Development and validation of a predictive model for diabetic peripheral neuropathy with type 2 diabetes mellitus in...

This study aims to identify risk factors associated with diabetic peripheral neuropathy (DPN) in patients with type 2 diabetesmellitus (T2DM) and to develop a predictive model to support clinical decision-making. A total of 1,001 patients with T2DM were retrospectively enrolled from the Department of Endocrinology, First Affiliated Hospital of Xinjiang Medical University, between January 2023 and January 2024. All patients were residents of Xinjiang. Patients were divided into two groups according to the diagnosis of peripheral neuropathy: 603 patients with DPN and 398 without DPN (NDPN). Missing data were handled using the "VIM" and "mice" packages in R. Statistical analyses were performed using independent t-tests and chi-square tests. Two machine learning algorithms were used to identify key risk factors, and shared features were visualized using a Venn diagram. Subsequently, four diagnostic models-GBM, GLM, RF, and SVM-were constructed using the "caret" package, and their predictive performance was rigorously evaluated.Fifteen factors, including age, duration of diabetes mellitus (DM), body mass index (BMI), diastolic blood pressure (DBP), 2-h postprandial glucose (2hPG), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), triglyceride-glucose (TyG) index, blood urea, estimated glomerular filtration rate (eGFR), urinary uric acid, urinary creatinine, urinary microalbumin, and urinary albumin-to-creatinine ratio (UACR), were significantly associated with the occurrence of diabetic peripheral neuropathy (DPN), all showing statistical significance (P < 0.05).SVM-RFE and LASSO regression identified seven core risk factors for model construction. The RF model achieved the best performance, with AUCs of 1.000 (95% CI 0.990-1.000) in the training set, 0.904 (95% CI 0.869-0.940) in the validation set, and 0.953 (95% CI 0.940-0.966) in the external dataset. To assess potential overfitting in the Random Forest model, we performed model simplification, bootstrap resampling to estimate confidence intervals, and DeLong's test (P = 0.748), all of which confirmed that the model maintained robust generalization performance rather than merely fitting the training data. This study successfully identified significant predictors of DPN using machine learning techniques and developed a validated diagnostic model. The model demonstrated high accuracy and may aid in early detection and clinical management of DPN.