<![CDATA[Heart disease remains one of the leading causes of death worldwide, making early detection of its risk crucial to reducing mortality and morbidity rates. This study aims to develop a heart disease risk prediction model based on machine learning using a Decision Tree algorithm optimized with Bald Eagle Search (BES). The research employed a quantitative approach utilizing a clinical dataset containing demographic and medical variables such as age, gender, blood pressure, cholesterol levels, electrocardiographic results, and heart disease status. The baseline Decision Tree model was compared with the BES-optimized model (BES-DT) through evaluations of accuracy, confusion matrix, prediction probability distribution, feature importance analysis, and learning curves with respect to the max_depth parameter. The analysis revealed that the baseline Decision Tree achieved an accuracy of 70.5%, with 43 correct predictions out of 61 test samples, while the BES-DT model achieved an accuracy of 68.9%, with 42 correct predictions. Although the overall accuracy showed a slight decrease, BES-DT demonstrated greater consistency in identifying at-risk patients, with fewer misclassifications (4 cases compared to 7 in the baseline). Furthermore, the prediction probability distribution in BES-DT was more stable, with values concentrated near 0 and 1, indicating higher confidence in classification. The feature importance analysis highlighted chest pain type, oldpeak, and thal as dominant variables in risk classification. The learning curve confirmed that BES-DT reduced the risk of overfitting and improved the model’s generalization capability. This study contributes to the development of more accurate and interpretable machine learning classification methods in healthcare. Future work may involve testing the model on larger and more diverse datasets, integrating other optimization algorithms for performance comparison, and implementing web-based or clinical applications to support medical decision-making.]]>
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