Development and Validation of a Machine Learning-Based Predictive Model for Peripheral Neuropathy Risk in Elderly Patients with Type 2 Diabetes.

Background: Diabetic peripheral neuropathy (DPN) is highly prevalent among elderly patients with type 2 diabetes; however, existing models exhibit suboptimal performance and lack specificity. This study aims to develop and validate a machine learning-based model for early identification of DPN risk. Methods: We retrospectively collected the data of 1450 elderly patients with type 2 diabetes using the electronic medical record system of the National Metabolic Management Center (MMC) at a tertiary hospital in Shanghai's Pudong New Area from March 2022 to March 2025. The dataset included general information, disease-related indicators, and laboratory results. We randomly divided the dataset into training and testing sets in a 7:3 ratio. After feature preprocessing and selection, four machine learning algorithms-logistic regression, naïve Bayes, random forest, and extreme gradient boosting (XGBoost)-were used to construct prediction models. Hyperparameter tuning was executed through grid search combined with 5-fold cross-validation, and model performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), accuracy, precision, recall, F1-score, calibration curves, and Decision Curve Analysis (DCA). The SHapley Additive exPlanations (SHAP) analysis was applied for model interpretation. Results: The prevalence of DPN was 42.9% (623/1450). Nine variables were identified as independent predictors: diabetes duration, HbA1c, sleep quality, Charlson Comorbidity Index, sugar-sweetened beverage intake, peripheral arterial disease, sedentary behavior, smoking, and hypertension. Among the models, XGBoost performed best with an AUC of 0.951, accuracy of 0.878, precision of 0.876, recall of 0.834, F1-score of 0.855, and Brier score of 0.087. SHAP analysis confirmed the dominant contribution of diabetes duration and HbA1c to model predictions. Conclusion: The XGBoost-based risk prediction model exhibited robust predictive performance and clinical utility for DPN in elderly patients with type 2 diabetes, offering potential for early identification of high-risk individuals and guiding targeted clinical interventions.