An In-Depth Analysis of COVID-19 Symptoms Considering the Co-Occurrence of Symptoms Using Clustering Algorithms

• Published in: IEEE access 2024, Vol.12, p.127792-127804
• Main Authors: Benito, Diego Javier, Robles, Jesus Rufino, Ramirez, Juan, Anta, Antonio Fernandez, Aguilar, Jose
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; clustering algorithms; COVID-19; COVID-19 symptoms; Datasets; Disease; Indexes; machine learning; Medical diagnostic imaging; Pain; Pandemics; Signs and symptoms; Social networking (online); Surveys; symptomatic patterns
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3456246

A comprehensive analysis of the COVID-19 pandemic is necessary to prepare for future healthcare challenges. In this regard, the large number of datasets collected during the pandemic has allowed various studies on disease behavior and characteristics. For example, collected datasets can be used to extract knowledge about the symptomatic behavior of the disease. In this work, we are interested in analyzing the relationships between the different symptoms of the disease, considering various dimensions, such as countries, variants of COVID-19, and age groups. To this end, we consider the co-occurrence of symptoms as a fundamental element. More precisely, we implemented clustering techniques to discover symptomatic patterns across the various dimensions. For instance, in analyzing the dominant patterns, we observe that symptom congestion or runny nose almost always appears with the symptom muscle pain across many dimensions. Hence, the information on symptom patterns can be helpful in decision-making processes to detect and combat COVID-19 and similar diseases.