# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method
### Stacking Ensemble Model Construction

Table 8: Summary of performance results of Type 2 severity for Groups 1-5.

these approaches limit the capacity for detailed hyperparameter tuning and restrict the use of a diverse range of models to adequately capture intricate patterns. As a result, they prevent the AutoML tools from fully understanding the complexities and specific interdependencies present in complex clinical datasets. Consequently, this could lead to the unique requirements or the most effective configurations that are essential for particular clinical tasks being overlooked. Moreover, Logistic Regression shows high specificity across all groups but has a comparatively lower F1 score than other models, indicating a limitation in handling multiclass classifications with higher complexity. Overall, the proposed model outperformed single models, such as Logistic Regression, XGBoost, and LGBM, in most groups. Specifically, Group 5 achieved an F1 score of 0.9318 and AUC score of 0.9121, indicating good performance. These results demonstrated the potential advantages of the ensemble approach compared to single classifiers and AutoML for complex clinical classification tasks. Table 9 presents the average performance results of the proposed adaptive stacking ensemble model in comparison to other single classifiers and AutoML approaches for predicting Type 2 severity across all groups. The prediction of Type 3 severity requires a more detailed classification than that required for Type 2 severity. Type 3 is classified into five classes: _'asymptomatic'_, _'mild'_, _'moderate'_, _'severe'_, and _'fatal'_. The classification of the severity classes for Type 3 increased in complexity when compared with those for Types 1 and 2. As a result, our model's average F1 score decreased by 18.8% and 10.2% when compared with the results for Types 1 and 2, respectively. Similarly, the average AUC scores decreased by 16.4% and 5.9%, respectively. However, this reduction was also observed across other single classifiers and AutoML approaches. The proposed model achieved an F1 score of 0.7981 and an AUC score of 0.7736, outperforming the average of other models, including Logistic Regression, XGBoost, and two AutoML approaches. Table 10 provides a summary of the comparative performance results for Type 3 severity across Groups 1-5. Comprehensive performance results of the comparisons with all 14 individual classifiers and AutoML approaches can be found in Table A3 of Appendix A. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Model** & **Precision** & **Recall** & **F1 Score** & **Specificity** & **AUC** \\ \hline \multirow{7}{*}{Group 1} & Logistic Regression (LR) & **0.7172** & 0.5643 & 0.6316 & **0.6878** & 0.6396 \\ \cline{2-6}  & XGBoost (XGB) & 0.6709 & 0.7050 & 0.6875 & 0.3913 & 0.6458 \\ \cline{2-6}  & Light Gradient Boosting Machine (LGBM) & 0.6825 & 0.7098 & 0.6959 & 0.4239 & 0.6568 \\ \cline{2-6}  & PyCaret (Logistic Regression (LR)) & 0.7109 & 0.7509 & 0.7304 & 0.6571 & 0.6461 \\ \cline{2-6}  & H2O.ai (Stacking: 1 st layer: 5 GBM, 1 GLM, 2 & 0.6849 & 0.7907 & 0.7340 & 0.4725 & 0.5425 \\ \cline{2-6}  & DRF, Meta: GLM) & & & & & \\ \cline{2-6}  & **Proposed Model (Group 1-CSS Type** & & & & & \\ \cline{2-6}  & **3)**(Stacking: 1 st layer: LGBM, GNB, 2 nd layer: MLP, LR, Meta: LR) & 0.7157 & **0.7691** & **0.7414** & 0.5333 & **0.6584** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of performance results of Type 3 severity for Groups 1-5.