Using Machine Learning to Predict the Requirement for Revascularization in Patients with Chest Pain in the Emergency Department

Table 1

Features for analysis.

		Features
Demographic data	1	Gender
	2	Age
Clinical data at emergency or outpatient department	3	SBP
	4	DBP
	5	HR
	6	Arrhythmia
	7	ST-segment changes
	8	Killip classification
History	9	CAD
	10	MI
	11	PCI
	12	CABG
	13	Chest pain
	14	Diabetes
	15	Hypertension
	16	Stroke
	17	Hyperlipidemia
	18	PAD
	19	Smoking
	20	Drinking
	21	Family history of CHD
Laboratory data at emergency or outpatient department	22	WBC
	23	Monocyte
	24	Lymphocyte
	25	RBC
	26	HBG
	27	HCT
	28	PLT
	29	FBG
	30	Hs-CRP
	31	HCY
	32	Uric acid
	33	CRE
	34	BUN
	35	TC
	36	TG
	37	LDL-C
	38	HDL-C
	39	LDH
	40	Cardiac markers change
Calculation data	41	NLR

SBP, systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate; CHD, coronary heart disease; MI, myocardial infarction; PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; PAD, peripheral arterial disease; WBC, white blood cell; RBC, red blood cell; HGB, hemoglobin; HCT, hematocrit; PLT, platelet; FBG, fasting blood glucose; HCY, homocysteine; CRE, creatinine; BUN, blood urea nitrogen; TC, total cholesterol; TG, triglyceride; LDL, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; NLR, neutrophil-to-lymphocyte ratio.