Evaluating Machine Learning Models for Heart Disease Classification Using a Hybrid Ensemble Framework

Our contributions are as follows.:
(i) an evidence-driven stacking framework built
from the three strongest of eight benchmarked
classifiers;
(ii) a joint evaluation of predictive performance and
predictive uncertainty; and
(iii) agreeing global and local explanations from
permutation importance, SHAP, and LIME.