Research Article
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 |
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