Comparison of statistical and machine learning methods for survival prediction of diabetic foot ulcers

Objectives: This prospective cohort study aims to provide the first survival prediction of diabetic foot ulcers (DFUs) of diabetes mellitus type 2 patients using machine learning (ML) and statistical prediction methods. Methods: A total of 319 DFU patients who visited the Diabetic Foot Care Clinic at Dow University Hospital in Karachi, Pakistan, were selected for the study. The selected participants were followed from October 2024 to January 2025 to monitor the occurrence of diabetic foot amputations (DFAs). ML - artificial neural network and statistical method (survival analysis) are employed to predict the likelihood of DFA occurring after 5 years of a DFU diagnosis. The accuracy of both models is compared using the receiver operating characteristic (ROC) curve and performance matrix. Results: In this study, 319 DFU patients’ data consisting of 220 (68.96%) males and 99 (31.04%) females are taken. During the follow-up period, 31 (9.72%) patients experienced DFAs, while 288 (90.28%) remained unaffected by foot amputation. Gender, age, body mass index, blood pressure systolic, total cholesterol, high-density lipoprotein, low-density lipoprotein, triglyceride, hemoglobin A1c, creatinine, neuropathy, nephropathy, retinopathy, and cardiovascular disease are statistically significant at the 5% level of significance. The ML model performance of the training data was 0.9417, and that of the testing data was 0.9271. The analysis shows that the accuracy of the ML model (area under the curve [AUC] = 0.850) was higher than that the accuracy of the statistical model (AUC = 0.782). Conclusion: The ML model provides better performance for predicting the DFA as compared to the traditional statistical model of survival analysis.