DETECTION OF ASSOCIATIONS BETWEEN ASTHMA AND AIR POLLUTION IN URBAN REGIONS USING SUPERVISED LEARNING ALGORITHMS

Authors:

K. Rajkamal, C. Dinadhayalan

Page No: 42-56

Abstract:

Traffic and power generation are the main sources of urban air pollution. The idea that outdoor air pollution can cause exacerbations of pre-existing asthma is supported by an evidence base that has been accumulating for several decades, with several studies suggesting a contribution to new-onset asthma as well. In this Series paper, we discuss the effects of particulate matter (PM), gaseous pollutants (ozone, nitrogen dioxide, and Sulphur dioxide), and mixed traffic-related air pollution. We focus on clinical studies, both epidemiological and experimental, published in the previous 5 years. From a mechanistic perspective, air pollutants probably cause oxidative injury to the airways, leading to inflammation, remodeling, and increased risk of sensitization. Although several pollutants have been linked to new-onset asthma, the strength of the evidence is variable. We also discuss clinical implications, policy issues, and research gaps relevant to air pollution and asthma.

Description:

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Volume & Issue

Volume-14,ISSUE-3

Keywords

Keywords: Air pollution, Asthma prediction, Supervised learning, light gradient boosting model.

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