Conference
Aug 2026
A Dual Explainability Framework for Cardiovascular Disease Prediction Using SHAP and LIME with Tree-Based Machine Learning Models
Cardiovascular disease (CVD) remains one of the main cause of mortality worldwide and although ensemble learning models such as Random Forest, XGBoost, and LightGBM offers strong predictive performance, their black box nature limits adoption in clinical settings. This study proposes a dual explainability framework that...
Cecilia Euginia, Charles Charles, David et al.
· International Conferences on... · 0 citations