# 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 \hline \multicolumn{2}{c}{Logistic Regression (LR)} & 0.8060 & 0.7186 & 0.7598 & **0.7670** & **0.7632** \\ \hline \multicolumn{2}{c}{Support Vector Machine 1 (SVM 1)} & 0.8031 & 0.7265 & 0.7629 & 0.6833 & 0.7562 \\ \hline \multicolumn{2}{c}{Support Vector Machine 2 (SVM 2)} & 0.7718 & 0.6659 & 0.7149 & 0.5858 & 0.6890 \\ \hline \multicolumn{2}{c}{Stochastic Gradient Descent (SGD)} & 0.7766 & 0.7341 & 0.7548 & 0.6144 & 0.6720 \\ \hline \multicolumn{2}{c}{K-Nearest Neighbor (KNN)} & 0.7769 & 0.7721 & 0.7745 & 0.5030 & 0.6243 \\ \hline \multicolumn{2}{c}{Multi-Layer Perceptron (MLP)} & 0.8133 & 0.6558 & 0.7261 & 0.7047 & 0.7590 \\ \hline \multicolumn{2}{c}{Gaussian Naive Bayes (GNB)} & 0.7960 & 0.7447 & 0.7695 & 0.6366 & 0.7314 \\ \hline \multicolumn{2}{c}{Decision Tree (DT)} & 0.7629 & 0.7619 & 0.7624 & 0.4485 & 0.5652 \\ \hline \multicolumn{2}{c}{Extra Trees Classifier (ExTree)} & 0.7590 & 0.7416 & 0.7502 & 0.4641 & 0.5615 \\ \hline \multicolumn{2}{c}{XGBoost (XGB)} & 0.7690 & **0.8243** & 0.7957 & 0.4468 & 0.7470 \\ \hline \multicolumn{2}{c}{Light Gradient Boosting Machine (LGBM)} & 0.7783 & 0.8162 & 0.7968 & 0.5067 & 0.7569 \\ \hline \multicolumn{2}{c}{Random Forest (RF)} & 0.7699 & 0.7977 & 0.7836 & 0.4472 & 0.7469 \\ \hline \multicolumn{2}{c}{AdaBoost (ADA)} & 0.7699 & 0.7977 & 0.7836 & 0.4472 & 0.7086 \\ \hline \multicolumn{2}{c}{CatBoost (CAT)} & 0.7767 & 0.8163 & 0.7960 & 0.5172 & 0.7426 \\ \hline \multicolumn{2}{c}{PyCaret (AdaBoost (ADA))} & 0.7971 & 0.8226 & 0.8096 & 0.5954 & 0.7299 \\ \hline \multicolumn{2}{c}{H2O.ai (Generalized Linear Modeling (GLM))} & 0.8047 & 0.8150 & 0.8098 & 0.6021 & 0.7511 \\ \hline \multicolumn{2}{c}{**Proposed Model (Group 1-CSS Type 2)** (Stacking: 1 st layer: LGBM, ADA, 2 nd layer: RF, GNB, SVM2 Meta: LR)} & **0.8119** & 0.8204 & **0.8161** & 0.5340 & 0.7522 \\ \hline \multicolumn{2}{c}{Logistic Regression (LR)} & 0.8172 & 0.6952 & 0.7513 & **0.7561** & 0.7852 \\ \hline \multicolumn{2}{c}{Support Vector Machine 1 (SVM 1)} & 0.8226 & 0.7023 & 0.7577 & 0.7458 & 0.7861 \\ \hline \multicolumn{2}{c}{Support Vector Machine 2 (SVM 2)} & 0.7134 & 0.7620 & 0.7369 & 0.1542 & 0.7154 \\ \hline \multicolumn{2}{c}{Stochastic Gradient Descent (SGD)} & 0.8139 & 0.7218 & 0.7651 & 0.7074 & 0.7740 \\ \hline \multicolumn{2}{c}{K-Nearest Neighbor (KNN)} & 0.7790 & 0.7572 & 0.7679 & 0.4741 & 0.6349 \\ \hline \multicolumn{2}{c}{Multi-Layer Perceptron (MLP)} & 0.8186 & 0.6736 & 0.7391 & 0.7554 & 0.7662 \\ \hline \multicolumn{2}{c}{Gaussian Naive Bayes (GNB)} & 0.8105 & 0.7059 & 0.7546 & 0.6960 & 0.7727 \\ \hline \multicolumn{2}{c}{Decision Tree (DT)} & 0.7720 & 0.7377 & 0.7545 & 0.4820 & 0.5773 \\ \hline \multicolumn{2}{c}{Extra Trees Classifier (ExTree)} & 0.7806 & 0.7253 & 0.7519 & 0.5345 & 0.6011 \\ \hline \multicolumn{2}{c}{XGBoost (XGB)} & 0.7938 & 0.7987 & 0.7962 & 0.4634 & 0.7736 \\ \hline \multicolumn{2}{c}{Light Gradient Boosting Machine (LGBM)} & 0.8007 & 0.8010 & 0.8008 & 0.4928 & 0.7890 \\ \hline \multicolumn{2}{c}{Random Forest (RF)} & 0.7903 & 0.7979 & 0.7941 & 0.4562 & **0.7913** \\ \hline \multicolumn{2}{c}{AdaBoost (ADA)} & 0.7898 & 0.7961 & 0.7938 & 0.4537 & 0.7663 \\ \hline \multicolumn{2}{c}{CatBoost (CAT)} & 0.7909 & 0.7890 & 0.7899 & 0.4820 & 0.7697 \\ \hline \multicolumn{2}{c}{PyCaret (AdaBoost (ADA))} & 0.8058 & 0.8212 & 0.8134 & 0.6072 & 0.7996 \\ \hline \multicolumn{2}{c}{H2O.ai (Generalized Linear Modeling (GLM))} & 0.8116 & 0.8532 & 0.8318 & 0.6102 & 0.7629 \\ \hline \multicolumn{2}{c}{**Proposed Model (Group 2-CSS Type 2)** (Stacking: 1 st layer: RF, Meta: LR)} & **0.8303** & **0.8480** & **0.8391** & 0.6105 & 0.7769 \\ \hline \multicolumn{2}{c}{Logistic Regression (LR)} & 0.8213 & 0.6815 & 0.7449 & **0.7701** & 0.7886 \\ \hline \multicolumn{2}{c}{Support Vector Machine 1 (SVM 1)} & 0.8257 & 0.6873 & 0.7502 & 0.7687 & 0.7923 \\ \hline \multicolumn{2}{c}{Support Vector Machine 2 (SVM 2)} & 0.7038 & 0.7740 & 0.7372 & 0.1092 & 0.7231 \\ \hline \multicolumn{2}{c}{Stochastic Gradient Descent (SGD)} & 0.8202 & 0.6802 & 0.7437 & 0.7565 & 0.7780 \\ \hline \multicolumn{2}{c}{K-Nearest Neighbor (KNN)} & 0.7895 & 0.7448 & 0.7665 & 0.4365 & 0.6327 \\ \hline \multicolumn{2}{c}{K-Nearest Perceptron (MLP)} & 0.8130 & 0.7041 & 0.7546 & 0.6997 & 0.7663 \\ \hline \multicolumn{2}{c}{Gaussian Naive Bayes (GNB)} & 0.8507 & 0.1030 & 0.1838 & 0.7396 & 0.7589 \\ \hline \multicolumn{2}{c}{Decision Tree (DT)} & 0.7839 & 0.7443 & 0.7636 & 0.5162 & 0.5975 \\ \hline \multicolumn{2}{c}{Extra Trees Classifier (ExTree)} & 0.7669 & 0.7178 & 0.7415 & 0.4705 & 0.5748 \\ \hline \multicolumn{2}{c}{XGBoost (XGB)} & 0.8018 & 0.8045 & 0.8031 & 0.4783 & 0.7812 \\ \hline \multicolumn{2}{c}{Light Gradient Boosting Machine (LGBM)} & 0.8078 & 0.8067 & 0.8072 & 0.5004 & 0.7950 \\ \hline \end{tabular}
\end{table}