Development and External Validation of a Machine Learning Model for Predicting Wound Infection in Diabetic Foot Ulcers

Abstract

Purpose: Wound infection is a major determinant of poor prognosis in patients with diabetic foot ulcers (DFUs). This study aimed to develop, compare, and externally validate multiple machine learning (ML) models for predicting wound infection in DFUs using routinely collected clinical indicators. Methods: A total of 800 patients with DFUs were retrospectively enrolled. The primary cohort (n=500) was randomly divided into training (70%, n=350) and internal testing (30%, n=150) sets, while an independent cohort (n=300) was used for external validation. Eight ML algorithms were constructed and compared, including logistic regression, decision tree, random forest, support vector machine, k-nearest neighbor, naive Bayes, extreme gradient boosting, and light gradient boosting machine. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, and other metrics in internal cross-validation and external validation. SHapley Additive exPlanations (SHAP) were applied for feature interpretability. Results: The RF model demonstrated the best performance, with an AUC of 0.937 (95% CI 0.906 to 0.969) in training, 0.853 (95% CI 0.804 to 0.901) in internal testing, and 0.832 (95% CI 0.779 to 0.885) in external validation. Six key variables (age, duration of diabetes, ankle brachial index, ulcer area, vascular complications, and osteomyelitis) were identified as the most influential predictors. SHAP analysis provided interpretable insights into their contributions to infection risk. Conclusion: The RF model showed robust predictive performance and generalizability for wound infection in DFUs. Its integration into clinical practice could enable early risk stratification and personalized interventions, potentially reducing amputations and improving outcomes. Future prospective studies are needed for further validation.