Mapping the Vulnerability of Asthmatic Allergy Prevalence Based on Environmental Characteristics through Fuzzy Spatial Association Rule Mining
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abstract
The prevalence of allergic diseases has highly increased in recent decades due to contamination of the environment with the allergy stimuli. A common treat is identifying the allergy stimulus, and then avoiding the patient to be exposed with it. There are, however, many unknown allergic diseases stimuli that are related to the characteristics of the living environment. In this article, we focus on the effect of air pollution on asthmatic allergies and investigate the association between prevalence of such allergies with those characteristics of the environment that may affect the air pollution. This investigation, eventually, leads to map the vulnerability of asthmatic allergy prevalence based on environmental characteristics. For this, spatial association rule mining has been deployed to mine the association between spatial distribution of allergy prevalence and the air pollution parameters such as CO, SO2, NO2, PM10, PM2.5, and O3 (compiled by the air pollution monitoring stations) as well as living distance to parks and roads. The categories of attributes have been defined as fuzzy sets in order to handle the data uncertainty. The results for the case study (i.e., Tehran metropolitan area) indicates that distance to parks and roads as well as CO, NO2, PM10, and PM2.5 is related to the allergy prevalence in December (the most polluted month of the year in Tehran), while SO2 and O3 have no effect on that. In June, however, the distance to parks and roads as well as NO2, PM10, and PM2.5 affect the allergy prevalence, but CO, SO2 and O3 are ineffective.
