Fig. 4

Different performance of the eight ML models on the training and test sets. AUC represents the area under the ROC curve and is used to evaluate the performance of the model in binary classification problems. Precision represents the precision rate, the higher the precision rate, the higher the proportion of real examples in the model prediction results, and the stronger the model’s ability to identify positive samples. Recall represents the recall rate, the higher the recall rate, the higher the proportion of positive samples successfully predicted by the model, and the more comprehensive the recognition ability of the model. F1 value represents the harmonic mean of precision and recall ratio are used to comprehensively evaluate the performance of the model. Accuracy refers to the proportion of the samples that are correctly predicted as positive cases to the total number of samples that are predicted as positive cases. Abbreviations: RF, Random Forest; GLM: Generalized Linear Model; KNN, K-Nearest Neighbor; SVM, Support Vector Machine; GBM, Gradient Boosting Machine; NNET, Neural Network; DT, Decision Tree; LASSO, Least Absolute Shrinkage and Selection Operator; AUC, Area Under the Receiver Operating Characteristic Curve; ML, Machine Learning