Use of the Apriori algorithm to trace the sociodemographic profile of brazilian men with prostate cancer
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/29168 |
Resumo: | Among the diseases that affect the male population, prostate cancer has increased the mortality rate among them, where it is the sixth malignant neoplasm in the world and in Brazil the first. Despite the initiatives to help the male population against prostate cancer, there is still a lack of guidance regarding diagnosis and treatment. However, the initiatives would be better targeted if they had the profiles of patients assisted by them, but it is still a field of research with gaps. In addition, data that can help are stored in large databases with a lot of information, mainly due to the computerization process of the health sector, which makes manual analysis of this data difficult. This work aims to determine the sociodemographic profile of Brazilians with prostate cancer through the Apriori algorithm with data from 2010 to 2019. With this, we applied the Apriori algorithm to the INCA database in order to have the rules of Association. In the end, it is clear that the factors of smoking, alcoholism, race and marital status are the factors that stood out the most as they appear in the rules with the highest levels of confidence. However, we infer that the brown race has a higher incidence of prostate cancer in Brazil. Despite the incompleteness of the optional data in the INCA database, the analysis carried out at the national level and the possibility of using it to guide campaigns in the context of men's health stands. |
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Use of the Apriori algorithm to trace the sociodemographic profile of brazilian men with prostate cancerUso del algoritmo Apriori para trazar el perfil sociodemográfico de hombres brasileños con cáncer de próstataUtilização do algoritmo Apriori para traçar o perfil sociodemográfico do homem brasileiro com câncer de próstataProcesamiento de datosReglas de asociaciónAlgoritmo aprioriCáncer de próstataEnseñanza en salud.Câncer de PróstataMineração de DadosRegras de AssociaçãoAlgoritmo AprioriEnsino em saúde.Prostate CancerData MiningAssociation RulesApriori AlgorithmTeaching in health.Among the diseases that affect the male population, prostate cancer has increased the mortality rate among them, where it is the sixth malignant neoplasm in the world and in Brazil the first. Despite the initiatives to help the male population against prostate cancer, there is still a lack of guidance regarding diagnosis and treatment. However, the initiatives would be better targeted if they had the profiles of patients assisted by them, but it is still a field of research with gaps. In addition, data that can help are stored in large databases with a lot of information, mainly due to the computerization process of the health sector, which makes manual analysis of this data difficult. This work aims to determine the sociodemographic profile of Brazilians with prostate cancer through the Apriori algorithm with data from 2010 to 2019. With this, we applied the Apriori algorithm to the INCA database in order to have the rules of Association. In the end, it is clear that the factors of smoking, alcoholism, race and marital status are the factors that stood out the most as they appear in the rules with the highest levels of confidence. However, we infer that the brown race has a higher incidence of prostate cancer in Brazil. Despite the incompleteness of the optional data in the INCA database, the analysis carried out at the national level and the possibility of using it to guide campaigns in the context of men's health stands.Entre las enfermedades que afectan a la población masculina, el cáncer de próstata ha aumentado la tasa de mortalidad entre ellos, donde es la sexta neoplasia maligna en el mundo y en Brasil la primera. A pesar de las iniciativas para ayudar a la población masculina contra el cáncer de próstata, aún falta orientación en cuanto al diagnóstico y tratamiento. Pero las iniciativas estarían mejor dirigidas si tuvieran los perfiles de los pacientes atendidos por ellas, pero aún es un campo de investigación con lagunas. Además, los datos que pueden ayudar se almacenan en grandes bases de datos con mucha información, principalmente debido al proceso de informatización del sector salud, lo que dificulta el análisis manual de estos datos. Este trabajo tiene como objetivo determinar el perfil sociodemográfico de los brasileños con cáncer de próstata a través del algoritmo Apriori con datos de 2010 a 2019. Con eso, aplicamos el algoritmo Apriori a la base de datos INCA para tener las reglas de Asociación. Al final, se puede apreciar que los factores tabaquismo, alcoholismo, raza y estado civil son los factores que más se destacaron por aparecer en las reglas con mayores niveles de confianza. Sin embargo, inferimos que la raza parda tiene mayor incidencia de cáncer de próstata en Brasil. A pesar de lo incompleto de los datos opcionales en la base de datos del INCA, se destaca el análisis realizado a nivel nacional y la posibilidad de utilizarlo para orientar campañas en el contexto de la salud del hombre.Entre as doenças que acometem a população masculina o câncer de próstata tem aumentado a taxa de mortalidade entre eles, onde no mundo é a sexta neoplasia maligna e no Brasil a primeira. Apesar das inciativas de ajuda a população masculina contra a neoplasia prostática, ainda falta um direcionamento quanto ao diagnóstico e tratamento. Mas as iniciativas seriam mais bem direcionadas se tivessem os perfis dos pacientes assistidos por elas, porém ainda é um campo de pesquisa com lacunas. Além disso, dados que possam ajudar se encontram armazenados em grandes bases de dados com muitas informações, principalmente devido ao processo de informatização do setor de saúde, que dificulta uma análise manual desses dados. Este trabalho tem o objetivo de determinar o perfil sociodemográfico do brasileiro com o câncer de próstata por meio do algoritmo Apriori com dados de 2010 a 2019. Com isso, aplicamos na base de dados do INCA o algoritmo Apriori com a finalidade de termos as regras de associação. Ao final percebe-se que os fatores de tabagismo, alcoolismo, raça e estado conjugal são os fatores que mais destacaram por aparecerem nas regras com os maiores índices de confiança. Porém, depreendemos que a raça parda é de maior incidência do câncer de próstata no Brasil. Apesar da incompletude dos dados opcionais na base do INCA, destaca-se a análise realizada a nível nacional e a possibilidade de utilização para nortear campanhas no contexto da saúde do homem.Research, Society and Development2022-04-29info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/2916810.33448/rsd-v11i6.29168Research, Society and Development; Vol. 11 No. 6; e33811629168Research, Society and Development; Vol. 11 Núm. 6; e33811629168Research, Society and Development; v. 11 n. 6; e338116291682525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/29168/25576Copyright (c) 2022 Gustavo Dias da Silva; Wellington Candeia de Araújohttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSilva, Gustavo Dias da Araújo, Wellington Candeia de 2022-05-13T18:04:10Zoai:ojs.pkp.sfu.ca:article/29168Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:46:19.130110Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Use of the Apriori algorithm to trace the sociodemographic profile of brazilian men with prostate cancer Uso del algoritmo Apriori para trazar el perfil sociodemográfico de hombres brasileños con cáncer de próstata Utilização do algoritmo Apriori para traçar o perfil sociodemográfico do homem brasileiro com câncer de próstata |
title |
Use of the Apriori algorithm to trace the sociodemographic profile of brazilian men with prostate cancer |
spellingShingle |
Use of the Apriori algorithm to trace the sociodemographic profile of brazilian men with prostate cancer Silva, Gustavo Dias da Procesamiento de datos Reglas de asociación Algoritmo apriori Cáncer de próstata Enseñanza en salud. Câncer de Próstata Mineração de Dados Regras de Associação Algoritmo Apriori Ensino em saúde. Prostate Cancer Data Mining Association Rules Apriori Algorithm Teaching in health. |
title_short |
Use of the Apriori algorithm to trace the sociodemographic profile of brazilian men with prostate cancer |
title_full |
Use of the Apriori algorithm to trace the sociodemographic profile of brazilian men with prostate cancer |
title_fullStr |
Use of the Apriori algorithm to trace the sociodemographic profile of brazilian men with prostate cancer |
title_full_unstemmed |
Use of the Apriori algorithm to trace the sociodemographic profile of brazilian men with prostate cancer |
title_sort |
Use of the Apriori algorithm to trace the sociodemographic profile of brazilian men with prostate cancer |
author |
Silva, Gustavo Dias da |
author_facet |
Silva, Gustavo Dias da Araújo, Wellington Candeia de |
author_role |
author |
author2 |
Araújo, Wellington Candeia de |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Silva, Gustavo Dias da Araújo, Wellington Candeia de |
dc.subject.por.fl_str_mv |
Procesamiento de datos Reglas de asociación Algoritmo apriori Cáncer de próstata Enseñanza en salud. Câncer de Próstata Mineração de Dados Regras de Associação Algoritmo Apriori Ensino em saúde. Prostate Cancer Data Mining Association Rules Apriori Algorithm Teaching in health. |
topic |
Procesamiento de datos Reglas de asociación Algoritmo apriori Cáncer de próstata Enseñanza en salud. Câncer de Próstata Mineração de Dados Regras de Associação Algoritmo Apriori Ensino em saúde. Prostate Cancer Data Mining Association Rules Apriori Algorithm Teaching in health. |
description |
Among the diseases that affect the male population, prostate cancer has increased the mortality rate among them, where it is the sixth malignant neoplasm in the world and in Brazil the first. Despite the initiatives to help the male population against prostate cancer, there is still a lack of guidance regarding diagnosis and treatment. However, the initiatives would be better targeted if they had the profiles of patients assisted by them, but it is still a field of research with gaps. In addition, data that can help are stored in large databases with a lot of information, mainly due to the computerization process of the health sector, which makes manual analysis of this data difficult. This work aims to determine the sociodemographic profile of Brazilians with prostate cancer through the Apriori algorithm with data from 2010 to 2019. With this, we applied the Apriori algorithm to the INCA database in order to have the rules of Association. In the end, it is clear that the factors of smoking, alcoholism, race and marital status are the factors that stood out the most as they appear in the rules with the highest levels of confidence. However, we infer that the brown race has a higher incidence of prostate cancer in Brazil. Despite the incompleteness of the optional data in the INCA database, the analysis carried out at the national level and the possibility of using it to guide campaigns in the context of men's health stands. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-04-29 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/29168 10.33448/rsd-v11i6.29168 |
url |
https://rsdjournal.org/index.php/rsd/article/view/29168 |
identifier_str_mv |
10.33448/rsd-v11i6.29168 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/29168/25576 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2022 Gustavo Dias da Silva; Wellington Candeia de Araújo https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2022 Gustavo Dias da Silva; Wellington Candeia de Araújo https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 11 No. 6; e33811629168 Research, Society and Development; Vol. 11 Núm. 6; e33811629168 Research, Society and Development; v. 11 n. 6; e33811629168 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
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1797052711441530880 |