Discovering the Symptom Patterns of COVID-19 from Recovered and Deceased Patients Using Apriori Association Rule Mining
This work addresses the need for optimized decision-making in COVID-19 treatment by providing symptom patterns, but it is incremental as it uses an existing method on new medical data.
The study applied the Apriori association rule mining algorithm to analyze 2,875 COVID-19 patient records, identifying common symptoms such as apnea (72%), cough (64%), and fever (59%) to help clinicians manage and treat the disease more effectively.
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 patient's records, identified the most common signs and 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.