Table 5.
Performance of different machine learning methods on fingerprints.
| Parameters | Main dataset | Validation dataset | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Sen | Spc | Acc | MCC | AUROC | Sen | Spc | Acc | MCC | AUROC | ||
| SVM | g = 0.005, c = 15, j = 1 | 90.19 | 88.12 | 89.16 | 0.78 | 0.95 | 93.33 | 89.33 | 91.33 | 0.83 | 0.96 |
| Random Forest | Ntree = 600 | 94.32 | 90.19 | 92.25 | 0.85 | 0.98 | 96.67 | 88.00 | 92.33 | 0.85 | 0.98 |
| SMO | g = 0.0005, c = 4 | 85.54 | 85.03 | 85.28 | 0.71 | 0.85 | 88.67 | 85.33 | 87.00 | 0.74 | 0.87 |
| J48 | c = 0.25, m = 1 | 90.02 | 89.33 | 89.67 | 0.79 | 0.89 | 88.67 | 88.67 | 88.67 | 0.77 | 0.90 |
| Naive Bayes | Default | 86.40 | 84.34 | 85.37 | 0.71 | 0.90 | 82.67 | 85.33 | 84.00 | 0.68 | 0.90 |