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

Figure 1 Machine learning model development and validation workflow. DFUs: Diabetic foot ulcers; LASSO: Least absolute shrinkage and selection operator; SHAP: SHapley Additive exPlanations.

Figure 2 Least absolute shrinkage and selection operator coefficient path plot and cross-validation curve for feature selection. A: Coefficient path plot showing variable trajectories against the log(λ) sequence; each colored line represents one variable, with variable names labeled at the right margin; B: Cross-validation curve showing binomial deviance vs log(λ); the left vertical dashed line indicates the minimum deviance (λ_min), and the right dashed line indicates the 1-standard-error criterion (λ_1se). LASSO: Least absolute shrinkage and selection operator; SII: Systemic immune-inflammation index; FIB: Fibrinogen; PLT: Platelet count; Hb: Hemoglobin; NPR: Neutrophil-platelet ratio; HbA1c: Hemoglobin A1c; CRP: C-reactive protein; NEUT: Neutrophil count; LYM: Lymphocyte count; Ca: Serum calcium; BUA: Blood uric acid; NLR: Neutrophil-lymphocyte ratio; CALLY: C-reactive protein-albumin-lymphocyte; HDL-C: High-density lipoprotein cholesterol; RBC: Red blood cell count; WBC: White blood cell count; MHR: Monocyte-high-density lipoprotein cholesterol ratio.

Figure 3 Receiver operating characteristic analysis of six machine learning models in training and validation sets. A: Training set (n = 480); B: Validation set (n = 205). The X-axis represents 1-specificity, and the Y-axis represents sensitivity. ROC: Receiver operating characteristic; KNN: K-nearest neighbors; LR: Logistic regression; MLP: Multilayer perceptron; RF: Random forest; SVM: Support vector machine; XGBoost: Extreme gradient boosting.

Figure 4 Validation and evaluation of the random forest model. A and B: Receiver operating characteristic curves for the training set (n = 480; area under the receiver operating characteristic curve =0.844) and validation set (n = 205; area under the receiver operating characteristic curve = 0.766), respectively; C and D: Calibration curves for the training set (Brier score = 0.161) and validation set (Brier score = 0.198), respectively; the dashed diagonal line represents ideal calibration; E and F: Decision curve analysis for the training set and validation set, respectively. The Y-axis denotes mean net benefit, and the X-axis denotes threshold probability (%). AUC: Area under the receiver operating characteristic curve; ROC: Receiver operating characteristic.

Figure 5 SHapley Additive exPlanations-based interpretation of the random forest model. A: Mean SHapley Additive exPlanations (SHAP) value bar plot ranking the four key features by importance (fibrinogen > C-reactive protein-albumin-lymphocyte > albumin > pedal vessel calcification); B: SHAP summary (beeswarm) plot, where each dot represents one patient, color indicates feature value (yellow = high, blue = low), and the X-axis shows SHAP value; C-F: SHAP dependence plots for fibrinogen, pedal vessel calcification, C-reactive protein-albumin-lymphocyte, and albumin; blue dots indicate non-amputation and orange dots indicate amputation, and the horizontal dashed line marks SHAP value = 0. ALB: Albumin; CALLY: C-reactive protein-albumin-lymphocyte; FIB: Fibrinogen; PVC: Pedal vessel calcification; SHAP: SHapley Additive exPlanations.