Background: Heart disease continues to be one of the foremost causes of mortality worldwide, emphasizing the urgent need for reliable and early diagnostic tools. Accurate prediction methods can support timely interventions and improve patient outcomes. Methods: This study presents the development and comparative evaluation of multiple machine learning models for heart disease prediction using a structured clinical dataset. Algorithms such as Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, and Deep Neural Networks were implemented. Additionally, a hybrid ensemble model combining XGBoost and SVM was proposed. Models were evaluated using key performance metrics including accuracy, precision, recall, and F1-score. Results: Among all models, the proposed hybrid model demonstrated the best performance, achieving an accuracy of 89.3%, a precision of 0.90, recall of 0.91, and an F1-score of 0.905, and outperforming all individual classifiers. These results highlight the benefits of combining complementary algorithms for improved generalization and diagnostic reliability. Conclusions: The findings underscore the effectiveness of ensemble and deep learning techniques in addressing key challenges such as data imbalance, feature selection, and model interpretability. The proposed hybrid model shows significant potential as a clinical decision-support tool, contributing to enhanced diagnostic accuracy and supporting medical professionals in real-world settings.
Almutairi et al. (2025) studied this question.