Document Type

Article

Publication Date

7-8-2026

Journal / Book Title

Bmj Health & Care Informatics

Abstract

Objectives To develop a robust and interpretable machine learning framework for arthritis risk prediction and to identify important risk factors associated with the disease.Methods This study used four datasets, which are Behavioral Risk Factor Surveillance System (BRFSS) 2019, BRFSS 2021, National Health Interview Survey (NHIS) 2020 and NHIS 2022. We evaluated 11 machine learning and deep learning architectures including a custom stacked ensemble combined with five resampling techniques to address class imbalance and 9 imputation methods to handle missing data. Area under the receiver operating characteristic curve (AUROC), sensitivity, specificity and balanced accuracy are used to evaluate model performance, with fivefold cross-validation to ensure robustness. SHapley Additive exPlanations (SHAP) provided feature interpretability in prediction.Results The combination of the stacked ensemble, Generative Adversarial Network (GAN) imputation and random undersampling (RUS) achieved the best performance with an AUROC of 0.8007 and sensitivity of 0.8060 in the test set of BRFSS 2019. This combination also showed a balanced performance across both subgroups of male and female by maintaining high performance with AUROC of 0.808 and 0.800, respectively, in the test set of BRFSS 2021. The stacked ensemble model with GAN imputation and either Random Undersampling (RUS) or Random Oversampling (ROS) achieved the best performance across all four datasets. SHAP analysis identified higher age, walking difficulty and high body mass index (BMI) as the top physical factors. Among the psychosocial factors, parental separation showed a high impact on predicting arthritis.Discussion The risk of arthritis is influenced by health, lifestyle and childhood experiences. SHAP shows that difficulty walking, age, BMI and childhood stress play important roles in predicting arthritis risk.Conclusion These results confirm important predictors of arthritis risk and provide a clear, interpretable framework.

DOI

10.1136/bmjhci-2025-101955

Rights

© Author(s) (or their employer(s)) 2026. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.

Published Citation

Shams MA, Fatema S, Hasibul Islam DM, Datta A, Eisenberg D, Tasaouf Mridula D, et al. Explainable machine learning revealing the impact of mental and physical health on arthritis. BMJ Health & Care Informatics. 2026;33:e101955. https://doi.org/10.1136/bmjhci-2025-101955

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