Machine learning-based risk prediction model for neuropathic foot ulcers in patients with diabetic peripheral neuropathy

Abstract

Background

Diabetic peripheral neuropathy (DPN) is a common chronic complication of diabetes, marked by symptoms like hyperalgesia, numbness, and swelling that impair quality of life. Nerve conduction abnormalities in DPN significantly increase the risk of neuropathic foot ulcers (NFU), which can progress rapidly and lead to severe outcomes, including infection, gangrene, and amputation. Early prediction of NFU in DPN patients is crucial for timely intervention.

Methods

Clinical data from 400 DPN patients treated at the China–Japan Friendship Hospital (September 2022–2024) were retrospectively analyzed. Data included medical histories, physical exams, biochemical tests, and imaging. After feature selection and data balancing, the dataset was split into training and validation subsets (8:2 ratio). Six machine learning algorithms—random forest, decision tree, logistic regression, K-nearest neighbor, extreme gradient boosting, and multilayer perceptron—were evaluated using k-fold cross-validation. Model performance was assessed via accuracy, precision, recall, F1 score, and AUC. The SHAP method was employed for interpretability.

Results

The multilayer perceptron model showed the best performance (accuracy: 0.875; AUC: 0.901). SHAP analysis highlighted triglycerides, high-density lipoprotein cholesterol, diabetes duration, age, and fasting blood glucose as key predictors.

Conclusions

A machine learning-based prediction model using a multilayer perceptron algorithm effectively identifies DPN patients at high NFU risk, offering clinicians an accurate tool for early intervention.