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

Table 6: Summary of performance results of Type 1 severity for groups 1-5.

further reducing this to five variables, thus offering fewer features compared to other groups. Consequently, the performance of other single classifiers and AutoML approaches in Group \(1\) was also observed to be lower. In Group 2, which was formed by expanding Group 1 through the addition of symptoms, the proposed model showed a performance improvement of 1.33% in the F1 score and 3.76% in the AUC score when compared with the results for Group 1. Other single classifiers and AutoML approaches also demonstrated that the inclusion of symptoms has a positive impact on prediction performance. In Group 3, the addition of symptoms and past or current medical history as clinical variables resulted in a performance improvement of 1.91% in the F1 score and 4.07% in the AUC score when compared with the results for Group 1. However, a comparison of Groups 3 and 2 shows that the inclusion of past or current medical history did not significantly enhance the severity prediction performance of Group 3. The improvement was only 0.58% in the F1 score and 0.31% in the AUC score. Other single classifiers and AutoML approaches also showed a less than 1% rise in the F1 score, and for logistic regression, a decrease in AUC scores was observed. This indicates that including a patient's past medical information in situations where patient symptom information is available does not significantly enhance the severity prediction performance. In Groups 4 and 5, which utilized blood test results, there was a significant improvement in the performance of all models when compared with the results of Groups 2 and 3. Specifically, the proposed model demonstrated exceptional performance in Groups 4 and 5, with F1 scores of 0.9871 and 0.9899 and AUC scores of 0.9974 and 0.9977, respectively. These results underscore the importance of blood test results for accurately predicting the severity of COVID-19. However, the AutoML approaches exhibited a relatively lower performance in Groups 4 and 5, showing only slight improvements or even decreases in AUC scores from Group 4 to Group 5. This observation confirms the feasibility and effectiveness of our proposed model for predicting COVID-19 severity. Table 7 presents the average performance results of the proposed adaptive stacking ensemble model in comparison to other single classifiers and AutoML approaches for predicting Type 2 severity across all groups. The prediction of Type 2 severity was classified into three classes: _\"mild\"_, _'severe'_, and _'critical'_. This detailed classification increased complexity compared to Type 1, resulting in an 8.6% decrease in the average F1 score and 10.5% decrease in the average AUC score for our proposed model. Despite these challenges, the proposed model demonstrated a superior performance in predicting Type 2 severity relative to other single classifiers and AutoML approaches. Despite these challenges, the proposed model demonstrated a superior performance in predicting Type 2 severity compared to other single classifiers and AutoML approaches. Specifically, the proposed model achieved an F1 score of 0.8720 and an AUC score of 0.8198, outperforming the averages of other models, including logistic regression, XGBoost, and two AutoML approaches. This underscores our model's robustness and effectiveness in more complex classification scenarios, highlighting its utility in accurately predicting the severity of COVID-19. Table 8 provides a summary of the comparative performance results for Type 2 severity across Groups 1-5. Comprehensive performance results of the comparisons with all 14 individual classifiers and AutoML approaches can be found in Table A2 of Appendix A.