S. Oka

Beyond predictive performance: Interpretability challenges and feature importance bias in XGBoost-based readmission models.

Song et al. report a machine-learning framework based on the eXtreme Gradient Boosting (XGBoost) algorithm for predicting 1-year unplanned readmissions among elderly patients with coronary heart disease (CHD). This commentary examines critical limitations in the interpretability and methodological robustness of such mo...