Machine Learning-Based Model to Predict Heart Disease in Early Stage Employing Different Feature Selection Techniques

Table 1

Heart disease dataset description.

Serial no.	Feature name	Code	Description
1	Age	AGE	The patient’s age in years.
2	Sex	SEX	The patient’s sex: ,
3	cp	CPT	Chest pain type: angina,1 = atypical angina, pain,
4	trestbps	RBP	Resting blood pressure (in mm)
5	chol	CM	The patient’s cholesterol measurement in mg/dl
6	fbs	FBS	The patient’s fasting blood  mg/dl. ,
7	restecg	REC	Resting electrocardiographic results: to note, ST-T wave abnormality, or definite left ventricular hypertrophy
8	Thalach	MHR	Maximum heart rate achieved
9	exang	EIA	Exercise-induced angina: ,
10	Oldpeak	OP	ST depression induced by exercise relative to rest checks the stress of the heart during exercise. The weak heart will stress more.
11	Slope	PES	The slope of the peak exercise ST segment sloping, 1,
12	ca	NMV	Number of primary vessels (0-3) colored by fluoroscopy.
13	thal	TS	Thallium stress result: 1, , defect, defect