# 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** & **F1 Score** & **Specificity** & **AUC** \\ \hline \multirow{8}{*}{Group 3} & Random Forest (RF) & 0.8043 & 0.8054 & 0.8048 & 0.4761 & 0.8010 \\ \cline{2-7}  & AdaBoost (ADA) & 0.8043 & 0.8054 & 0.8048 & 0.4761 & 0.7319 \\ \cline{2-7}  & CatBoost (CAT) & 0.7985 & 0.7943 & 0.7964 & 0.4992 & 0.7800 \\ \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}  & **Proposed Model (Group 3-CSS Type 2)** & **0.8628** & **0.8550** & **0.8589** & 0.7585 & **0.8187** \\ \cline{2-7}  & (Stacking: 1 st layer: LGBM, SGD, Meta: LR) & 0.8577 & 0.7665 & 0.8095 & **0.8453** & 0.8716 \\ \hline \multirow{8}{*}{Group 4} & Support Vector Machine 1 (SVM 1) & 0.8596 & 0.7576 & 0.8054 & 0.8345 & 0.8720 \\ \cline{2-7}  & Support Vector Machine 2 (SVM 2) & 0.6891 & 0.7974 & 0.7393 & 0.0845 & 0.8003 \\ \cline{2-7}  & Stochastic Gradient Descent (SGD) & 0.8471 & 0.7757 & 0.8098 & 0.8011 & 0.8352 \\ \cline{2-7}  & K-Nearest Neighbor (KNN) & 0.8275 & 0.8023 & 0.8147 & 0.6232 & 0.7293 \\ \cline{2-7}  & Multi-Layer Perceptron (MLP) & 0.8629 & 0.8089 & 0.8350 & 0.8057 & 0.8743 \\ \cline{2-7}  & Gaussian Naive Bayes (GNB) & 0.8768 & 0.1114 & 0.1977 & 0.8193 & 0.8127 \\ \cline{2-7}  & Decision Tree (DT) & 0.8353 & 0.7961 & 0.8152 & 0.7075 & 0.6899 \\ \cline{2-7}  & Extra Trees Classifier (ExTree) & 0.8175 & 0.7634 & 0.7895 & 0.6439 & 0.6722 \\ \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}  & Random Forest (RF) & 0.8703 & 0.8492 & 0.8596 & 0.7365 & 0.9011 \\ \cline{2-7}  & AdaBoost (ADA) & 0.8703 & 0.8492 & 0.8596 & 0.7365 & 0.7862 \\ \cline{2-7}  & CatBoost (CAT) & 0.8739 & 0.8567 & 0.8652 & 0.7301 & 0.9021 \\ \hline \multirow{8}{*}{Group 5} & 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}  & **Proposed Model (Group 4-CSS Type 2)** & **0.9203** & **0.9169** & **0.9186** & 0.8683 & **0.9031** \\ \cline{2-7}  & (Stacking: 1 st layer: LGBM, RF, Meta: LR) & **0.9203** & **0.9169** & **0.9186** & 0.8683 & **0.9031** \\ \hline \multirow{8}{*}{Group 6} & Logistic Regression (LR) & 0.8624 & 0.7700 & 0.8136 & **0.8683** & 0.8654 \\ \cline{2-7}  & Support Vector Machine 1 (SVM 1) & 0.8645 & 0.7616 & 0.8098 & 0.8444 & 0.8661 \\ \cline{2-7}  & Support Vector Machine 2 (SVM 2) & 0.6888 & 0.7974 & 0.7391 & 0.0821 & 0.7721 \\ \cline{2-7}  & Stochastic Gradient Descent (SGD) & 0.8500 & 0.7607 & 0.8029 & 0.8135 & 0.8111 \\ \cline{2-7}  & K-Nearest Neighbor (KNN) & 0.8130 & 0.7846 & 0.7985 & 0.5535 & 0.7180 \\ \cline{2-7}  & Multi-Layer Perceptron (MLP) & 0.8548 & 0.8094 & 0.8315 & 0.7621 & 0.8753 \\ \cline{2-7}  & Gaussian Naive Bayes (GNB) & 0.8735 & 0.4325 & 0.5785 & 0.8233 & 0.8290 \\ \cline{2-7}  & Decision Tree (DT) & 0.8537 & 0.8182 & 0.8356 & 0.7337 & 0.7369 \\ \cline{2-7}  & Extra Trees Classifier (ExTree) & 0.8001 & 0.7536 & 0.7762 & 0.5801 & 0.6399 \\ \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}  & Random Forest (RF) & 0.8715 & 0.8514 & 0.8613 & 0.7367 & 0.9036 \\ \cline{2-7}  & AdaBoost (ADA) & 0.8715 & 0.8514 & 0.8613 & 0.7367 & 0.6562 \\ \cline{2-7}  & CatBoost (CAT) & 0.8726 & 0.8554 & 0.8639 & 0.7276 & 0.9038 \\ \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}  & **Proposed Model (Group 5-CSS Type 2)** & **0.9330** & **0.9307** & **0.9318** & 0.8269 & **0.9121** \\ \cline{2-7}  & (Stacking: 1 st layer: LGBM, DT, SGD, Meta: LR) & **0.9330** & **0.9307** & **0.9318** & 0.8269 & **0.9121** \\ \hline \multirow{8}{*}{Average} & Logistic Regression (LR) & 0.8329 & 0.7264 & 0.7758 & **0.8014** & 0.8148 \\ \cline{2-7}  & XGBoost (XGB) & 0.8225 & 0.8283 & 0.8252 & 0.5668 & 0.8227 \\ \cline{2-7}  & PyCaret & 0.8024 & 0.8324 & 0.8170 & 0.6889 & 0.7971 \\ \cline{2-7}  & H2O.ai & 0.8112 & 0.8458 & 0.8281 & 0.6903 & 0.7775 \\ \cline{2-7}  & Average of 14 Single Classifiers & 0.8127 & 0.7501 & 0.7705 & 0.6101 & 0.7627 \\ \hline \end{tabular}
\end{table}