Detection of congestive heart failures using C4.5 Decision Tree

Abstract

Automatic electrocardiogram (ECG) heart beat classification is significant for diagnosis of heart failures. The purpose of this study is to evaluate the effect of C4.5 decision tree method in creating the model that will detect and separate normal and congestive heart failures (CHF) on the long-term ECG time series. The research was conducted in two stages: feature extraction using autoregressive (AR) module and classification by applying C4.5 decision tree method. The ECG signals were obtained from BIDMC Congestive heart failure database and classified by applying different experiments. The experimental results showed that the proposed method reached 99.86% classification accuracy (sensitivity 99.77%, specificity 99.93%, area under the ROC curve 0.998) and has potential in detecting the congestive heart failures.