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Explainable residual ensemble modelling for EuroQol-5 dimensions-based quality-of-life assessment and stratification
Jaehyuk Lee1, Sejun Oh2,3, Jun Hwan Choi4
1Institute of IT Convergence Technology, Seoul National University of Science and Technology, Seoul, Republic of Korea.
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, F1-score, ROC-AUC, and PR-AUC. Model interpretability was examined using LR coefficients and SHAP analysis.
Results:
The proposed model achieved superior performance on the independent test set (accuracy = 0.88, precision = 0.85, recall = 0.83, F1 = 0.84, ROC-AUC = 0.93, PR-AUC = 0.91), outperforming individual baseline models. Key predictors included functional limitation (WOMAC), pain severity (VAS), and surgical history (TKA). Incorporating interaction features further improved accuracy to 0.89 without compromising interpretability.
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.
