Zhong-Ming Zhao

Lung cancer risk prediction using interpretable ensemble models on lifestyle and clinical data

The results suggest that stacking-based ensembles can improve risk prediction from lifestyle and clinical indicators while maintaining model transparency through SHAP and LIME explanations, highlighting the potential of interpretable ensemble learning as a decision-support tool for lung cancer risk assessment.