# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
###### Abstract
Therefore, an adaptive approach that can predict severity using readily available clinical variables or by selecting variables that significantly impact actual clinical severity is needed. **Constrained classification of severity levels by a single classifier:** Most studies have employed a single classifier, such as Logistic Regression or XGBoost, to predict COVID-19 severity [14; 15; 16]. While these models have shown reasonable predictive capabilities, their performance can decrease with insufficient data or when the number of severity levels increases, making decision boundaries more complex. As a result, some studies have focused on simple binary classifications, categorizing patient cases as either low or high severity, with an emphasis on identifying mortality risk [17; 18; 19]. Moreover, single classifiers, which utilize different learning methods, are limited in their ability to capture complex patterns or the diversity within a dataset, potentially resulting in failure to generalize effectively across the entire dataset. Consequently, each single classifier has different performance levels, and important clinical variables for severity prediction appear inconsistently across different classifiers. Furthermore, actual COVID-19 patient data, which were not collected for research purposes, present challenging issues, such as irregular sampling and data imbalance. Irregular sampling complicates data extraction and contributes to the large amounts of missing data, while data imbalances can cause classifiers to be biased towards the majority class, leading to unsatisfactory results. In this context, the performance of single classifiers can significantly vary, limiting their ability to create stable, reliable prediction models and often resulting in biased predictions towards the majority class without effectively learning from the minority class. To address these issues, we propose an adaptive stacking ensemble technique to effectively identify various severity levels of COVID-19 patients. The main contributions of this paper are as follows. **Use of a large patient dataset for generalization:** In contrast to previous methods developed with limited patient datasets from a single institution in a specific local region, we utilized a nationwide clinical epidemiology dataset that includes data from 5644 confirmed COVID-19 patients. Our dataset, collected by the South Korean government, particularly the Korea Centers for Disease Control and Prevention (KCDC), comprises patient data from over 100 hospitals nationwide. To validate our approach with an actual patient dataset, we enriched our dataset by incorporating additional data from a single institution, Korea University Ansan Hospital. Consequently, our approach not only improves the performance of our model but also enhances its generalizability, making it applicable in diverse clinical settings. **Adaptive data strategies for clinical variable availability:** The adaptive characteristics of our technique allow for the classification of COVID-19 severity using only the available clinical variables, even when certain variables are unavailable or time-consuming to collect. In contrast to previous methods that depend on extensive medical testing devices, we utilize clinical variables that can be easily obtained through patient interviews or health applications to support early patient triage. Recognizing that even easily obtainable clinical variables can vary depending on the hospital's situation, we categorize the variables within the dataset into five groups: patient demographics, vital signs, symptoms, underlying conditions, and blood test results. We employ a data-driven strategy to group these variables and develop adaptive models for their various combinations, evaluating each model's performance. Additionally, we enhance our clinical severity prediction models by using only the selected features identified through our feature selection method, considering the clinical settings that can complicate data acquisition.