Explainable machine learning model for predicting minor amputation in diabetic foot ulcers

Abstract

BACKGROUND

Minor amputations severely impact the quality of life in patients with diabetic foot ulcers (DFUs), yet early and transparent risk stratification tools remain limited.

AIM

To identify clinical risk factors and develop an explainable machine learning model for predicting minor amputation within 3 months after admission in patients with DFUs, and to apply SHapley Additive exPlanations (SHAP) analysis to interpret the model’s predictions.

METHODS

A total of 685 patients with DFUs were enrolled and randomly partitioned into training and validation sets based on minor amputation status within 3 months after admission. Predictive factors were identified using least absolute shrinkage and selection operator regression. Six machine learning algorithms were trained and compared, with the optimal model selected based on the area under the receiver operating characteristic curve. SHAP analysis was applied to evaluate model interpretability and feature importance.

RESULTS

Least absolute shrinkage and selection operator regression identified four independent predictors of minor amputation: (1) Fibrinogen (FIB); (2) Pedal vessel calcification (PVC); (3) Albumin (ALB); and (4) The C-reactive protein-ALB-lymphocyte (CALLY) index (all P < 0.05). Subsequent model comparison revealed the random forest model as the best predictive model, achieving area under the receiver operating characteristic curves of 0.844 (95%CI: 0.810-0.878) in the training set and 0.766 (95%CI: 0.700-0.828) in the validation set. SHAP analysis confirmed that FIB made the greatest contribution to the model’s prediction, followed by the CALLY index, ALB, and PVC, with elevated FIB and a reduced CALLY index strongly driving higher predicted risk.

CONCLUSION

A random forest model including FIB, PVC, serum ALB, and the CALLY index demonstrates significant potential for the early prediction of minor amputation in patients with DFUs, providing clinicians with a transparent and interpretable tool to optimize risk stratification.

Keywords: Diabetic foot ulcers; Minor amputation; Clinical prediction model; Machine learning; C-reactive protein-albumin-lymphocyte index; SHapley Additive exPlanations

Core Tip: This study developed and validated an explainable machine learning model to predict minor amputation within 3 months of admission in patients with diabetic foot ulcers. Among six compared algorithms, the random forest model performed best (validation area under the receiver operating characteristic curve = 0.766), identifying fibrinogen, pedal vessel calcification, albumin, and the C-reactive protein-albumin-lymphocyte index as independent predictors. SHapley Additive exPlanations analysis provided visual, patient-level interpretation of feature contributions. The model relies exclusively on routinely available laboratory and radiographic variables, offering a practical and objective tool for early risk stratification that may help clinicians identify high-risk patients with diabetic foot ulcers and initiate timely targeted interventions.