TABLE 6.

Performance of optimized ML methods

Abbreviation	Accuracy [95% Cl]	Precision [95% Cl]	Recall [95% Cl]	F1 [95% Cl]
SVM*	0.774 ± 0.003 [0.770, 0.779]	0.826 ± 0.004 [0.820, 0.832]	0.774 ± 0.003 [0.770, 0.779]	0.794 ± 0.003 [0.790, 0.798]
LR*	0.739 ± 0.004 [0.734, 0.744]	0.840 ± 0.004 [0.834, 0.846]	0.739 ± 0.004 [0.734, 0.744]	0.770 ± 0.003 [0.766, 0.775]
NB*	0.840 ± 0.005 [0.833, 0.847]	0.814 ± 0.007 [0.805, 0.824]	0.840 ± 0.005 [0.833, 0.847]	0.822 ± 0.006 [0.815, 0.830]
ANN*	0.863 ± 0.004 [0.856, 0.869]	0.841 ± 0.009 [0.829, 0.853]	0.863 ± 0.005 [0.856, 0.869]	0.832 ± 0.007 [0.823, 0.841]
RF*	0.857 ± 0.003 [0.853, 0.862]	0.859 ± 0.003 [0.854, 0.863]	0.859 ± 0.003 [0.855, 0.864]	0.860 ± 0.004 [0.855, 0.865]
XGBoost*	0.867 ± 0.004 [0.860, 0.872]	0.846 ± 0.007 [0.836, 0.856]	0.867 ± 0.004 [0.860, 0.872]	0.841 ± 0.006 [0.832, 0.850]

^* The data set used for model building contains 19 feature variables