Table 6.
Comparison of the performance of different classifiers for healthy and unhealthy discrimination
| Classifier | Sensitivity(%) | Specificity(%) | Harmonic mean(%) |
|---|---|---|---|
| RLR | 96.4 | 96.7 | 96.5 |
| SVM | 95.5 | 81.7 | 88.1 |
| DT | 95.0 | 96.7 | 95.8 |
| NB | 92.9 | 100.0 | 96.3 |
| hDT_RLR | 98.6 | 96.7 | 97.6 |
| hDT_SVM | 98.6 | 8.3 | 87.3 |
| hDT_NB | 95.0 | 6.7 | 95.8 |
| hLR_SVM | 95.6 | 78.3 | 86.1 |
| hLR_DT | 95.0 | 96.7 | 95.8 |
| hLR_NB | 95.0 | 96.7 | 95.8 |
| hSVM_LR | 95.6 | 96.7 | 96.1 |
| hSVM_DT | 92.9 | 96.7 | 94.7 |
| hSVM_NB | 92.9 | 100.0 | 96.3 |
| hNB_LR | 92.9 | 96.7 | 94.7 |
| hNB_DT | 92.9 | 96.7 | 94.7 |
| hNB_SVM | 98.6 | 78.3 | 87.3 |
Classification methods used in the experiments. Regularized Logistic Regression (RLR), Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), combined DT+RLR (hDT_RLR), DT+SVM (hDT_SVM), DT+NB (hDT_NB), LR+SVM (hLR_SVM), LR+DT (hLR_DT), LR+NB (hLR_NB), SVM+LR (hSVM_LR), SVM+DT (hSVM_DT), SVM+NB (hSVM_NB), NB+LR (hNB_LR), NB+DT (hNB_DT), NB+SVM (hNB_SVM)