Machine Learning-Based Prediction Model for Type 2 Diabetic Peripheral Neuropathy: Role of Bilateral Brachial-Ankle Pulse Wave Velocity and Anthropometric Indicators.

PURPOSE: Establishing a machine learning model to predict diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes mellitus (T2DM) and exploring the role of bilateral brachial-ankle pulse wave velocity and anthropometric indices.

PATIENTS AND

METHODS

Clinical data of 966 T2DM patients were retrospectively analyzed. According to sensory nerve conduction test results, they were divided into a DPN group and a non-DPN group. The BorutaShap method was employed to screen influencing factors, based on which nine machine learning models were established and compared. Interpretative analysis was performed using the SHAP (SHapley Additive exPlanations) package in Python. The mean absolute SHAP value of feature parameters was defined as their importance and ranked accordingly. The relationship between each feature and DPN was determined based on SHAP values, and quantitative analysis was conducted for continuous variables.

RESULTS

Among 966 T2DM patients, 469 were diagnosed with DPN and 13 influencing factors identified. Of nine machine learning models, the Support Vector Machine (SVM) model performed best (accuracy 0.74[95% CI: 0.69-0.79], AUC 0.82[95% CI: 0.77-0.87], recall 0.66[95% CI: 0.58-0.74], precision 0.80[95% CI: 0.73-0.86], F1 0.72[95% CI: 0.66-0.78]). SHAP analysis of the SVM model showed left brachial-ankle pulse wave velocity (LBAPWV) as the most influential predictor (SHAP=0.70), followed by gender, Glucose 0min, fT3, diabetes duration, and hip circumference. Right brachial-ankle pulse wave velocity (RBAPWV) contributed less (SHAP=0.20). Risk factors included LBAPWV, Gender, Glucose 0min, Diabetes duration, Insulin therapy, RBAPWV, UACR, Smoking, Height, and In-hospital blood glucose value; protective factors were fT3, Hip circumference, and C-peptide 180min.

CONCLUSION

Machine learning enables robust DPN prediction. Our model revealed asymmetric importance between LBAPWV and RBAPWV, with LBAPWV showing stronger DPN associations. Hip circumference was a protective anthropometric predictor. These findings enhance DPN risk stratification.