Unveiling Symptomatic Pathways of COVID-19: An In-depth Analysis Employing SLIM Association Rule Mining

Abstract

In order to achieve a thorough comprehension of COVID-19, it is imperative to elucidate the interconnections among its symptoms. The proposed methodology aims to discern patterns that can effectively diagnose the emergence of symptoms, thereby improving diagnostic accuracy and potentially yielding significant breakthroughs. The current study facilitates identifying patterns that signify symptoms' occurrence and convergence. The SLIM algorithm is introduced as a novel method for systematically analyzing large symptom datasets, and its application is presented in this study. The reason for this goes beyond the capabilities of conventional methods, enabling greater diagnostic precision and illuminating new investigational avenues. The present study predicts and identifies COVID-19-related symptoms with greater accuracy, which enhances our understanding of the disease's symptomatology and encourages further study in the area. The results of this study have improved the experience and management of COVID-19's diagnosis, treatment, and pathophysiology, thereby contributing to a better grasp of the virus and bolstering global efforts to manage and mitigate it.