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


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