# 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{6}{*}{Group 1} & Logistic Regression (LR) & 0.9416 & 0.7740 & 0.8496 & 0.7604 & 0.8989 \\ \cline{2-7}  & XGBoost (XGB) & 0.8883 & 0.8735 & 0.8808 & 0.4644 & 0.8007 \\ \cline{2-7}  & Light Gradient Boosting Machine (LGBM) & 0.8932 & 0.8770 & 0.8850 & 0.5059 & 0.8105 \\ \cline{2-7}  & PyCaret (CatBoost (CAT)) & 0.9188 & 0.8997 & 0.9091 & 0.6092 & 0.7849 \\ \cline{2-7}  & H2O.ai (Gradient Boosting Machine (GBM)) & 0.9347 & 0.9101 & 0.9223 & 0.7278 & 0.8406 \\ \cline{2-7}  & \multicolumn{2}{c}{**Proposed Model (Group 1-CSS Type 1)**} & \multirow{2}{*}{(Stacking: 1 st layer: LGBM, 2 nd layer: XGB, MLP, Meta: LR)} & \multirow{2}{*}{0.9262} & \multirow{2}{*}{0.9301} & \multirow{2}{*}{0.9281} & \multirow{2}{*}{0.6026} & \multirow{2}{*}{0.8722} \\ \cline{2-7}  & Logistic Regression (LR) & 0.9479 & 0.7939 & 0.8641 & 0.8168 & 0.9327 \\ \cline{2-7}  & XGBoost (XGB) & 0.9040 & 0.8876 & 0.8957 & 0.5823 & 0.8495 \\ \cline{2-7}  & CatBoost (CAT) & 0.9084 & 0.8847 & 0.8974 & 0.5759 & 0.8645 \\ \cline{2-7}  & PyCaret (Random Forest (RF)) & 0.8993 & 0.8933 & 0.8963 & 0.5423 & 0.8462 \\ \cline{2-7}  & H2O.ai (Stacking: 1 st layer: 1 GBM, 1 GLM, 2 DRF, Meta: GLM) & 0.9354 & 0.8592 & 0.8957 & 0.8096 & 0.8410 \\ \cline{2-7}  & \multicolumn{2}{c}{**Proposed Model (Group 2-CSS Type 1)**} & \multirow{2}{*}{0.9407} & \multirow{2}{*}{0.9421} & \multirow{2}{*}{0.9414} & \multirow{2}{*}{0.7214} & \multirow{2}{*}{0.9098} \\ \cline{2-7}  & (Stacking: 1 st layer: CAT, 2 nd layer: CAT, LR, Meta: LR)} & \multirow{2}{*}{0.9407} & \multirow{2}{*}{0.8129} & \multirow{2}{*}{0.8760} & \multirow{2}{*}{0.8180} & \multirow{2}{*}{0.8880} \\ \cline{2-7}  & Logistic Regression (LR) & 0.9497 & 0.8129 & 0.8760 & 0.8180 & 0.8880 \\ \cline{2-7}  & XGBoost (XGB) & 0.9054 & 0.8916 & 0.8984 & 0.5619 & 0.8578 \\ \cline{2-7}  & AdaBoost (ADA) & 0.9098 & 0.8972 & 0.9035 & 0.5554 & 0.8777 \\ \cline{2-7}  & PyCaret (CatBoost (CAT)) & 0.9032 & 0.9075 & 0.9053 & 0.5237 & 0.8532 \\ \cline{2-7}  & H2O.ai (Gradient Boosting Machine (GBM)) & 0.9358 & 0.8760 & 0.9049 & 0.8085 & 0.8426 \\ \cline{2-7}  & \multicolumn{2}{c}{**Proposed Model (Group 3-CSS Type 1)**} & \multirow{2}{*}{(Stacking: 1 st layer: LGBM, CAT, RF, 2 nd layer: LGBM, GNB, Meta: LR)} & \multirow{2}{*}{0.9471} & \multirow{2}{*}{0.9474} & \multirow{2}{*}{0.9472} & \multirow{2}{*}{0.6526} & \multirow{2}{*}{0.9129} \\ \cline{2-7}  & Logistic Regression (LR) & 0.9363 & 0.8850 & 0.9099 & 0.8914 & 0.9412 \\ \cline{2-7}  & XGBoost (XGB) & 0.9573 & 0.9363 & 0.9467 & 0.8741 & 0.9469 \\ \cline{2-7}  & CatBoost (CAT) & 0.9575 & 0.9363 & 0.9468 & 0.8741 & 0.9479 \\ \cline{2-7}  & PyCaret (Logistic Regression (LR)) & 0.9313 & 0.8997 & 0.9152 & 0.8852 & 0.9410 \\ \cline{2-7}  & H2O.ai (Stacking: 1 st layer: 1 GBM, 1 GLM, 2 DRF, Meta: GLM) & 0.9357 & 0.9136 & 0.9245 & 0.8685 & 0.8439 \\ \cline{2-7}  & \multicolumn{2}{c}{**Proposed Model (Group 4-CSS Type 1)**} & \multirow{2}{*}{0.9871} & \multirow{2}{*}{0.9872} & \multirow{2}{*}{0.9871} & \multirow{2}{*}{0.8959} & \multirow{2}{*}{0.9774} \\ \cline{2-7}  & (Stacking: 1 st layer: RF, 2 nd layer: RF, LR, Meta: LR)} & \multirow{2}{*}{0.9376} & \multirow{2}{*}{0.9965} & \multirow{2}{*}{0.9166} & \multirow{2}{*}{0.8646} & \multirow{2}{*}{0.9407} \\ \cline{2-7}  & Logistic Regression (LR) & 0.9376 & 0.8965 & 0.9166 & 0.8646 & 0.9407 \\ \cline{2-7}  & XGBoost (XGB) & 0.9561 & 0.9350 & 0.9454 & 0.8671 & 0.9460 \\ \cline{2-7}  & Random Forest (RF) & 0.9588 & 0.9389 & 0.9487 & 0.8329 & 0.9467 \\ \cline{2-7}  & PyCaret (CatBoost (CAT)) & 0.9176 & 0.9318 & 0.9246 & 0.8321 & 0.9024 \\ \cline{2-7}  & H2O.ai (Stacking: 1 st layer: 1 GBM, 1 GLM, 2 DRF, Meta: GLM) & 0.9355 & 0.9180 & 0.9267 & 0.8679 & 0.8433 \\ \cline{2-7}  & \multicolumn{2}{c}{**Proposed Model (Group 5-CSS Type 1)**} & \multirow{2}{*}{(Stacking: 1 st layer: RF, MLP, SGD 2 nd layer: RF, SGD, Meta: LR)} & \multirow{2}{*}{0.9901} & \multirow{2}{*}{0.9908} & \multirow{2}{*}{0.9904} & \multirow{2}{*}{0.9443} & \multirow{2}{*}{0.9877} \\ \cline{2-7}  & Meta: LR & & & & & & \\ \hline \hline \end{tabular}
\end{table}