## 4 Results
### Predictive Performance of the Machine Learning Models  
For each model, the area under the receiving operator characteristic (AUROC) was calculated. Moreover, we calculated the training time, accuracy ((TP + TN)/n), recall  
Figure 3: Correlation of all the significant features. (TP/(TP + FN)), and precision (TP/(TP + FP)), where TP = true positive, FN = false negative, FP = false positive. In Table 4, the results for each ML model for the entire training window are shown \(t_{i}=-48\)). In addition, Figure 4 shows the corresponding ROC curves for each model. Using the entire training window (\(t_{i}=-48\)), we found that all non-linear models performed superior compared with the linear models. In particular, for each metric within this training window, we found that RandF performed the best (AUROC score: 0.973) and NB (AUROC score: 0.734) performed the worst (except for training time). Furthermore, all models performed best on the normal dataset without any dimension reduction (i.e., \(\Delta\) Significant_Features), Figure A9. The results also show that relatively adequate results were achieved using the conventional ML models, KNN and SVM, with both yielding an AUROC score of 0.950 and 0.951, respectively, which was comparable to the best performing ensemble models (i.e., GB and RandF). Interestingly, these models even outperformed ADA when trained on a longer time window (\(t_{i}=-48\) to \(t_{i}=-24\); see Figure 5 and Table 5). The strongest overfitting model was found to be the DT model, yielding a 17% lower AUROC score for the test than the training set. The least overfitting models were found to be LogR and NB, yielding \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Fit Time** & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** \\  & & **Accuracy** & **Accuracy** & **Precision** & **Precision** & **Recall** & **Recall** & **AUROC** & **AUROC** \\ \hline ADA & 0.519 & 0.899 & 0.865 & 0.899 & 0.864 & 0.899 & 0.865 & 0.966 & 0.936 \\  & [0.516, 0.521] & [0.897, 0.9] & [0.861, 0.868] & [0.897, 0.9] & [0.865, 0.868] & [0.897, 0.9] & [0.861, 0.868] & [0.966, 0.967] & [0.933, 0.939] \\ DT & 0.109 & 0.842 & 0.842 & 0.843 & 0.842 & 0.842 & 0.831 & 0.831 \\  & [0.107, 0.11] & 1.0 [1.0] & [0.839, 0.846] & [0.1 [1.0] & [0.839, 0.847] & [0.839, 0.846] & [0.810, 0.827, 0.836] \\ GB & 2.249 [2.236, 0.942] & 0.899 & 0.944 & 0.902 & 0.942 & 0.899 & 0.998 & 0.963 \\  & [0.941, 0.943] & [0.896, 0.950] & [0.943, 0.945] & [0.899, 0.905] & [0.941, 0.943] & [0.896, 0.902] & [0.988, 0.989] & [0.961, 0.965] \\ KNN & 0.023 & 0.937 & 0.898 & 0.937 & 0.898 & 0.937 & 0.898 & 0.985 & 0.95 \\  & [0.023, 0.023] & [0.935, 0.938] & [0.985, 0.91] & [0.936, 0.938] & [0.966, 0.99] & [0.935, 0.938] & [0.895, 0.91] & [0.984, 0.958] & [0.948, 0.952] \\ LogR & 0.021 & 0.748 & 0.743 & 0.744 & 0.739 & 0.748 & 0.743 & 0.794 & 0.782 \\  & [0.019, 0.022] & [0.747, 0.749] & [0.74, 0.746] & [0.734, 0.755] & [0.742, 0.749] & [0.744, 0.746] & [0.763, 0.793] & [0.797, 0.796] \\ NB & **0.005** & 0.072 & 0.7 & 0.702 & 0.699 & 0.702 & 0.7 & 0.742 & 0.734 \\  & [**0.005, 0.005] & [0.699, 0.704] & [0.694, 0.705] & [0.7, 0.705] & [0.693, 0.705] & [0.699, 0.704] & [0.694, 0.705] & [0.74, 0.744] & [0.729, 0.74] \\ RandF & 1.145 & 1.0 [1.0, 1.0] & **0.916** & 1.0 [1.0, 1.0] & **0.922** & 1.0 [1.0, 1.0] & **0.919** & 1.0 [1.0, 1.0] & **0.973** \\  & [1.139, 1.152] & 1.0 [**1.0, 1.0] & **0.916** & **0.922** & **1.0 [1.0, 9.922]** & **1.0 [1.0, 1.0] & **0.972** & **0.975** \\ SVM & 1.220 & 0.921 & 0.891 & 0.924 & 0.894 & 0.921 & 0.891 & 0.974 & 0.951 \\  & [1.216, 1.225] & [0.92, 0.922] & [0.888, 0.894] & [0.923, 0.925] & [0.891, 0.897] & [0.92, 0.922] & [0.888, 0.894] & [0.974, 0.975] & [0.949, 0.954] \\ \hline \hline \end{tabular} Bold is best performance.  
\end{table} 
Table 4: Predictive performance of each model for the entire training window \(t_{i}=-48\). The results are presented with a 95% Confidence Interval.