Table 3. Performance of different machine learning pipelines.

Machine learning pipeline	Training AUC	Cross-validation mean AUC	Test AUC
BorutaShap + RF*	0.98*	0.85*	0.88*
BorutaShap + SVM	0.99	0.82	0.84
BorutaShap + LR	0.96	0.82	0.83
BorutaShap + MLP	0.99	0.83	0.85
Boruta + RF	0.98	0.84	0.87
Boruta + SVM	0.97	0.84	0.86
Boruta + LR	0.95	0.83	0.84
Boruta + MLP	0.99	0.85	0.87
LASSO + RF	0.94	0.78	0.80
LASSO + SVM	0.94	0.78	0.80
LASSO + LR	0.98	0.84	0.86
LASSO + MLP	0.97	0.83	0.85
RFE + RF	0.97	0.84	0.86
RFE + SVM	0.96	0.85	0.87
RFE + LR	0.97	0.80	0.86
RFE + MLP	0.97	0.82	0.85

*, the best-performing pipeline. AUC, area under the receiver operating characteristic curve; RF, random forest; SVM, support vector machine; LR, logistic regression; MLP, multilayer perceptron; LASSO, least absolute shrinkage and selection operator; RFE, recursive feature elimination.