Cardiac Arrhythmia Classification Using Boosted Decision Trees

Alaa Elsayyad; Ahmed M. Nassef; Abdel Karim Mohammad Ali Baareh

doi:10.15866/irecos.v10i3.5359

Cardiac Arrhythmia Classification Using Boosted Decision Trees

Alaa Elsayyad⁽¹⁾, Ahmed M. Nassef⁽²⁾, Abdel Karim Mohammad Ali Baareh^(3*)

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/irecos.v10i3.5359

Abstract

An intelligent system for the classification of Electrocardiograph (ECG) beat signal would play an important role in the diagnosis of cardiac arrhythmias. This paper employed a recently invented C5.0 decision trees (DTs) algorithm to develop a supervised ECG beat classifier. In general, decision tree algorithms have proved remarkable ability to derive meaning from complicated or imprecise data. Accordingly, they can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computational techniques. They are nonparametric methods with no assumptions about the space distribution and the classifier structure. This study investigated the performance of the C5.0 decision tree model with boosting on the diagnosis of ECG features' dataset. Boosting process significantly improves the accuracy of a C5.0 model. The algorithm builds up multiple decision-tree models in a sequential manner; the first model is built in the standard way. Then, each of the subsequent models focuses on the misclassified samples by the preceding model. Finally, new samples are classified by ensemble these models using a weighted voting procedure to combine the separate decisions into one overall choice. The objective of this work is to classify an ECG characteristic feature vector as either normal or arrhythmia. The classification performance of boosted C5.0 DTs is evaluated and compared to the one that achieved by multilayer feed-forward neural network. Experimental results showed that the boosted C5.0 DTs model has achieved a remarkable performance that reached 99% classification accuracy on both training and testing subsets.
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Keywords

Electrocardiogram; Arrhythmia; Neural Network; Decision Tree; Feature Selection and Boosting

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