What is the influence of lifestyle habits and socioeconomic factors on the occurrence of prostate cancer in Brazil?
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | preprint |
Idioma: | por |
Título da fonte: | SciELO Preprints |
Texto Completo: | https://preprints.scielo.org/index.php/scielo/preprint/view/7566 |
Resumo: | Objective: the investigation of physical, lifestyle and socioeconomic features that may be associated with the occurrence of prostate cancer in Brazil. Methods: a microdata base referring to the 2019 National Health Survey in Brazil was used, with the selection of 42,799 male individuals; this group was analyzed using statistical methods and machine learning modeling (logistic regression and decision tree). Results: the models applied allowed us to identify with a good level of accuracy individuals with prostate cancer diagnosis (DCP), in addition to groups with specific features more strongly associated with such a disease. Conclusion: the models indicate a significant influence of socioeconomic, physical and dietary factors on the frequency of DCP in the analyzed group. The high level of accuracy and sensitivity of the models demonstrates the potential of machine learning methods for predicting DCP. |
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What is the influence of lifestyle habits and socioeconomic factors on the occurrence of prostate cancer in Brazil?¿Cuál es la influencia de los hábitos de vida y de los factores socioeconómicos en la aparición del cáncer de próstata en Brasil?Qual a influência de hábitos de vida e fatores socioeconômicos na ocorrência de câncer de próstata no Brasil?câncer de próstataestilo de vidaestudos transversaisAprendizado de máquinaprostate cancerlifestyleCross-Sectional Studiesmachine learningcáncer de próstataestilo de vidaEstudios Transversalesaprendizaje automáticoObjective: the investigation of physical, lifestyle and socioeconomic features that may be associated with the occurrence of prostate cancer in Brazil. Methods: a microdata base referring to the 2019 National Health Survey in Brazil was used, with the selection of 42,799 male individuals; this group was analyzed using statistical methods and machine learning modeling (logistic regression and decision tree). Results: the models applied allowed us to identify with a good level of accuracy individuals with prostate cancer diagnosis (DCP), in addition to groups with specific features more strongly associated with such a disease. Conclusion: the models indicate a significant influence of socioeconomic, physical and dietary factors on the frequency of DCP in the analyzed group. The high level of accuracy and sensitivity of the models demonstrates the potential of machine learning methods for predicting DCP.Objetivo: investigar características físicas, de estilo de vida y socioeconómicas que pueden estar asociadas con la aparición de cáncer de próstata en Brasil. Métodos: se utilizó una base de microdatos referente a la Encuesta Nacional de Salud de 2019, con la selección de 42.799 individuos del sexo masculino; este grupo fue analizado mediante métodos estadísticos y modelado de machine learning (regresión logística y árbol de decisión). Resultados: los modelos aplicados permitieron identificar con buen nivel de exactitud a los individuos con diagnóstico de cáncer de próstata (DCP), además de grupos con características específicas más fuertemente asociadas a esta enfermedad. Conclusión: los modelos indican influencia significativa de factores socioeconómicos, físicos y dietéticos sobre la frecuencia de DCP en el grupo analizado. El alto nivel de exactitud y sensibilidad de los modelos demuestra el potencial de los métodos de machine learning para predecir la DCP.Objetivo: investigar características físicas, de hábitos de vida e socioeconômicas que podem estar associadas à ocorrência de câncer de próstata no Brasil. Métodos: uma base de microdados referente à Pesquisa Nacional de Saúde 2019 foi utilizada, com a seleção de 42.799 indivíduos do sexo masculino; este grupo foi analisado por meio de métodos estatísticos e modelagem por machine learning (regressão logística e árvore de decisão). Resultados: os modelos aplicados permitiram identificar com bom nível de acurácia os indivíduos que receberam o diagnóstico de câncer de próstata (DCP), além de grupos com características específicas mais fortemente associados a esta doença. Conclusão: os modelos indicam uma influência significativa de fatores socioeconômicos, físicos e alimentares na frequência de DCP no grupo analisado. O alto nível de acurácia e sensibilidade dos modelos demonstra o potencial dos métodos de machine learning para a previsão de DCP.SciELO PreprintsSciELO PreprintsSciELO Preprints2023-12-08info:eu-repo/semantics/preprintinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://preprints.scielo.org/index.php/scielo/preprint/view/756610.1590/SciELOPreprints.7566porhttps://preprints.scielo.org/index.php/scielo/article/view/7566/14213Copyright (c) 2023 Marco Antonio de Souza, Camila Nascimento Monteiro, Cláudia Renata dos Santos Barroshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSouza, Marco Antonio deMonteiro, Camila NascimentoBarros, Cláudia Renata dos Santosreponame:SciELO Preprintsinstname:Scientific Electronic Library Online (SCIELO)instacron:SCI2023-11-30T23:52:11Zoai:ops.preprints.scielo.org:preprint/7566Servidor de preprintshttps://preprints.scielo.org/index.php/scieloONGhttps://preprints.scielo.org/index.php/scielo/oaiscielo.submission@scielo.orgopendoar:2023-11-30T23:52:11SciELO Preprints - Scientific Electronic Library Online (SCIELO)false |
dc.title.none.fl_str_mv |
What is the influence of lifestyle habits and socioeconomic factors on the occurrence of prostate cancer in Brazil? ¿Cuál es la influencia de los hábitos de vida y de los factores socioeconómicos en la aparición del cáncer de próstata en Brasil? Qual a influência de hábitos de vida e fatores socioeconômicos na ocorrência de câncer de próstata no Brasil? |
title |
What is the influence of lifestyle habits and socioeconomic factors on the occurrence of prostate cancer in Brazil? |
spellingShingle |
What is the influence of lifestyle habits and socioeconomic factors on the occurrence of prostate cancer in Brazil? Souza, Marco Antonio de câncer de próstata estilo de vida estudos transversais Aprendizado de máquina prostate cancer lifestyle Cross-Sectional Studies machine learning cáncer de próstata estilo de vida Estudios Transversales aprendizaje automático |
title_short |
What is the influence of lifestyle habits and socioeconomic factors on the occurrence of prostate cancer in Brazil? |
title_full |
What is the influence of lifestyle habits and socioeconomic factors on the occurrence of prostate cancer in Brazil? |
title_fullStr |
What is the influence of lifestyle habits and socioeconomic factors on the occurrence of prostate cancer in Brazil? |
title_full_unstemmed |
What is the influence of lifestyle habits and socioeconomic factors on the occurrence of prostate cancer in Brazil? |
title_sort |
What is the influence of lifestyle habits and socioeconomic factors on the occurrence of prostate cancer in Brazil? |
author |
Souza, Marco Antonio de |
author_facet |
Souza, Marco Antonio de Monteiro, Camila Nascimento Barros, Cláudia Renata dos Santos |
author_role |
author |
author2 |
Monteiro, Camila Nascimento Barros, Cláudia Renata dos Santos |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Souza, Marco Antonio de Monteiro, Camila Nascimento Barros, Cláudia Renata dos Santos |
dc.subject.por.fl_str_mv |
câncer de próstata estilo de vida estudos transversais Aprendizado de máquina prostate cancer lifestyle Cross-Sectional Studies machine learning cáncer de próstata estilo de vida Estudios Transversales aprendizaje automático |
topic |
câncer de próstata estilo de vida estudos transversais Aprendizado de máquina prostate cancer lifestyle Cross-Sectional Studies machine learning cáncer de próstata estilo de vida Estudios Transversales aprendizaje automático |
description |
Objective: the investigation of physical, lifestyle and socioeconomic features that may be associated with the occurrence of prostate cancer in Brazil. Methods: a microdata base referring to the 2019 National Health Survey in Brazil was used, with the selection of 42,799 male individuals; this group was analyzed using statistical methods and machine learning modeling (logistic regression and decision tree). Results: the models applied allowed us to identify with a good level of accuracy individuals with prostate cancer diagnosis (DCP), in addition to groups with specific features more strongly associated with such a disease. Conclusion: the models indicate a significant influence of socioeconomic, physical and dietary factors on the frequency of DCP in the analyzed group. The high level of accuracy and sensitivity of the models demonstrates the potential of machine learning methods for predicting DCP. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-12-08 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/preprint info:eu-repo/semantics/publishedVersion |
format |
preprint |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://preprints.scielo.org/index.php/scielo/preprint/view/7566 10.1590/SciELOPreprints.7566 |
url |
https://preprints.scielo.org/index.php/scielo/preprint/view/7566 |
identifier_str_mv |
10.1590/SciELOPreprints.7566 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://preprints.scielo.org/index.php/scielo/article/view/7566/14213 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
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https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
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application/pdf |
dc.publisher.none.fl_str_mv |
SciELO Preprints SciELO Preprints SciELO Preprints |
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SciELO Preprints SciELO Preprints SciELO Preprints |
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Scientific Electronic Library Online (SCIELO) |
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SciELO Preprints |
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SciELO Preprints |
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SciELO Preprints - Scientific Electronic Library Online (SCIELO) |
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