Table 1 The average values of the evaluation metrics in 5 folds for different classifiers based on their optimal number of features
Classifier (optimal feature vector size) | Acc (%) | F1 (%) | Pre (%) | Sen (%) | Spe (%) | AUC (%) |
|---|---|---|---|---|---|---|
ABRF (10) | 89.74 | 89.37 | 92.6 | 86.44 | 93.04 | 96.58 |
LR (80) | 71.3 | 71.32 | 71.25 | 71.48 | 71.13 | 77.48 |
MP (10) | 89.56 | 89.59 | 89.37 | 89.91 | 89.22 | 95.54 |
RF (10) | 90.09 | 89.78 | 92.61 | 87.13 | 93.04 | 96.44 |
XGB (20) | 92.09 | 92.078 | 92.36 | 91.82 | 92.35 | 97.77 |
SVM (90) | 72.09 | 73.26 | 70.16 | 76.69 | 67.48 | 76.41 |