A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

Table 1

Features information and description of Cleveland heart disease dataset 2016 [13].

S. no.	Feature name	Feature code	Description	Domain of values (min-max)
1	Age	AGE	Age in years	30 < age < 77
2	Sex	SEX	Male = 1	1
			Female = 0	0
3	Type of chest pain	CPT	1 = atypical angina	1
			2 = typical angina	2
			3 = asymptomatic	3
			4 = nonanginal pain	4
4	Resting blood pressure	RBP	mm Hg admitted at the hospital	94–200
5	Serum cholesterol	SCH	In mg/dl	120–564
6	Fasting blood sugar >120 mg/dl	FBS	Fasting blood sugar >120 mg/dl (1 = true; 0 = false)	1
				0
7	Resting electrocardiographic results	RES	0 = normal	0
			1 = having ST-T	1
			2 = hypertrophy	2
8	Maximum heart rate achieved	MHR	—	71–202
9	Exercise-induced angina	EIA	1 = yes	0
			0 = no	1
10	Old peak = ST depression induced by exercise relative to rest	OPK	—	0–6.2
11	Slope of the peak exercise ST segment	PES	1 = up sloping	1
			2 = flat	2
			3 = down sloping	3
12	Number of major vessels (0–3) colored by fluoroscopy	VCA	—	0
				1
				2
				3
13	Thallium scan	THA	3 = normal	3
			6 = fixed defect	6
			7 = reversible defect	7