Table 1. The performances of SVM model and RF model in classification of MFEs.
Positives | Negatives | TP | FP | TN | FN | SP (%) | SE (%) | PPV (%) | Q (%) | |
SVM | 6,782 | 10,714 | 5,642 | 1,435 | 9,279 | 1,140 | 86.6 | 83.2 | 79.7 | 85.3 |
RF | 6,782 | 10,714 | 6,368 | 632 | 10,082 | 414 | 94.1 | 93.9 | 91.0 | 94.0 |
The prediction were evaluated by parameters of TP (true positive), FN (false negative), TN (true negative), FP (false positive), specificity SP = TN/(TN+FP), sensitivity SE = TP/(TP+FN), positive prediction value PPV = TP/ (TP+FP) and overall accuracy Q = (TP+TN)/ (TP+FN+TN+FP).