Table 5.

Performance of ML model II with different classification algorithms for training and test datasets

5-fold cross validation performance of classification algorithms on the training dataset
Algorithms	Precision	Recall	F1-score	AUC-ROC	Accuracy
SVM	77.47 ± 6.38	89.67 ± 8.52	82.97 ± 6.56	89.03 ± 5.68	80.76 ± 7.15
Extra Trees	79.21 ± 7.69	82.17 ± 5.98	80.59 ± 6.56	86.91 ± 6.62	79.11 ± 7.26
Random Forest	77.65 ± 7.58	80.81 ± 8.48	79.14 ± 7.78	85.03 ± 6.17	77.67 ± 8.28
AdaBoost	74.58 ± 6.30	70.20 ± 2.64	72.19 ± 3.57	75.87 ± 3.80	71.52 ± 4.78
XGBoost	78.87 ± 7.36	70.13 ± 1.84	74.09 ± 3.47	77.83 ± 7.53	74.09 ± 4.50
LR	79.05 ± 5.79	81.63 ± 9.58	80.09 ± 6.45	84.93 ± 8.15	78.98 ± 6.28