# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method
### Stacking Ensemble Model Construction
In Type 2, when compared with the results obtained when no feature selection was performed, the average F1 score decreased by 0.41% and the AUC score decreased by 0.1%. In Type 3, the F1 score decreased by 0.32% and the AUC score decreased by 0.85% when compared with the results for the \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Feature Selection** & \multicolumn{2}{c}{**Group 1**} & \multicolumn{2}{c}{**Group 2**} & \multicolumn{2}{c}{**Group 3**} & \multicolumn{2}{c}{**Group 4**} & \multicolumn{2}{c}{**Group 5**} \\
**Algorithm** & **F1-Score** & **AUC** & **F1-Score** & **AUC** & **F1-Score** & **AUC** & **F1-Score** & **AUC** & **F1-Score** & **AUC** \\ \hline None & 0.7334 & 0.6379 & 0.7515 & 0.6853 & 0.7586 & 0.7233 & 0.8262 & 0.8266 & 0.8313 & 0.8901 \\ \hline Forward Selection & 0.7345 & 0.6277 & 0.7541 & 0.6743 & 0.7622 & 0.7142 & 0.8285 & 0.8244 & 0.8356 & 0.8799 \\ \hline Backward Elimination & 0.7379 & 0.6220 & 0.7488 & 0.6823 & 0.7488 & 0.7246 & 0.8295 & 0.8255 & 0.8373 & 0.9019 \\ \hline RFECV & 0.7414 & 0.6431 & 0.7526 & 0.6890 & 0.7605 & 0.7285 & 0.8332 & 0.8309 & 0.8389 & 0.9043 \\ \hline Proposed Algorithms & 0.7414 & 0.6584 & 0.7581 & 0.6914 & 0.7726 & 0.7355 & 0.8430 & 0.8955 & 0.8754 & 0.9115 \\ \hline \end{tabular}
\end{table} 
Table 17: Performance results of feature selection for each group for Type 3. Figure 14: Average F1 and AUC scores for feature selection algorithm across groups 1-5 for Type 3. Figure 13: Average F1 and AUC scores for feature selection algorithm across groups 1-5 for Type 2.

case when no feature selection was performed. With backward elimination, there was no significant improvement in the performance when compared with the results obtained when no feature selection was performed. In Type 2, the average F1 score increased by 0.25% and the AUC score increased by 1.54%, whereas in Type 3, the average F1 score increased by 0.03% and the AUC score decreased by 0.14%. This was because backward elimination removes a relatively small number of features. The performance of RFECV was better than the performance of forward selection and backward elimination. This was because RFECV can remove or include important features through recursive model retraining during the feature selection process. In Type 2, RFECV showed an average increase of 1.19% in the F1 score and 2.54% in the AUC score when compared with the results obtained when no feature selection was performed. In Type 3, average increases of 0.83% in the F1 score and 1.51% in the AUC score were observed when compared with the results when no feature selection was performed. Finally, the proposed algorithm outperformed the other feature-selection algorithms. Type 2 demonstrated an average increase of 2.09% in the F1 score and 4.64% in the AUC score. Similarly, for Type 3, the proposed algorithm showed an average increase of 1.76% in the F1 score and 2.72% in the AUC score. ## 5 Conclusions

In this paper, we presented an adaptive stacking ensemble technique to effectively identify various COVID-19 severity levels in patients. This technique could be particularly useful in the early stages of the healthcare response to pandemics, such as COVID-19, even when resources are limited. To enhance the generalizability of our model, we utilized a nationwide dataset provided by the South Korean government, which included data on 5644 patients from more than 100 hospitals. Using a data-driven strategy, we grouped the clinical variables, developed adaptive models for various combinations, and presented the results for each group and severity type. We also analyzed important clinical variables using feature selection methods to improve our severity prediction model's performance by focusing on highly important features and removing variables of low relevance. Finally, we proposed a method for automatically constructing an adaptive stacking ensemble model that predicts the severity levels more accurately than existing single classifiers and AutoML approaches. Despite our contributions, our research faced several limitations.