The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis

Detalhes bibliográficos
Autor(a) principal: Cordeiro, JR
Data de Publicação: 2023
Outros Autores: Mosca, S, Correia-Costa, A, Ferreira, C, Pimenta, J, Correia-Costa, L, Barros, H, Postolache, O
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/154634
Resumo: The increasing prevalence of overweight and obesity is a worldwide problem, with several well-known consequences that might start to develop early in life during childhood. The present research based on data from children that have been followed since birth in a previously established cohort study (Generation XXI, Porto, Portugal), taking advantage of State-of-the-Art (SoA) data science techniques and methods, including Neural Architecture Search (NAS), explainable Artificial Intelligence (XAI), and Deep Learning (DL), aimed to explore the hidden value of data, namely on electrocardiogram (ECG) records performed during follow-up visits. The combination of these techniques allowed us to clarify subtle cardiovascular changes already present at 10 years of age, which are evident from ECG analysis and probably induced by the presence of obesity. The proposed novel combination of new methodologies and techniques is discussed, as well as their applicability in other health domains.
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spelling The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science AnalysisThe increasing prevalence of overweight and obesity is a worldwide problem, with several well-known consequences that might start to develop early in life during childhood. The present research based on data from children that have been followed since birth in a previously established cohort study (Generation XXI, Porto, Portugal), taking advantage of State-of-the-Art (SoA) data science techniques and methods, including Neural Architecture Search (NAS), explainable Artificial Intelligence (XAI), and Deep Learning (DL), aimed to explore the hidden value of data, namely on electrocardiogram (ECG) records performed during follow-up visits. The combination of these techniques allowed us to clarify subtle cardiovascular changes already present at 10 years of age, which are evident from ECG analysis and probably induced by the presence of obesity. The proposed novel combination of new methodologies and techniques is discussed, as well as their applicability in other health domains.MDPI20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10216/154634eng2227-906710.3390/children10101655Cordeiro, JRMosca, SCorreia-Costa, AFerreira, CPimenta, JCorreia-Costa, LBarros, HPostolache, Oinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T14:27:33Zoai:repositorio-aberto.up.pt:10216/154634Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:01:42.868717Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis
title The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis
spellingShingle The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis
Cordeiro, JR
title_short The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis
title_full The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis
title_fullStr The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis
title_full_unstemmed The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis
title_sort The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis
author Cordeiro, JR
author_facet Cordeiro, JR
Mosca, S
Correia-Costa, A
Ferreira, C
Pimenta, J
Correia-Costa, L
Barros, H
Postolache, O
author_role author
author2 Mosca, S
Correia-Costa, A
Ferreira, C
Pimenta, J
Correia-Costa, L
Barros, H
Postolache, O
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Cordeiro, JR
Mosca, S
Correia-Costa, A
Ferreira, C
Pimenta, J
Correia-Costa, L
Barros, H
Postolache, O
description The increasing prevalence of overweight and obesity is a worldwide problem, with several well-known consequences that might start to develop early in life during childhood. The present research based on data from children that have been followed since birth in a previously established cohort study (Generation XXI, Porto, Portugal), taking advantage of State-of-the-Art (SoA) data science techniques and methods, including Neural Architecture Search (NAS), explainable Artificial Intelligence (XAI), and Deep Learning (DL), aimed to explore the hidden value of data, namely on electrocardiogram (ECG) records performed during follow-up visits. The combination of these techniques allowed us to clarify subtle cardiovascular changes already present at 10 years of age, which are evident from ECG analysis and probably induced by the presence of obesity. The proposed novel combination of new methodologies and techniques is discussed, as well as their applicability in other health domains.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv https://hdl.handle.net/10216/154634
url https://hdl.handle.net/10216/154634
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2227-9067
10.3390/children10101655
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dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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