Table 7. The performance of the Different Machine Learning Models evaluated using the Hold Out method (80/20) using SMOTE.
The RTF model achieves the highest AUC (0.89), Sensitivity (75%), Precision (73%) and F-Score (74%). The SVM model achieves the highest Specificity (88.9%).
| ANN | LB | LWB | RTF | BN | SVM | |
|---|---|---|---|---|---|---|
| Sensitivity | 40% | 31.3% | 43% | 75% | 49.5% | 28.2% |
| Specificity | 88.4% | 88.5% | 80.92% | 86.2% | 79.8% | 88.9% |
| Precision | 65.2% | 59.3% | 54.8% | 73% | 56.8% | 57.7% |
| F-score | 49.8% | 40.9% | 48.23% | 74% | 52.9% | 37.9% |
| AUC | 0.74 | 0.7 | 0.7 | 0.89 | 0.72 | 0.59 |
| RMSE | 0.44 | 0.45 | 0.46 | 0.46 | 0.42 | 0.57 |