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Hybrid Unsupervised Modeling of Air Pollution Impact to Cardiovascular and Respiratory Diseases

Hybrid Unsupervised Modeling of Air Pollution Impact to Cardiovascular and Respiratory Diseases

Lazaros Iliadis, Vardis-Dimitris Anezakis, Konstantinos Demertzis, Georgios Mallinis
Copyright: © 2017 |Volume: 9 |Issue: 3 |Pages: 23
ISSN: 1937-9390|EISSN: 1937-9420|EISBN13: 9781522512530|DOI: 10.4018/IJISCRAM.2017070102
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MLA

Iliadis, Lazaros, et al. "Hybrid Unsupervised Modeling of Air Pollution Impact to Cardiovascular and Respiratory Diseases." IJISCRAM vol.9, no.3 2017: pp.13-35. http://doi.org/10.4018/IJISCRAM.2017070102

APA

Iliadis, L., Anezakis, V., Demertzis, K., & Mallinis, G. (2017). Hybrid Unsupervised Modeling of Air Pollution Impact to Cardiovascular and Respiratory Diseases. International Journal of Information Systems for Crisis Response and Management (IJISCRAM), 9(3), 13-35. http://doi.org/10.4018/IJISCRAM.2017070102

Chicago

Iliadis, Lazaros, et al. "Hybrid Unsupervised Modeling of Air Pollution Impact to Cardiovascular and Respiratory Diseases," International Journal of Information Systems for Crisis Response and Management (IJISCRAM) 9, no.3: 13-35. http://doi.org/10.4018/IJISCRAM.2017070102

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Abstract

During the last few decades, climate change has increased air pollutant concentrations with a direct and serious effect on population health in urban areas. This research introduces a hybrid computational intelligence approach, employing unsupervised machine learning (UML), in an effort to model the impact of extreme air pollutants on cardiovascular and respiratory diseases of citizens. The system is entitled Air Pollution Climate Change Cardiovascular and Respiratory (APCCCR) and it combines the fuzzy chi square test (FUCS) with the UML self organizing maps algorithm. A major innovation of the system is the determination of the direct impact of air pollution (or of the indirect impact of climate change) to the health of the people, in a comprehensive manner with the use of fuzzy linguistics. The system has been applied and tested thoroughly with spatiotemporal data for the Thessaloniki urban area for the period 2004-2013.

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