Table 5 Predictive performance of all machine learning models
Models | Accuracy | AUC | Specificity | False negative rate | False positive rate |
|---|---|---|---|---|---|
All features | |||||
Decision tree | 0.627 (95% CI, 0.598–0.656) | 0.575 (95% CI, 0.545–0.603) | 0.963 | 0.915 | 0.037 |
Random forest | 0.646 (95% CI, 0.617–0.675) | 0.596 (95% CI, 0.567–0.652) | 0.869 | 0.755 | 0.131 |
Artificial neural network | 0.650 (95% CI, 0.607–0.675) | 0.625 (95% CI, 0.579–0.672) | 0.861 | 0.665 | 0.139 |
Feature selection | |||||
Decision tree | 0.642 (95% CI, 0.613–0.671) | 0.592 (95% CI, 0.563–0.648) | 0.963 | 0.915 | 0.037 |
Random forest | 0.648 (95% CI, 0.601–0.695) | 0.605 (95% CI, 0.558 –0.652 | 0.913 | 0.802 | 0.087 |
Artificial neural network | 0.668 (95% CI, 0.621–0.714) | 0.654 (95% CI, 0.625–0.683) | 0.922 | 0.755 | 0.078 |
Grace variable sets | |||||
Decision tree | 0.622 (95% CI, 0.576–0.668) | 0.554 (95% CI, 0.508–0.601) | 0.973 | 0.927 | 0.027 |
Random forest | 0.627 (95% CI, 0.598–0.656) | 0.575 (95% CI, 0.545–0.603) | 0.966 | 0.904 | 0.034 |
Artificial neural network | 0.644 (95% CI, 0.615–0.673) | 0.594 (95% CI, 0.565–0.65) | 0.892 | 0.778 | 0.108 |