Development of machine learning Predictive Model for Type 2 Diabetic Retinopathy Using the Triglyceride-glucose index explained by SHAP method

Xiaoqin Liu¹Shuying Wu¹Yue Yang¹Yang Li¹Xinting Zhang¹Ri-Hui Liu²Ling Qin^3*Fei Li^1*

• ¹The First hospital of Jilin University, Changchun, China
• ²Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
• ³Meihekou Central Hospital, Meihekou, China

Introduction: This study aimed to develop a diabetic retinopathy (DR) Prediction model using various machine learning algorithms incorporating the novel predictor Triglyceride-glucose index (TyG). Furthermore, the model was interpreted using the SHapley Additive exPlanations (SHAP) method. Method: Real-world data were collected from a general hospital in a major city and a county clinic, then divided into the DR Group (1392) and non-DR group (2358). Baseline data were collected, and variables were selected using Recursive Feature Elimination with Cross-Validation (RFECV). The performance of five machine learning algorithms, including Logistic Regression model (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB), was assessed based on accuracy, sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating characteristic Curve (ROC). The optimal model was interpreted using SHAP. Result:SVM and LR demonstrated superior performance in both the test set and training set (ROC, 0.85 and 0.82, respectively). The top five predictors identified by SHAP analysis included TyG, This is a provisional file, not the final typeset article Insulin therapy, HbA1c, Diabetes Course, HDL. HDL was identified as a protective factor, while the remaining factors were associated with retinopathy. Conclusion:LR and SVM demonstrated the best performance. To our knowledge, this is the first machine learning-based DR prediction model integrating the triglyceride-glucose index (TyG) as a core predictor, overcoming limitations of insulin resistance (IR) assessment in resource-limited settings. TyG provides a cost-effective alternative to conventional IR biomarkers (e.g., HOMA-IR), enabling practical DR risk stratification in primary care.