Supervised Kohonen Self-Organizing Maps of Acute Asthma from Air Pollution Exposure

There are unanswered questions with regards to acute respiratory outcomes, particularly asthma, due to environmental exposures. In contribution to asthma research, the current study explored a computational intelligence paradigm of artificial neural networks (ANNs) called self-organizing maps (SOM)....

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Autores principales:	Moses Mogakolodi Kebalepile, Loveness Nyaradzo Dzikiti, Kuku Voyi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	self-organizing maps classification model air quality asthma outcomes asthma research artificial neural networks Medicine R
Acceso en línea:	https://doaj.org/article/244bd7eeef034cc6b978a866cb24f090
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spelling	oai:doaj.org-article:244bd7eeef034cc6b978a866cb24f0902021-11-11T16:13:09ZSupervised Kohonen Self-Organizing Maps of Acute Asthma from Air Pollution Exposure10.3390/ijerph1821110711660-46011661-7827https://doaj.org/article/244bd7eeef034cc6b978a866cb24f0902021-10-01T00:00:00Zhttps://www.mdpi.com/1660-4601/18/21/11071https://doaj.org/toc/1661-7827https://doaj.org/toc/1660-4601There are unanswered questions with regards to acute respiratory outcomes, particularly asthma, due to environmental exposures. In contribution to asthma research, the current study explored a computational intelligence paradigm of artificial neural networks (ANNs) called self-organizing maps (SOM). To train the SOM, air quality data (nitrogen dioxide, sulphur dioxide and particulate matter), interpolated to geocoded addresses of asthmatics, were used with clinical data to classify asthma outcomes. Socio-demographic data such as age, gender and race were also used to perform the classification by the SOM. All pollutants and demographic traits appeared to be important for the correct classification of asthma outcomes. Age was more important: older patients were more likely to have asthma. The resultant SOM model had low quantization error. The study concluded that Kohonen self-organizing maps provide effective classification models to study asthma outcomes, particularly when using multidimensional data. SO<sub>2</sub> was concluded to be an important pollutant that requires strict regulation, particularly where frail subpopulations such as the elderly may be at risk.Moses Mogakolodi KebalepileLoveness Nyaradzo DzikitiKuku VoyiMDPI AGarticleself-organizing mapsclassification modelair qualityasthma outcomesasthma researchartificial neural networksMedicineRENInternational Journal of Environmental Research and Public Health, Vol 18, Iss 11071, p 11071 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	self-organizing maps classification model air quality asthma outcomes asthma research artificial neural networks Medicine R
spellingShingle	self-organizing maps classification model air quality asthma outcomes asthma research artificial neural networks Medicine R Moses Mogakolodi Kebalepile Loveness Nyaradzo Dzikiti Kuku Voyi Supervised Kohonen Self-Organizing Maps of Acute Asthma from Air Pollution Exposure
description	There are unanswered questions with regards to acute respiratory outcomes, particularly asthma, due to environmental exposures. In contribution to asthma research, the current study explored a computational intelligence paradigm of artificial neural networks (ANNs) called self-organizing maps (SOM). To train the SOM, air quality data (nitrogen dioxide, sulphur dioxide and particulate matter), interpolated to geocoded addresses of asthmatics, were used with clinical data to classify asthma outcomes. Socio-demographic data such as age, gender and race were also used to perform the classification by the SOM. All pollutants and demographic traits appeared to be important for the correct classification of asthma outcomes. Age was more important: older patients were more likely to have asthma. The resultant SOM model had low quantization error. The study concluded that Kohonen self-organizing maps provide effective classification models to study asthma outcomes, particularly when using multidimensional data. SO<sub>2</sub> was concluded to be an important pollutant that requires strict regulation, particularly where frail subpopulations such as the elderly may be at risk.
format	article
author	Moses Mogakolodi Kebalepile Loveness Nyaradzo Dzikiti Kuku Voyi
author_facet	Moses Mogakolodi Kebalepile Loveness Nyaradzo Dzikiti Kuku Voyi
author_sort	Moses Mogakolodi Kebalepile
title	Supervised Kohonen Self-Organizing Maps of Acute Asthma from Air Pollution Exposure
title_short	Supervised Kohonen Self-Organizing Maps of Acute Asthma from Air Pollution Exposure
title_full	Supervised Kohonen Self-Organizing Maps of Acute Asthma from Air Pollution Exposure
title_fullStr	Supervised Kohonen Self-Organizing Maps of Acute Asthma from Air Pollution Exposure
title_full_unstemmed	Supervised Kohonen Self-Organizing Maps of Acute Asthma from Air Pollution Exposure
title_sort	supervised kohonen self-organizing maps of acute asthma from air pollution exposure
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/244bd7eeef034cc6b978a866cb24f090
work_keys_str_mv	AT mosesmogakolodikebalepile supervisedkohonenselforganizingmapsofacuteasthmafromairpollutionexposure AT lovenessnyaradzodzikiti supervisedkohonenselforganizingmapsofacuteasthmafromairpollutionexposure AT kukuvoyi supervisedkohonenselforganizingmapsofacuteasthmafromairpollutionexposure
_version_	1718432342377234432

Supervised Kohonen Self-Organizing Maps of Acute Asthma from Air Pollution Exposure

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