Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques

Table 3

Dataset attribute, icon, detail, and range.

Sr. no.	Attribute	Representative icon	Details	Range
1	Age	Age	Patients age, in years	29–71
2	Sex	Sex	0 = female; 1 = male	0,1
3	Chest pain	Cp	4 types of chest pain (1—typical angina; 2—atypical angina; 3—nonanginal pain; 4—asymptomatic)	0,1,2,3
4	Rest blood pressure	Trestbps	Resting systolic blood pressure (in mm Hg on admission to the hospital)	94–200
5	Serum cholesterol	Chol	Serum cholesterol in mg/dl	126–564
6	Fasting blood sugar	Fbs	Fasting blood sugar >120 mg/dl (0—false; 1—true)	0,1
7	Rest electrocardiograph	Restecg	0—normal; 1—having ST-T wave abnormality; 2—left ventricular hypertrophy	0,1,2
8	MaxHeart rate	Thalch	Maximum heart rate achieved	71–202
9	Exercise-induced angina	Exang	Exercise-induced angina (0—no; 1—yes)	0,1
10	ST depression	Oldpeak	ST depression induced by exercise relative to rest	0–6.2
11	Slope	Slope	Slope of the peak exercise ST segment (1—upsloping; 2—flat; 3—down sloping)	1,2,3,
12	No. of vessels	Ca	No. of major vessels (0–3) colored by fluoroscopy	0,1,2,3
13	Thalassemia	Thal	Defect types; 3—normal; 6—fixed defect; 7—reversible defect	0,1,2,3
14	Num (class attribute)	Class	Diagnosis of heart disease status (0—nil risk; 1—low risk; 2—potential risk; 3—high risk; 4—very high risk)	0,1