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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

2023Journal article Open access
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dc.contributor.authorForte, Pedro
dc.contributor.authorEncarnação, Samuel
dc.contributor.authorMonteiro, A.M.
dc.contributor.authorTeixeira, José Eduardo
dc.contributor.authorHattabi, Soukaina
dc.contributor.authorSortwell, Andrew
dc.contributor.authorBranquinho, Luís
dc.contributor.authorAmaro, Bruna
dc.contributor.authorSampaio, Tatiana
dc.contributor.authorFlores, Pedro Miguel
dc.contributor.authorSilva-Santos, Sandra
dc.contributor.authorRibeiro, Joana
dc.contributor.authorBatista, Amanda
dc.contributor.authorFerraz, Ricardo
dc.contributor.authorRodrigues, Filipe
dc.date.accessioned2023-10-20T11:03:20Z
dc.date.available2023-10-20T11:03:20Z
dc.date.issued2023
dc.description.abstractThe 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.pt_PT
dc.description.sponsorshipThis project was supported by the National Funds through the FCT—Portuguese Foundation for Science and Technology (project UIDB/04045/2021).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationForte, 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. 1-17pt_PT
dc.identifier.doi10.3390/bs13070522pt_PT
dc.identifier.issn2076-328X
dc.identifier.urihttp://hdl.handle.net/10198/28805
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherBehav. Sci.pt_PT
dc.relationUIDB/04045/2021pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectMetabolic syndromept_PT
dc.subjectInflammationpt_PT
dc.subjectImmunitypt_PT
dc.subjectEnergy expenditurept_PT
dc.subjectPhysical exercisept_PT
dc.subjectPublic healthpt_PT
dc.subjectResearch Subject Categories::INTERDISCIPLINARY RESEARCH AREAS::Sportspt_PT
dc.titleA 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 Policiespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage17pt_PT
oaire.citation.issue7pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleBehavioral Sciencespt_PT
oaire.citation.volume13pt_PT
person.familyNameForte
person.familyNameEncarnação
person.familyNameMonteiro
person.familyNameTeixeira
person.familyNameSampaio
person.givenNamePedro
person.givenNameSamuel
person.givenNameAntónio M.
person.givenNameJosé Eduardo
person.givenNameTatiana
person.identifier.ciencia-id351B-B16B-79C7
person.identifier.ciencia-id9416-E2F5-E660
person.identifier.ciencia-idC41C-6CCD-A1F0
person.identifier.ciencia-idD11C-9591-7A8A
person.identifier.orcid0000-0003-0184-6780
person.identifier.orcid0000-0003-2965-2777
person.identifier.orcid0000-0003-4467-1722
person.identifier.orcid0000-0003-4612-3623
person.identifier.orcid0000-0001-8548-2907
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication3ecc6d1b-07a4-40d7-81f4-df6fd7b3d5b0
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