Fig. 5

Optimization and visualization of the XGBoost model. Note: A, B and D displayed the ROC curve of the train, validation and test of the XGBoost model by 10-fold cross-validation. C showed the learning curve of the XGBoost classifier. E showed the reliability curve of XGBoost model. F showed the summary plots of SHAP values for the XGBoost model. For each feature, one point corresponds to a single patient. A point’s position along the x axis represented the impact that feature had on the model’s output for that specific patient. Features were arranged along the y axis based on their importance, which was given by the mean of their absolute Shapley values. The higher the feature was positioned in the plot, the more important it was for the model