Phenotype identification and precise intervention for multimorbidity of rheumatoid arthritis and diabetes mellitus using interpretable machine learning.

Objective: Rheumatoid arthritis (RA) and diabetes mellitus (DM) frequently coexist, yet the heterogeneity of RA-DM multimorbidity remains unclear. This study aims to develop an interpretable machine learning framework to reveal the phenotypic subgroups of RA-DM multimorbidity, providing a potential direction for precision public health interventions. Methods: Utilizing data from the National Health and Nutrition Examination Survey (1999-2018), we developed a Bayesian-optimized eXtreme Gradient Boosting (XGBoost) model to classify RA-DM multimorbidity status and compared with other machine learning models. Shapley Additive Explanations (SHAP) was applied to interpret the optimal model and quantify the contributions of different features. A dual-clustering approach combining Self-Organizing Maps and K-means was used to identify RA-DM phenotypic subgroups with different feature contribution patterns based on SHAP profiles. Results: The optimized XGBoost model achieved the best classification performance, outperforming other models such as K-nearest neighbors, support vector machine and logistic regression. SHAP analysis identified nine key contributing features (homocysteine, age, glucose, etc), and revealed non-linear interactions among the features. The dual-clustering based on SHAP values identified four distinct RA-DM phenotypes-inflammatory, metabolically protective, age-related and non-obese protective-each exhibiting unique clinical and biochemical patterns. Conclusion: This study established an interpretable machine learning framework for identifying distinct phenotypes of RA-DM multimorbidity. These findings provide a data-driven basis for targeted interventions in precision public health, while offering a transferable paradigm for phenotype discovery in other multimorbid conditions.