Predicting Osteoarthritis in Older Adults Using Literature-Based, Non-Invasive Risk Factors: A Cross-Sectional Analysis of ELSA Wave 9

Abstract

Osteoarthritis (OA) is a prevalent joint disorder in older adults that is often diagnosed at a later stage, as clinical assessments typically rely on imaging and laboratory tests that are not readily accessible in all settings. This study aimed to develop and evaluate machine learning models that predict OA using non-invasive, self-reported features from Wave 9 of the English Longitudinal Study of Ageing (ELSA). A total of 4,723 participants aged 60 and above were included. An initial set of 32 features was selected based on existing literature and refined through a structured feature selection pipeline, resulting in a final set of 25 features, including joint pain and mobility limitations. Four supervised models -Logistic Regression, Random Forest, XGBoost, and CatBoost- were trained using a stratified train-test split and resampling to address class imbalance. The upsampled logistic regression model achieved the highest sensitivity (0.769) and strong overall performance (AUC = 0.755), while CatBoost showed the highest specificity (0.759) and an AUC of 0.747. A reduced logistic regression model using only the top 15 features retained similar accuracy and AUC. These findings demonstrate that OA can be predicted without imaging or biomarkers. The resulting models, particularly the logistic regression model, offer promise as cost-effective screening tools to support early identification and guide decisions about further clinical assessment. making them well-suited for primary care and digital health settings, especially where resources are limited.