TABLE 3.

Classification performance tests after gene selection by the proposed hybrid algorithm.

Model name	Accuracy	Recall	Precision	F1	AUC
XGBClassifier	0.815	0.850	0.815	0.827	0.91 ± 0.09
LGBMClassifier	0.830	0.815	0.868	0.837	0.89 ± 0.07
RandomForestClassifier	0.831	0.825	0.826	0.836	0.93 ± 0.08
ExtraTreesClassifier	0.874	0.855	0.927	0.868	0.94 ± 0.07
GaussianNB	0.850	0.810	0.902	0.845	0.93 ± 0.08
KNeighborsClassifier	0.840	0.79	0.915	0.835	0.92 ± 0.10
LogisticRegression	0.872	0.895	0.887	0.884	0.93 ± 0.07
DecisionTreeClassifier	0.690	0.685	0.798	0.757	0.71 ± 0.23
SupportVectorMachineClassifier	0.919	0.940	0.930	0.929	0.93 ± 0.07
LinearDiscriminantAnalysis	0.874	0.895	0.882	0.883	0.92 ± 0.07
Mean	0.840	0.836	0.875	0.850	0.901

Bold values represent the average performance.