A Machine Learning Framework for Osteoarthritis Risk Prediction in Metabolic Syndrome: NHANES-Based Model Development and Clinical Tool Validation

Objective: The aim of this study was to develop a machine-learning-based predictive model for assessing osteoarthritis (OA) risk in patients with metabolic syndrome (MetS), to identify key predictors and develop a clinical risk assessment tool. Methods: Data from the National Health and Nutrition Examination Survey (NHANES, 1999-2023) were utilized to screen the core predictors in combination with LASSO(Least Absolute Shrinkage and Selection Operator) regression, and predictive models were constructed by machine learning algorithms such as XGBoost. The SHAP framework was introduced to parse variable contributions, and a column-line diagram tool was developed to enable individualized risk assessment. Results: The study included 13,250 patients with MetS and screened 14 core predictors including age, body fat percentage (BFP), and sleep disorders. The XGBoost model demonstrated the best predictive performance in the validation set (AUC=0.761), and the SHAP analysis showed that age (29.6% contribution) and BFP (14.5%) were the strongest risk drivers. Column line plots categorized risk into low, moderate, and high tertiles to guide targeted interventions. Conclusion: This study is the first to construct a dynamic prediction model of OA risk in patients with MetS, which highlights established metabolic factors contributing to OA risk and provides an evidence-based tool for the “metabolic-joint co-management” strategy, with significant potential for clinical translation.