Discovering the Symptom Patterns of COVID-19 from Recovered and Deceased Patients Using Apriori Association Rule Mining

TL;DR

This study applies Apriori association rule mining to COVID-19 patient data to identify common symptom patterns, aiding clinicians in diagnosis and treatment decisions.

Contribution

It introduces a novel application of association rule mining to COVID-19 symptom analysis using a sizable patient dataset.

Findings

Identified key symptoms: apnea, cough, fever, weakness, myalgia, sore throat.

Provided insights for clinical management of COVID-19.

Demonstrated effectiveness of Apriori algorithm in medical data analysis.

Abstract

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.