# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 2 Related Work  
The rapid spread of COVID-19 has emphasized the need for effective early-stage severity prediction models to optimize resource allocation and patient triage. Consequently, numerous studies have proposed the development of machine learning models to predict COVID-19 severity in patients and improve prognoses. [PERSON] et al. [20], [PERSON] et al. [21], and [PERSON] et al. [22] utilized datasets from local hospitals in Wuhan, China, during the early stages of the COVID-19 outbreak. They predicted characteristics and risk factors related to the clinical severity and mortality of COVID-19 patients using single classifiers, including fuzzy logic and multivariate Logistic Regression. Their goal was to identify clinical variables that could significantly influence COVID-19 severity based on patients' clinical characteristics and in-hospital record information (e.g., patient demographics, blood test results, medical history, and real-time PCR). The epidemiological and clinical characteristics of the patients in these datasets were identified as key factors related to prognosis heterogeneity after COVID-19 diagnosis [23]. However, the generalizability and applicability of these methods are limited due to their reliance on small patient datasets, with sizes of 375, 443, and 663 patients, respectively. [PERSON] et al. [24] aimed to enhance their prediction model's generalizability by using a dataset of 1590 patients from hospitals in the Wuhan, Hubei, and Guangdong regions of China. They proposed a deep-learning-based Cox proportional hazards model to predict COVID-19 patients' survival rates. However, this model requires the time-series-based tracking of patient cohort data, which can be challenging and costly to obtain due to the need for continuous data formatting. A machine-learning-based analysis of CT images has been proposed to predict COVID-19 patient mortality rates and severity changes over time. [PERSON] et al. [25] used a deep-learning-based diagnostic model with chest CT imaging for the early detection, quantification, and tracking of COVID-19. They integrated a large-scale dataset that included CT scans of 11,356 patients from three medical centers in China and four publicly available databases. Similarly, [PERSON] et al. [26] explored the performance of single classifiers in rapidly triaging COVID-19 patients by combining CT image analysis with EHRs and clinical laboratory results. [PERSON] et al. [27] utilized a Vision Transformer (ViT) technique, known for its high performance in computer vision, to enhance accuracy by segmenting and processing the training data along with augmented CT images. Despite the advantages of these studies in tracking severity changes over time using image data, the need for medical testing devices to acquire training image data poses a significant challenge, especially where rapid initial classification is required. Furthermore, [PERSON] et al. [28] reported that approximately 20% of COVID-19 patients do not show significant imaging changes in the lungs, leading to unnecessary radiation exposure and the potential misallocation of limited testing resources. [PERSON] et al. [29] used a meta-analysis to investigate the correlation between various cardiac markers and the severity or mortality of COVID-19 patients. Their findings suggested that elevated levels of CK-MB, PCT, NT-proBNP, BNP, and D-dimer could serve as indicators for predicting COVID-19 severity. Meanwhile, [PERSON] et al. [30] applied pairwise correlation to compress a dataset composed of 70 clinical characteristics and used the XGBost model for prediction. They concluded that biomarkers such as ferritin, CRP, LDH, and D-dimer could potentially act as indicators for detecting COVID-19 infection. However, these datasets are obtained through standard laboratory tests, which are time-consuming and expensive. Since these tests are typically designed to diagnose specific health conditions, they cannot fully represent all potential clinical scenarios, necessitating additional tests or specialized procedures.