Table 9.

Performance of different classification algorithms for the grouped data of Caucasian race.

Classifier	Accuracy	TPR	TNR	AUC	Precision	F-Score	G-Mean	Mean Rank
FDT	0.7285	0.7935	0.6271	0.7103	0.7684	0.7807	0.7054	3.29
LDA	0.6908	0.7957	0.5254	0.6606	0.7255	0.7590	0.6466	6.71
LR	0.6887	0.7609	0.5763	0.6686	0.7368	0.7487	0.6622	6.21
GNB	0.6887	0.8261	0.4746	0.6503	0.7103	0.7638	0.6261	7.93
MGSVM	0.7219	0.8913	0.4576	0.6745	0.7193	0.7961	0.6387	5.79
CKNN	0.7483	0.9457	0.4407	0.6932	0.7250	0.8208	0.6455	4.36
Ensemble	0.7219	0.8696	0.4915	0.6805	0.7273	0.7921	0.6538	5.07
SVM*	0.7351	0.8478	0.5593	0.7036	0.7500	0.7959	0.6886	3.36
kNN*	0.7483	0.8804	0.5424	0.7114	0.7500	0.8100	0.6910	2.29