Table 17 Comparison of the performance of the different classification models using the SMOTE sampling methods. The models are: Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayesian Classifier (BC), Bayesian Network (BN), K-Nearest Neighbor (KNN) and Random Forest (RF)
| Â | DT | SVM | ANN | BC | BN | KNN | RF |
|---|---|---|---|---|---|---|---|
Sensitivity | 59.95% | 80.26% | 55.89% | 37.41% | 60.07% | 78.43% | 96.07% |
Specificity | 96.05% | 95.19% | 90.43% | 93.32% | 91.02% | 96.98% | 96.84% |
Precision | 70.91% | 62.63% | 24.37% | 52.20% | 27.32% | 77.18% | 75.50% |
F-score | 64.97% | 70.36% | 33.94% | 43.59% | 37.56% | 77.80% | 84.55% |
RMSE | 0.27 | 0.25 | 0.29 | 0.34 | 0.28 | 0.23 | 0.18 |
AUC | 0.88 | 0.8 | 0.82 | 0.82 | 0.84 | 0.88 | 0.97 |