A Reliable Machine Intelligence Model for Accurate Identification of Cardiovascular Diseases Using Ensemble Techniques

Table 2

Dataset attributes’ description [33].

S. no.	Cleveland dataset features	Comprehensive dataset features	Mendeley dataset features	Unit
1	Age	Age	Age	In years
2	Sex	Sex	Gender	1, 0 (0 = female; 1 = male)
3	cp	Chest pain type	Chest pain	Value 0: typical angina; value 1: atypical angina
4	trestbps	Resting bps	Resting BP	94–200 (in mmHg)
5	chol	Cholesterol	Serum cholesterol	126–564 (in mg/dl)
6	fbs	Fasting blood sugar	Fasting blood sugar	0, 1 > 120 mg/dl (0 = false; 1 = true)
7	restecg	Resting ECG	Restingrelectro	0, 1, 2 (value 0: normal; value 1: having ST-T-wave abnormality (T-wave inversions and/or ST elevation or depression of >0.05 mV); value 2: showing probable or definite left ventricular hypertrophy by Estes criteria
8	thalach	Max heart rate	Max heart rate	71–202
9	exang	Exercise angina	Exercise angina	0, 1 (0 = no; 1 = yes)
10	Oldpeak	Oldpeak	Oldpeak	0–6.2
11	Slope	ST slope	Slope	1, 2, 3 (1-upsloping, 2-flat, and 3-downsloping)
12	ca	—	No. of major vessels	0, 1, 2, 3
13	thal	—	—	Thalassemia display, 3 = normal, 6 = fixed, and 7 = reversible defect
14	Target	Target	Target	0, 1 (0 = absence of heart disease; 1 = presence of heart disease)