## 4 Results
### Predictive Performance of the Machine Learning Models
Figure 4: ROC plot of the models trained on the entire learning window (i.e., \(t_{i}=-48\)). Dashed line: Curve of a random classifier (the diagonal line). an AUROC score of 1% lower on the test set than on the training set. Except for DT, the non-linear models all scored an AUROC score between 2% and 3% lower for the test. With respect to the training times of the models, RandF turned out to be a less beneficial model. As indicated in Table 4 and Figure 5, we observed that RandF (1.145 ms) turned out to need roughly two times more training time than ADA (0.519 ms). GB turned out to need the most training time (2.249 ms). Moreover, we found that the SVM model also took a relatively long time to be trained (1.220 ms). If we take both training and predictive performance into account, KNN was shown to be the best overall performing model (training time: 0.023 ms; AUROC: 0.950). In Table 3 in the Appendix A, we present the predictive performance of the PCA variant of the dataset. As observed, all models performed worse using this type of dataset. Interestingly, however, when inspecting the relative scores of the models using this dataset, KNN (AUROC: 0.938) and SVM (AUROC: 0.929) performed best compared with the ensemble models RandF (AUROC: 0.921), ADA (AUROC: 0.806), and GB (AUROC: 0.884). Lastly, as we were dealing with an imbalanced dataset (i.e., 36% LOS and 64% control), we expected that the number of false positives would be bigger than the number of false positives. Figure 5: Training times of each model, trained on the entire training window (i.e., \(t_{i}=-48\)). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \(-\mathbf{48}\) & \(-\mathbf{42}\) & \(-\mathbf{36}\) & \(-\mathbf{30}\) & \(-\mathbf{24}\) & \(-\mathbf{18}\) & \(-\mathbf{12}\) & \(-\mathbf{6}\) \\ \hline ADA & 0.936 & 0.939 & 0.940 & 0.938 & 0.935 & 0.931 & 0.931 & 0.934 \\  & [0.933, 0.939] & [0.936, 0.941] & [0.938, 0.943] & [0.936, 0.941] & [0.932, 0.938] & [0.926, 0.935] & [0.925, 0.936] & [0.926, 0.943] \\  & 0.831 & 0.827 & 0.817 & 0.808 & 0.805 & 0.796 & 0.784 & 0.729 \\ DT & [0.827, 0.836] & [0.823, 0.831] & [0.813, 0.822] & [0.802, 0.813] & [0.798, 0.813] & [0.786, 0.805] & [0.775, 0.793] & [0.798, 0.806] \\ GB & 0.963 & 0.963 & 0.963 & 0.960 & **0.959** & **0.953** & **0.948** & **0.957** \\  & [0.964, 0.965] & [0.962, 0.965] & [0.961, 0.965] & [0.998, 0.962] & [**0.956, 0.962] & [**0.949, 0.956**] & [**0.935, 0.952] & [**0.950, 0.965]** \\ KNN & 0.950 & 0.947 & 0.948 & 0.942 & 0.939 & 0.926 & 0.918 & 0.928 \\  & [0.948, 0.952] & [0.945, 0.949] & [0.946, 0.951] & [0.939, 0.945] & [0.935, 0.943] & [0.921, 0.931] & [0.912, 0.924] & [0.935] \\ LogR & 0.782 & 0.793 & 0.810 & 0.818 & 0.821 & 0.822 & 0.858 & 0.915 \\  & [0.779, 0.786] & [0.788, 0.797] & [0.806, 0.814] & [0.813, 0.823] & [0.816, 0.827] & [0.813, 0.830] & [0.850, 0.865] & [0.980, 0.922] \\  & 0.734 & 0.737 & 0.742 & 0.746 & 0.750 & 0.764 & 0.792 & 0.810 \\ NB & [0.729, 0.740] & [0.731, 0.742] & [0.737, 0.747] & [0.737, 0.754] & [0.743, 0.758] & [0.757, 0.771] & [0.784, 0.801] & [0.706, 0.824] \\  & **0.973** & **0.971** & **0.970** & **0.966** & **0.964** & **0.955** & **0.957** & **0.957** \\ RandF & **[0.972, 0.975]** & **[0.970, 0.973]** & [0.968, 0.972] & [**0.964, 0.968] & [**0.961, 0.966] & [**0.952, 0.959] & [**0.943, 0.952] & [**0.948, 0.963]** \\  & 0.951 & 0.948 & 0.951 & 0.948 & 0.951 & 0.943 & 0.944 & 0.962 \\ SVM & [0.949, 0.954] & [0.947, 0.955] & [0.949, 0.954] & [0.945, 0.951] & [0.948, 0.954] & [0.939, 0.947] & [0.939, 0.949] & [**0.955, 0.969]** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: AUROC test scores per training window. The results are shown with a 95% confidence interval. negatives. Nevertheless, the results show that the precision and recall scores for each model performed equally well.