Table 4.
The 10-Fold Cross-Validation Performance of the Machine Learning Models in the Training Dataset
| Model | True Positive | False Positive | True Negative | False Negative | Sensitivity | Specificity | AUC | CA |
|---|---|---|---|---|---|---|---|---|
| Artificial Neural Network | 58 | 60 | 4441 | 288 | 16.76% | 98.67% | 0.926 | 0.928 |
| Logistic Regression | 54 | 61 | 4440 | 292 | 15.61% | 98.64% | 0.916 | 0.927 |
| Bayesian Network | 212 | 421 | 4080 | 134 | 61.27% | 90.65% | 0.904 | 0.885 |
| Random Forest | 97 | 127 | 4374 | 249 | 28.03% | 97.18% | 0.896 | 0.922 |
| K-NN | 50 | 75 | 4426 | 296 | 14.45% | 98.33% | 0.811 | 0.924 |
| Support Vector Machine | 195 | 895 | 3606 | 151 | 56.36% | 80.12% | 0.652 | 0.652 |
| Decision Trees | 103 | 158 | 4343 | 243 | 29.77% | 96.49% | 0.591 | 0.921 |
Abbreviations: AUC, the area under the curve; CA, classification accuracy; Precision, positive predictive value; Recall, true positive rate.