NOUN
ap-9918	174	19	.	.	PUNCT
ap-9918	175	1	this	this	PRON
ap-9918	175	2	indicates	indicate	VERB
ap-9918	175	3	that	that	SCONJ
ap-9918	175	4	catboost	catboost	NOUN
ap-9918	175	5	adeptly	adeptly	ADV
ap-9918	175	6	handles	handle	VERB
ap-9918	175	7	various	various	ADJ
ap-9918	175	8	data	datum	NOUN
ap-9918	175	9	distributions	distribution	NOUN
ap-9918	175	10	and	and	CCONJ
ap-9918	175	11	recognises	recognise	VERB
ap-9918	175	12	feature	feature	NOUN
ap-9918	175	13	importance	importance	NOUN
ap-9918	175	14	,	,	PUNCT
ap-9918	175	15	making	make	VERB
ap-9918	175	16	it	it	PRON
ap-9918	175	17	a	a	DET
ap-9918	175	18	reliable	reliable	ADJ
ap-9918	175	19	option	option	NOUN
ap-9918	175	20	for	for	ADP
ap-9918	175	21	a	a	DET
ap-9918	175	22	wide	wide	ADJ
ap-9918	175	23	range	range	NOUN
ap-9918	175	24	of	of	ADP
ap-9918	175	25	datasets	dataset	NOUN
ap-9918	175	26	.	.	PUNCT
ap-9918	176	1	it	it	PRON
ap-9918	176	2	uses	use	VERB
ap-9918	176	3	a	a	DET
ap-9918	176	4	unique	unique	ADJ
ap-9918	176	5	boosting	boosting	NOUN
ap-9918	176	6	method	method	NOUN
ap-9918	176	7	that	that	PRON
ap-9918	176	8	reduces	reduce	VERB
ap-9918	176	9	overfitting	overfitte	VERB
ap-9918	176	10	,	,	PUNCT
ap-9918	176	11	enhancing	enhance	VERB
ap-9918	176	12	generalisation	generalisation	NOUN
ap-9918	176	13	and	and	CCONJ
ap-9918	176	14	results	result	NOUN
ap-9918	176	15	in	in	ADP
ap-9918	176	16	high	high	ADJ
ap-9918	176	17	sensitivity	sensitivity	NOUN
ap-9918	176	18	and	and	CCONJ
ap-9918	176	19	specificity	specificity	NOUN
ap-9918	176	20	in	in	ADP
ap-9918	176	21	most	most	ADJ
ap-9918	176	22	datasets	dataset	NOUN
ap-9918	176	23	.	.	PUNCT
ap-9918	177	1	the	the	DET
ap-9918	177	2	catboost	catboost	PROPN
ap-9918	177	3	model	model	NOUN
ap-9918	177	4	is	be	AUX
ap-9918	177	5	tailored	tailor	VERB
ap-9918	177	6	for	for	ADP
ap-9918	177	7	efficiently	efficiently	ADV
ap-9918	177	8	handling	handle	VERB
ap-9918	177	9	categorical	categorical	ADJ
ap-9918	177	10	data	datum	NOUN
ap-9918	177	11	,	,	PUNCT
ap-9918	177	12	which	which	PRON
ap-9918	177	13	likely	likely	ADV
ap-9918	177	14	enhances	enhance	VERB
ap-9918	177	15	its	its	PRON
ap-9918	177	16	performance	performance	NOUN
ap-9918	177	17	on	on	ADP
ap-9918	177	18	complex	complex	ADJ
ap-9918	177	19	datasets	dataset	NOUN
ap-9918	177	20	,	,	PUNCT
ap-9918	177	21	such	such	ADJ
ap-9918	177	22	as	as	ADP
ap-9918	177	23	wdbc	wdbc	NOUN
ap-9918	177	24	and	and	CCONJ
ap-9918	177	25	bcrd	bcrd	VERB
ap-9918	177	26	.	.	PUNCT
ap-9918	178	1	from	from	ADP
ap-9918	178	2	this	this	DET
ap-9918	178	3	study	study	NOUN
ap-9918	178	4	,	,	PUNCT
ap-9918	178	5	the	the	DET
ap-9918	178	6	pros	pro	NOUN
ap-9918	178	7	and	and	CCONJ
ap-9918	178	8	cons	con	NOUN
ap-9918	178	9	of	of	ADP
ap-9918	178	10	the	the	DET
ap-9918	178	11	catboost	catboost	NOUN
ap-9918	178	12	model	model	NOUN
ap-9918	178	13	can	can	AUX
ap-9918	178	14	be	be	AUX
ap-9918	178	15	summarised	summarise	VERB
ap-9918	178	16	in	in	ADP
ap-9918	178	17	table	table	NOUN
ap-9918	178	18	3	3	NUM
ap-9918	178	19	.	.	NOUN
ap-9918	178	20	6	6	NUM
ap-9918	178	21	.	.	X
ap-9918	178	22	conclusion	conclusion	NOUN
ap-9918	178	23	based	base	VERB
ap-9918	178	24	on	on	ADP
ap-9918	178	25	a	a	DET
ap-9918	178	26	thorough	thorough	ADJ
ap-9918	178	27	evaluation	evaluation	NOUN
ap-9918	178	28	of	of	ADP
ap-9918	178	29	the	the	DET
ap-9918	178	30	results	result	NOUN
ap-9918	178	31	,	,	PUNCT
ap-9918	178	32	catboost	catboost	PROPN
ap-9918	178	33	is	be	AUX
ap-9918	178	34	an	an	DET
ap-9918	178	35	effective	effective	ADJ
ap-9918	178	36	model	model	NOUN
ap-9918	178	37	for	for	ADP
ap-9918	178	38	detecting	detect	VERB
ap-9918	178	39	and	and	CCONJ
ap-9918	178	40	predicting	predict	VERB
ap-9918	178	41	breast	breast	NOUN
ap-9918	178	42	cancer	cancer	NOUN
ap-9918	178	43	recurrence	recurrence	NOUN
ap-9918	178	44	across	across	ADP
ap-9918	178	45	different	different	ADJ
ap-9918	178	46	datasets	dataset	NOUN
ap-9918	178	47	.	.	PUNCT
ap-9918	179	1	140	140	NUM
ap-9918	179	2	vol	vol	NOUN
ap-9918	179	3	.	.	PUNCT
ap-9918	180	1	65	65	NUM
ap-9918	180	2	no	no	INTJ
ap-9918	180	3	.	.	PUNCT
ap-9918	181	1	2/2025	2/2025	PROPN
ap-9918	181	2	a	a	DET
ap-9918	181	3	comparative	comparative	ADJ
ap-9918	181	4	study	study	NOUN
ap-9918	181	5	of	of	ADP
ap-9918	181	6	breast	breast	NOUN
ap-9918	181	7	cancer	cancer	NOUN
ap-9918	181	8	detection	detection	NOUN
ap-9918	181	9	and	and	CCONJ
ap-9918	181	10	recurrence	recurrence	NOUN
ap-9918	181	11	.	.	PUNCT
ap-9918	181	12	.	.	PUNCT
ap-9918	182	1	.	.	PUNCT
ap-9918	183	1	figure	figure	NOUN
ap-9918	183	2	2	2	NUM
ap-9918	183	3	.	.	PUNCT
ap-9918	184	1	roc	roc	NOUN
ap-9918	184	2	and	and	CCONJ
ap-9918	184	3	auc	auc	VERB
ap-9918	184	4	values	value	NOUN
ap-9918	184	5	of	of	ADP
ap-9918	184	6	the	the	DET
ap-9918	184	7	comparative	comparative	ADJ
ap-9918	184	8	models	model	NOUN
ap-9918	184	9	when	when	SCONJ
ap-9918	184	10	used	use	VERB
ap-9918	184	11	with	with	ADP
ap-9918	184	12	the	the	DET
ap-9918	184	13	gse42568	gse42568	NOUN
ap-9918	184	14	dataset	dataset	PROPN
ap-9918	184	15	.	.	PUNCT
ap-9918	185	1	figure	figure	NOUN
ap-9918	185	2	3	3	NUM
ap-9918	185	3	.	.	PUNCT
ap-9918	186	1	roc	roc	NOUN
ap-9918	186	2	and	and	CCONJ
ap-9918	186	3	auc	auc	VERB
ap-9918	186	4	values	value	NOUN
ap-9918	186	5	of	of	ADP
ap-9918	186	6	the	the	DET
ap-9918	186	7	comparative	comparative	ADJ
ap-9918	186	8	models	model	NOUN
ap-9918	186	9	when	when	SCONJ
ap-9918	186	10	used	use	VERB
ap-9918	186	11	with	with	ADP
ap-9918	186	12	the	the	DET
ap-9918	186	13	bcrd	bcrd	NOUN
ap-9918	186	14	dataset	dataset	NOUN
ap-9918	186	15	.	.	PUNCT
ap-9918	187	1	the	the	DET
ap-9918	187	2	pros	pro	NOUN
ap-9918	187	3	the	the	DET
ap-9918	187	4	cons	con	NOUN
ap-9918	187	5	it	it	PRON
ap-9918	187	6	can	can	AUX
ap-9918	187	7	handle	handle	VERB
ap-9918	187	8	categorical	categorical	ADJ
ap-9918	187	9	features	feature	NOUN
ap-9918	187	10	.	.	PUNCT
ap-9918	188	1	this	this	PRON
ap-9918	188	2	can	can	AUX
ap-9918	188	3	decrease	decrease	VERB
ap-9918	188	4	the	the	DET
ap-9918	188	5	pre	pre	ADJ
ap-9918	188	6	-	-	ADJ
ap-9918	188	7	processing	processing	ADJ
ap-9918	188	8	time	time	NOUN
ap-9918	188	9	.	.	PUNCT
ap-9918	189	1	memory	memory	NOUN
ap-9918	189	2	-	-	PUNCT
ap-9918	189	3	intensive	intensive	ADJ
ap-9918	189	4	,	,	PUNCT
ap-9918	189	5	particularly	particularly	ADV
ap-9918	189	6	when	when	SCONJ
ap-9918	189	7	working	work	VERB
ap-9918	189	8	with	with	ADP
ap-9918	189	9	big	big	ADJ
ap-9918	189	10	datasets	dataset	NOUN
ap-9918	189	11	.	.	PUNCT
ap-9918	190	1	it	it	PRON
ap-9918	190	2	was	be	AUX
ap-9918	190	3	useful	useful	ADJ
ap-9918	190	4	for	for	ADP
ap-9918	190	5	learning	learn	VERB
ap-9918	190	6	on	on	ADP
ap-9918	190	7	small	small	ADJ
ap-9918	190	8	datasets	dataset	NOUN
ap-9918	190	9	because	because	SCONJ
ap-9918	190	10	it	it	PRON
ap-9918	190	11	reduces	reduce	VERB
ap-9918	190	12	overfitting	overfitte	VERB
ap-9918	190	13	well	well	ADV
ap-9918	190	14	.	.	PUNCT
ap-9918	191	1	takes	take	VERB
ap-9918	191	2	longer	long	ADJ
ap-9918	191	3	time	time	NOUN
ap-9918	191	4	than	than	ADP
ap-9918	191	5	other	other	ADJ
ap-9918	191	6	models	model	NOUN
ap-9918	191	7	to	to	PART
ap-9918	191	8	train	train	VERB
ap-9918	191	9	on	on	ADP
ap-9918	191	10	small	small	ADJ
ap-9918	191	11	datasets	dataset	NOUN
ap-9918	191	12	.	.	PUNCT
ap-9918	192	1	it	it	PRON
ap-9918	192	2	requires	require	VERB
ap-9918	192	3	little	little	ADJ
ap-9918	192	4	adjustment	adjustment	NOUN
ap-9918	192	5	of	of	ADP
ap-9918	192	6	the	the	DET
ap-9918	192	7	hyperparameters	hyperparameter	NOUN
ap-9918	192	8	.	.	PUNCT
ap-9918	193	1	it	it	PRON
ap-9918	193	2	works	work	VERB
ap-9918	193	3	well	well	ADV
ap-9918	193	4	with	with	ADP
ap-9918	193	5	imbalanced	imbalanced	ADJ
ap-9918	193	6	datasets	dataset	NOUN
ap-9918	193	7	.	.	PUNCT
ap-9918	194	1	table	table	NOUN
ap-9918	194	2	3	3	NUM
ap-9918	194	3	.	.	PUNCT
ap-9918	195	1	the	the	DET
ap-9918	195	2	pros	pro	NOUN
ap-9918	195	3	and	and	CCONJ
ap-9918	195	4	cons	con	NOUN
ap-9918	195	5	of	of	ADP
ap-9918	195	6	the	the	DET
ap-9918	195	7	catboost	catboost	PROPN
ap-9918	195	8	model	model	NOUN
ap-9918	195	9	.	.	PUNCT
ap-9918	196	1	this	this	DET
ap-9918	196	2	option	option	NOUN
ap-9918	196	3	is	be	AUX
ap-9918	196	4	recommended	recommend	VERB
ap-9918	196	5	for	for	ADP
ap-9918	196	6	the	the	DET
ap-9918	196	7	study	study	NOUN
ap-9918	196	8	as	as	SCONJ
ap-9918	196	9	it	it	PRON
ap-9918	196	10	consistently	consistently	ADV
ap-9918	196	11	performs	perform	VERB
ap-9918	196	12	well	well	ADV
ap-9918	196	13	in	in	ADP
ap-9918	196	14	terms	term	NOUN
ap-9918	196	15	of	of	ADP
ap-9918	196	16	f1	f1	NOUN
ap-9918	196	17	score	score	NOUN
ap-9918	196	18	,	,	PUNCT
ap-9918	196	19	accuracy	accuracy	NOUN
ap-9918	196	20	,	,	PUNCT
ap-9918	196	21	specificity	specificity	NOUN
ap-9918	196	22	,	,	PUNCT
ap-9918	196	23	precision	precision	NOUN
ap-9918	196	24	,	,	PUNCT
ap-9918	196	25	sensitivity	sensitivity	NOUN
ap-9918	196	26	,	,	PUNCT
ap-9918	196	27	and	and	CCONJ
ap-9918	196	28	auc	auc	NOUN
ap-9918	196	29	score	score	NOUN
ap-9918	196	30	.	.	PUNCT
ap-9918	197	1	although	although	SCONJ
ap-9918	197	2	there	there	PRON
ap-9918	197	3	is	be	VERB
ap-9918	197	4	still	still	ADV
ap-9918	197	5	great	great	ADJ
ap-9918	197	6	potential	potential	NOUN
ap-9918	197	7	in	in	ADP
ap-9918	197	8	other	other	ADJ
ap-9918	197	9	models	model	NOUN
ap-9918	197	10	,	,	PUNCT
ap-9918	197	11	such	such	ADJ
ap-9918	197	12	as	as	ADP
ap-9918	197	13	xgboost	xgboost	ADV
ap-9918	197	14	and	and	CCONJ
ap-9918	197	15	libsvm	libsvm	VERB
ap-9918	197	16	,	,	PUNCT
ap-9918	197	17	they	they	PRON
ap-9918	197	18	are	be	AUX
ap-9918	197	19	not	not	PART
ap-9918	197	20	as	as	ADV
ap-9918	197	21	consistent	consistent	ADJ
ap-9918	197	22	and	and	CCONJ
ap-9918	197	23	stable	stable	ADJ
ap-9918	197	24	as	as	ADP
ap-9918	197	25	catboost	catboost	NOUN
ap-9918	197	26	.	.	PUNCT
ap-9918	198	1	therefore	therefore	ADV
ap-9918	198	2	,	,	PUNCT
ap-9918	198	3	the	the	DET
ap-9918	198	4	catboost	catboost	NOUN
ap-9918	198	5	model	model	NOUN
ap-9918	198	6	can	can	AUX
ap-9918	198	7	be	be	AUX
ap-9918	198	8	trusted	trust	VERB
ap-9918	198	9	to	to	PART
ap-9918	198	10	detect	detect	VERB
ap-9918	198	11	and	and	CCONJ
ap-9918	198	12	predict	predict	VERB
ap-9918	198	13	the	the	DET
ap-9918	198	14	recurrence	recurrence	NOUN
ap-9918	198	15	of	of	ADP
ap-9918	198	16	breast	breast	NOUN
ap-9918	198	17	cancer	cancer	NOUN
ap-9918	198	18	.	.	PUNCT
ap-9918	199	1	depending	depend	VERB
ap-9918	199	2	on	on	ADP
ap-9918	199	3	the	the	DET
ap-9918	199	4	dataset	dataset	NOUN
ap-9918	199	5	,	,	PUNCT
ap-9918	199	6	certain	certain	ADJ
ap-9918	199	7	models	model	NOUN
ap-9918	199	8	offer	offer	VERB
ap-9918	199	9	benefits	benefit	NOUN
ap-9918	199	10	and	and	CCONJ
ap-9918	199	11	drawbacks	drawback	NOUN
ap-9918	199	12	.	.	PUNCT
ap-9918	200	1	for	for	ADP
ap-9918	200	2	example	example	NOUN
ap-9918	200	3	,	,	PUNCT
ap-9918	200	4	xgboost	xgboost	ADV
ap-9918	200	5	and	and	CCONJ
ap-9918	200	6	libsvm	libsvm	VERB
ap-9918	200	7	perform	perform	VERB
ap-9918	200	8	well	well	ADV
ap-9918	200	9	but	but	CCONJ
ap-9918	200	10	are	be	AUX
ap-9918	200	11	less	less	ADV
ap-9918	200	12	reliable	reliable	ADJ
ap-9918	200	13	.	.	PUNCT
ap-9918	201	1	this	this	DET
ap-9918	201	2	study	study	NOUN
ap-9918	201	3	shows	show	VERB
ap-9918	201	4	that	that	SCONJ
ap-9918	201	5	catboost	catboost	NOUN
ap-9918	201	6	is	be	AUX
ap-9918	201	7	reliable	reliable	ADJ
ap-9918	201	8	and	and	CCONJ
ap-9918	201	9	emphasises	emphasise	VERB
ap-9918	201	10	the	the	DET
ap-9918	201	11	importance	importance	NOUN
ap-9918	201	12	of	of	ADP
ap-9918	201	13	selecting	select	VERB
ap-9918	201	14	the	the	DET
ap-9918	201	15	right	right	ADJ
ap-9918	201	16	machine	machine	NOUN
ap-9918	201	17	learning	learning	NOUN
ap-9918	201	18	model	model	NOUN
ap-9918	201	19	for	for	ADP
ap-9918	201	20	medical	medical	ADJ
ap-9918	201	21	predictions	prediction	NOUN
ap-9918	201	22	.	.	PUNCT
ap-9918	202	1	141	141	NUM
ap-9918	202	2	r.	r.	PROPN
ap-9918	202	3	d.	d.	PROPN
ap-9918	202	4	abdu	abdu	PROPN
ap-9918	202	5	-	-	PUNCT
ap-9918	202	6	aljabar	aljabar	PROPN
ap-9918	202	7	,	,	PUNCT
ap-9918	202	8	k.	k.	PROPN
ap-9918	202	9	d.	d.	PROPN
ap-9918	202	10	aljafaar	aljafaar	PROPN
ap-9918	202	11	,	,	PUNCT
ap-9918	202	12	z.	z.	PROPN
ap-9918	202	13	j.	j.	PROPN
ap-9918	202	14	m.	m.	PROPN
ap-9918	202	15	ameen	ameen	PROPN
ap-9918	202	16	,	,	PUNCT
ap-9918	202	17	h.	h.	PROPN
ap-9918	202	18	a.	a.	PROPN
ap-9918	202	19	naman	naman	PROPN
ap-9918	202	20	acta	acta	PROPN
ap-9918	202	21	polytechnica	polytechnica	PROPN
ap-9918	202	22	7	7	NUM
ap-9918	202	23	.	.	PUNCT
ap-9918	203	1	future	future	ADJ
ap-9918	203	2	works	work	NOUN
ap-9918	203	3	based	base	VERB
ap-9918	203	4	on	on	ADP
ap-9918	203	5	the	the	DET
ap-9918	203	6	findings	finding	NOUN
ap-9918	203	7	and	and	CCONJ
ap-9918	203	8	recommendations	recommendation	NOUN
ap-9918	203	9	of	of	ADP
ap-9918	203	10	this	this	DET
ap-9918	203	11	work	work	NOUN
ap-9918	203	12	,	,	PUNCT
ap-9918	203	13	several	several	ADJ
ap-9918	203	14	avenues	avenue	NOUN
ap-9918	203	15	for	for	ADP
ap-9918	203	16	future	future	ADJ
ap-9918	203	17	research	research	NOUN
ap-9918	203	18	can	can	AUX
ap-9918	203	19	be	be	AUX
ap-9918	203	20	explored	explore	VERB
ap-9918	203	21	to	to	PART
ap-9918	203	22	improve	improve	VERB
ap-9918	203	23	the	the	DET
ap-9918	203	24	ability	ability	NOUN
ap-9918	203	25	of	of	ADP
ap-9918	203	26	catboost	catboost	ADJ
ap-9918	203	27	models	model	NOUN
ap-9918	203	28	to	to	PART
ap-9918	203	29	predict	predict	VERB
ap-9918	203	30	breast	breast	NOUN
ap-9918	203	31	cancer	cancer	NOUN
ap-9918	203	32	recurrence	recurrence	NOUN
ap-9918	203	33	:	:	PUNCT
ap-9918	203	34	(	(	PUNCT
ap-9918	203	35	1	1	X
ap-9918	203	36	.	.	PUNCT
ap-9918	203	37	)	)	PUNCT
ap-9918	203	38	model	model	NOUN
ap-9918	203	39	tuning	tuning	NOUN
ap-9918	203	40	and	and	CCONJ
ap-9918	203	41	optimisation	optimisation	NOUN
ap-9918	203	42	.	.	PUNCT
ap-9918	204	1	(	(	PUNCT
ap-9918	204	2	2	2	NUM
ap-9918	204	3	.	.	PUNCT
ap-9918	204	4	)	)	PUNCT
ap-9918	204	5	incorporating	incorporate	VERB
ap-9918	204	6	additional	additional	ADJ
ap-9918	204	7	data	datum	NOUN
ap-9918	204	8	.	.	PUNCT
ap-9918	205	1	(	(	PUNCT
ap-9918	205	2	3	3	NUM
ap-9918	205	3	.	.	PUNCT
ap-9918	205	4	)	)	PUNCT
ap-9918	205	5	integrating	integrate	VERB
ap-9918	205	6	the	the	DET
ap-9918	205	7	benefits	benefit	NOUN
ap-9918	205	8	of	of	ADP
ap-9918	205	9	multiple	multiple	ADJ
ap-9918	205	10	models	model	NOUN
ap-9918	205	11	could	could	AUX
ap-9918	205	12	lead	lead	VERB
ap-9918	205	13	to	to	ADP
ap-9918	205	14	more	more	ADV
ap-9918	205	15	accurate	accurate	ADJ
ap-9918	205	16	predictions	prediction	NOUN
ap-9918	205	17	.	.	PUNCT
ap-9918	206	1	by	by	ADP
ap-9918	206	2	addressing	address	VERB
ap-9918	206	3	these	these	DET
ap-9918	206	4	next	next	ADJ
ap-9918	206	5	areas	area	NOUN
ap-9918	206	6	,	,	PUNCT
ap-9918	206	7	research	research	NOUN
ap-9918	206	8	can	can	AUX
ap-9918	206	9	make	make	VERB
ap-9918	206	10	a	a	DET
ap-9918	206	11	significant	significant	ADJ
ap-9918	206	12	contribution	contribution	NOUN
ap-9918	206	13	to	to	ADP
ap-9918	206	14	advancing	advance	VERB
ap-9918	206	15	predictive	predictive	ADJ
ap-9918	206	16	modelling	modelling	NOUN
ap-9918	206	17	in	in	ADP
ap-9918	206	18	the	the	DET
ap-9918	206	19	detection	detection	NOUN
ap-9918	206	20	of	of	ADP
ap-9918	206	21	recurrent	recurrent	ADJ
ap-9918	206	22	breast	breast	NOUN
ap-9918	206	23	cancer	cancer	NOUN
ap-9918	206	24	.	.	PUNCT
ap-9918	207	1	as	as	ADP
ap-9918	207	2	a	a	DET
ap-9918	207	3	result	result	NOUN
ap-9918	207	4	,	,	PUNCT
ap-9918	207	5	more	more	ADV
ap-9918	207	6	accurate	accurate	ADJ
ap-9918	207	7	,	,	PUNCT
ap-9918	207	8	reliable	reliable	ADJ
ap-9918	207	9	,	,	PUNCT
ap-9918	207	10	and	and	CCONJ
ap-9918	207	11	therapeutically	therapeutically	ADV
ap-9918	207	12	useful	useful	ADJ
ap-9918	207	13	instruments	instrument	NOUN
ap-9918	207	14	can	can	AUX
ap-9918	207	15	be	be	AUX
ap-9918	207	16	produced	produce	VERB
ap-9918	207	17	,	,	PUNCT
ap-9918	207	18	improving	improve	VERB
ap-9918	207	19	patient	patient	ADJ
ap-9918	207	20	outcomes	outcome	NOUN
ap-9918	207	21	.	.	PUNCT
ap-9918	208	1	references	reference	NOUN
ap-9918	208	2	[	[	X
ap-9918	208	3	1	1	NUM
ap-9918	208	4	]	]	PUNCT
ap-9918	208	5	m.	m.	NOUN
ap-9918	208	6	m.	m.	PROPN
ap-9918	208	7	y.	y.	PROPN
ap-9918	208	8	al	al	PROPN
ap-9918	208	9	-	-	PUNCT
ap-9918	208	10	hashimi	hashimi	PROPN
ap-9918	208	11	.	.	PUNCT
ap-9918	209	1	trends	trend	NOUN
ap-9918	209	2	in	in	ADP
ap-9918	209	3	breast	breast	NOUN
ap-9918	209	4	cancer	cancer	NOUN
ap-9918	209	5	incidence	incidence	NOUN
ap-9918	209	6	in	in	ADP
ap-9918	209	7	iraq	iraq	PROPN
ap-9918	209	8	during	during	ADP
ap-9918	209	9	the	the	DET
ap-9918	209	10	period	period	NOUN
ap-9918	209	11	2000–2019	2000–2019	NUM
ap-9918	209	12	.	.	PUNCT
ap-9918	210	1	asian	asian	PROPN
ap-9918	210	2	pacific	pacific	PROPN
ap-9918	210	3	journal	journal	PROPN
ap-9918	210	4	of	of	ADP
ap-9918	210	5	cancer	cancer	NOUN
ap-9918	210	6	prevention	prevention	PROPN
ap-9918	210	7	22(12):3889–3896	22(12):3889–3896	PROPN
ap-9918	210	8	,	,	PUNCT
ap-9918	210	9	2021	2021	NUM
ap-9918	210	10	.	.	PUNCT
ap-9918	211	1	https://doi.org/10.31557/apjcp.2021.22.12.3889	https://doi.org/10.31557/apjcp.2021.22.12.3889	VERB
ap-9918	212	1	[	[	X
ap-9918	212	2	2	2	NUM
ap-9918	212	3	]	]	PUNCT
ap-9918	212	4	i.	i.	PROPN
ap-9918	212	5	j.	j.	PROPN
ap-9918	212	6	mustafa	mustafa	PROPN
ap-9918	212	7	,	,	PUNCT
ap-9918	212	8	o.	o.	PROPN
ap-9918	212	9	r.	r.	PROPN
ap-9918	212	10	abdullah	abdullah	PROPN
ap-9918	212	11	,	,	PUNCT
ap-9918	212	12	n.	n.	PROPN
ap-9918	212	13	al	al	PROPN
ap-9918	212	14	-	-	PUNCT
ap-9918	212	15	saffar	saffar	PROPN
ap-9918	212	16	,	,	PUNCT
ap-9918	212	17	et	et	PROPN
ap-9918	212	18	al	al	PROPN
ap-9918	212	19	.	.	PUNCT
ap-9918	213	1	quality	quality	NOUN
ap-9918	213	2	of	of	ADP
ap-9918	213	3	life	life	NOUN
ap-9918	213	4	assessment	assessment	NOUN
ap-9918	213	5	in	in	ADP
ap-9918	213	6	women	woman	NOUN
ap-9918	213	7	with	with	ADP
ap-9918	213	8	breast	breast	NOUN
ap-9918	213	9	cancer	cancer	NOUN
ap-9918	213	10	in	in	ADP
ap-9918	213	11	nineveh	nineveh	PROPN
ap-9918	213	12	,	,	PUNCT
ap-9918	213	13	iraq	iraq	PROPN
ap-9918	213	14	.	.	PUNCT
ap-9918	214	1	cureus	cureus	PROPN
ap-9918	214	2	16(1):e51589	16(1):e51589	NUM
ap-9918	214	3	,	,	PUNCT
ap-9918	214	4	2024	2024	NUM
ap-9918	214	5	.	.	PUNCT
ap-9918	215	1	https://doi.org/10.7759/cureus.51589	https://doi.org/10.7759/cureus.51589	PRON
ap-9918	216	1	[	[	X
ap-9918	216	2	3	3	NUM
ap-9918	216	3	]	]	X
ap-9918	216	4	e.	e.	PROPN
ap-9918	216	5	deniz	deniz	PROPN
ap-9918	216	6	,	,	PUNCT
ap-9918	216	7	a.	a.	NOUN
ap-9918	216	8	şengür	şengür	NOUN
ap-9918	216	9	,	,	PUNCT
ap-9918	216	10	z.	z.	PROPN
ap-9918	216	11	kadiroğlu	kadiroğlu	PROPN
ap-9918	216	12	,	,	PUNCT
ap-9918	216	13	et	et	PROPN
ap-9918	216	14	al	al	PROPN
ap-9918	216	15	.	.	PUNCT
ap-9918	216	16	transfer	transfer	NOUN
ap-9918	216	17	learning	learning	NOUN
ap-9918	216	18	based	base	VERB
ap-9918	216	19	histopathologic	histopathologic	ADJ
ap-9918	216	20	image	image	NOUN
ap-9918	216	21	classification	classification	NOUN
ap-9918	216	22	for	for	ADP
ap-9918	216	23	breast	breast	NOUN
ap-9918	216	24	cancer	cancer	NOUN
ap-9918	216	25	detection	detection	NOUN
ap-9918	216	26	.	.	PUNCT
ap-9918	217	1	health	health	NOUN
ap-9918	217	2	information	information	NOUN
ap-9918	217	3	science	science	NOUN
ap-9918	217	4	and	and	CCONJ
ap-9918	217	5	systems	system	NOUN
ap-9918	217	6	6(1):18	6(1):18	PROPN
ap-9918	217	7	,	,	PUNCT
ap-9918	217	8	2018	2018	NUM
ap-9918	217	9	.	.	PUNCT
ap-9918	218	1	https://doi.org/10.1007/s13755-018-0057-x	https://doi.org/10.1007/s13755-018-0057-x	NOUN
ap-9918	219	1	[	[	X
ap-9918	219	2	4	4	X
ap-9918	219	3	]	]	PUNCT
ap-9918	219	4	a.	a.	NOUN
ap-9918	219	5	yala	yala	PROPN
ap-9918	219	6	,	,	PUNCT
ap-9918	219	7	c.	c.	PROPN
ap-9918	219	8	lehman	lehman	PROPN
ap-9918	219	9	,	,	PUNCT
ap-9918	219	10	t.	t.	PROPN
ap-9918	219	11	schuster	schuster	PROPN
ap-9918	219	12	,	,	PUNCT
ap-9918	219	13	et	et	PROPN
ap-9918	219	14	al	al	PROPN
ap-9918	219	15	.	.	PUNCT
ap-9918	220	1	a	a	DET
ap-9918	220	2	deep	deep	ADJ
ap-9918	220	3	learning	learn	VERB
ap-9918	220	4	mammography	mammography	NOUN
ap-9918	220	5	-	-	PUNCT
ap-9918	220	6	based	base	VERB
ap-9918	220	7	model	model	NOUN
ap-9918	220	8	for	for	ADP
ap-9918	220	9	improved	improved	ADJ
ap-9918	220	10	breast	breast	NOUN
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ap-9918	221	5	.	.	PUNCT
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ap-9918	223	2	5	5	NUM
ap-9918	223	3	]	]	PUNCT
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ap-9918	223	16	.	.	PUNCT
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ap-9918	224	3	,	,	PUNCT
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ap-9918	224	5	.	.	PUNCT
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ap-9918	226	2	6	6	NUM
ap-9918	226	3	]	]	PUNCT
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ap-9918	226	10	,	,	PUNCT
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ap-9918	226	13	,	,	PUNCT
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ap-9918	227	4	,	,	PUNCT
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ap-9918	229	2	7	7	X
ap-9918	229	3	]	]	X
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ap-9918	229	7	s.	s.	PROPN
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ap-9918	230	8	cancer	cancer	NOUN
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ap-9918	230	12	images	image	NOUN
ap-9918	230	13	.	.	PUNCT
ap-9918	231	1	materials	material	NOUN
ap-9918	231	2	today	today	NOUN
ap-9918	231	3	:	:	PUNCT
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ap-9918	231	5	37:2738–2743	37:2738–2743	NUM
ap-9918	231	6	,	,	PUNCT
ap-9918	231	7	2021	2021	NUM
ap-9918	231	8	.	.	PUNCT
ap-9918	232	1	https://doi.org/10.1016/j.matpr.2020.08.543	https://doi.org/10.1016/j.matpr.2020.08.543	PROPN
ap-9918	233	1	[	[	X
ap-9918	233	2	8	8	NUM
ap-9918	233	3	]	]	PUNCT
ap-9918	233	4	s.	s.	PROPN
ap-9918	233	5	joo	joo	PROPN
ap-9918	233	6	,	,	PUNCT
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ap-9918	233	8	s.	s.	PROPN
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ap-9918	233	10	,	,	PUNCT
ap-9918	233	11	s.	s.	PROPN
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ap-9918	233	13	,	,	PUNCT
ap-9918	233	14	et	et	PROPN
ap-9918	233	15	al	al	PROPN
ap-9918	233	16	.	.	PUNCT
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ap-9918	234	8	of	of	ADP
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ap-9918	234	11	to	to	ADP
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ap-9918	234	14	in	in	ADP
ap-9918	234	15	breast	breast	NOUN
ap-9918	234	16	cancer	cancer	NOUN
ap-9918	234	17	.	.	PUNCT
ap-9918	235	1	scientific	scientific	ADJ
ap-9918	235	2	reports	report	NOUN
ap-9918	235	3	11(1):18800	11(1):18800	NUM
ap-9918	235	4	,	,	PUNCT
ap-9918	235	5	2021	2021	NUM
ap-9918	235	6	.	.	PUNCT
ap-9918	236	1	https://doi.org/10.1038/s41598-021-98408-8	https://doi.org/10.1038/s41598-021-98408-8	X
ap-9918	237	1	[	[	X
ap-9918	237	2	9	9	NUM
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ap-9918	237	4	y.	y.	PROPN
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ap-9918	237	6	,	,	PUNCT
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ap-9918	238	17	-	-	PUNCT
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ap-9918	239	4	,	,	PUNCT
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ap-9918	239	6	.	.	PUNCT
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ap-9918	241	2	10	10	NUM
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ap-9918	242	4	,	,	PUNCT
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ap-9918	242	6	.	.	PUNCT
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ap-9918	244	3	]	]	PUNCT
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ap-9918	244	8	s.	s.	PROPN
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ap-9918	248	2	12	12	NUM
ap-9918	248	3	]	]	PUNCT
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ap-9918	249	4	(	(	PUNCT
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ap-9918	249	6	)	)	PUNCT
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ap-9918	251	2	13	13	NUM
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ap-9918	255	4	of	of	ADP
ap-9918	255	5	biharmonic	biharmonic	NOUN
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ap-9918	255	8	-	-	ADJ
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ap-9918	255	10	distances	distance	NOUN
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ap-9918	255	12	clustering	clustering	NOUN
ap-9918	255	13	.	.	PUNCT
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ap-9918	256	8	,	,	PUNCT
ap-9918	256	9	corvallis	corvallis	PROPN
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ap-9918	256	11	oregon	oregon	PROPN
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ap-9918	257	2	15	15	NUM
ap-9918	257	3	]	]	X
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ap-9918	257	5	v.	v.	ADP
ap-9918	257	6	dorogush	dorogush	ADJ
ap-9918	257	7	,	,	PUNCT
ap-9918	257	8	v.	v.	PROPN
ap-9918	257	9	ershov	ershov	PROPN
ap-9918	257	10	,	,	PUNCT
ap-9918	257	11	a.	a.	PROPN
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ap-9918	259	6	support	support	NOUN
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ap-9918	260	4	.	.	PUNCT
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ap-9918	285	27	1	1	NUM
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ap-9918	285	29	2	2	NUM
ap-9918	285	30	dataset	dataset	NOUN
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ap-9918	285	32	3	3	NUM
ap-9918	285	33	materials	material	NOUN
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ap-9918	285	35	methods	method	NOUN
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ap-9918	285	62	4	4	NUM
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ap-9918	285	65	5	5	NUM
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ap-9918	285	71	7	7	NUM
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ap-9918	285	74	references	reference	NOUN