Classification of the Cardiac Arrhythmia Using Combined Feature Selection Algorithms

Abstract

The prediction of heart disease has gained great importance in recent years. Efficient monitoring of cardiac patients can save tremendous number of lives. This paper presents a method for classification and prediction of electrocardiogram data obtained from 452 patients representing the risk of cardiac arrhythmia. The aim of the study is to select highly related features with arrhythmia risk by using three different feature selection algorithms. In addition, various machine learning models are utilized for the classification task such as k-Nearest Neighbors (k-NN), Support Vector Machines (SVM) and Decision Tree (DT). The experimental results show that combination of a purposed feature selection method which later is called “Matched Selection” using SVM classifier outperforms other combinations and have an accuracy of 81.27% while k-NN and DT classifiers have an accuracy of 69.66% and 73.50% respectively. The study, in which detailed analyses are presented comparatively, is promising for the future studies.