# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method
### Stacking Ensemble Model Construction
### Effect of Feature Selection Algorithm on Performance Results

To select the important features that influence clinical severity, we evaluated the effects of 37 input clinical variables on the severity and selected an optimal subset of features that could improve the prediction performance by removing less relevant features. For this purpose, we proposed a feature selection algorithm based on greedy search algorithms, including forward selection, backward elimination, and RFECV. To evaluate the effectiveness of our proposed feature selection algorithm, we conducted comparative experiments with no feature selection and with forward selection, backward elimination, RFECV, or the proposed algorithm. Table 14 presents the features selected by each of the feature selection methods for Groups 1, 3, and 5, which were defined in the data-driven strategy for the 37 clinical variables. The detailed results for all groups are presented in Table 4(Appendix B). \begin{table}
\begin{tabular}{c c c} \hline
**Groups** & **Feature Selection** & **Selected Feature (Clinical Variables)** \\ \hline \hline \multirow{4}{*}{Group 1} & None (9) & AGE, SEX, PREG, PREGW, BMI, SBP, DBP, HRI, TEMPI \\ \cline{2-3}  & Forward Selection (5) & AGE, PREG, BMI, HRI, TEMPI \\ \cline{2-3}  & Backward Elimination (5) & AGE, PREGW, SBP, HRI, TEMPI \\ \cline{2-3}  & RFECV (5) & AGE, BMI, SBP, HRI, TEMPI \\ \cline{2-3}  & Proposed Algorithm (5) & AGE, BMI, SBP, HRI, TEMPI \\ \hline \multirow{4}{*}{Group 3} & \multirow{4}{*}{None (32)} & AGE, SEX, PREG, PREGW, BMI, SBP, DBP, HRI, TEMPI, \\  & & FEVER, COUGH, SPUTUM, ST, RNR, MAM, FM, SOB, \\  & & HEADA, ACC, VN, DIARR, DM, HTN, HF, CCD, \\  & & ASTHMA, COPD, CKD, MALIG, CLD, RDAD, DEMEN \\ \cline{2-3}  & Forward Selection (9) & AGE, TEMPI, FEVER, FM, SOB, ACC, HTN, RDAD, \\  & & DEMEN \\ \cline{2-3}  & \multirow{4}{*}{Backward Elimination (28)} & AGE, SEX, PREG, PREGW, SBP, HRI, TEMPI, FEVER, \\  & & COUGH, SPUTUM, ST, RNR, MAM, FM, SOB, HEADA, \\  & & ACC, DIARR, DM, HTN, CCD, ASTHMA, COPD, CKD, \\  & & MALIG, CLD, DEMEN \\ \cline{2-3}  & \multirow{4}{*}{RFECV (26)} & AGE, SEX, BMI, SBP, DBP, HRI, TEMPI, FEVER, COUGH, \\  & & SPUTUM, ST, RNR, MAM, FM, SOB, HEADA, \\  & DIARR, DM, HTN, CCD, ASTHMA, MALIG, DEMEN \\ \cline{2-3}  & \multirow{4}{*}{Proposed Algorithm (22)} & AGE, SEX, SBP, HRI, TEMPI, FEVER, COUGH, SPUTUM, \\  & & ST, RNR, MAM, FM, SOB, HEADA, \\  & & HTN, CCD, ASTHMA, MALIG, DEMEN \\ \cline{2-3}  & \multirow{4}{*}{RFECV (26)} & AGE, SEX, BMI, SBP, DBP, HRI, TEMPI, FEVER, COUGH, \\  & & SPUTUM, ST, RNR, MAM, FM, SOB, HEADA, \\  & & DIARR, DM, HTN, CCD, ASTHMA, MALIG, DEMEN \\ \cline{2-3}  & \multirow{4}{*}{Proposed Algorithm (22)} & AGE, SEX, SBP, HRI, TEMPI, FEVER, COUGH, SPUTUM, \\  & & ST, RNR, MAM, FM, SOB, HEADA, \\  & & HTN, CCD, ASTHMA, MALIG, DEMEN \\ \cline{2-3}  & \multirow{4}{*}{Proposed Algorithm (22)} & AGE, SEX, SBP, DBP, HRI, TEMPI, FEVER, COUGH, SPUTUM, \\  & & ST, RNR, MAM, FM, SOB, HEADA, \\  & & HTN, CCD, ASTHMA, MALIG, DEMEN \\ \cline{2-3}  & \multirow{4}{*}{Proposed Algorithm (22)} & AGE, SEX, SBP, DBP, HRI, TEMPI, FEVER, COUGH, SPUTUM, \\  & & ST, RNR, MAM, FM, SOB, HEADA, \\  & & HTN, CCD, ASTHMA, MALIG, CLD, DEMEN \\ \cline{2-3}  & \multirow{4}{*}{Proposed Algorithm (22)} & AGE, SEX, SBP, DBP, HRI, TEMPI, FEVER, COUGH, \\  & & ST, RNR, MAM, FM, SOB, HEADA, \\  & & HTN, CCD, ASTHMA, MALIG, DEMEN \\ \cline{2-3}  & \multirow{4}{*}{Proposed Algorithm (22)} & AGE, SEX, SBP, DBP, HRI, TEMPI, FEVER, COUGH, SPUTUM, \\  & & ST, RNR, MAM, FM, SOB, HEADA, \\  & & HTN, CCD, ASTHMA, MALIG, CLD, DEMEN \\ \cline{2-3}  & \multirow{4}{*}{Proposed Algorithm (22)} & AGE, SEX, SBP, DBP, HRI, TEMPI, FEVER, COUGH, SPUTUM, \\  & & ST, RNR, MAM, FM, SOB, HE



Each feature selection method selects a different number of features, except for Group 1, which has a smaller total number of features. The forward selection method begins without any selected features and incrementally adds features that contribute to performance improvements.