Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study

Detalhes bibliográficos
Autor(a) principal: Paula, Daniela Polessa
Data de Publicação: 2022
Outros Autores: Aguiar, Odaleia Barbosa, Marques, Larissa Pruner, Bensenor, Isabela, Suemoto, Claudia Kimie, Fonseca, Maria de Jesus Mendes da, Griep, Rosane Härter
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da FIOCRUZ (ARCA)
Texto Completo: https://www.arca.fiocruz.br/handle/icict/55289
Resumo: Instituto Brasileiro de Geografia e Estatística. Escola Nacional de Ciências Estatísticas, Rio de Janeiro, RJ, Brasil.
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spelling Paula, Daniela PolessaAguiar, Odaleia BarbosaMarques, Larissa PrunerBensenor, IsabelaSuemoto, Claudia KimieFonseca, Maria de Jesus Mendes daGriep, Rosane Härter2022-10-25T13:27:45Z2022-10-25T13:27:45Z2022PAULA, Daniela Polessa et al. Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study. Plos One, v. 17, n. 10, e0275619, p. 1 - 14, Oct. 2022.1932-6203https://www.arca.fiocruz.br/handle/icict/5528910.1371/journal.pone.0275619engPublic Library of ScienceEstudo Elsa-BrasilComparando algoritmosPrevisão de multimorbidadeAprendizado de máquinaElsa-Brasil studyComparing machineLearning algorithmsMultimorbidity predictionComparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil studyinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleInstituto Brasileiro de Geografia e Estatística. Escola Nacional de Ciências Estatísticas, Rio de Janeiro, RJ, Brasil.Universidade do Estado do Rio de Janeiro. Instituto de Nutrição. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Escola Nacional de Saúde Pública. Rio de Janeiro, RJ, Brasil.Universidade de São Paulo. Faculdade de Medicina da Universidade de São Paulo & Hospital Universitário. Departamento de Medicina Interna. São Paulo, SP, Brasil.Universidade de São Paulo. Faculdade de Medicina. Departamento de Clínica Médica. Divisão de Geriatriia. São Paulo, SP, Brasil.Fundação Oswaldo Cruz. Escola Nacional de Saúde Pública. Departamento de Epidemiologia. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Educação em Saúde e Meio Ambiente. Rio de Janeiro, RJ, Brasil.Background Multimorbidity is a worldwide concern related to greater disability, worse quality of life, and mortality. The early prediction is crucial for preventive strategies design and integrative medical practice. However, knowledge about how to predict multimorbidity is limited, possibly due to the complexity involved in predicting multiple chronic diseases. Methods In this study, we present the use of a machine learning approach to build cost-effective multimorbidity prediction models. Based on predictors easily obtainable in clinical practice (sociodemographic, clinical, family disease history and lifestyle), we build and compared the performance of seven multilabel classifiers (multivariate random forest, and classifier chain, binary relevance and binary dependence, with random forest and support vector machine as base classifiers), using a sample of 15105 participants from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). We developed a web application for the building and use of prediction models. Results Classifier chain with random forest as base classifier performed better (accuracy = 0.34, subset accuracy = 0.15, and Hamming Loss = 0.16). For different feature sets, random forest based classifiers outperformed those based on support vector machine. BMI, blood pressure, sex, and age were the features most relevant to multimorbidity prediction. Conclusions - Our results support the choice of random forest based classifiers for multimorbidity prediction.info:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; charset=utf-82991https://www.arca.fiocruz.br/bitstream/icict/55289/1/license.txt5a560609d32a3863062d77ff32785d58MD51ORIGINALRosaneHGriep_MariaMFonseca_etal_IOC_2022.pdfRosaneHGriep_MariaMFonseca_etal_IOC_2022.pdfapplication/pdf930544https://www.arca.fiocruz.br/bitstream/icict/55289/2/RosaneHGriep_MariaMFonseca_etal_IOC_2022.pdf3b9c9231e11efee1e255ea96c37ce92aMD52icict/552892023-09-14 16:01:58.857oai:www.arca.fiocruz.br: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ório InstitucionalPUBhttps://www.arca.fiocruz.br/oai/requestrepositorio.arca@fiocruz.bropendoar:21352023-09-14T19:01:58Repositório Institucional da FIOCRUZ (ARCA) - Fundação Oswaldo Cruz (FIOCRUZ)false
dc.title.en_US.fl_str_mv Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study
title Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study
spellingShingle Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study
Paula, Daniela Polessa
Estudo Elsa-Brasil
Comparando algoritmos
Previsão de multimorbidade
Aprendizado de máquina
Elsa-Brasil study
Comparing machine
Learning algorithms
Multimorbidity prediction
title_short Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study
title_full Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study
title_fullStr Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study
title_full_unstemmed Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study
title_sort Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study
author Paula, Daniela Polessa
author_facet Paula, Daniela Polessa
Aguiar, Odaleia Barbosa
Marques, Larissa Pruner
Bensenor, Isabela
Suemoto, Claudia Kimie
Fonseca, Maria de Jesus Mendes da
Griep, Rosane Härter
author_role author
author2 Aguiar, Odaleia Barbosa
Marques, Larissa Pruner
Bensenor, Isabela
Suemoto, Claudia Kimie
Fonseca, Maria de Jesus Mendes da
Griep, Rosane Härter
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Paula, Daniela Polessa
Aguiar, Odaleia Barbosa
Marques, Larissa Pruner
Bensenor, Isabela
Suemoto, Claudia Kimie
Fonseca, Maria de Jesus Mendes da
Griep, Rosane Härter
dc.subject.other.en_US.fl_str_mv Estudo Elsa-Brasil
Comparando algoritmos
Previsão de multimorbidade
Aprendizado de máquina
topic Estudo Elsa-Brasil
Comparando algoritmos
Previsão de multimorbidade
Aprendizado de máquina
Elsa-Brasil study
Comparing machine
Learning algorithms
Multimorbidity prediction
dc.subject.en.en_US.fl_str_mv Elsa-Brasil study
Comparing machine
Learning algorithms
Multimorbidity prediction
description Instituto Brasileiro de Geografia e Estatística. Escola Nacional de Ciências Estatísticas, Rio de Janeiro, RJ, Brasil.
publishDate 2022
dc.date.accessioned.fl_str_mv 2022-10-25T13:27:45Z
dc.date.available.fl_str_mv 2022-10-25T13:27:45Z
dc.date.issued.fl_str_mv 2022
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.citation.fl_str_mv PAULA, Daniela Polessa et al. Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study. Plos One, v. 17, n. 10, e0275619, p. 1 - 14, Oct. 2022.
dc.identifier.uri.fl_str_mv https://www.arca.fiocruz.br/handle/icict/55289
dc.identifier.issn.en_US.fl_str_mv 1932-6203
dc.identifier.doi.none.fl_str_mv 10.1371/journal.pone.0275619
identifier_str_mv PAULA, Daniela Polessa et al. Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study. Plos One, v. 17, n. 10, e0275619, p. 1 - 14, Oct. 2022.
1932-6203
10.1371/journal.pone.0275619
url https://www.arca.fiocruz.br/handle/icict/55289
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Public Library of Science
publisher.none.fl_str_mv Public Library of Science
dc.source.none.fl_str_mv reponame:Repositório Institucional da FIOCRUZ (ARCA)
instname:Fundação Oswaldo Cruz (FIOCRUZ)
instacron:FIOCRUZ
instname_str Fundação Oswaldo Cruz (FIOCRUZ)
instacron_str FIOCRUZ
institution FIOCRUZ
reponame_str Repositório Institucional da FIOCRUZ (ARCA)
collection Repositório Institucional da FIOCRUZ (ARCA)
bitstream.url.fl_str_mv https://www.arca.fiocruz.br/bitstream/icict/55289/1/license.txt
https://www.arca.fiocruz.br/bitstream/icict/55289/2/RosaneHGriep_MariaMFonseca_etal_IOC_2022.pdf
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