In-Depth Analysis of Asthma Severity Factors in Omani Patients Using Artificial Intelligence and Machine Learning

This research investigates the association between various elements and the severity of asthma among individuals living in the Al Batinah North Governorate of Oman. It addresses both indoor and outdoor parameters that participate in the development of severity of asthma, with indoor elements including smoking, bakhoor (incense), perfume, and dust, while outdoor elements focus on pollution from nearby industrial areas such as Sohar Industrial Zone (SIZ), Majan Industrial Area (MIA), and Sohar Industrial Port (SIP). Additionally, other health-related factors are examined. The study uses the Knowledge Data Discovery (KDD) methodology to employ Artificial Intelligence (AI) and Machine Learning (ML) methods to identify hidden patterns that influence asthma severity. Additionally, a comprehensive analysis is conducted to find the association between parameters. The dataset of the study was acquired from an electronic health recording system at the Ministry of Health called Al-Shifa. For this study, the system encompasses patient records from three health centers in the Al Batinah North Governorate between 2014 and 2022. The findings indicate a significant positive relationship between proximity to MIA and the severity of asthma, with age also emerging as an influential factor in certain cases. This research aims to enhance asthma understanding and support personalized healthcare development, evidence-based policies, and effective management and prevention strategies for this population.