Asthma-prone areas modeling using a machine learning model

Abstract Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased. Therefore, the purpose of our study was to determine the asthma-prone areas in Tehran, Iran considering environmental, spatial factors. Init...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Soo-Mi Choi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8d6decd62e484710947f6961f8c0eafd
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8d6decd62e484710947f6961f8c0eafd
record_format	dspace
spelling	oai:doaj.org-article:8d6decd62e484710947f6961f8c0eafd2021-12-02T13:57:12ZAsthma-prone areas modeling using a machine learning model10.1038/s41598-021-81147-12045-2322https://doaj.org/article/8d6decd62e484710947f6961f8c0eafd2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81147-1https://doaj.org/toc/2045-2322Abstract Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased. Therefore, the purpose of our study was to determine the asthma-prone areas in Tehran, Iran considering environmental, spatial factors. Initially, we built a spatial database using 872 locations of children with asthma and 13 environmental factors affecting the disease—distance to parks and streets, rainfall, temperature, humidity, pressure, wind speed, particulate matter (PM 10 and PM 2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), and nitrogen dioxide (NO2). Subsequently, utilizing this spatial database, a random forest (RF) machine learning model, and a geographic information system, we prepared a map of asthma-prone areas. For modeling and validation, we deployed 70% and 30%, respectively, of the locations of children with asthma. The results of spatial autocorrelation and RF model showed that the criteria of distance to parks and streets as well as PM 2.5 and PM 10 had the greatest impact on asthma occurrence in the study area. Spatial autocorrelation analyses indicated that the distribution of asthma cases was not random. According to receiver operating characteristic results, the RF model had good accuracy (the area under the curve was 0.987 and 0.921, respectively, for training and testing data).Seyed Vahid Razavi-TermehAbolghasem Sadeghi-NiarakiSoo-Mi ChoiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Seyed Vahid Razavi-Termeh Abolghasem Sadeghi-Niaraki Soo-Mi Choi Asthma-prone areas modeling using a machine learning model
description	Abstract Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased. Therefore, the purpose of our study was to determine the asthma-prone areas in Tehran, Iran considering environmental, spatial factors. Initially, we built a spatial database using 872 locations of children with asthma and 13 environmental factors affecting the disease—distance to parks and streets, rainfall, temperature, humidity, pressure, wind speed, particulate matter (PM 10 and PM 2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), and nitrogen dioxide (NO2). Subsequently, utilizing this spatial database, a random forest (RF) machine learning model, and a geographic information system, we prepared a map of asthma-prone areas. For modeling and validation, we deployed 70% and 30%, respectively, of the locations of children with asthma. The results of spatial autocorrelation and RF model showed that the criteria of distance to parks and streets as well as PM 2.5 and PM 10 had the greatest impact on asthma occurrence in the study area. Spatial autocorrelation analyses indicated that the distribution of asthma cases was not random. According to receiver operating characteristic results, the RF model had good accuracy (the area under the curve was 0.987 and 0.921, respectively, for training and testing data).
format	article
author	Seyed Vahid Razavi-Termeh Abolghasem Sadeghi-Niaraki Soo-Mi Choi
author_facet	Seyed Vahid Razavi-Termeh Abolghasem Sadeghi-Niaraki Soo-Mi Choi
author_sort	Seyed Vahid Razavi-Termeh
title	Asthma-prone areas modeling using a machine learning model
title_short	Asthma-prone areas modeling using a machine learning model
title_full	Asthma-prone areas modeling using a machine learning model
title_fullStr	Asthma-prone areas modeling using a machine learning model
title_full_unstemmed	Asthma-prone areas modeling using a machine learning model
title_sort	asthma-prone areas modeling using a machine learning model
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8d6decd62e484710947f6961f8c0eafd
work_keys_str_mv	AT seyedvahidrazavitermeh asthmaproneareasmodelingusingamachinelearningmodel AT abolghasemsadeghiniaraki asthmaproneareasmodelingusingamachinelearningmodel AT soomichoi asthmaproneareasmodelingusingamachinelearningmodel
_version_	1718392333496483840

Asthma-prone areas modeling using a machine learning model

Ejemplares similares