# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method
### Stacking Ensemble Model Construction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & **Model** & **Precision** & **Recall** & **F1 Score** & **Specificity** & **AUC** \\ \hline \multirow{8}{*}{Group 2} & Logistic Regression (LR) & 0.7299 & 0.5263 & 0.6116 & **0.7353** & 0.6900 \\ \cline{2-7}  & XGBoost (XGB) & 0.6957 & 0.7253 & 0.7102 & 0.4394 & 0.6972 \\ \cline{2-7}  & Light Gradient Boosting Machine (LGBM) & 0.7013 & 0.7306 & 0.7157 & 0.4510 & **0.7096** \\ \cline{2-7}  & PyCaret (Gaussian Naive Bayes (GNB)) & 0.7328 & 0.7717 & 0.7517 & 0.6951 & 0.6897 \\ \cline{2-7}  & H2O.ai (Stacking: 1 st layer: 22 GBM, 1 GLM, 2 DRF, Meta: GLM) & 0.7001 & 0.7654 & 0.7313 & 0.4936 & 0.6975 \\ \cline{2-7}  & **Proposed Model (Group 2-CSS Type** & & & & & \\
**3**)(Stacking: 1 st layer: LGBM, RF, ADA, 2 nd layer: MLP, LR, Meta: LR) & 0.7420 & 0.7749 & 0.7581 & 0.5805 & 0.6914 \\ \hline \multirow{8}{*}{Group 3} & Logistic Regression (LR) & 0.7386 & 0.5343 & 0.6251 & **0.7472** & 0.7017 \\ \cline{2-7}  & XGBoost (XGB) & 0.7025 & 0.7297 & 0.7234 & 0.4512 & 0.7024 \\ \cline{2-7}  & Light Gradient Boosting Machine (LGBM) & 0.7105 & 0.7368 & 0.7225 & 0.4549 & 0.7084 \\ \cline{2-7}  & PyCaret (Random Forest (RF)) & 0.7231 & 0.7897 & 0.7549 & 0.4928 & 0.7128 \\ \cline{2-7}  & H2O.ai (Generalized Linear Modeling (GLM)) & 0.6954 & 0.7825 & 0.7408 & 0.7036 & 0.7367 \\ \cline{2-7}  & **Proposed Model (Group 3-CSS Type** & & & & & \\
**3**)(Stacking: 1 st layer: LGBM, RF, ADA, 2 nd layer: MLP, LR, Meta: LR) & 0.7566 & 0.7892 & 0.7726 & 0.5906 & **0.7355** \\ \hline \multirow{8}{*}{Group 4} & Logistic Regression (LR) & 0.7899 & 0.6249 & 0.6978 & **0.8429** & 0.8122 \\ \cline{2-7}  & XGBoost (XGB) & 0.7915 & 0.7948 & 0.7931 & 0.6450 & 0.8660 \\ \cline{2-7}  & Light Gradient Boosting Machine (LGBM) & 0.7916 & 0.7917 & 0.7916 & 0.6607 & 0.8744 \\ \cline{2-7}  & PyCaret (Gaussian Naive Bayes (GNB)) & 0.7346 & 0.7775 & 0.7554 & 0.7558 & 0.7305 \\ \cline{2-7}  & H2O.ai (Stacking: 1 st layer: 22 GBM, 1 GLM, 2 DRF, Meta: GLM) & 0.6965 & 0.7966 & 0.7432 & 0.7214 & 0.7484 \\ \cline{2-7}  & **Proposed Model (Group 4-CSS Type** & & & & & \\
**3**)(Stacking: 1 st layer: LGBM, LR, 2 nd layer: MLP, Meta: LR) & 0.8239 & 0.8631 & 0.8430 & 0.7626 & **0.8995** \\ \hline \multirow{8}{*}{Group 5} & Logistic Regression (LR) & 0.7887 & 0.6240 & 0.6967 & **0.8264** & 0.8079 \\ \cline{2-7}  & XGBoost (XGB) & 0.7855 & 0.7956 & 0.7905 & 0.6272 & 0.8658 \\ \cline{1-1} \cline{2-7}  & Light Gradient Boosting Machine (LGBM) & 0.7854 & 0.7925 & 0.7889 & 0.6363 & 0.8720 \\ \cline{1-1} \cline{2-7}  & PyCaret (CatBoost (CAT)) & 0.7231 & 0.7883 & 0.7543 & 0.6782 & 0.7362 \\ \cline{1-1} \cline{2-7}  & H2O.ai (Gradient Boosting Machine (GBM)) & 0.7054 & 0.7895 & 0.7451 & 0.6926 & 0.7187 \\ \cline{1-1} \cline{2-7}  & **Proposed Model (Group 5-CSS Type** & & & & & \\
**3**)(Stacking: 1 st layer: LGBM, SVM2, 2 nd layer: MLP, LR, Meta: LR) & 0.8564 & 0.8952 & 0.8754 & 0.7068 & **0.9115** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._rates multiple models in stacking configurations, presented competitive results, with F1 scores of 0.7340 in Group 1 and 0.7313 in Group 2. However, despite these achievements, both AutoML approaches exhibited only slight enhancements in Groups 4 and 5. This issue becomes particularly apparent in scenarios requiring finer parameter calibration and model tuning due to the increased complexity introduced by a larger number of clinical variables. Specifically, H2O.ai's optimization focuses on model parameters, without directly optimizing the data itself, making its performance highly dependent on the characteristics of the data. This leads to potential shortcomings in effectively capturing the intricate patterns present in blood tests and other clinical variables. Similar to the classification of severity in Type 2, Logistic Regression showed higher specificity but lower F1 and AUC scores than our proposed model. This implies that logistic regression has a low prediction rate for true positives and high number of false negatives for a specific class.