# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method
### Stacking Ensemble Model Construction
In other words, it often misclassifies the severity classes, potentially leading to incorrect negative results. Therefore, the use of logistic regression tends to critical severity classes being overlooked, indicating that essential severity information that is necessary for treatment might be missed. When predicting Type 3 severity, the proposed model in Group 5 showed the highest performance, with an F1 score of 0.8754 and AUC score of 0.9115. However, the specificity was observed to be relatively low. This means that the model accurately identified almost all individuals with severe disease, but also incorrectly classified a significant number of healthy individuals. Such a situation often occurs during the early diagnosis of serious diseases that can have drastic consequences if undiagnosed. Therefore, our proposed model for early diagnosis may misclassify some healthy individuals as having critical severity. However, it is crucial that the model does not overlook or miss actual severe cases. Therefore, prioritizing high F1 and AUC scores is an important strategy when making critical medical decisions. Accordingly, our results can play a critical role in making important diagnostic decisions in life-threatening situations. Figure 10 illustrates a comparison of the performance between Logistic Regression, XGBoost, PyCaret, H2O.ai, and our proposed model, specifically focusing on F1 and AUC scores for Groups 1, 3, and 5 within our data-driven strategy across all data types. Based on these experiments, our observations can be summarized as follows:

1. As the number of classified classes (i.e., types of severity) increases, the need for detailed classification leads to increased complexity, which, in turn, degrades the performance of each model. However, our proposed model demonstrated a superior performance compared to existing single models and AutoML approaches, providing better adaptive predictive results even when not all clinical variables could be collected due to the hospital's circumstances. 2. By comparing Groups 2 and 3, it was found that the patient's current or past medical history was not an essential clinical variable when information about patient symptoms is available. Therefore, an appropriate classifier performance can be achieved using the patient's personal information and symptom details. 3. The performance improves as the number of clinical variables increases. Clinical variables related to blood tests are particularly important for severity classification. However, AutoML approaches are constrained by their limited scope for detailed hyperparameter tuning and the restriction on the variety of models they can utilize. This can lead to a relative decrease in performance when dealing with complex clinical datasets with a larger number of clinical variables. 4. Our proposed stacking ensemble classifier consistently exhibited a superior performance to various types of single classifiers in all cases. ### Effectiveness of Data-Driven Strategy and Oversampling

To evaluate the effectiveness of the data-driven strategy, we evaluated the performances of the original eight classes, each sub-classified into eight severity classes based on the _CSS_, and compared them with those of Types 1, 2, and 3, which were developed to address data imbalance issues. In this experiment, we compared the clinical variables of Group 5, which included blood test results, by utilizing 37 clinical variables. The performance of the original eight classes was measured using the LGBM, which was the most effective of the single classifiers. We evaluated the performances of Types 1, 2, and 3 using the proposed model. Table 11 shows the precision, recall, F1 score, specificity, and AUC score for the clinical severities of the eight original classes obtained using the LGBM. Table 12 presents the precision, recall, F1 score, specificity, and AUC score for the clinical severity of Types 1, 2, and 3 using the proposed model. Figure 10: Performance comparison of Logistic Regression, XGBoost, PyCaret, H2O.ai, and our proposed model.