Figure 2
Model performance. Fivefold cross-validation was used to evaluate model performance in the training set. ROC curves and calibration curves were used to compare the strengths and weaknesses of the models. (A,B) ROC andibration curves of the five machine learning models on the training set using fivefold cross-validation. (C,D) internal validation on the training set. (E,F) ROC andibration curves of the five machine learning models in the test set. From Supplementary Table 4, we can find that the model obtained by Logistic Regression (L1 regularization) performs best with AUC = 0.819, F1 = 0.357 in the validation set.
