Abstract
This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients’ necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN’s optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.
Highlights
Coronavirus disease 2019 (COVID-19) is a highly contagious viral disease, and the World Health Organization (WHO) declared that the COVID-19 was an international public health emergency [1], [2]
Huang and colleagues revealed that the pathological Liver profile of COVID-19 patients, including moderate microvascular steatosis and mild lobular and portal activity, might have been attributable to SARS-CoV-2 infection. These findings demonstrate a relationship between liver impairment and COVID-19, which plays a critical role in disease progression and is associated with risks of COVID-19 [142]
WORKS Based on patients’ necessary information, pre-existing diseases, symptom, immune index, and complication, this study established a useful Harris hawks optimization (HHO)-Fuzzy K-nearest neighbor (FKNN) model to distinguish the severity of COVID-19, of which innovations are as follows: on the one hand, it is proposed for the first time to use the immune index to distinguish the severity of COVID-19, and on the other hand, the HHO algorithm is used for the first time to screen the parameters and features of the FKNN simultaneously
Summary
Coronavirus disease 2019 (COVID-19) is a highly contagious viral disease, and the World Health Organization (WHO) declared that the COVID-19 was an international public health emergency [1], [2]. First described COVID-19 in December 2019 in Wuhan, Hubei Province, China. The ongoing outbreak of COVID-19 is affecting multiple countries in the world [1]. 4,292 deaths have been triggered by COVID-19 [3]. A great deal of studies is focused on using traditional statistical methods to identify risk factors of COVID-19 patients. Older age, pre-existing diseases, abnormal liver function, and T-lymphocyte count were correlated closely with COVID-19 progression and prognosis [4]–[6]. Traditional statistical methods could not rapidly identify changes
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