Cardiac arrhythmia classification using sequential feature selection and decision tree classifier method

Abstract

Cardiac arrhythmia is referred to as a condition in which the heart's normal functionality is restricted resulting in cardiovascular diseases. Effective and well-timed monitoring is very much essential to save human life. During the past few years, keeping track of when and how arrhythmias occur has gained a lot of significance as it leads way towards many life-threatening issues like stroke, sudden cardiac arrest and also heart failure. This paper provides an evaluation of various classification algorithms based on feature selection techniques that improve the performance of the cardio monitoring system. The pre-eminent features are sorted out using feature selection methods. The feature selection methods enable to decide the features that can contribute to improve the performance. The paper also gives efficient combinations of distinct classification algorithms along with feature selections which improves the accuracy. Few popular machine learning algorithms namely naive Bayes, support vector machine and decision tree from contemporary literatures were applied to evaluate the performance with feature selection methods. The experimental result shows that the decision tree classifier with sequential feature selection provides improved accuracy of 84.13%.