Application of Association Rules for Discovering Patterns between Respiratory Diseases and Environmental Variables in the Municipality of Copiapó, Chile - Andrés ALFARO; Servando CAMPILLAY; Carlo TRONCOS; Luis ESPEJO (Communications of International Proceedings, 2024)
The study focuses on identifying patterns between respiratory diseases and environmental variables using data mining techniques and association rules. Information was collected from various data sources between January 2017 and December 2021, categorized to facilitate analysis. Although literature exists linking environmental variables to respiratory diseases, similar studies had not been conducted in Copiapó, highlighting the relevance of this work. A data mining model was employed to extract association rules that reveal relationships between respiratory diseases, environmental conditions, and pollution levels. The results show that, in autumn, acute respiratory infections (ARI) are commonly associated with influenza and bronchitis, while in spring, climatic conditions and pollution are the main factors influencing bronchitis, rhinitis, pharyngitis, and tonsillitis. The study's conclusions also suggest that levels of air pollution (PM2.5 and PM10) are related to the number of consultations for respiratory diseases, affecting individuals of all ages. Furthermore, it is recommended to include COVID-19 in future studies, as it was not considered in this analysis due to its unexpected emergence during the five-year data collection period. This study provides evidence that air pollution is a public health issue in Copiapó and demonstrates that data mining can be a novel tool for identifying patterns and associations between environmental variables and respiratory diseases.
Keywords: Descriptive model; Relationship between variables; Apriori algorithm.