A Deep Learning Neural Network to Classify Obesity Risk in Portuguese Adolescents Based on Physical Fitness Levels and Body Mass Index Percentiles: Insights for National Health Policies

Detalhes bibliográficos
Autor(a) principal: Forte, Pedro
Data de Publicação: 2023
Outros Autores: Encarnação, Samuel Gonçalves, Monteiro, A.M., Teixeira, José Eduardo, Hattabi, Soukaina, Sortwell, Andrew, Branquinho, Luís, Amaro, Bruna, Sampaio, Tatiana, Flores, Pedro Miguel, Silva-Santos, Sandra, Ribeiro, Joana, Batista, Amanda, Ferraz, Ricardo, Rodrigues, Filipe
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10198/28805
Resumo: The increasing prevalence of overweight and obesity among adults is a risk factor for many chronic diseases and death. In addition, obesity among children and adolescents has reached unprecedented levels and studies show that obese children and adolescents are more likely to become obese adults. Therefore, both the prevention and treatment of obesity in adolescents are critical. This study aimed to develop an artificial intelligence (AI) neural network (NNET) model that identifies the risk of obesity in Portuguese adolescents based on their body mass index (BMI) percentiles and levels of physical fitness. Using datasets from the FITescola® project, 654 adolescents aged between 10–19 years old, male: 334 (51%), female: n = 320 (49%), age 13.8 ± 2 years old, were selected to participate in a cross-sectional observational study. Physical fitness variables, age, and sex were used to identify the risk of obesity. The NNET had good accuracy (75%) and performance validation through the Receiver Operating Characteristic using the Area Under the Curve (ROC AUC = 64%) in identifying the risk of obesity in Portuguese adolescents based on the BMI percentiles. Correlations of moderate effect size were perceived for aerobic fitness (AF), upper limbs strength (ULS), and sprint time (ST), showing that some physical fitness variables contributed to the obesity risk of the adolescents. Our NNET presented a good accuracy (75%) and was validated with the K-Folds Cross-Validation (K-Folds CV) with good accuracy (71%) and ROC AUC (66%). According to the NNET, there was an increased risk of obesity linked to low physical fitness in Portuguese teenagers.
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spelling A Deep Learning Neural Network to Classify Obesity Risk in Portuguese Adolescents Based on Physical Fitness Levels and Body Mass Index Percentiles: Insights for National Health PoliciesMetabolic syndromeInflammationImmunityEnergy expenditurePhysical exercisePublic healthResearch Subject Categories::INTERDISCIPLINARY RESEARCH AREAS::SportsThe increasing prevalence of overweight and obesity among adults is a risk factor for many chronic diseases and death. In addition, obesity among children and adolescents has reached unprecedented levels and studies show that obese children and adolescents are more likely to become obese adults. Therefore, both the prevention and treatment of obesity in adolescents are critical. This study aimed to develop an artificial intelligence (AI) neural network (NNET) model that identifies the risk of obesity in Portuguese adolescents based on their body mass index (BMI) percentiles and levels of physical fitness. Using datasets from the FITescola® project, 654 adolescents aged between 10–19 years old, male: 334 (51%), female: n = 320 (49%), age 13.8 ± 2 years old, were selected to participate in a cross-sectional observational study. Physical fitness variables, age, and sex were used to identify the risk of obesity. The NNET had good accuracy (75%) and performance validation through the Receiver Operating Characteristic using the Area Under the Curve (ROC AUC = 64%) in identifying the risk of obesity in Portuguese adolescents based on the BMI percentiles. Correlations of moderate effect size were perceived for aerobic fitness (AF), upper limbs strength (ULS), and sprint time (ST), showing that some physical fitness variables contributed to the obesity risk of the adolescents. Our NNET presented a good accuracy (75%) and was validated with the K-Folds Cross-Validation (K-Folds CV) with good accuracy (71%) and ROC AUC (66%). According to the NNET, there was an increased risk of obesity linked to low physical fitness in Portuguese teenagers.This project was supported by the National Funds through the FCT—Portuguese Foundation for Science and Technology (project UIDB/04045/2021).Behav. Sci.Biblioteca Digital do IPBForte, PedroEncarnação, Samuel GonçalvesMonteiro, A.M.Teixeira, José EduardoHattabi, SoukainaSortwell, AndrewBranquinho, LuísAmaro, BrunaSampaio, TatianaFlores, Pedro MiguelSilva-Santos, SandraRibeiro, JoanaBatista, AmandaFerraz, RicardoRodrigues, Filipe2023-10-20T11:03:20Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/28805engForte, Pedro; Encarnação, Samuel Gonçalves; Monteiro, A.M.; Teixeira, José Eduardo; Hattabi, Soukaina; Sortwell, Andrew; Branquinho, Luís; Amaro, Bruna; Sampaio, Tatiana; Flores, Pedro Miguel; Silva-Santos, Sandra; Ribeiro, Joana; Batista, Amanda; Ferraz, Ricardo; Rodrigues, Filipe (2023). A Deep Learning Neural Network to Classify Obesity Risk in Portuguese Adolescents Based on Physical Fitness Levels and Body Mass Index Percentiles: Insights for National Health Policies. Behavioral Sciences. ISSN 2076-328X. 13:7, p. 1-172076-328X10.3390/bs13070522info: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:RCAAP2023-11-21T11:02:53Zoai:bibliotecadigital.ipb.pt:10198/28805Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:18:45.999888Repositório 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 A Deep Learning Neural Network to Classify Obesity Risk in Portuguese Adolescents Based on Physical Fitness Levels and Body Mass Index Percentiles: Insights for National Health Policies
title A Deep Learning Neural Network to Classify Obesity Risk in Portuguese Adolescents Based on Physical Fitness Levels and Body Mass Index Percentiles: Insights for National Health Policies
spellingShingle A Deep Learning Neural Network to Classify Obesity Risk in Portuguese Adolescents Based on Physical Fitness Levels and Body Mass Index Percentiles: Insights for National Health Policies
Forte, Pedro
Metabolic syndrome
Inflammation
Immunity
Energy expenditure
Physical exercise
Public health
Research Subject Categories::INTERDISCIPLINARY RESEARCH AREAS::Sports
title_short A Deep Learning Neural Network to Classify Obesity Risk in Portuguese Adolescents Based on Physical Fitness Levels and Body Mass Index Percentiles: Insights for National Health Policies
title_full A Deep Learning Neural Network to Classify Obesity Risk in Portuguese Adolescents Based on Physical Fitness Levels and Body Mass Index Percentiles: Insights for National Health Policies
title_fullStr A Deep Learning Neural Network to Classify Obesity Risk in Portuguese Adolescents Based on Physical Fitness Levels and Body Mass Index Percentiles: Insights for National Health Policies
title_full_unstemmed A Deep Learning Neural Network to Classify Obesity Risk in Portuguese Adolescents Based on Physical Fitness Levels and Body Mass Index Percentiles: Insights for National Health Policies
title_sort A Deep Learning Neural Network to Classify Obesity Risk in Portuguese Adolescents Based on Physical Fitness Levels and Body Mass Index Percentiles: Insights for National Health Policies
author Forte, Pedro
author_facet Forte, Pedro
Encarnação, Samuel Gonçalves
Monteiro, A.M.
Teixeira, José Eduardo
Hattabi, Soukaina
Sortwell, Andrew
Branquinho, Luís
Amaro, Bruna
Sampaio, Tatiana
Flores, Pedro Miguel
Silva-Santos, Sandra
Ribeiro, Joana
Batista, Amanda
Ferraz, Ricardo
Rodrigues, Filipe
author_role author
author2 Encarnação, Samuel Gonçalves
Monteiro, A.M.
Teixeira, José Eduardo
Hattabi, Soukaina
Sortwell, Andrew
Branquinho, Luís
Amaro, Bruna
Sampaio, Tatiana
Flores, Pedro Miguel
Silva-Santos, Sandra
Ribeiro, Joana
Batista, Amanda
Ferraz, Ricardo
Rodrigues, Filipe
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Biblioteca Digital do IPB
dc.contributor.author.fl_str_mv Forte, Pedro
Encarnação, Samuel Gonçalves
Monteiro, A.M.
Teixeira, José Eduardo
Hattabi, Soukaina
Sortwell, Andrew
Branquinho, Luís
Amaro, Bruna
Sampaio, Tatiana
Flores, Pedro Miguel
Silva-Santos, Sandra
Ribeiro, Joana
Batista, Amanda
Ferraz, Ricardo
Rodrigues, Filipe
dc.subject.por.fl_str_mv Metabolic syndrome
Inflammation
Immunity
Energy expenditure
Physical exercise
Public health
Research Subject Categories::INTERDISCIPLINARY RESEARCH AREAS::Sports
topic Metabolic syndrome
Inflammation
Immunity
Energy expenditure
Physical exercise
Public health
Research Subject Categories::INTERDISCIPLINARY RESEARCH AREAS::Sports
description The increasing prevalence of overweight and obesity among adults is a risk factor for many chronic diseases and death. In addition, obesity among children and adolescents has reached unprecedented levels and studies show that obese children and adolescents are more likely to become obese adults. Therefore, both the prevention and treatment of obesity in adolescents are critical. This study aimed to develop an artificial intelligence (AI) neural network (NNET) model that identifies the risk of obesity in Portuguese adolescents based on their body mass index (BMI) percentiles and levels of physical fitness. Using datasets from the FITescola® project, 654 adolescents aged between 10–19 years old, male: 334 (51%), female: n = 320 (49%), age 13.8 ± 2 years old, were selected to participate in a cross-sectional observational study. Physical fitness variables, age, and sex were used to identify the risk of obesity. The NNET had good accuracy (75%) and performance validation through the Receiver Operating Characteristic using the Area Under the Curve (ROC AUC = 64%) in identifying the risk of obesity in Portuguese adolescents based on the BMI percentiles. Correlations of moderate effect size were perceived for aerobic fitness (AF), upper limbs strength (ULS), and sprint time (ST), showing that some physical fitness variables contributed to the obesity risk of the adolescents. Our NNET presented a good accuracy (75%) and was validated with the K-Folds Cross-Validation (K-Folds CV) with good accuracy (71%) and ROC AUC (66%). According to the NNET, there was an increased risk of obesity linked to low physical fitness in Portuguese teenagers.
publishDate 2023
dc.date.none.fl_str_mv 2023-10-20T11:03:20Z
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
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10198/28805
url http://hdl.handle.net/10198/28805
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Forte, Pedro; Encarnação, Samuel Gonçalves; Monteiro, A.M.; Teixeira, José Eduardo; Hattabi, Soukaina; Sortwell, Andrew; Branquinho, Luís; Amaro, Bruna; Sampaio, Tatiana; Flores, Pedro Miguel; Silva-Santos, Sandra; Ribeiro, Joana; Batista, Amanda; Ferraz, Ricardo; Rodrigues, Filipe (2023). A Deep Learning Neural Network to Classify Obesity Risk in Portuguese Adolescents Based on Physical Fitness Levels and Body Mass Index Percentiles: Insights for National Health Policies. Behavioral Sciences. ISSN 2076-328X. 13:7, p. 1-17
2076-328X
10.3390/bs13070522
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dc.publisher.none.fl_str_mv Behav. Sci.
publisher.none.fl_str_mv Behav. Sci.
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