Table 4.

The comparison between unimproved traditional algorithms and machine learning algorithms.

	Model	Accuracy	ROC_AUC	Precision	Recall	F1
Traditional Model	Logistic Regression	0.554	0.559	0.649	0.267	0.379
Traditional Machine Learning Model	Random Forest	0.814	0.813	0.819	0.815	0.816
	Decision Tree	0.767	0.767	0.771	0.771	0.771
	BernoulliNB	0.698	0.698	0.697	0.719	0.708
	KNeighbors	0.675	0.674	0.664	0.732	0.696
	GaussianNB	0.600	0.595	0.567	0.908	0.698
Ensemble Learning Models	XGBoost	0.806	0.806	0.802	0.821	0.811
	Catboost	0.763	0.762	0.759	0.788	0.772
	Lightgbm	0.803	0.803	0.802	0.813	0.808