Fig. 4

Factors effect size. The a–i histogram describes the proportion of factoric importance of different predictors in the model. For each model, the relative importance is quantified by assigning a weight between 0 and 1 for each variable. The models XGBoost and RF allow the importance of variables to be derived during model training; the coefficients of the Elastic Net, Lasso, and Ridge models are used as the basis for factor importance; the k-NN, LR, NN, and SVM models are obtained by the Mean decrease accuracy method. j The average factor importance of the top 5 models according to AU-ROC