# 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
**Group** & **Model** & **Precision** & **Recall** & **FI.Score** & **Specificity** & **AUC** \\ \hline \multirow{4}{*}{Group 1} & 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}  & **Proposed Model (Group 1-CSS Type 1)** & & & & & \\ (Stacking: 1 st layer: LGBM, 2 nd layer: XGB, MLP, Meta: LR) & 0.9262 & **0.9301** & **0.9281** & 0.6026 & 0.8722 \\ \hline \multirow{4}{*}{Group 2} & Logistic Regression (LR) & **0.9479** & 0.7939 & 0.8641 & **0.8168** & **0.9327** \\ \cline{2-7}  & Support Vector Machine 1 (SVM 1) & 0.9155 & 0.7925 & 0.8496 & 0.7892 & 0.8799 \\ \cline{2-7}  & Support Vector Machine 2 (SVM 2) & 0.8691 & 0.8671 & 0.8726 & 0.2650 & 0.7755 \\ \cline{2-7}  & Stochastic Gradient Descent (SGD) & 0.9146 & 0.7585 & 0.8293 & 0.8076 & 0.8735 \\ \cline{2-7}  & K-Nearest Neighbor (KNN) & 0.8962 & 0.9138 & 0.9049 & 0.6083 & 0.7117 \\ \cline{2-7}  & Multi-Layer Perceptron (MLP) & 0.9105 & 0.8107 & 0.8577 & 0.7077 & 0.8616 \\ \cline{2-7}  & Gaussian Naive Bayes (GNB) & 0.9150 & 0.7996 & 0.8534 & 0.7758 & 0.8540 \\ \cline{2-7}  & Decision Tree (DT) & 0.8944 & 0.8695 & 0.8818 & 0.5605 & 0.6250 \\ \cline{2-7}  & Extra Trees Classifier (ExTree) & 0.8970 & 0.8589 & 0.8775 & 0.6355 & 0.6571 \\ \cline{2-7}  & XGBost (XGB) & 0.9040 & 0.8876 & 0.8957 & 0.5823 & 0.8495 \\ \cline{2-7}  & Light Gradient Boosting Machine (LGBM) & 0.9055 & 0.8894 & 0.8964 & 0.5893 & 0.8562 \\ \cline{2-7}  & Random Forest (RF) & 0.9006 & 0.8721 & 0.8861 & 0.5482 & 0.8626 \\ \hline \multirow{4}{*}{Group 2} & AdaBoost (ADA) & 0.9014 & 0.8729 & 0.8869 & 0.5482 & 0.8739 \\ \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}  & **Proposed Model (Group 2-CSS Type 1)** & & & & & \\ (Stacking: 1 st layer: CAT, 2 nd layer: CAT, LR, Meta: LR) & 0.9407 & **0.9421** & **0.9414** & 0.7214 & 0.9098 \\ \hline \multirow{4}{*}{Group 3} & Logistic Regression (LR) & **0.9497** & 0.8129 & 0.8760 & **0.8180** & 0.8880 \\ \cline{2-7}  & Support Vector Machine 1 (SVM 1) & 0.9185 & 0.8005 & 0.8554 & 0.8172 & 0.8851 \\ \cline{2-7}  & Support Vector Machine 2 (SVM 2) & 0.8595 & 0.8744 & 0.8669 & 0.0167 & 0.7862 \\ \cline{2-7}  & Stochastic Gradient Descent (SGD) & 0.9170 & 0.7903 & 0.8489 & 0.8097 & 0.8863 \\ \cline{2-7}  & K-Nearest Neighbor (KNN) & 0.9029 & 0.8775 & 0.8900 & 0.4367 & 0.7164 \\ \cline{2-7}  & Multi-Layer Perceptron (MLP) & 0.9100 & 0.8324 & 0.8695 & 0.6678 & 0.8549 \\ \cline{2-7}  & Gaussian Naive Bayes (GNB) & 0.9148 & 0.8138 & 0.8613 & 0.7561 & 0.8403 \\ \cline{2-7}  & Decision Tree (DT) & 0.8949 & 0.8629 & 0.8786 & 0.5945 & 0.6385 \\ \cline{2-7}  & Extra Trees Classifier (ExTree) & 0.8927 & 0.8567 & 0.8743 & 0.5872 & 0.6319 \\ \cline{2-7}  & XGBost (XGB) & 0.9054 & 0.8916 & 0.8984 & 0.5619 & 0.8578 \\ \cline{2-7}  & Light Gradient Boosting Machine (LGBM) & 0.9008 & 0.8947 & 0.8977 & 0.6240 & 0.8698 \\ \cline{2-7}  & Random Forest (RF) & 0.9098 & 0.8925 & 0.9011 & 0.5554 & 0.8654 \\ \cline{2-7}  & AdaBoost (ADA) & 0.9098 & 0.8972 & 0.9035 & 0.5554 & 0.8777 \\ \cline{2-7}  & CatBoost (CAT) & 0.9080 & 0.8943 & 0.9011 & 0.5758 & 0.8715 \\ \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}  & **Proposed Model (Group 3-CSS Type 1)** & & & & & \\ (Stacking: 1 st layer: LGBM, CAT, RF, 2 nd layer: LGBM, GNB, Meta: LR) & 0.9471 & **0.9474** & **0.9472** & 0.6526 & **0.9129** \\ \hline \multirow{4}{*}{Group 4} & Logistic Regression (LR) & 0.9363 & 0.8850 & 0.9099 & 0.8914 & 0.9412 \\ \cline{2-7}  & Support Vector Machine 1 (SVM 1) & 0.9340 & 0.8744 & 0.9032 & 0.8907 & 0.9412 \\ \cline{2-7}  & Support Vector Machine 2 (SVM 2) & 0.8533 & 0.8898 & 0.8712 & 0.0602 & 0.8965 \\ \cline{2-7}  & Stochastic Gradient Descent (SGD) & 0.9354 & 0.8810 & 0.9074 & 0.8912 & 0.9401 \\ \cline{2-7}  & K-Nearest Neighbor (KNN) & 0.9395 & 0.9199 & 0.9296 & 0.6803 & 0.8510 \\ \cline{2-7}  & Multi-Layer Perceptron (MLP) & 0.9467 & 0.9182 & 0.9322 & 0.8798 & 0.9445 \\ \hline \end{tabular}
\end{table}