# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
###### Abstract  
During outbreaks of infectious diseases, such as COVID-19, it is critical to rapidly determine treatment priorities and identify patients requiring hospitalization based on clinical severity. Although various machine learning models have been developed to predict COVID-19 severity, most have limitations, such as small dataset sizes, the limited availability of clinical variables, or a constrained classification of severity levels by a single classifier. In this paper, we propose an adaptive stacking ensemble technique that identifies various COVID-19 patient severity levels and separates them into three formats: Type 1 (low or high severity), Type 2 (mild, severe, critical), and Type 3 (asymptomatic, mild, moderate, severe, fatal). To enhance the model's generalizability, we utilized a nationwide dataset from the South Korean government, comprising data from 5644 patients across over 100 hospitals. To address the limited availability of clinical variables, our technique employs data-driven strategies and a proposed feature selection method. This ensures the availability of clinical variables across diverse hospital environments. To construct optimal stacking ensemble models, our technique adaptively selects candidate base classifiers by analyzing the correlation between their predicted outcomes and performance. It then automatically determines the optimal multi-layer combination of base and meta-classifiers using a greedy search algorithm. To further improve the performance, we applied various techniques, including imputation of missing values and oversampling. The experimental results demonstrate that our stacking ensemble models significantly outperform existing single classifiers and AutoML approaches, with improvements of 6.42% and 8.86% in F1 and AUC scores for Type 1, 9.59% and 6.68% for Type 2, and 11.94% and 9.24% for Type 3, respectively. Consequently, our approach improves the prediction of COVID-19 severity levels and potentially assists frontline healthcare providers in making informed decisions. COVID-19; early triage; clinical servility prediction; adaptive stacking ensemble +
Footnote †: journal: Medical  
identify patients requiring hospitalization. COVID-19 treatment relies on symptomatic relief, supportive care, oxygen therapy, and intensive care, depending on the disease severity [3]. Therefore, it is essential to appropriately allocate the limited healthcare resources, such as quarantine facilities, hospital beds, and intensive care units, based on the different severity level classifications of COVID-19 patients. Several machine learning model approaches have been proposed to predict the severity of COVID-19 patients. However, these studies have the following limitations. **Small patient datasets:** Many studies relied on limited information or clinical variables, such as medical images [4], blood or urine tests [5; 6], clinical characteristics [7], and Electronic Health Records (EHRs) [8] obtained during hospitalization. These data sources have the advantage of not requiring additional expenditure on materials, as the necessary information is already included in the patient's medical records. However, these studies typically derived their datasets from the patient pool at a single institution in a specific local region, usually involving 300-600 patients. Thus, these studies have limited generalizability due to their relatively small sample sizes. **Limited availability of clinical variables:** Some studies have shown promising results using patient data obtained from extensive medical testing devices, such as blood tests [9], CT scans [10; 11], and MRIs [12]. However, the time required to confirm the test results can delay the rapid identification of patients with critically severe COVID-19. In real-world clinical settings, it is often necessary to triage and refer COVID-19 patients immediately after diagnosis, even with limited available clinical variables [13].