The COVID-19 pandemic has a devastating impact globally, claiming millions of
lives and causing significant social and economic disruptions. In order to
optimize decision-making and allocate limited resources, it is essential to
identify COVID-19 symptoms and determine the severity of each case. Machine
learning algorithms offer a potent tool in the medical field, particularly in
mining clinical datasets for useful information and guiding scientific
decisions. Association rule mining is a machine learning technique for
extracting hidden patterns from data. This paper presents an application of
association rule mining based Apriori algorithm to discover symptom patterns
from COVID-19 patients. The study, using 2875 records of patient, identified
the most common symptoms as apnea (72%), cough (64%), fever (59%), weakness
(18%), myalgia (14.5%), and sore throat (12%). The proposed method provides
clinicians with valuable insight into disease that can assist them in managing
and treating it effectively