Table 13. Comparison of SVM, LR, bagging SVM and bagging LR.

Machine Learning model	Feature selection / extraction	No. of features/dimension	Precision (%)	Recall (%)	Miss rate (%)	F1-score (%)	Testing accuracy (%)	ROC-AUC
SVM	No	12	88.24	91.84	8.16	90	85.50	93.26
	Filter based feature	6	90	91.84	8.16	90.91	86.96	94.08
	PCA	11	88.24	91.84	8.16	90	85.51
Bagging SVM (linear kernel with c = 7)	Filter based feature	5	87.04	95.92	4.08	91.26	86.96	93.77
LR	No	12	89.80	89.80	10.20	89.80	85.51	93.67
	Filter based feature	5	90	91.84	8.16	90.91	86.96	94.48
	PCA	11	89.80	89.80	10.20	89.80	85.51
Bagging LR	Filter based feature	5	90	91.84	8.16	90.91	86.96	94.79