Investigating New Patterns in Symptoms of COVID-19 Patients by Association Rule Mining (ARM)

Abstract

Background: COVID-19 is a major public health emergency wreaking havoc on public health, happiness, and liberty of travel, as well as the worldwide economy. Scientists from all over the world are working to develop treatments and vaccines; the WHO has given emergency approval to eight vaccines from around the world. However, it is also seen that the efficiency of vaccines is not up to the mark in different age groups. COVID-19 symptoms come in many different shapes and sizes, so it’s important to learn about them as soon as possible so that medical attention and management can be easier. Method: The GitHub Data Repository-made COVID-19 patient data is available on the internet, which is used in this investigation. We have used the association rule mining method to look for common patterns in a targeted class or segment and then look at the symptoms based on them. Result: The result is that this study involves individuals with a median age of 52 years old. Few frequent symptoms like respiratory failure (1%), septic shock (1.4%), respiratory distress syndrome (1.8%), diarrhoea (1.8%), nausea (2%), sputum (3%), headache (5%), sore throat (8%), pneumonia (8%), weakness (7%), malaise/body pain (11%), cough (37%), fever (67%) and remaining diseases like myocardial infarction, cardiac failure, and renal illness (less than 1%) were present. If a patient had chronic disease, respiratory failure, and pneumonia, there was a higher risk of death; if a patient had a combination of chronic disease, respiratory failure, and pneumonia, respiratory failure in the age range of 45 to 84 years there was a higher risk of death. Patients having chronic conditions like pneumonia or renal disease symptoms that died as a result of the corona virus had more serious indication patterns than those without chronic diseases.