Explainable residual ensemble modelling for EuroQol-5 dimensions-based quality-of-life assessment and stratification in patients with knee osteoarthritis.

Objective: To develop an interpretable machine-learning framework for supporting quality-of-life (QoL) assessment and stratification in patients with knee osteoarthritis (OA) by integrating linear and nonlinear modelling strategies. Methods: This retrospective study utilised de-identified clinical data from 1,102 patients with knee OA collected at a university hospital in South Korea between September 2013 and January 2022 and made available via the AI Hub platform. QoL was assessed using the EuroQol-5 Dimensions (EQ-5D) index and dichotomised at 0.7. A residual ensemble model combining logistic regression (LR) and a Random Forest residual learner was developed and evaluated using stratified train-test split and three-fold cross-validation. Model performance was assessed using accuracy, F Results: The proposed model achieved superior performance on the independent test set (accuracy = 0.88, precision = 0.85, recall = 0.83, F Conclusions: The proposed residual ensemble framework effectively balances predictive performance and interpretability, providing a clinically meaningful framework for QoL assessment and risk stratification in knee OA. This approach supports the development of explainable decision-support tools in digital musculoskeletal health.