# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method
### Data-Driven Strategy  
Acquiring all clinical variables of a patient in real-world clinical settings can be challenging due to limitations related to the medical staff and facilities. To address these challenges, in this phase, we prioritize and group the clinical variables based on their ease of collection during patient visits to a hospital. We organized these variables into five categories: patient demographics, vital signs, symptoms, underlying diseases, and blood test results. Figure 3 illustrates our data-driven strategy. Figure 3: Description of data-driven strategy. * Group 1 comprised nine clinical variables related to patient demographics and vital signs, including _AGE_, _BMI_, _SBP_, and _HRI_. These variables can be obtained simply by querying the patient and can be easily collected upon arrival at the hospital. * Group 2 was obtained by expanding Group 1 by adding symptoms such as _FEVER_ and _HEADA_, resulting in 21 clinical variables. These variables were binary data types that indicated whether a COVID-19 patient exhibited the related symptom. * Group 3 was obtained by expanding Group 2 by adding 32 clinical variables, including underlying diseases related to the patient's current or past medical history, such as _DM_ and _HTN_. * Group 4 comprised 26 clinical variables and was obtained by adding blood test results, such as _HGB_ and _LYMPHO_, to the Group 2 data in anticipation of difficulties accessing the patient's current/past medical history. * Finally, Group 5 was based on Group 3 and included underlying diseases related to the patient's current/past medical history, along with the addition of blood test results, resulting in 37 clinical variables. The target variable in our original dataset, _CSS_, was divided into eight different severity classes, as shown in Table 2. However, the original dataset contained a significant data imbalance. Out of 5664 patients, 4456 (78.85%) were classified as Class 1, leaving only 1188 patients (21.05%) across Classes 2-8. Furthermore, Classes 4-7 contained less than 1% of the total patient data. This data imbalance presented a challenging issue for model training because some classes were not adequately represented. To address these issues, we restructured the severity classes into three types, considering the various COVID-19 severity levels. These three types of restructured severity classes were utilized in model training as the target variables. * Type 1 comprises two classes aimed at distinguishing between general ward and Intensive Care Unit (ICU) patients for efficient ward allocation. Patients from classes 1-4 in the original dataset were classified as _low-severity_ (5312 patients, 94.12%) and patients from classes 5-8 were classified as _high-severity_ (332 patients, 5.88%). * For Type 2, the target variables were restructured into three classes. Classes 1 and 2 were considered _mild_ (4791 patients, 84.89%), classes 3-6 were considered _severe_ (599 patients, 10.61%), and classes 7 and 8 were considered _critical_ (254 patients, 4.50%). * Type 3 provided a more detailed classification by subdividing _low-_ and _high-severity_ levels into five classes. Class 1 was _asymptomatic_ (4456 patients, 78.95%), Class 2 was _mild_ (335 patients, 5.94%), Classes 3-4 were _moderate_ (521 patients, 9.23%), Classes 5-7 were _severe_ (91 patients, 1.61%), and Class 8 was associated with _fatal_ (241 patients, 4.27%).