Home > Published Issues > 2024 > Volume 15, No. 4, 2024 >
JAIT 2024 Vol.15(4): 467-479
doi: 10.12720/jait.15.4.467-479

Binary Classification of Heart Disease Based on Differential Evolution-Optimised Machine Learning Approach

Theodore Nicholas Richard Egling 1, Sumbwanyambe Mbuyu 2,*, and Zenghui Wang 2,*
1. Department of Computer Science, University of South Africa, Florida, South Africa
2. Department of Electrical Engineering, University of South Africa, Florida, South Africa

Email: 14801264@mylife.unisa.ac.za (T.N.R.E); sumbwm@unisa.ac.za (S.W); wangz@unisa.ac.za (Z.W)
*Corresponding author

Manuscript received September 27, 2023; revised November 11, 2023; accepted November 27, 2023; published April 9, 2024.

Abstract—Accurate and timely diagnosis of heart disease is a persistent challenge in healthcare, necessitating innovative diagnostic methodologies. This study investigates the efficacy of Differential Evolution (DE) for hyperparameter optimisation in machine learning algorithms, targeting improved performance in heart disease binary classification. DE was selected for its robustness and ability to efficiently navigate high-dimensional parameter spaces, essential attributes for the fine-tuning of complex models. Employing the Cleveland Heart Disease dataset, the study optimised three machine learning classifiers: Random Forest, AdaBoost, and Gradient Boosting. Post-optimization, the DE-enhanced Random Forest Classifier achieved a standout performance with an accuracy of 93.3% and an F1-Score of 90.9%. Likewise, AdaBoost and Gradient Boosting classifiers also exhibited performance gains, reaching accuracies of 88.9% and 86.7%, and F1-Scores of 85.7% and 83.3%, respectively. These results not only outperform various existing models but also offer insights into the differential impacts of DE on multiple algorithms. The study lays a solid foundation for future research and clinical applications, indicating that DE-optimised machine learning algorithms hold significant promise for advancements in cardiovascular disease diagnostics.
Keywords—differential evolution, hyperparameter optimization, machine learning, heart disease diagnosis, binary classification, Cleveland heart disease dataset, random forest, AdaBoost, gradient boosting

Cite: Theodore Nicholas Richard Egling, Sumbwanyambe Mbuyu, and Zenghui Wang, "Binary Classification of Heart Disease Based on Differential Evolution-Optimised Machine Learning Approach," Journal of Advances in Information Technology, Vol. 15, No. 4, pp. 467-479, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.