An Interpretable Two-Stage Machine Learning Framework for Musculoskeletal Symptom Presence and Extent: A Cross-Sectional Study of the European Workforce

Abstract

Musculoskeletal symptoms may affect a single body region or multiple region groups, representing distinct classification tasks. This cross-sectional secondary analysis evaluated an interpretable two-stage machine-learning framework using data from 30,278 employees across 35 countries in the 2024 European Working Conditions Survey. Stage A distinguished employees with and without musculoskeletal symptoms, whereas Stage B differentiated single-region from multiregional symptoms among 20,593 symptomatic employees. Elastic Net logistic regression, penalised spline logistic regression, and extreme gradient boosting (XGBoost) were evaluated using five repetitions of five-fold outer cross-validation with three-fold inner tuning, while preserving primary sampling units and incorporating survey weights. Model performance was assessed using discrimination, probability accuracy, calibration, and threshold-based measures. Overall, 68.28% of employees were symptomatic. For the selected Elastic Net model, the area under the receiver operating characteristic curve was 0.744 (95% confidence interval: 0.728–0.758) in Stage A and 0.654 (0.634–0.673) in Stage B. Performance differences across models were limited, and Elastic Net was selected based on the prespecified Brier-score parsimony criterion. Tiring or painful positions showed the highest held-out permutation importance in both stages. Predictor patterns remained consistent across sensitivity analyses. Symptom presence was classified with moderate discrimination, whereas classification of regional extent was more limited.