The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an ongoing global pandemic with over 208,795,446 cases and more than 4,386,181 deaths worldwide as of August 17, 2021 (1, 2). The coronavirus disease 2019 (COVID-19) has created significantly impact and tension on global healthcare systems and continues to be a main worldwide concern as the number of patients with COVID-19 and death rates are still quickly rising(3, 4). The clinical manifestation spectrum of COVID-19 patients ranges from asymptomatic to mild disease or severe viral pneumonia with acute respiratory failure, multi-organ failure, shock and even death (5–7). A large proportion of people infected with the virus, around 40–45%, experienced little or no symptoms(8), and severe illness or ICU admission occurs between five and approximately 30% of hospitalized patients(9). Older people and those with underlying medical problems such as cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illnesses (6, 9, 10).
The high mortality rate due to COVID-19, insufficient drugs and hospital beds, low speed of vaccination, and overtiredness of medical staffs have enforced the world to consider COVID-19 pandemic management as a critical priority. These challenges are more prominent in the developing and low-income countries in which there are not enough hospital facilities (i.e., ICU beds, oxygen generators, medical providers and etc.) for efficient management of the disease.
In this scenario, recognition of the key predictive variables of COVID-19 severity and prognosis the patient's status into one of the four classes (need to home quarantine, ward admission, ICU admission, or expired) is highly important. It helps health care providers to have more efficient decision making, better resource allocation, and also better patient triage and treatment planning. Difficulties for the identification of key predictor variables of COVID-19 outcomes and patient infection severity categorization are consequences of COVID-19 complexity. Machine learning(ML) models can be helpful by analyzing large sets of data to discover patterns rapidly, identifying the important predictors through feature selection, and providing techniques that can help predict the severity category of patients involved with SARS-CoV-2 infection(10, 11).
To date, there have been several efforts to apply ML algorithms for f forecasting the prognosis of patients infected with SARS-CoV-2, and also for automatic diagnosis and classifying SARS-CoV-2 infected patients (5, 10, 12). However, most of these studies included a small size of data sets with a low quantity of cases in each outcome class that enforced them to use binary classifiers instead of having multiple classes. For instance, because of limited number of patients with mild and critical types of SARS-CoV-2, several studies used only two strict classes (non-severe and severe), instead of four classes types (i.e., mild, common, severe, and critical) (13).
Therefore, because of the importance of exact classifying of a patient in the multiple classes for assignment of health care resources, utilization of ICU facilities and early and active treatment of highrisk cases with a high risk of involving with severe covid-19, it is essential to develop an intelligence system to address these issues.
In the present study, we aimed to use a modified several ML model on large data set to identify the most important predictor factors of Severity of SARS-CoV-2 Infection and also, to develop an intelligent clinical decision support system for prediction of the severity of SARS-CoV-2 infection into one of the four different categories, including home quarantine, ward admission, ICU admission and expired cases status by using ML models.