Heart Disease Prediction Using a Comparison of Naïve Bayes and Random Forest Algorithms

Abstract

Heart disease is one of the leading causes of death worldwide, making early detection essential. This study compares the performance of the Naive Bayes and Random Forest algorithms in predicting heart disease using clinical data. The dataset includes attributes such as chest pain type (cp), maximum heart rate achieved (thalach), and slope of the ST segment (slope). The research process consists of data preprocessing, feature selection, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The results show that Random Forest outperformed Naive Bayes in heart disease prediction. Random Forest achieved an accuracy of 75%, precision of 69%, and recall of 86%, while Naive Bayes achieved an accuracy of 69%, precision of 66%, and recall of 72%. These findings indicate that Random Forest is more effective in handling the complexity of heart disease data and provides better predictive performance. This study demonstrates the potential of machine learning methods, particularly Random Forest, in supporting heart disease diagnosis and may serve as a reference for the development of medical decision support systems.