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Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach
Objectives: Our aim was to test if machine learning algorithms can predict cancer mortality (CM) at an ecological level and use these results to identify statistically significant spatial clusters of excess cancer mortality (eCM). Methods: Age-standardized CM was extracted from the official database...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10397398/ https://www.ncbi.nlm.nih.gov/pubmed/37546351 http://dx.doi.org/10.3389/ijph.2023.1604789 |
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author | Casaes Teixeira, Bruno Toporcov, Tatiana Natasha Chiaravalloti-Neto, Francisco Chiavegatto Filho, Alexandre Dias Porto |
author_facet | Casaes Teixeira, Bruno Toporcov, Tatiana Natasha Chiaravalloti-Neto, Francisco Chiavegatto Filho, Alexandre Dias Porto |
author_sort | Casaes Teixeira, Bruno |
collection | PubMed |
description | Objectives: Our aim was to test if machine learning algorithms can predict cancer mortality (CM) at an ecological level and use these results to identify statistically significant spatial clusters of excess cancer mortality (eCM). Methods: Age-standardized CM was extracted from the official databases of Brazil. Predictive features included sociodemographic and health coverage variables. Machine learning algorithms were selected and trained with 70% of the data, and the performance was tested with the remaining 30%. Clusters of eCM were identified using SatScan. Additionally, separate analyses were performed for the 10 most frequent cancer types. Results: The gradient boosting trees algorithm presented the highest coefficient of determination (R (2) = 0.66). For total cancer, all algorithms overlapped in the region of Bagé (27% eCM). For esophageal cancer, all algorithms overlapped in west Rio Grande do Sul (48%–96% eCM). The most significant cluster for stomach cancer was in Macapá (82% eCM). The most important variables were the percentage of the white population and residents with computers. Conclusion: We found consistent and well-defined geographic regions in Brazil with significantly higher than expected cancer mortality. |
format | Online Article Text |
id | pubmed-10397398 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103973982023-08-04 Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach Casaes Teixeira, Bruno Toporcov, Tatiana Natasha Chiaravalloti-Neto, Francisco Chiavegatto Filho, Alexandre Dias Porto Int J Public Health Public Health Archive Objectives: Our aim was to test if machine learning algorithms can predict cancer mortality (CM) at an ecological level and use these results to identify statistically significant spatial clusters of excess cancer mortality (eCM). Methods: Age-standardized CM was extracted from the official databases of Brazil. Predictive features included sociodemographic and health coverage variables. Machine learning algorithms were selected and trained with 70% of the data, and the performance was tested with the remaining 30%. Clusters of eCM were identified using SatScan. Additionally, separate analyses were performed for the 10 most frequent cancer types. Results: The gradient boosting trees algorithm presented the highest coefficient of determination (R (2) = 0.66). For total cancer, all algorithms overlapped in the region of Bagé (27% eCM). For esophageal cancer, all algorithms overlapped in west Rio Grande do Sul (48%–96% eCM). The most significant cluster for stomach cancer was in Macapá (82% eCM). The most important variables were the percentage of the white population and residents with computers. Conclusion: We found consistent and well-defined geographic regions in Brazil with significantly higher than expected cancer mortality. Frontiers Media S.A. 2023-07-20 /pmc/articles/PMC10397398/ /pubmed/37546351 http://dx.doi.org/10.3389/ijph.2023.1604789 Text en Copyright © 2023 Casaes Teixeira, Toporcov, Chiaravalloti-Neto and Chiavegatto Filho. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Archive Casaes Teixeira, Bruno Toporcov, Tatiana Natasha Chiaravalloti-Neto, Francisco Chiavegatto Filho, Alexandre Dias Porto Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach |
title | Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach |
title_full | Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach |
title_fullStr | Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach |
title_full_unstemmed | Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach |
title_short | Spatial Clusters of Cancer Mortality in Brazil: A Machine Learning Modeling Approach |
title_sort | spatial clusters of cancer mortality in brazil: a machine learning modeling approach |
topic | Public Health Archive |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10397398/ https://www.ncbi.nlm.nih.gov/pubmed/37546351 http://dx.doi.org/10.3389/ijph.2023.1604789 |
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