Table 4.
Performance evaluation on different classifiers.
Dataset | Classifier | Accuracy (%) | Precision (%) | Sensitivity (%) | Specificity (%) | MCC (%) |
---|---|---|---|---|---|---|
RPI369 | LGBM | 73.81 | 72.18 | 68.75 | 78.81 | 48.03 |
SVM | 71.60 | 71.70 | 70.74 | 72.51 | 43.62 | |
GBDT | 71.74 | 71.79 | 70.74 | 72.79 | 43.90 | |
RPI488 | LGBM | 89.52 | 93.28 | 94.30 | 84.17 | 79.02 |
SVM | 86.22 | 88.62 | 89.86 | 82.27 | 72.44 | |
GBDT | 86.01 | 88.54 | 89.86 | 81.81 | 72.04 |
The bold value indicates this measure performance is the best among the compared methods.