Objective:

To develop an interpretable framework for integrating multidimensional clinical and patient-reported information associated with QoL status in knee osteoarthritis patients.

Approach:

Key Findings:

• The dataset comprised 1,102 patients, with 64.0% classified as lower QoL (EQ-5D < 0.7).
• Logistic regression provided interpretable relationships, while Random Forest and other nonlinear models enhanced predictive capacity.

Interpretation:

The study aims to provide insights into QoL management for knee OA patients through an explainable modeling framework that balances interpretability and predictive accuracy.

Limitations:

• The study is based on a single-center dataset, which may limit generalizability.
• The EQ-5D index was binarized, potentially oversimplifying the QoL assessment.

Conclusion:

The proposed residual ensemble framework seeks to enhance personalized care in knee OA rehabilitation through improved QoL evaluation.

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