Table 1.

Comparison of algorithm performances

ML algorithms	Model 1 AUROC	Model 2 AUROC	Model 3 AUROC	Model 4 AUROC	Model 5 AUROC	Model 6 AUROC
Extreme gradient boosting	0.926	0.959	0.944	0.975	0.940	0.978
CatBoost classifier	0.924	0.961	0.946	0.977	0.945	0.984
Extra Trees classifier	0.904	0.961	0.939	0.989	0.947	0.990
Random forest classifier	0.864	0.953	0.908	0.945	0.925	0.978
MLP classifier	0.859	0.959	0.922	0.977	0.930	0.983
Logistic regression	0.811	0.957	0.878	0.864	0.942	0.949
Support vector machine- linear kernel	0.808	0.956	0.874	0.876	0.932	0.943
K neighbors classifier	0.848	0.945	0.890	0.904	0.912	0.932

The bold numbers are the AUROC values that received the highest score in each model.