Table 2.
Performance of the models with a predefined positive prediction fraction of 20% for primary outcome
| Positive prediction fraction 20% | TP/FP | FN/TN | Sensitivity/Precision % | MCC % | AUROC % | AUPRC % | Brier % | P (sensitivity) % |
|---|---|---|---|---|---|---|---|---|
| Full machine-learning model | 106 / 676 | 76 / 3055 | 58.2 / 13.6 | 21.1 | 76.3 | 15.5 | 4.19 | - |
| Full logistic regression model | 97 / 685 | 85 / 3046 | 53.3 / 12.4 | 18.4 | 74.7 | 15.6 | 4.32 | 17.2 |
| Parsimonious machine-learning model | 100 / 682 | 82 / 3049 | 54.9 / 12.8 | 19.3 | 75.9 | 17.3 | 4.34 | 26.4 |
| Parsimonious logistic regression model | 90 / 692 | 92 / 3039 | 49.5 / 11.5 | 16.3 | 73.8 | 15.8 | 4.33 | 4.86 |
| Age-only model | 87 / 676 | 95 / 3055 | 47.8 / 11.4 | 15.8 | 69.7 | 12.1 | 38.8 | 3.55 |
TP true positives, FP false positives, FN false negatives, TN true negatives, MCC Matthews correlation coefficient, AUROC area under the operating receiver curve, AUPRC area under the precision recall curve P(sensitivity): probability that a model performs better than the full machine-learning model relative to sensitivity