Comparative Evaluation of C4.5, CART, and C5.0 Decision Tree Algorithms on Heart Disease Prediction: Performance Metrics and Model Effectiveness

Accurately predicting heart disease is crucial for effective diagnosis and treatment. Decision tree algorithms, such as C4.5, CART, and C5.0, are widely used in medical diagnostics due to their interpretability and performance. This study compares these three prominent decision tree algorithms to a heart disease dataset. This research aims to assess and compare their effectiveness in predicting heart disease using various performance metrics, including accuracy, precision, recall, and F1 score. The analysis involves training and validating each algorithm on the dataset, followed by a detailed examination of their classification results. Our findings reveal distinct strengths and weaknesses among the algorithms, providing insights into their suitability for heart disease prediction. The results suggest that while all three algorithms perform well, C5.0 exhibits superior accuracy and robustness, making it a potentially more effective tool for heart disease prediction. This paper contributes valuable information for selecting the most appropriate decision tree algorithm for medical diagnostics and highlights the importance of performance metrics in evaluating predictive models.