Integrating traditional Chinese medicine syndromes with clinical variables to predict lower extremity arterial disease in type 2 diabetes

Figure 1  Coefficient path of the LASSO regression model.

Figure 2  Relationship between the tuning parameter (λ) and binomial deviance in the LASSO regression model with ten-fold cross-validation.

Figure 3  Receiver operating characteristic curves of five machine learning prediction models in the training and validation sets.

Figure 4 Calibration curves of each model in the training and validation sets. GBDT: Gradient boosting decision tree.

Figure 5 Receiver operating characteristic curves of the XGBoost model based on ten-fold cross-validation. ROC: Receiver operating characteristic; AUC: Area under the receiver operating characteristic curve; 95%CI: 95% confidence interval.

Figure 6  SHapley Additive exPlanations analysis of the machine learning prediction model.

Figure 7 Decision curve analysis of the five models in the training and validation sets. GBDT: Gradient boosting decision tree.

Figure 8 "Traditional Chinese medicine-only" and "Western-only" denote models built using traditional Chinese medicine features or Western medicine features alone, respectively. "Integrated" denotes the model combining both feature sets. The "All" and "None" strategies represent treating all patients or no patients. Net benefit was computed across threshold probabilities from 0 to 1. TCM: Traditional Chinese medicine; AUC: Area under the receiver operating characteristic curve.

Figure 9 Nomogram for the prediction model of lower extremity arterial disease in type 2 diabetes mellitus. RBC: Red blood cell; BUN: Blood urea nitrogen.