The use of Artificial Neural Networks in the diagnosis of manageable factors on Brazilian Primary Health Care
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/11740 |
Resumo: | The social determinants of health are relevant in the main strategies of Primary Health Care. However, it is known the difficulties of the health sector to overcome the factors that negatively interfere with the health of the population. Thus, it was aimed to create a computer model to present in detail the factors that somehow are related to the Primary Health Care, enabling public health managers to make decisions efficiently. Using artificial neural networks, it was possible to create a classifier model that could show which variables are related to the efficiency in Primary Care and which lead to inefficiency. Moreover, it was used the NICeSim simulator as a tool to evaluate the behavior of each variable identified as relevant to the efficiency in Primary Care of cities. The results demonstrate that the created model was superior to previously proposed models. Furthermore, our model has been demonstrated to be very effective in identifying variables that affect Primary Health. The created model shows that factors, such as illiteracy and welfare programs, considerably affect the efficiency of health care, reinforcing the argument that the focus of the public policies should be dealt in an intersectoral way, improving the factors that positively influence the population health. |
id |
UNIFEI_38ce6fea0450641cc2e09c371b21f60f |
---|---|
oai_identifier_str |
oai:ojs.pkp.sfu.ca:article/11740 |
network_acronym_str |
UNIFEI |
network_name_str |
Research, Society and Development |
repository_id_str |
|
spelling |
The use of Artificial Neural Networks in the diagnosis of manageable factors on Brazilian Primary Health CareEl uso de Redes Neuronales Artificiales en el diagnóstico de factores de gestión en Atención Primaria de Salud en BrasilO uso de Redes Neurais Artificiais no diagnóstico de fatores gerenciáveis na Atenção Primária à Saúde no BrasilArtificial neural networksTechnology for management in public healthEfficiency.Redes neuronalesTecnología para la gestión en salud públicaEficiencia.Redes neuraisTecnologia para gestão em saúde públicaEficiência.The social determinants of health are relevant in the main strategies of Primary Health Care. However, it is known the difficulties of the health sector to overcome the factors that negatively interfere with the health of the population. Thus, it was aimed to create a computer model to present in detail the factors that somehow are related to the Primary Health Care, enabling public health managers to make decisions efficiently. Using artificial neural networks, it was possible to create a classifier model that could show which variables are related to the efficiency in Primary Care and which lead to inefficiency. Moreover, it was used the NICeSim simulator as a tool to evaluate the behavior of each variable identified as relevant to the efficiency in Primary Care of cities. The results demonstrate that the created model was superior to previously proposed models. Furthermore, our model has been demonstrated to be very effective in identifying variables that affect Primary Health. The created model shows that factors, such as illiteracy and welfare programs, considerably affect the efficiency of health care, reinforcing the argument that the focus of the public policies should be dealt in an intersectoral way, improving the factors that positively influence the population health.Los determinantes sociales de la salud son relevantes en las principales estrategias de Atención Primaria de Salud. Sin embargo, se conocen las dificultades del sector de la salud para superar los factores que interfieren negativamente con la salud de la población. Por lo tanto, su objetivo fue crear un modelo de computadora para presentar en detalle los factores que de alguna manera están relacionados con la Atención Primaria de Salud, permitiendo a los gerentes de salud pública tomar decisiones de manera eficiente. Utilizando redes neuronales, fue posible crear un modelo clasificador que pudiera mostrar qué variables están relacionadas con la eficiencia en Atención Primaria y cuáles conducen a la ineficiencia. Los resultados demuestran que el modelo creado fue superior a los modelos que ya se utilizaron una vez que mostraron una mayor precisión en la ubicación de las variables que afectan la salud primaria. Concluyó que factores como el analfabetismo y los programas de asistencia social afectan considerablemente la eficiencia de la atención de la salud.Os determinantes sociais da saúde são relevantes nas principais estratégias da Atenção Primária à Saúde. No entanto, sabe-se das dificuldades do setor saúde em superar os fatores que interferem negativamente na saúde da população. Assim, objetivou-se criar um modelo computacional para apresentar em detalhes os fatores que de alguma forma estão relacionados à Atenção Primária à Saúde, possibilitando aos gestores de saúde pública a tomada de decisões de forma eficiente. Utilizando redes neurais, foi possível criar um modelo de classificador que pudesse mostrar quais variáveis estão relacionadas à eficiência na Atenção Básica e quais levam à ineficiência. Os resultados demonstram que o modelo criado foi superior aos modelos já utilizados, uma vez que mostrou maior precisão na localização das variáveis que afetam a Saúde Primária. Concluiu que fatores como analfabetismo e programas de bem-estar afetam consideravelmente a eficiência dos cuidados de saúde.Research, Society and Development2021-01-17info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/1174010.33448/rsd-v10i1.11740Research, Society and Development; Vol. 10 No. 1; e31010111740Research, Society and Development; Vol. 10 Núm. 1; e31010111740Research, Society and Development; v. 10 n. 1; e310101117402525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIenghttps://rsdjournal.org/index.php/rsd/article/view/11740/14363Copyright (c) 2021 Kerla Fabiana Dias Cabral; Fábio Ribeiro Cerqueira; Rodrigo Siqueira -Batista; Marco Aurélio Marques Ferreira; Bruna Rodrigues de Freitashttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessCabral, Kerla Fabiana DiasCerqueira, Fábio Ribeiro Siqueira-Batista, RodrigoFerreira, Marco Aurélio Marques Freitas, Bruna Rodrigues de 2021-02-20T21:19:23Zoai:ojs.pkp.sfu.ca:article/11740Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:33:28.694573Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
The use of Artificial Neural Networks in the diagnosis of manageable factors on Brazilian Primary Health Care El uso de Redes Neuronales Artificiales en el diagnóstico de factores de gestión en Atención Primaria de Salud en Brasil O uso de Redes Neurais Artificiais no diagnóstico de fatores gerenciáveis na Atenção Primária à Saúde no Brasil |
title |
The use of Artificial Neural Networks in the diagnosis of manageable factors on Brazilian Primary Health Care |
spellingShingle |
The use of Artificial Neural Networks in the diagnosis of manageable factors on Brazilian Primary Health Care Cabral, Kerla Fabiana Dias Artificial neural networks Technology for management in public health Efficiency. Redes neuronales Tecnología para la gestión en salud pública Eficiencia. Redes neurais Tecnologia para gestão em saúde pública Eficiência. |
title_short |
The use of Artificial Neural Networks in the diagnosis of manageable factors on Brazilian Primary Health Care |
title_full |
The use of Artificial Neural Networks in the diagnosis of manageable factors on Brazilian Primary Health Care |
title_fullStr |
The use of Artificial Neural Networks in the diagnosis of manageable factors on Brazilian Primary Health Care |
title_full_unstemmed |
The use of Artificial Neural Networks in the diagnosis of manageable factors on Brazilian Primary Health Care |
title_sort |
The use of Artificial Neural Networks in the diagnosis of manageable factors on Brazilian Primary Health Care |
author |
Cabral, Kerla Fabiana Dias |
author_facet |
Cabral, Kerla Fabiana Dias Cerqueira, Fábio Ribeiro Siqueira-Batista, Rodrigo Ferreira, Marco Aurélio Marques Freitas, Bruna Rodrigues de |
author_role |
author |
author2 |
Cerqueira, Fábio Ribeiro Siqueira-Batista, Rodrigo Ferreira, Marco Aurélio Marques Freitas, Bruna Rodrigues de |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Cabral, Kerla Fabiana Dias Cerqueira, Fábio Ribeiro Siqueira-Batista, Rodrigo Ferreira, Marco Aurélio Marques Freitas, Bruna Rodrigues de |
dc.subject.por.fl_str_mv |
Artificial neural networks Technology for management in public health Efficiency. Redes neuronales Tecnología para la gestión en salud pública Eficiencia. Redes neurais Tecnologia para gestão em saúde pública Eficiência. |
topic |
Artificial neural networks Technology for management in public health Efficiency. Redes neuronales Tecnología para la gestión en salud pública Eficiencia. Redes neurais Tecnologia para gestão em saúde pública Eficiência. |
description |
The social determinants of health are relevant in the main strategies of Primary Health Care. However, it is known the difficulties of the health sector to overcome the factors that negatively interfere with the health of the population. Thus, it was aimed to create a computer model to present in detail the factors that somehow are related to the Primary Health Care, enabling public health managers to make decisions efficiently. Using artificial neural networks, it was possible to create a classifier model that could show which variables are related to the efficiency in Primary Care and which lead to inefficiency. Moreover, it was used the NICeSim simulator as a tool to evaluate the behavior of each variable identified as relevant to the efficiency in Primary Care of cities. The results demonstrate that the created model was superior to previously proposed models. Furthermore, our model has been demonstrated to be very effective in identifying variables that affect Primary Health. The created model shows that factors, such as illiteracy and welfare programs, considerably affect the efficiency of health care, reinforcing the argument that the focus of the public policies should be dealt in an intersectoral way, improving the factors that positively influence the population health. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-17 |
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/11740 10.33448/rsd-v10i1.11740 |
url |
https://rsdjournal.org/index.php/rsd/article/view/11740 |
identifier_str_mv |
10.33448/rsd-v10i1.11740 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/11740/14363 |
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. 1; e31010111740 Research, Society and Development; Vol. 10 Núm. 1; e31010111740 Research, Society and Development; v. 10 n. 1; e31010111740 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 |
_version_ |
1797052831852658688 |