Diagnostic model based on machine learning with multi‐feature analysis for diabetic peripheral neuropathy in type 2 diabetes: A single‐center retrospective study

OBJECTIVE: Diabetic peripheral neuropathy (DPN) is a common and serious complication of diabetes, which imposes a significant economic burden on both society and individuals. Therefore, early identification and diagnosis are crucial for the prevention and treatment of DPN. This study aims to develop machine learning (ML) models based on a variety of different characteristics to predict the risk of DPN in patients with type 2 diabetes mellitus. METHODS: We retrospectively reviewed the data of 770 patients screened for DPN with type 2 diabetes mellitus in the Department of General Practice, Jiangwan Town Subdistrict Community Health Service Center between May 2024 and July 2024. The data included 91 variables for baseline description analysis. The dataset was divided into a training and test set for the model evaluation in a 7:3 ratio, and feature selection was performed with the Boruta algorithm. Eleven ML algorithms were used to develop diagnostic models for DPN. The area under the receiver operating characteristic curve, calibration curves, and decision curves analysis were applied to evaluate the performance of the models, and the Shapley Additive Explanations (SHAP) method was adopted to interpret the performance of the models and the importance of features. RESULTS: The study included 770 type 2 diabetes mellitus inpatients, with a mean age of 70.88 ± 5.88 years, and 26.62% (205/770) had DPN. Integrated with the results of evaluation, we selected logistic regression (LR), extreme gradient boosting (XGBoost), and light gradient boosting machine (LGBM) models including neurological examinations as the top-performing algorithms. Based on variable importance ranking from the three models with overlap analysis, we identified 12 key predictors. Among the three models, the tuning fork test, ankle reflex, and temperature sensation test were all identified as the top three important predictors. Meanwhile, the low-density lipoprotein cholesterol (LDL-C) in the XGBoost model and the total cholesterol (Tch) in the LGBM were the important features for predicting DPN. In addition, excluding neurological examination findings, both LDL-C and Tch were important indicators with high contribution degree. CONCLUSIONS: The ML approach helped establish diagnostic models with stable performance. Meanwhile, the use of interpretable SHAP methods can enhance the clinical applicability of the models.