Heart Disease Diagnosis Based on Stacking Classifiers: Insights from a Comprehensive Machine Learning Approach

This study addresses the global health issue of cardiovascular diseases by using machine learning techniques to improve early-stage prediction of heart diseases.K-nearest neighbors, naive Bayes, logistic regression, decision trees, support vector machines, and random forest were used as individual classifiers to be carefully evaluated and compared.Additionally, the efficacy of the stacking ensemble technique was examined.The data from the UCI Machine Learning Repository were preprocessed and examined using performance metrics (accuracy, precision, recall, F1-score, and AUC) and stratified cross-validation.The results indicate that while the suggested stacking classifier with a logistic regression meta-learner performed competitively (84.78%), the random forest attained the best accuracy (86.41%).These findings emphasize the value of model diversity and meta-learner selection, while highlighting the promise of ensemble approaches in clinical diagnosis.Future research will focus on advanced ensemble strategies, improved model interpretability, comprehensive parameter optimization, and the validation of larger and more diverse clinical datasets to enhance robustness and generalizability.