Table 9.
Criteria | ADTree | RF | RS | BA | MB | |||||
---|---|---|---|---|---|---|---|---|---|---|
T | V | T | V | T | V | T | V | T | V | |
True positive | 89 | 10 | 96 | 10 | 88 | 10 | 87 | 10 | 92 | 10 |
True negative | 92 | 10 | 94 | 10 | 92 | 9 | 93 | 10 | 95 | 10 |
False positive | 11 | 1 | 4 | 1 | 12 | 1 | 13 | 1 | 8 | 1 |
False negative | 8 | 1 | 6 | 1 | 8 | 2 | 7 | 1 | 5 | 1 |
Sensitivity | 0.918 | 0.909 | 0.941 | 0.909 | 0.917 | 0.833 | 0.926 | 0.909 | 0.948 | 0.909 |
Specificity | 0.893 | 0.909 | 0.959 | 0.909 | 0.885 | 0.900 | 0.877 | 0.909 | 0.922 | 0.909 |
Accuracy | 0.905 | 0.909 | 0.950 | 0.909 | 0.900 | 0.864 | 0.900 | 0.909 | 0.935 | 0.909 |
AUROC | 0.957 | 0.876 | 0.983 | 0.913 | 0.968 | 0.884 | 0.968 | 0.921 | 0.992 | 0.926 |
T, training dataset; V, validation dataset.