Table 1. Performance of the best selected model from each ML-based method.
Method | MCC | Accuracy | Sensitivity | Specificity | Pt | Py |
---|---|---|---|---|---|---|
SVM | 0.660 | 0.871 | 0.655 | 0.952 | 0.624 | 0.607 |
RF | 0.638 | 0.861 | 0.669 | 0.934 | 0.625 | 0.603 |
ET | 0.635 | 0.848 | 0.668 | 0.931 | 0.622 | 0.599 |
k-NN | 0.632 | 0.860 | 0.642 | 0.943 | 0.605 | 0.585 |
The first column represents the ML-based method developed in this study. The second, the third, the fourth and the fifth, the sixth and the seventh respectively represent the MCC, accuracy, sensitivity, specificity, Pt and Py. Bold font denotes the best result.