Journal of Healthcare Engineering / 2022 / Article / Tab 2 / Research Article
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)