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Machine learning prediction of adolescent obesity using physical fitness data

2026Artigo científico Acesso aberto
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datacite.subject.fosCiências Naturais
datacite.subject.sdg03:Saúde de Qualidade
dc.contributor.authorSampaio, Tatiana
dc.contributor.authorEncarnação, Samuel
dc.contributor.authorAmaro, Bruna
dc.contributor.authorRibeiro, Joana
dc.contributor.authorBranquinho, Luís
dc.contributor.authorMonteiro, António M.
dc.contributor.authorTeixeira, José Eduardo
dc.contributor.authorHattabi, Soukaina
dc.contributor.authorSortwell, Andrew
dc.contributor.authorLeite, Luciano Bernardes
dc.contributor.authorMalheiro, Alexandra
dc.contributor.authorRodrigues, Pedro M.
dc.contributor.authorBeat Knechtle
dc.contributor.authorFlores, Pedro Miguel
dc.contributor.authorForte, Pedro
dc.date.accessioned2026-02-25T16:54:08Z
dc.date.available2026-02-25T16:54:08Z
dc.date.issued2026
dc.description.abstractThe escalating prevalence of obesity among adolescents has emerged as a critical global public health challenge. Machine learning techniques have been used to predict obesity in adolescents. This study aimed to develop and validate a robust obesity prediction model for adolescents using this hybrid approach, leveraging data from a diverse cross-sectional population-based study. The hybrid method combines statistical inference with non-linear machine learning to enhance prediction accuracy. Physical fitness data were collected from the FITescola® tests. Multiple tests were employed to evaluate physical fitness. Multiple Poisson's multiple regression method was applied to identify the most predictive variables set of the adolescent's body mass index (BMI) classification. The model's goodness-of-fit statistics indicate a strong fit, with a log-likelihood of 􀀀 8068.6 and a Pseudo R-squared value of 0.8853, where the aerobic fitness (AF), upper limb strength (ULS) and lower limb flexibility (LLF) presented an inverse association with the adolescent's BMI. In contrast the adolescent's core strength presented a positive association with their body mass. The random forest regression showed that an average of 35 repetition on the yo-yo test predicted a healthy BMI percentile [predBMIperc = 0.31]. In addition, the model presented good validity [MAE = 0.36, MSE = 0.20, RMSE = 0.45, R2 = 0.54]. The model's strong fit and accurate random forest regression's predictions suggest that physical fitness components, such as aerobic fitness, upper limb strength, lower limb power, and core strength, play a significant role in obesity risk among adolescents.por
dc.identifier.citationSampaio, Tatiana; Encarnação, Samuel; Amaro, Bruna; Ribeiro, Joana; Branquinho, Luís; Monteiro, António M.; Teixeira, José Eduardo; Hattabi, Soukaina; Sortwell, Andrew; Leite, Luciano Bernardes; Malheiro, Alexandra; Rodrigues, Pedro M.; Knechtle, Beat; Flores, Pedro Miguel; Forte, Pedro (2026). Machine learning prediction of adolescent obesity using physical fitness data. Obesity Medicine. ISSN 2451-8476. 59, p. 1-9
dc.identifier.doi10.1016/j.obmed.2025.100679
dc.identifier.issn2451-8476
dc.identifier.urihttp://hdl.handle.net/10198/35866
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relation.ispartofseries100679
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleMachine learning prediction of adolescent obesity using physical fitness dataeng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage9
oaire.citation.startPage1
oaire.citation.titleObesity Medicine
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameMonteiro
person.familyNameTeixeira
person.familyNameRodrigues
person.familyNameForte
person.givenNameAntónio M.
person.givenNameJosé Eduardo
person.givenNamePedro M.
person.givenNamePedro
person.identifier.ciencia-idC41C-6CCD-A1F0
person.identifier.ciencia-idD11C-9591-7A8A
person.identifier.ciencia-idC919-D413-5E83
person.identifier.ciencia-id351B-B16B-79C7
person.identifier.orcid0000-0003-4467-1722
person.identifier.orcid0000-0003-4612-3623
person.identifier.orcid0009-0002-4920-7398
person.identifier.orcid0000-0003-0184-6780
person.identifier.scopus-author-id56108282100
relation.isAuthorOfPublication5b5d8601-e683-42d5-a1b5-c8e29a4e0a41
relation.isAuthorOfPublication79042f92-ce53-4b79-a33f-76ac63c55b8d
relation.isAuthorOfPublication55f77b39-347e-4ad5-97c6-def43d656014
relation.isAuthorOfPublication3ecc6d1b-07a4-40d7-81f4-df6fd7b3d5b0
relation.isAuthorOfPublication.latestForDiscovery5b5d8601-e683-42d5-a1b5-c8e29a4e0a41

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