Table 3a: Performance metrics of models trained on original data using hold-out test set.
| Model | AUC | Accuracy | F1 | Sensitivity | Specificity | PPV | NPV |
|---|---|---|---|---|---|---|---|
| Elastic Net Logistic Regression | 81.58% | 85.42%* | 46.05% | 36.56% | 95.44% | 62.20% | 88.00% |
| SVM | 80.75% | 85.23% | 49.16%* | 41.94% | 94.12% | 59.39% | 88.77% |
| KNN | 66.48% | 83.40% | 21.84% | 13.62% | 97.72%* | 55.07% | 84.65% |
| Naïve Bayes | 74.72% | 70.23% | 43.52% | 67.38%* | 70.81% | 32.14% | 91.37%* |
| CaRT | 77.56% | 82.18% | 44.70% | 42.29% | 90.37% | 47.39% | 88.42% |
| Random Forest | 81.03% | 85.11% | 47.64% | 39.79% | 94.41% | 59.36% | 88.43% |
| XGBoost | 83.18%* | 84.87% | 47.68% | 40.50% | 93.97% | 57.95% | 88.50% |
| Feedforward NN | 78.20% | 84.87% | 35.32% | 24.37% | 97.28% | 64.76%* | 86.25% |
*Highest value achieved for each metric.