Table 2.

Comparative performance among different GA-based machine learning algorithms in predicting the TMB group.

Algorithm	GA Features	Sensitivity	Specificity	Precision	Accuracy	Running Time (s)
LR	13	0.64 ± 0.265	0.9 ± 0.079	0.8367 ± 0.162	0.7936 ± 0.132	0.159057
SVM	10	0.56 ± 0.15	0.8714 ± 0.177	0.7733 ± 0.228	0.7462 ± 0.133	0.050138
RF	6	0.64 ± 0.15	0.9179 ± 0.064	0.8833 ± 0.108	0.8089 ± 0.041	0.817011
LDA	4	0.56 ± 0.16	0.8714 ± 0.131	0.77 ± 0.131	0.7449 ± 0.056	0.125044
LGBM	11	0.72 ± 204	0.8893 ± 0.131	0.8367 ± 0.131	0.8218 ± 0.1	0.094271
XGB	7	0.6 ± 204	0.9 ± 0.009	0.8 ± 0.106	0.7808 ± 0.08	1.63018

GA: genetic algorithm, logistic regression (LR), random forest (RF), support vector machine (SVM), linear discriminant analysis (LDA), light gradient boosting machine (LGBM), extreme gradient boosting (XGB).