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Spatial distribution of esophageal cancer mortality in China: a machine learning approach

BACKGROUND: Esophageal cancer (EC) is one of the most common cancers, causing many people to die every year worldwide. Accurate estimations of the spatial distribution of EC are essential for effective cancer prevention. METHODS: EC mortality surveillance data covering 964 surveyed counties in China...

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Autores principales: Liao, Yilan, Li, Chunlin, Xia, Changfa, Zheng, Rongshou, Xu, Bing, Zeng, Hongmei, Zhang, Siwei, Wang, Jinfeng, Chen, Wanqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7807241/
https://www.ncbi.nlm.nih.gov/pubmed/32478387
http://dx.doi.org/10.1093/inthealth/ihaa022
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author Liao, Yilan
Li, Chunlin
Xia, Changfa
Zheng, Rongshou
Xu, Bing
Zeng, Hongmei
Zhang, Siwei
Wang, Jinfeng
Chen, Wanqing
author_facet Liao, Yilan
Li, Chunlin
Xia, Changfa
Zheng, Rongshou
Xu, Bing
Zeng, Hongmei
Zhang, Siwei
Wang, Jinfeng
Chen, Wanqing
author_sort Liao, Yilan
collection PubMed
description BACKGROUND: Esophageal cancer (EC) is one of the most common cancers, causing many people to die every year worldwide. Accurate estimations of the spatial distribution of EC are essential for effective cancer prevention. METHODS: EC mortality surveillance data covering 964 surveyed counties in China in 2014 and three classes of auxiliary data, including physical condition, living habits and living environment data, were collected. Genetic programming (GP), a hierarchical Bayesian model and sandwich estimation were used to estimate the spatial distribution of female EC mortality. Finally, we evaluated the accuracy of the three mapping methods. RESULTS: The results show that compared with the root square mean error (RMSE) of the hierarchical Bayesian model at 6.546 and the sandwich estimation at 7.611, the RMSE of GP is the lowest at 5.894. According to the distribution estimated by GP, the mortality of female EC was low in some regions of Northeast China, Northwest China and southern China; in some regions downstream of the Yellow River Basin, north of the Yangtze River in the Yangtze River Basin and in Southwest China, the mortality rate was relatively high. CONCLUSIONS: This paper provides an accurate map of female EC mortality in China. A series of targeted preventive measures can be proposed based on the spatial disparities displayed on the map.
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spelling pubmed-78072412021-01-21 Spatial distribution of esophageal cancer mortality in China: a machine learning approach Liao, Yilan Li, Chunlin Xia, Changfa Zheng, Rongshou Xu, Bing Zeng, Hongmei Zhang, Siwei Wang, Jinfeng Chen, Wanqing Int Health Original Article BACKGROUND: Esophageal cancer (EC) is one of the most common cancers, causing many people to die every year worldwide. Accurate estimations of the spatial distribution of EC are essential for effective cancer prevention. METHODS: EC mortality surveillance data covering 964 surveyed counties in China in 2014 and three classes of auxiliary data, including physical condition, living habits and living environment data, were collected. Genetic programming (GP), a hierarchical Bayesian model and sandwich estimation were used to estimate the spatial distribution of female EC mortality. Finally, we evaluated the accuracy of the three mapping methods. RESULTS: The results show that compared with the root square mean error (RMSE) of the hierarchical Bayesian model at 6.546 and the sandwich estimation at 7.611, the RMSE of GP is the lowest at 5.894. According to the distribution estimated by GP, the mortality of female EC was low in some regions of Northeast China, Northwest China and southern China; in some regions downstream of the Yellow River Basin, north of the Yangtze River in the Yangtze River Basin and in Southwest China, the mortality rate was relatively high. CONCLUSIONS: This paper provides an accurate map of female EC mortality in China. A series of targeted preventive measures can be proposed based on the spatial disparities displayed on the map. Oxford University Press 2020-06-01 /pmc/articles/PMC7807241/ /pubmed/32478387 http://dx.doi.org/10.1093/inthealth/ihaa022 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Article
Liao, Yilan
Li, Chunlin
Xia, Changfa
Zheng, Rongshou
Xu, Bing
Zeng, Hongmei
Zhang, Siwei
Wang, Jinfeng
Chen, Wanqing
Spatial distribution of esophageal cancer mortality in China: a machine learning approach
title Spatial distribution of esophageal cancer mortality in China: a machine learning approach
title_full Spatial distribution of esophageal cancer mortality in China: a machine learning approach
title_fullStr Spatial distribution of esophageal cancer mortality in China: a machine learning approach
title_full_unstemmed Spatial distribution of esophageal cancer mortality in China: a machine learning approach
title_short Spatial distribution of esophageal cancer mortality in China: a machine learning approach
title_sort spatial distribution of esophageal cancer mortality in china: a machine learning approach
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7807241/
https://www.ncbi.nlm.nih.gov/pubmed/32478387
http://dx.doi.org/10.1093/inthealth/ihaa022
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