A Machine-Learning-Based Clinical Decision Model for Predicting Amputation Risk in Patients with Diabetic Foot...

Objective: To establish a reliable machine-learning-based model for predicting the risk of lower limb amputation in patients with diabetic foot ulcers and to provide quantitative evidence for clinical decision-making and individualized prevention strategies. Methods: This retrospective study analyzed data from 149 hospitalized diabetic foot ulcer patients treated at Beijing Shijitan Hospital between January 2019 and December 2022. Patients were divided into amputation and non-amputation groups according to clinical outcomes. Candidate predictors-including infection biomarkers, vascular parameters, and nutritional indices-were first screened using the least absolute shrinkage and selection operator algorithm. Subsequently, a support vector machine model was trained and internally validated via five-fold cross-validation to estimate amputation risk. Model performance was evaluated by discrimination, calibration, and clinical utility analysis. Results: Among all enrolled variables, C-reactive protein and Wagner grade were identified as independent predictors of amputation (p < 0.05). The optimized support vector machine model achieved excellent discrimination, with an area under the Receiver Operating Characteristic curve of 0.89, and demonstrated a moderate level of calibration (Hosmer-Lemeshow χ² = 19.614, p = 0.012). Decision curve analysis showed a net clinical benefit of 0.351 when the threshold probability was set at 0.30. The model correctly classified 82.4% of cases in internal validation, confirming its predictive robustness and potential for clinical application. Conclusions: C-reactive protein and Wagner grade are key determinants of amputation risk in diabetic foot ulcer patients. The support vector machine-based prediction model exhibits strong accuracy, clinical interpretability, and personalized interventions.