# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method
### Stacking Ensemble Model Construction
Consequently, this method selects a relatively small number of features across all groups, thereby increasing the risk of overall performance degradation due to the inclusion of a few features. In contrast, the backward elimination method begins by including all features and sequentially removes those with the least effect on the performance. This method tends to consider most features as important, and therefore eliminates only a small number of features across all groups. As a result, it selects a larger number of features for all groups than the other methods. However, this method can lead to inefficient feature selection because an insufficient number of features are properly removed. The RFECV method operates by iteratively training the model and removing less-significant features. With each iteration, the model is retrained with the remaining features and less important features are discarded. This process continues until the optimal number of features is determined through cross-validation; thus, a greater number of features is removed from all groups when compared with those removed in backward elimination. Finally, the proposed algorithm combines the features extracted by forward selection, backward elimination, and RFECV, ultimately returning only the important features selected by at least two methods. This approach has the advantage of encompassing features that might have been overlooked by other feature selection algorithms. Table 15 shows the performance results of the feature selection for each group in Type 1 in terms of the F1 and AUC scores. To assess the feature-selection performance, we evaluated the performances of various feature-selection algorithms when applied to the proposed models derived for each type. The bold and underlined results in the table represent the most outstanding performances. The results for Type 1, which included simple binary classification, generally showed high performance in terms of both F1 and AUC scores. Most algorithms exhibited the highest F1 and AUC scores in Group 5. In terms of the F1 score, the proposed algorithm showed a superior performance across all groups when compared with the other algorithms. Regarding the AUC, the other algorithms showed a competitive performance in Groups 2 and 3, but there was no significant difference when compared with the proposed algorithm. In Group 1, both RFECV and the proposed algorithm resulted in identical feature selection outcomes, leading to identical F1 and AUC scores of 0.9281 and 0.8722, respectively. For Type 1, there was no significant difference in performance because the number and types \begin{table}
\begin{tabular}{c c c} \hline
**Groups** & **Feature Selection** & **Selected Feature (Clinical Variables)** \\ \hline \multirow{8}{*}{Group 5} & \multirow{4}{*}{None (37)} & AGE, SEX, PREG, PREGW, BMI, SBP, DBP, HRI, TEMPI, FEVER, COUGH, SPUTUM, ST, RNR, MAM, FM, SOB, HEADA, ACC, MN, DIARR, DHTN, HF, CCD, ASTHMA, COPD, CKD, MALIG, CHD, RDAD, DEMEN, HGB, HCT, LYMPHO, PLT, WBC \\ \cline{2-3}  & Forward Selection (13) & AGE, TEMPI, ST, MAM, SOB, ACC, DM, DEMEN, HGB, HCT, LYMPHO, PLT, WBC \\ \cline{2-3}  & Backward Elimination (32) & AGE, SEX, PREG, PREGW, BMI, SBP, DBP, TEMPI, FEVER, COUGH, SPUTUM, ST, RNR, MAM, FM, SOB, HEADA, ACC, DM, HTN, HF, ASTHMA, COPD, CKD, MALIG, RDAD, DEMEN, HGB, HCT, LYMPHO, PLT, WBC \\ \cline{2-3}  & RFECV (22) & AGE, SEX, BMI, TEMPI, FEVER, ST, SOB, HEADA, ACC, VN, DIARR, DM, ASTHMA, COPD, MALIG, CD, DEMEN, HGB, HCT, LYMPHO, PLT, WBC \\ \cline{2-3}  & Proposed Algorithm (19) & AGE, SEX, BMI, TEMPI, FEVER, ST, MAM, SOB, HEADA, ACC, DM, HTN, COPD, MALIG, DEMEN, HGB, HCT, LYMPHO, PLT, WBC \\ \hline \end{tabular}
\end{table} 
Table 14: Cont. In the case of forward selection, the F1 and AUC scores for Types 2 and 3 decreased when compared with that in the case where no feature selection was performed, mainly because the number of selected features was too small.