Table 6.
Comparison on random forest classification efficiency (using the unbalanced dataset) with the literature
| Proposal | Accuracy | Sensitivity | Specificity | AUC |
|---|---|---|---|---|
| Wu et al. [1] | – | 0.960 | 0.800 | 0.910 |
| Tartar et al. [9] | 0.837 | 0.854 | 0.816 | 0.908 |
| Reeves et al. [10] | – | – | – | 0.772 |
| Dilger et al. [11] | 0.920 | 0.909 | 0.928 | 0.935 |
| Zhang et al. [12] | 0.880 | – | – | – |
| Kaya et al. [13] | 0.849 | 0.831 | 0.921 | – |
| Ferreira Jr et al. [14] | 0.792 | – | – | 0.816 |
| 48 combined features | 0.800 | 0.702 | 0.856 | 0.858 |
| 6 selected features | 0.788 | 0.662 | 0.860 | 0.843 |