سامانه همایش های دانشگاه آزاد اسلامی واحد نجف آباد

صفحه اصلی / International Conference on Artificial Intelligence; City, Industry and Health

Analyzing Symptom Patterns of COVID 19 Using Apriori Association Rule Mining

نویسندگان :

Hasti Mokhtari Karchegani¹ Homa Movahednejad² Mahdi Sharifi³

1- Department of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran 2- Department of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran 3- Department of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

کلمات کلیدی :

Association rule mining،Apriori algorithm،COVID-19،Machine learning،Symptom analysis

چکیده :

The COVID-19 crisis has had a profound impact on global health systems, economies, and communities. As healthcare providers faced growing demands and resource constraints, early identification of symptoms and understanding the severity of cases became essential. Artificial intelligence, particularly machine learning, has emerged as a powerful approach in medical data analysis, offering insights that support clinical decision-making. This study applies an association rule mining method, based on the Apriori algorithm, to analyze clinical records of patients diagnosed with COVID-19. These findings highlight the most common indicators of the disease and present a data-driven approach to symptom tracking. By uncovering hidden patterns, the proposed method enables healthcare professionals to better understand symptom correlations and improve response strategies. The results of this research may contribute to more accurate diagnosis, better resource allocation, and more informed treatment planning during infectious disease outbreaks.