Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification

Table 1

Types of features of the dataset.

Feature no.	Feature description	Feature code	(Mean  std)Healthy	(Mean  std)Patients
1	Age (AGE)		52.64  9.52	56.84  7.42
2	Sex (SEX)		0.55  0.49	0.81  0.385
3	Chest pain type (CPT)		2.79  0.92	3.60  0.79
4	Resting blood pressure (RBP)		129.17  16.32	134.85  18.69
5	Serum cholesterol (SCH)		243.49  53.58	250.73  49.83
6	Fasting blood sugar (FBS)		0.14  0.35	0.15  0.35
7	Resting electrocardiographic results (RES)		0.84  0.98	1.14  0.97
8	Maximum heart rate achieved (MHR)		158.59  18.98	138.89  22.74
9	Exercise induced angina (EIA)		0.14  0.35	0.54  0.49
10	Old peak (OPK)		0.59  0.78	1.64  1.29
11	Peak exercise slope (PES)		1.41  0.59	1.83  0.56
12	Number of major vessels colored by fluoroscopy (VCA)		0.27  0.63	1.13  1.01
13	Thallium scan (THA)		3.78  1.55	5.90  1.70