INVESTIGATING THE RELATION BETWEEN PREVALENCE OF ASTHMATIC ALLERGY WITH THE CHARACTERISTICS OF THE ENVIRONMENT USING ASSOCIATION RULE MINING

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 paper, 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. 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 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.

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Air Pollution Monitoring in Intelligent Cities Using Weighted Association Rule Mining

Developing and Monitoring Smart Environments for Intelligent Cities - Advances in Civil and Industrial Engineering ◽

10.4018/978-1-7998-5062-5.ch007 ◽

2021 ◽

pp. 171-197

Author(s):

Goksu Tuysuzoglu ◽

Derya Birant

Keyword(s):

Air Pollution ◽

Air Quality ◽

Association Rules ◽

Association Rule ◽

Association Rule Mining ◽

Experimental Studies ◽

Rule Mining ◽

Air Pollution Monitoring ◽

Real World Datasets ◽

Monitoring Stations

Through the use of internet of things-based sensors in air quality monitoring stations, concentration of different pollutants and meteorological parameters can be regularly measured. In case of unusual conditions (e.g., increased levels of dangerous pollutants), a smart assessment system can produce warning so that appropriate air quality management process can be initiated. In this context, the objective of this study is to discover relationships and patterns among air pollution features and characteristics. In this case, determination of frequently observed association rules can trigger an appropriate background smart environment system when a critical situation is detected. In the experimental studies in the current project, traditional association rule mining and weighted association rule mining methods have been employed using real-world datasets collected from 21 monitoring stations in Turkey. In consequence, useful and outstanding association rules exceeding the user-defined support and confidence levels were obtained that can form basis for further research.

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FUZZY SPATIAL ASSOCIATION RULE MINING TO ANALYZE THE EFFECT OF ENVIRONMENTAL VARIABLES ON THE RISK OF ALLERGIC ASTHMA PREVALENCE

Geodesy and Cartography ◽

10.3846/20296991.2015.1051339 ◽

2015 ◽

Vol 41 (2) ◽

pp. 101-112 ◽

Cited By ~ 3

Author(s):

Yousef Kanani Sadat ◽

Tina Nikaein ◽

Farid Karimipour

Keyword(s):

Allergic Asthma ◽

Association Rule ◽

Environmental Variables ◽

Association Rule Mining ◽

Spatial Association ◽

Pollution Monitoring ◽

Asthma Prevalence ◽

Rule Mining ◽

Inference System ◽

Spatial Association Rule Mining

The prevalence of allergic diseases has greatly increased in recent decades, likely due to contamination of the environment with allergy irritants. One common treatment is identifying that allergy irritant, and then avoiding exposure to it. This article studies the relation between the prevalence of allergic asthma and certain allergy irritants that are related to environmental variables. To that end, we use spatial association rule mining to determine the association between the spatial distribution of allergic asthma prevalence and air pollutants such as CO, SO2, NO2, PM10, PM2.5, and O3 (from data compiled by air pollution monitoring stations), as well as other factors, such as the distance of residence from parks and roads. In order to clear up the uncertainties inherent in the attributes linked to the spatial data, the dimensions in question have been defined as fuzzy sets. Results for the case study (i.e. Tehran metropolitan area) indicate that distance to parks and roads, as well as CO, NO2, PM10, and PM2.5 levels are related to allergic asthma prevalence, while SO2 and O3 are not. Finally, we use the extracted association rules in fuzzy inference system to produce the spatial risk map of allergic asthma prevalence, which shows how much is the risk of allergic asthma prevalence at each point of the city.

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