Authors:
Hlaing Htake Khaung Tin, Khin Shin Thant, Myat Mon Khaing, Thet Thet Aung

Addresses:
Faculty of Information Science, University of Information Technology, Yangon, Myanmar. Faculty of Information Science, University of Computer Studies, Hinthada, Myanmar.

Abstract:

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.

Keywords: C4.5 and C5.0; Decision Tree; Heart Disease Prediction; Diagnostic Tools; Decision Trees; Distinctive Approaches; Medical Diagnostics; Predictive Accuracy; Performance and Accuracy.

Received on: 12/05/2024, Revised on: 03/08/2024, Accepted on: 09/09/2024, Published on: 03/12/2024

DOI: 10.69888/FTSHSL.2024.000273

FMDB Transactions on Sustainable Health Science Letters, 2024 Vol. 2 No. 4, Pages: 188-197

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