Table 3 Classification performance of the three classification trees using different misclassification costs and the logistic regression models using different cut-off points
Classification trees | Misclassification costs | |||
|---|---|---|---|---|
Tree 1:1 | Tree 5:1 | Tree 10:1 | Tree 20:1 | |
Sensitivity | 0% | 16% | 64.3% | 91% |
PPV | 0% | 23.2% | 15%. | 11.9% |
Specificity | 100% | 95% | 65.5% | 36.1% |
NPV | 91.3% | 65.8% | 95% | 97.7% |
Error rate | 8.7% | 11.9% | 34.6% | 59.1% |
AUC | 71% | 71% | 67.6% | |
Logistic regression | Classification cut-offs | |||
0.5 | 0.3 | 0.2 | 0.1 | |
Sensitivity | 0% | 1.1% | 10.8% | 65.5% |
PPV | 0% | 22.3% | 27.6% | 15.5% |
Specificity | 100% | 99.7% | 97.4% | 67.3% |
NPV | 91.64% | 91.7% | 92.3% | 95.5% |
Error rate | 8.4% | 8.6% | 9.8% | 32.8% |
AUC | 72% | 72% | 72% | 72% |