# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method
### Stacking Ensemble Model Construction

Table 1: _Cont._

**Table A1. **_Cont._ \begin{tabular}{c c c c c c c} \hline
**Group** & **Model** & **Precision** & **Recall** & **F1 Score** & **Specificity** & **AUC** \\ \hline \hline \multirow{9}{*}{Group 4} & Gaussian Naive Bayes (GNB) & 0.9277 & 0.8536 & 0.8891 & 0.8550 & 0.9035 \\ \cline{2-7}  & Decision Tree (DT) & 0.9491 & 0.9292 & 0.9390 & 0.7635 & 0.8564 \\ \cline{2-7}  & Extra Trees Classifier (ExTree) & 0.9310 & 0.9075 & 0.9191 & 0.6726 & 0.8001 \\ \cline{2-7}  & XGBoost (XGB) & 0.9573 & 0.9363 & 0.9467 & 0.8741 & 0.9469 \\ \cline{2-7}  & Light Gradient Boosting Machine (LGBM) & 0.9565 & 0.9358 & 0.9460 & 0.8534 & 0.9476 \\ \cline{2-7}  & Random Forest (RF) & 0.9566 & 0.9358 & 0.9461 & 0.8603 & 0.9474 \\ \cline{2-7}  & AdaBoost (ADA) & 0.9566 & 0.9343 & 0.9453 & 0.8603 & 0.9478 \\ \cline{2-7}  & CatBoost (CAT) & 0.9575 & 0.9343 & 0.9458 & 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 & 0.9357 & 0.9136 & 0.9245 & 0.8685 & 0.8439 \\ \cline{2-7}  & DRF, Meta: GLM) & & & & & & \\ \cline{2-7}  & **Proposed Model (Group 4-CSS Type 1)** & & & & & & \\ (Stacking: 1 st layer: RF, 2 nd layer: RF, LR, & **0.9871** & **0.9872** & **0.9871** & **0.8959** & **0.9974** \\ \cline{2-7}  & Logistic Regression (LR) & 0.9376 & 0.8965 & 0.9166 & 0.8646 & 0.9407 \\ \cline{2-7}  & Support Vector Machine 1 (SVM 1) & 0.9358 & 0.8903 & 0.9125 & 0.8642 & 0.9405 \\ \cline{2-7}  & Support Vector Machine 2 (SVM 2) & 0.8633 & 0.8908 & 0.8768 & 0.0602 & 0.8571 \\ \cline{2-7}  & Stochastic Gradient Descent (SGD) & 0.9386 & 0.8996 & 0.9187 & 0.8648 & 0.9384 \\ \cline{2-7}  & K-Nearest Neighbor (KNN) & 0.9322 & 0.9115 & 0.9217 & 0.6454 & 0.8145 \\ \cline{2-7}  & Multi-Layer Perceptron (MLP) & 0.9455 & 0.9173 & 0.9312 & 0.8660 & 0.9420 \\ \cline{2-7}  & Gaussian Naive Bayes (GNB) & 0.9221 & 0.8447 & 0.8817 & 0.7994 & 0.8935 \\ \cline{2-7}  & Decision Tree (DT) & 0.9500 & 0.9270 & 0.9384 & 0.8459 & 0.8965 \\ \cline{2-7}  & Extra Trees Classifier (ExTree) & 0.9148 & 0.8938 & 0.9042 & 0.5066 & 0.7102 \\ \cline{2-7} Group 5 & XGBoost (XGB) & 0.9561 & 0.9350 & 0.9454 & 0.8671 & 0.9460 \\ \cline{2-7}  & Light Gradient Boosting Machine (LGBM) & 0.9558 & 0.9345 & 0.9450 & 0.8671 & 0.9473 \\ \cline{2-7}  & Random Forest (RF) & 0.9588 & 0.9389 & 0.9487 & 0.8329 & 0.9467 \\ \cline{2-7}  & AdaBoost (ADA) & 0.9588 & 0.9389 & 0.9487 & 0.8329 & 0.9469 \\ \cline{2-7}  & CatBoost (CAT) & 0.9583 & 0.9372 & 0.9476 & 0.8879 & 0.9476 \\ \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 & 0.9355 & 0.9180 & 0.9267 & 0.8679 & 0.8433 \\ \cline{2-7}  & **Proposed Model (Group 5-CSS Type 1)** & & & & & \\ (Stacking: 1 st layer: RF, MLP, SGD 2 nd layer: RF, SGD, Meta: LR) & **0.9901** & **0.9908** & **0.9904** & **0.9443** & **0.9977** \\ \hline \multirow{9}{*}{Average} & Logistic Regression (LR) & 0.9426 & 0.8325 & 0.8832 & 0.8302 & 0.9203 \\ \cline{2-7}  & XGBoost (XGB) & 0.9222 & 0.9048 & 0.9134 & 0.6700 & 0.8802 \\ \cline{2-7}  & PyCaret & 0.9140 & 0.9064 & 0.9101 & 0.6785 & 0.8655 \\ \cline{2-7}  & H2O.ai & 0.9354 & 0.8954 & 0.9148 & 0.8165 & 0.8423 \\ \cline{2-7}  & Average of 14 Single Classifiers & 0.9168 & 0.8698 & 0.8920 & 0.6676 & 0.8487 \\ \cline{2-7}  & Average of 2 AutoML Approaches & 0.9247 & 0.9009 & 0.9125 & 0.7475 & 0.8539 \\ \cline{2-7}  & Average of All Single Classifiers and AutoML & 0.9178 & 0.8737 & 0.8946 & 0.6776 & 0.8494 \\ \cline{2-7}  & **Proposed Model (CSS Type 1)** & **0.9582** & **0.9595** & **0.9588** & **0.7634** & **0.9380** \\ \hline \end{tabular}