Developing an interpretable machine learning model for diagnosing gout using clinical and ultrasound features.

OBJECTIVE

To develop a machine learning (ML) model using clinical data and ultrasound features for gout prediction, and apply SHapley Additive exPlanations (SHAP) for model interpretation.

METHODS

This study analyzed 609 patients' first metatarsophalangeal (MTP1) joint ultrasound data from two institutions. Institution 1 data (n = 571) were split into training cohort (TC) and internal testing cohort (ITC) (8:2 ratio), while Institution 2 data (n = 92) served as external testing cohort (ETC). Key predictors were selected using Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Extreme Gradient Boosting (XGBoost) algorithms. Six ML models were evaluated using standard performance metrics, with SHAP analysis for model interpretation.

RESULTS

Five key predictors were identified: serum uric acid (SUA), deep learning (DL) model predictions, tophus, bone erosion, and double contour sign (DCs). The logistic regression (LR) model demonstrated optimal performance, achieving Area Under the Curve (AUC) values of 0.870 (95% CI: 0.820-0.920) in ITC and 0.854 (95% CI: 0.804-0.904) in ETC. The model showed good calibration with Brier scores of 0.138 and 0.159 in ITC and ETC, respectively.

CONCLUSION

This study developed an interpretable ML model for gout prediction and utilized SHAP to elucidate feature contributions, establishing a foundation for future applications in clinical decision support for gout diagnosis.