Application of logistic regression in the analysis of risk factor associated with arterial hypertension
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Data de Publicação: | 2021 |
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/22964 |
Resumo: | Logistic regression is an important technique for data modeling when you want to analyze the relationship between a response variable and one or more independent variables. The technique allows one to estimate the chances related to the probability of occurrence of an event of interest. Logistic regression differs from linear regression due to the dichotomous nature of the dependent variable and has been used in several areas of knowledge, including studies in the health area. This study used the logistic regression technique to analyze the association between Hypertension and certain risk factors. The data used comes from the National Health Survey (PNS) for the year 2019, carried out by the Brazilian Institute of Geography and Statistics (IBGE) in the country. Two models were adjusted, the final model being composed of seven variables with a statistical significance of 5%. Diagnostic techniques indicated an adequate fit of the model, as well as its accuracy for predictions. The results show that factors such as increasing age, high body mass index (BMI) and a positive diagnosis for diabetes increase the chances of an individual being hypertensive. |
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Application of logistic regression in the analysis of risk factor associated with arterial hypertensionAplicación de la regresión logística en el análisis de factores de riesgo asociados a la hipertensión arterialAplicação da regressão logística na análise dos dados dos fatores de risco associados à hipertensão arterialAssociationRisk FactorsFitted model.AssociaçãoFatores de riscoModelo ajustado.AsociaciónFactores de riesgoModelo de ajuste.Logistic regression is an important technique for data modeling when you want to analyze the relationship between a response variable and one or more independent variables. The technique allows one to estimate the chances related to the probability of occurrence of an event of interest. Logistic regression differs from linear regression due to the dichotomous nature of the dependent variable and has been used in several areas of knowledge, including studies in the health area. This study used the logistic regression technique to analyze the association between Hypertension and certain risk factors. The data used comes from the National Health Survey (PNS) for the year 2019, carried out by the Brazilian Institute of Geography and Statistics (IBGE) in the country. Two models were adjusted, the final model being composed of seven variables with a statistical significance of 5%. Diagnostic techniques indicated an adequate fit of the model, as well as its accuracy for predictions. The results show that factors such as increasing age, high body mass index (BMI) and a positive diagnosis for diabetes increase the chances of an individual being hypertensive.La regresión logística es una técnica importante para el modelado de datos cuando desea analizar la relación entre una variable de respuesta y una o más variables independientes. La técnica permite estimar las posibilidades relacionadas con la probabilidad de que ocurra un evento de interés. La regresión logística se diferencia de la lineal por la naturaleza dicotómica de la variable dependiente y se ha utilizado en varias áreas del conocimiento, incluidos estudios en el área de la salud. Este estudio utilizó la técnica de regresión logística para analizar la asociación entre Hipertensión y ciertos factores de riesgo. Los datos utilizados provienen de la Encuesta Nacional de Salud (PNS) del año 2019, realizada por el Instituto Brasileño de Geografía y Estadística (IBGE) en el país. Se ajustaron dos modelos, estando el modelo final compuesto por siete variables con una significancia estadística del 5%. Las técnicas de diagnóstico indicaron un ajuste adecuado del modelo, así como su precisión para las predicciones. Los resultados muestran que factores como la edad avanzada, el índice de masa corporal (IMC) alto y un diagnóstico positivo de diabetes aumentan las posibilidades de que una persona sea hipertensa.A regressão logística é uma técnica importante para modelagem de dados quando se deseja analisar a relação entre uma variável resposta e uma ou mais variáveis independentes. A técnica permite que se estime as chances relacionadas à probabilidade da ocorrência de um evento de interesse. A regressão logística diferencia-se da regressão linear devido à natureza dicotômica da variável dependente e vem sendo utilizada em diversas áreas do conhecimento, incluindo estudos na área da saúde. O presente trabalho utilizou a técnica da regressão logística com o objetivo de analisar a associação entre Hipertensão Arterial e determinados fatores de risco. Os dados utilizados provém da Pesquisa Nacional de Saúde (PNS) do ano de 2019, realizada pelo Instituto Brasileiro de Geografia e Estatística (IBGE) em território nacional. Foram ajustados dois modelos, sendo o modelo final composto por sete variáveis com significância estatística de 5%. As técnicas de diagnóstico indicaram um ajuste adequado do modelo, bem como sua precisão para predições. Os resultados apontam que fatores como o aumento da idade, índice de massa corporal (IMC) alto e o diagnóstico positivo para diabetes aumentam as chances de um indivíduo ser hipertenso.Research, Society and Development2021-12-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/2296410.33448/rsd-v10i16.22964Research, Society and Development; Vol. 10 No. 16; e20101622964Research, Society and Development; Vol. 10 Núm. 16; e20101622964Research, Society and Development; v. 10 n. 16; e201016229642525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/22964/20655Copyright (c) 2021 Maria Beatriz Galdino da Silveira; Nyedja Fialho Morais Barbosa; Ana Patrícia Bastos Peixoto; Érika Fialho Morais Xavier; Sílvio Fernando Alves Xavier Júniorhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSilveira, Maria Beatriz Galdino da Barbosa, Nyedja Fialho Morais Peixoto, Ana Patrícia Bastos Xavier, Érika Fialho Morais Xavier Júnior, Sílvio Fernando Alves2021-12-20T11:03:07Zoai:ojs.pkp.sfu.ca:article/22964Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:41:58.552898Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Application of logistic regression in the analysis of risk factor associated with arterial hypertension Aplicación de la regresión logística en el análisis de factores de riesgo asociados a la hipertensión arterial Aplicação da regressão logística na análise dos dados dos fatores de risco associados à hipertensão arterial |
title |
Application of logistic regression in the analysis of risk factor associated with arterial hypertension |
spellingShingle |
Application of logistic regression in the analysis of risk factor associated with arterial hypertension Silveira, Maria Beatriz Galdino da Association Risk Factors Fitted model. Associação Fatores de risco Modelo ajustado. Asociación Factores de riesgo Modelo de ajuste. |
title_short |
Application of logistic regression in the analysis of risk factor associated with arterial hypertension |
title_full |
Application of logistic regression in the analysis of risk factor associated with arterial hypertension |
title_fullStr |
Application of logistic regression in the analysis of risk factor associated with arterial hypertension |
title_full_unstemmed |
Application of logistic regression in the analysis of risk factor associated with arterial hypertension |
title_sort |
Application of logistic regression in the analysis of risk factor associated with arterial hypertension |
author |
Silveira, Maria Beatriz Galdino da |
author_facet |
Silveira, Maria Beatriz Galdino da Barbosa, Nyedja Fialho Morais Peixoto, Ana Patrícia Bastos Xavier, Érika Fialho Morais Xavier Júnior, Sílvio Fernando Alves |
author_role |
author |
author2 |
Barbosa, Nyedja Fialho Morais Peixoto, Ana Patrícia Bastos Xavier, Érika Fialho Morais Xavier Júnior, Sílvio Fernando Alves |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Silveira, Maria Beatriz Galdino da Barbosa, Nyedja Fialho Morais Peixoto, Ana Patrícia Bastos Xavier, Érika Fialho Morais Xavier Júnior, Sílvio Fernando Alves |
dc.subject.por.fl_str_mv |
Association Risk Factors Fitted model. Associação Fatores de risco Modelo ajustado. Asociación Factores de riesgo Modelo de ajuste. |
topic |
Association Risk Factors Fitted model. Associação Fatores de risco Modelo ajustado. Asociación Factores de riesgo Modelo de ajuste. |
description |
Logistic regression is an important technique for data modeling when you want to analyze the relationship between a response variable and one or more independent variables. The technique allows one to estimate the chances related to the probability of occurrence of an event of interest. Logistic regression differs from linear regression due to the dichotomous nature of the dependent variable and has been used in several areas of knowledge, including studies in the health area. This study used the logistic regression technique to analyze the association between Hypertension and certain risk factors. The data used comes from the National Health Survey (PNS) for the year 2019, carried out by the Brazilian Institute of Geography and Statistics (IBGE) in the country. Two models were adjusted, the final model being composed of seven variables with a statistical significance of 5%. Diagnostic techniques indicated an adequate fit of the model, as well as its accuracy for predictions. The results show that factors such as increasing age, high body mass index (BMI) and a positive diagnosis for diabetes increase the chances of an individual being hypertensive. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-04 |
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/22964 10.33448/rsd-v10i16.22964 |
url |
https://rsdjournal.org/index.php/rsd/article/view/22964 |
identifier_str_mv |
10.33448/rsd-v10i16.22964 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/22964/20655 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
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. 10 No. 16; e20101622964 Research, Society and Development; Vol. 10 Núm. 16; e20101622964 Research, Society and Development; v. 10 n. 16; e20101622964 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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1797052758800465920 |