Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms

Table 1

Description of Cleveland heart disease dataset.

Feature ID	Feature name	Feature type	Description	Values
F1	AGE	Numerical	Age in years	28–77; Mean: 51.9
F2	SEX	Nominal	Gender (1: male; 0: female)	1: (206); 0: (97)
F3	CP	Nominal	Chest pain type (1: typical angina; 2: atypical angina; 3: nonanginal pain; 4: asymptomatic)	1: (23); 2: (50); 3: (86); 4: (144)
F4	TRESTBPS	Numerical	Resting blood pressure (in mmHg on admission to the hospital)	94–200; mean: 131.6
F5	CHOL	Numerical	Serum cholesterol (in mg/dl)	126–564; mean: 246.6
F6	FBS	Nominal	Fasting blood sugar >120 mg/dl (1: true; 0: false)	1:(45); 0: (258)
F7	RESTECG	Nominal	Resting electrocardiographic results (2: showing probable or definite left ventricular hypertrophy by Estes' criteria, month of exercise ECG reading; 1: having ST-T wave abnormality; 0: normal)	2: (148); 1: (4); 0: (151)
F8	THALACH	Numerical	Maximum heart rate achieved	71–202; mean: 149.6
F9	EXANG	Nominal	Exercise-induced angina (1: yes; 0: no)	1: (99); 0: (204)
F10	OLDPEAK	Numerical	ST depression induced by exercise relative to rest	0–6.2; mean: 1.03
F11	SLOPE	Nominal	The slope of the peak exercise ST segment (3: downsloping; 2: flat; 1: upsloping)	3: (21); 2: (140); 1: (142)
F12	CA	Nominal	Number of major vessels (0–3)	3: (24); 2: (38); 1: (65); 0: (176)
F13	THAL	Nominal	The heart status (7: reversible defect; 6: fixed defect; 3: normal)	7: (117); 6: (18); 3: (168)
F14	TARGET	Nominal	Diagnosis of heart disease (1: presence; 0: absence)	1: (139); 0: (164)