Ten different machine learning models were employed, and internal cross-validation along with the Wilcoxon signed-rank test was utilized to identify the most efficient machine learning model based on two criteria. The first criterion focused on selecting the model with an average F1-score derived from cross-validation that was significantly higher than the other models. As for the second criterion, the model with an average precision (AP) derived from cross-validation showed significantly higher performance compared to the other models and required a shorter training time. We continued to use average precision (AP) to evaluate model performance ( Fig 7). Two models including XGBoost (0.929 ± 0.029), and CatBoost (0.927 ± 0.030) remained the best performers. In this case, XGBoost showed significant difference when being compared to most of the others (p < 0.05). In addition, XGBoost had a shorter training time than CatBoost. Thus, in terms of the second criterion for this step, XGBoost model was selected for the optimization.