Table 3.
Compare with widely used machine learning models.
| Method | Acc. (%) | Sen. (%) | Spec. (%) | Prec. (%) | MCC (%) | AUC (%) |
|---|---|---|---|---|---|---|
| MAN-HOPE-LR | 83.75 ± 0.11 | 83.21 ± 0.47 | 84.30 ± 0.32 | 84.13 ± 0.20 | 67.52 ± 0.22 | 91.58 ± 0.13 |
| MAN-HOPE-Ada | 84.73 ± 0.18 | 85.53 ± 0.29 | 83.93 ± 0.22 | 84.19 ± 0.18 | 69.48 ± 0.36 | 92.07 ± 0.13 |
| MAN-HOPE-RF | 92.66 ± 0.12 | 92.03 ± 0.15 | 93.29 ± 0.22 | 93.21 ± 0.20 | 85.33 ± 0.24 | 97.12 ± 0.05 |
| MAN-HOPE-XGB | 89.56 ± 0.41 | 90.60 ± 0.28 | 88.51 ± 0.95 | 88.75 ± 0.81 | 79.13 ± 0.79 | 96.02 ± 0.24 |
| Proposed method | 93.30 ± 0.12 | 91.50 ± 0.14 | 95.10 ± 0.11 | 94.91 ± 0.11 | 86.66 ± 0.24 | 97.93 ± 0.08 |
The boldface indicates this measure performance is the best in this item among the compared methods.