# 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 1} & Logistic Regression (LR) & 0.8060 & 0.7186 & 0.7598 & **0.7670** & **0.7632** \\ \cline{2-7}  & XGBoost (XGB) & 0.7690 & **0.8243** & 0.7957 & 0.4468 & 0.7470 \\ \cline{2-7}  & Light Gradient Boosting Machine (LGBM) & 0.7783 & 0.8162 & 0.7968 & 0.5067 & 0.7569 \\ \cline{2-7}  & PyCaret (AdaBoost (ADA)) & 0.7971 & 0.8226 & 0.8096 & 0.5954 & 0.7299 \\ \cline{2-7}  & H2O.ai (Generalized Linear Modeling (GLM)) & 0.8047 & 0.8150 & 0.8098 & 0.6021 & 0.7511 \\ \cline{2-7}  & \multicolumn{2}{c}{**Proposed Model (Group 1-CSS Type 2)**} & \multirow{2}{*}{0.8119} & \multirow{2}{*}{0.8204} & \multirow{2}{*}{0.8161} & \multirow{2}{*}{0.5340} & \multirow{2}{*}{0.7522} \\  & (Stacking: 1 st layer: LGBM, ADA, 2 nd layer: RF, GNB, SVM2 Meta: LR) & & & & \\ \hline \multirow{8}{*}{Group 2} & Logistic Regression (LR) & 0.8172 & 0.6952 & 0.7513 & **0.7561** & 0.7852 \\ \cline{2-7}  & XGBoost (XGB) & 0.7938 & 0.7987 & 0.7962 & 0.4634 & 0.7736 \\ \cline{2-7}  & Random Forest (RF) & 0.7903 & 0.7979 & 0.7941 & 0.4562 & **0.7913** \\ \cline{2-7}  & PyCaret (AdaBoost (ADA)) & 0.8058 & 0.8212 & 0.8134 & 0.6072 & 0.7996 \\ \cline{2-7}  & H2O.ai (Generalized Linear Modeling (GLM)) & 0.8116 & 0.8532 & 0.8318 & 0.6102 & 0.7629 \\ \cline{2-7}  & \multicolumn{2}{c}{**Proposed Model (Group 2-CSS Type 2)**} & \multirow{2}{*}{0.8303} & \multirow{2}{*}{0.8480} & \multirow{2}{*}{0.8391} & \multirow{2}{*}{0.6105} & \multirow{2}{*}{0.7769} \\  & (Stacking: 1 st layer: RF, Meta: LR) & & & & & \\ \hline \multirow{8}{*}{Group 3} & Logistic Regression (LR) & 0.8213 & 0.6815 & 0.7449 & **0.7701** & 0.7886 \\ \cline{2-7}  & XGBoost (XGB) & 0.8018 & 0.8045 & 0.8031 & 0.4783 & 0.7812 \\ \cline{2-7}  & Light Gradient Boosting Machine (LGBM) & 0.8078 & 0.8067 & 0.8072 & 0.5004 & 0.7950 \\ \cline{2-7}  & PyCaret (Logistic Regression (LR)) & 0.8073 & 0.8428 & 0.8247 & 0.6142 & 0.8003 \\ \cline{2-7}  & H2O.ai (Gradient Boosting Machine (GBM)) & 0.8069 & 0.8467 & 0.8263 & 0.6115 & 0.7721 \\ \cline{2-7}  & \multicolumn{2}{c}{**Proposed Model (Group 3-CSS Type 2)**} & \multirow{2}{*}{0.8628} & \multirow{2}{*}{0.8550} & \multirow{2}{*}{0.8589} & \multirow{2}{*}{0.7585} & \multirow{2}{*}{**0.8187**} \\  & (Stacking: 1 st layer: LGBM, SGD, Meta: LR) & & & & & \\ \hline \multirow{8}{*}{Group 4} & Logistic Regression (LR) & 0.8577 & 0.7665 & 0.8095 & **0.8453** & 0.8716 \\ \cline{2-7}  & XGBoost (XGB) & 0.8714 & 0.8540 & 0.8626 & 0.7299 & 0.9057 \\ \cline{2-7}  & Light Gradient Boosting Machine (LGBM) & 0.8834 & 0.8624 & 0.8728 & 0.7645 & **0.9126** \\ \cline{2-7}  & PyCaret (Gaussian Naive Bayes (GNB)) & 0.8084 & 0.8248 & 0.8165 & 0.8193 & 0.8262 \\ \cline{2-7}  & H2O.ai (Gradient Boosting Machine (GBM)) & 0.8191 & 0.8590 & 0.8386 & 0.8025 & 0.7931 \\ \cline{2-7}  & \multicolumn{2}{c}{**Proposed Model (Group 4-CSS Type 2)**} & \multirow{2}{*}{0.9203} & \multirow{2}{*}{0.9169} & \multirow{2}{*}{0.9186} & \multirow{2}{*}{0.8193} & \multirow{2}{*}{0.9031} \\  & (Stacking: 1 st layer: LGBM, RF, Meta: LR) & & & & \\ \hline \multirow{8}{*}{Group 5} & Logistic Regression (LR) & 0.8624 & 0.7700 & 0.8136 & **0.8331** & 0.8654 \\ \cline{2-7}  & XGBoost (XGB) & 0.8767 & 0.8598 & 0.8682 & 0.7157 & 0.9062 \\ \cline{2-7}  & Light Gradient Boosting Machine (LGBM) & 0.8807 & 0.8611 & 0.8708 & 0.7425 & 0.9115 \\ \cline{2-7}  & PyCaret (CatBoost (CAT)) & 0.7936 & 0.8504 & 0.8210 & 0.8083 & 0.8295 \\ \cline{2-7}  & H2O.ai (Generalized Linear Modeling (GLM)) & 0.8139 & 0.8550 & 0.8339 & 0.8251 & 0.8084 \\ \cline{2-7}  & \multicolumn{2}{c}{**Proposed Model (Group 5-CSS Type 2)**} & \multirow{2}{*}{0.9330} & \multirow{2}{*}{0.9307} & \multirow{2}{*}{0.9318} & \multirow{2}{*}{0.8269} & \multirow{2}{*}{**0.9121**} \\  & (Stacking: 1 st layer: LGBM, DT, SGD, Meta: LR) & & & & \\ \hline \hline \end{tabular}
\end{table}