SARS-COV-2: Symptoms Severity Assessment using Data Mining

ABSTRACT

COVID-19 spreads from person to person in communities (local transmission). Symptoms experienced vary from one person to another. Some may be asymptomatic, while others can experience mild to severe respiratory illness. Adults with existing medical problems are said to have more severe respiratory symptoms. As SARS-Cov-2 cases continually increase in the Philippines, hospitals and quarantine facilities can no longer accommodate new patients. People exposed to the virus are considered Person Under Investigation (PUI). And those experiencing COVID-like symptoms are Person Under Monitoring (PUM). Patients assessed as PUI/PUM are no longer admitted to the hospitals but observe their condition at home. In this study, the researchers developed a System that will closely monitor the symptoms of the probable SARS-COV-2 patients. It aims to assist healthcare professionals, including Barangay health workers, in tracking the patient's condition and informing them once the System evaluates the severity of the patient's symptoms. A clinical symptom dataset was used in this analysis to identify the COVID-like symptoms using the Decision Tree algorithm. The result shows that the System could determine if the patient's symptoms are severe or not. © 2021 IEEE.