Evaluating the methodological suitability of partial dependence plots and Shapley additive explanations for population-level interpretation of machine learning models in total joint arthroplasty

PDPs appear more methodologically appropriate than SHAP for population-level clinical guideline development, offering actionable dose-response relationships and population risk thresholds that SHAP's individualized attribution framework cannot provide. The dominance of interaction effects among the most influential predictors validates that PDPs accurately capture complementary relationships while presenting them in a format directly applicable to evidence-based perioperative protocols and institutional quality improvement initiatives. Video Abstract.