Karli Eka Setiawan

Enhancing Cardiovascular Risk Stratification with Interpretable Ensemble Learning: A Comparative Analysis and Explainable AI-Driven Insights

Results show that computationally efficient and interpretable ensembles provide a viable alternative to more complex models, particularly in data-constrained clinical settings, and highlight the potential of interpretable ensemble learning to develop dependable, deployable clinical decision support tools for integratio...