# 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} \hline
**Group** & **Model** & **Precision** & **Recall** & **Fl Score** & **Specificity** & **AUC** \\ \hline \hline Logistic Regression (LR) & 0.7386 & 0.5343 & 0.6251 & **0.7472** & 0.7017 \\ \hline Support Vector Machine 1 (SVM 1) & 0.7313 & 0.5458 & 0.6318 & 0.7264 & 0.7101 \\ \hline Support Vector Machine 2 (SVM 2) & 0.6078 & 0.6577 & 0.5639 & 0.2054 & 0.6346 \\ \hline Stochastic Gradient Descent (SGD) & 0.7243 & 0.4617 & 0.6622 & 0.7515 & 0.6781 \\ \hline K-Nearest Neighbor (KNN) & 0.6786 & 0.6466 & 0.6228 & 0.4842 & 0.5539 \\ \hline Multi-Layer Perceptron (MLP) & 0.7189 & 0.5493 & 0.1050 & 0.6778 & 0.6886 \\ \hline Gaussian Naive Bayes (GNB) & 0.7284 & 0.0566 & 0.6692 & 0.8529 & 0.6083 \\ \hline Decision Tree (DT) & 0.6910 & 0.6488 & 0.6410 & 0.5159 & 0.5405 \\ \hline Extra Trees Classifier (ExTree) & 0.6827 & 0.6041 & 0.7158 & 0.5336 & 0.5423 \\ \hline XGBoost (XGB) & 0.7025 & 0.7297 & 0.7234 & 0.4512 & 0.7024 \\ \hline Light Gradient Boosting Machine (LGBM) & 0.7105 & 0.7368 & 0.7225 & 0.4549 & 0.7084 \\ \hline Random Forest (RF) & 0.7125 & 0.7328 & 0.7225 & 0.4754 & 0.7236 \\ \hline AdaBoost (ADA) & 0.7125 & 0.7328 & 0.7111 & 0.4754 & 0.5969 \\ \hline CatBoost (CAT) & 0.7012 & 0.7213 & 0.6201 & 0.4535 & 0.7112 \\ \hline PyCaret (Random Forest (RF)) & 0.7231 & 0.7897 & 0.7549 & 0.4928 & 0.7128 \\ \hline H2O.ai (Generalized Linear Modeling (GLM)) & 0.6954 & 0.7825 & 0.7408 & 0.7036 & 0.7367 \\ \hline \multicolumn{5}{c}{**Proposed Model (Group 3-CSS Type 3)**} \\ (Stacking: 1 st layer: LGBM, RF, ADA, 2 nd layer: & **0.7566** & **0.7892** & **0.7726** & 0.5906 & **0.7355** \\ \hline \multicolumn{5}{c}{Logistic Regression (LR)} & 0.7899 & 0.6249 & 0.6978 & **0.8429** & 0.8122 \\ \hline Support Vector Machine 1 (SVM 1) & 0.7884 & 0.6134 & 0.6900 & 0.8038 & 0.8186 \\ \hline Support Vector Machine 2 (SVM 2) & 0.5916 & 0.7368 & 0.6563 & 0.1413 & 0.7181 \\ \hline Stochastic Gradient Descent (SGD) & 0.7766 & 0.6280 & 0.6944 & 0.7861 & 0.7685 \\ \hline K-Nearest Neighbor (KNN) & 0.7340 & 0.6966 & 0.7148 & 0.5933 & 0.6222 \\ \hline Multi-Layer Perceptron (MLP) & 0.7882 & 0.6851 & 0.7330 & 0.7816 & 0.8123 \\ \hline Gaussian Naive Bayes (GNB) & 0.7872 & 0.1004 & 0.1781 & 0.8111 & 0.7191 \\ \hline Decision Tree (DT) & 0.7615 & 0.7174 & 0.7388 & 0.6804 & 0.6283 \\ \hline Extra Trees Classifier (ExTree) & 0.7308 & 0.6568 & 0.6918 & 0.6370 & 0.5978 \\ \hline XGBoost (XGB) & 0.7915 & 0.7948 & 0.7931 & 0.6450 & 0.8660 \\ \hline Light Gradient Boosting Machine (LGBM) & 0.7916 & 0.7917 & 0.7916 & 0.6607 & 0.8744 \\ \hline Random Forest (RF) & 0.7908 & 0.7890 & 0.7899 & 0.6780 & 0.8541 \\ \hline AdaBoost (ADA) & 0.7908 & 0.7890 & 0.7899 & 0.6780 & 0.6274 \\ \hline CatBoost (CAT) & 0.7830 & 0.7855 & 0.7842 & 0.6507 & 0.6274 \\ \hline PyCaret (Gaussian Naive Bayes (GNB)) & 0.7346 & 0.7775 & 0.7554 & 0.7558 & 0.7305 \\ \hline H2O.ai (Stacking: 1 st layer: 22 GBM, 1 GLM, 2 DRF, Meta: GLM) & 0.6965 & 0.7966 & 0.7432 & 0.7214 & 0.7484 \\ \hline \multicolumn{5}{c}{**Proposed Model (Group 4-CSS Type 3)**} \\ (Stacking: 1 st layer: LGBM, LR, 2 nd layer: MLP, & **0.8239** & **0.8631** & **0.8430** & 0.7626 & **0.8995** \\ \hline \multicolumn{5}{c}{Logistic Regression (LR)} & 0.7887 & 0.6240 & 0.6967 & **0.8264** & 0.8079 \\ \hline \multicolumn{5}{c}{Support Vector Machine 1 (SVM 1)} & 0.7923 & 0.5975 & 0.6812 & 0.8183 & 0.8140 \\ \hline Support Vector Machine 2 (SVM 2) & 0.5917 & 0.7377 & 0.6567 & 0.1414 & 0.6941 \\ \hline Stochastic Gradient Descent (SGD) & 0.7761 & 0.6117 & 0.6842 & 0.7980 & 0.7692 \\ \hline \multicolumn{5}{c}{K-Nearest Neighbor (KNN)} & 0.7237 & 0.6846 & 0.7036 & 0.5756 & 0.6066 \\ \hline \multicolumn{5}{c}{Multi-Layer Perceptron (MLP)} & 0.7714 & 0.7129 & 0.7410 & 0.7137 & 0.8088 \\ \hline Gaussian Naive Bayes (GNB) & 0.7678 & 0.3883 & 0.5158 & 0.8107 & 0.6786 \\ \hline Decision Tree (DT) & 0.7639 & 0.7147 & 0.7385 & 0.6815 & 0.6257 \\ \hline Extra Trees Classifier (ExTree) & 0.7110 & 0.6417 & 0.6746 & 0.5775 & 0.5797 \\ \hline XGBoost (XGB) & 0.7855 & 0.7956 & 0.7905 & 0.6272 & 0.8658 \\ \hline \end{tabular}
\end{table}