A Cost-Aware Stacking Ensemble Framework for Early Heart Disease Detection

Heart disease remains a leading cause of mortality worldwide, emphasizing the need for reliable and early screening mechanisms. Although machine learning techniques have been widely explored for heart disease prediction, many existing approaches primarily optimize overall accuracy and provide limited attention to misclassification cost asymmetry and interpretability, which are critical for clinical screening applications. This paper proposes a cost-aware stacking ensemble framework for early heart disease detection that integrates heterogeneous base classifiers, optimized decision thresholding, and explainable artificial intelligence techniques within a unified pipeline. Logistic regression, support vector machine, and random forest models are employed as base learners, and their outputs are combined using a logistic regression–based meta-learner. Cost-sensitive learning is incorporated to prioritize reducing false negatives, while the final classification threshold is optimized using receiver operating characteristic analysis. Model transparency is enhanced through Shapley value–based local explanations and permutation-based global feature importance analysis. Experimental evaluation on the UCI Heart Disease dataset using stratified cross-validation demonstrates that the proposed framework achieves an accuracy of 87.17%, a sensitivity of 0.92, a specificity of 0.84, and an area under the ROC curve of 0.87. The results indicate that the proposed approach provides a robust and interpretable decision-support system suitable for early heart disease screening.