A Comparative Study for Predicting Heart Diseases Using Data Mining Classification Methods

TL;DR

This study compares five data mining classification algorithms to predict heart diseases, finding that decision trees achieve the highest accuracy, demonstrating the effectiveness of machine learning in medical diagnosis.

Contribution

It provides a comparative analysis of five classification algorithms for heart disease prediction using large datasets, highlighting the superior performance of decision trees.

Findings

Decision tree achieved 99.0% accuracy.

Random forest performed second best, close to decision trees.

All classifiers demonstrated predictive capability.

Abstract

Improving the precision of heart diseases detection has been investigated by many researchers in the literature. Such improvement induced by the overwhelming health care expenditures and erroneous diagnosis. As a result, various methodologies have been proposed to analyze the disease factors aiming to decrease the physicians practice variation and reduce medical costs and errors. In this paper, our main motivation is to develop an effective intelligent medical decision support system based on data mining techniques. In this context, five data mining classifying algorithms, with large datasets, have been utilized to assess and analyze the risk factors statistically related to heart diseases in order to compare the performance of the implemented classifiers (e.g., Na\"ive Bayes, Decision Tree, Discriminant, Random Forest, and Support Vector Machine). To underscore the practical viability of our approach, the selected classifiers have been implemented using MATLAB tool with two datasets. Results of the conducted experiments showed that all classification algorithms are predictive and can give relatively correct answer. However, the decision tree outperforms other classifiers with an accuracy rate of 99.0% followed by Random forest. That is the case because both of them have relatively same mechanism but the Random forest can build ensemble of decision tree. Although ensemble learning has been proved to produce superior results, but in our case the decision tree has outperformed its ensemble version.