# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method
### Stacking Ensemble Model Construction
For each model included in the performance evaluation, a total of 20 experiments were conducted, and the average performance results were recorded. Through these extensive evaluation processes, we comprehensively assessed each model's effectiveness in accurately predicting the various severity levels of COVID-19. Table 5 presents the average performance results of our adaptive stacking ensemble model in comparison to other single classifiers and AutoML approaches for predicting Type 1 severity across all groups. The highlighted sections in each table indicate the highest-performing classifiers and their corresponding scores. The prediction of Type 1 severity across Groups 1-5 employs a binary classification to distinguish between general and ICU patients. Our proposed model demonstrated considerable robustness, achieving an average F1 score of 0.9588 and AUC score of 0.9380 across all groups. Compared to existing single classifiers, Logistic Regression achieved an average F1 score of 0.8832 and an AUC of 0.9203, indicating our model's improvement of 7.56% in F1 and 1.77% in AUC scores. XGBoost achieved an F1 score of 0.9134 and an AUC of 0.8802, with our model showing an improvement of 4.54% in F1 and 5.78% in AUC scores. Moreover, our model significantly outperformed AutoML approaches. Compared to PyCaret, which recorded an F1 score of 0.9101 and an AUC of 0.8655, our model demonstrated improvements of 5.35% in F1 and 8.37% in AUC scores. Similarly, compared to H2O.ai, which achieved an F1 score of 0.9180 and an AUC of 0.8423, our model showed improvements of 4.40% in F1 and 9.57% in AUC scores. Furthermore, when compared to the average of all single classifiers and AutoML approaches, which had an F1 of 0.8946 and an AUC of 0.8494, our model showed an improvement of 7.18% in F1 and 10.43% in AUC scores for Type 1 severity. The enhanced performance of our model, especially evident in its recall and F1 score, signifies its effectiveness in accurately identifying patients at risk of severe COVID-19 outcomes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Model** & **Precision** & **Recall** & **F1 Score** & **Specificity** & **AUC** \\ \hline \multirow{8}{*}{Average} & Logistic Regression (LR) & 0.9426 & 0.8325 & 0.8832 & 0.8302 & 0.9203 \\ \cline{2-6}  & XGBoost (XGB) & 0.9222 & 0.9048 & 0.9134 & 0.6700 & 0.8802 \\ \cline{2-6}  & PyCaret & 0.9140 & 0.9064 & 0.9101 & 0.6785 & 0.8655 \\ \cline{2-6}  & H2O.ai & 0.9354 & 0.8954 & 0.9148 & 0.8165 & 0.8423 \\ \cline{2-6}  & Average of All Single Classifiers and AutoML & 0.9178 & 0.8737 & 0.8946 & 0.6776 & 0.8494 \\ \cline{2-6}  & **Proposed Model (CSS Type 1)** & **0.9582** & **0.9595** & **0.9588** & 0.7634 & **0.9380** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average performance results of Type 1 severity for all groups. For a more detailed understanding, Table 6 provides a summary of the comparative performance results for Type 1 severity across Groups 1-5. Comprehensive performance results of the comparisons with all 14 individual classifiers and AutoML approaches can be found in Table A1 of Appendix A. Based on a detailed observation of each group's performance, the proposed model's severity prediction for Group 1 was lower than that of other groups, with an F1 score of 0.9281 and AUC score of 0.8772. This lower performance is due to Group 1 considering only nine clinical variables related to demographics and vital signs, with feature selection