Machine Learning Insights Into Amputation Risk: Evaluating Wound Classification Systems in Diabetic Foot Ulcers

Abstract

This study compares the performance of various wound classification systems to determine which system most effectively predicts amputation risk in diabetic foot ulcer (DFU) patients. Additionally, it identifies the key clinical and socioeconomic factors that influence this risk. A total of 616 DFUs from 400 outpatient participants in a prospective cohort study were followed over 6 months. Ten machine learning (ML) algorithms were employed to evaluate the predictive accuracy of various wound classification systems. The SHapley Additive exPlanations (SHAP) method was used to interpret the predictions of the selected model. The DIAFORA (diabetic foot risk assessment) and WIFI (Wound, Ischaemia and foot Infection) classification systems demonstrated the highest predictive power for predicting amputation within 6 months. SHAP analysis revealed that wound penetration to bone, presence of ischaemia and infection, renal failure, delayed first specialist visit, longer diabetes duration, high baseline HbA1c, low education levels and high body mass index were significant risk factors for amputation. Conversely, higher education levels served as a protective factor. Occupation showed variable effects, with private-sector employment associated with increased risk, while being a housewife was linked to lower risk. Infection and ischaemia are significant factors affecting DFU outcomes. Addressing treatment adherence barriers and implementing tailored interventions that consider patients' occupational needs can reduce amputation rates.