Early identification of older individuals at risk of mobility decline with machine learning

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dc.contributor.authorNascimento, Carla Ferreira do
dc.contributor.authorANDRE FILIPE DE MORAES BATISTA
dc.contributor.authorDuarte, Yeda Aparecida Oliveira
dc.contributor.authorChiavegatto Filho, Alexandre Dias Porto
dc.creatorNascimento, Carla Ferreira do
dc.creatorDuarte, Yeda Aparecida Oliveira
dc.creatorChiavegatto Filho, Alexandre Dias Porto
dc.date.accessioned2024-11-21T17:23:39Z
dc.date.available2024-11-21T17:23:39Z
dc.date.issued2022
dc.description.abstractBackground: : The early identification of individuals at risk of mobility decline can improve targeted strategies of prevention. Aims: : To evaluate the predictive performance of machine learning (ML) algorithms in identifying older in dividuals at risk of future mobility decline. Methods: : We used data from the SABE Study (Health, Well-being and Aging Study), a representative sample of people aged 60 years and more, living in the Municipality of São Paulo, Brazil. Mobility decline was assessed 5 years after admission in the study by self-reported difficulty to walk a block, climb steps, being able to stoop, crouch and kneel, or lifting or carrying weights greater than 5 kg. Popular machine learning algorithms were trained in 70% of the sample with 10-fold cross-validation, and predictive performance metrics were obtained from applying the trained algorithms to the other 30% (test set). Results: : Of the 1,615 individuals, 48% developed difficulty in at least one of the four tasks, 32% in stooping, crouching and kneeling, and 30% in carrying weights. The random forest algorithm had the best predictive performance for most outcomes. The tasks that the algorithm was able to predict with better performance were crouching and kneeling (AUC-ROC: 0.81[0.76–0.85]), and lifting or carrying weights (AUC-ROC: 0.80 [0.75–0.84]). Age was the most important variable for the algorithms, followed by education and back pain, according to the SHAP (SHapley Additive exPlanations) values. Conclusion: : Applications of ML algorithms are a promising tool to identify older patients at risk of mobility decline, with the potential of improving targeted preventive programs.pt
dc.formatDigital
dc.format.extent7 p.
dc.identifier.doi10.1016/j.archger.2022.104625
dc.identifier.issn0167-4943
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/7230
dc.language.isoInglês
dc.relation.ispartofArchives of Gerontology and Geriatrics
dc.subjectPrediction modelingen
dc.subjectDisabilityen
dc.subjectFunctional limitationen
dc.subjectDecision support toolen
dc.titleEarly identification of older individuals at risk of mobility decline with machine learning
dc.typejournal article
dspace.entity.typePublication
local.identifier.sourceUrihttps://www.sciencedirect.com/science/article/pii/S0167494322000061?via%3Dihub
local.publisher.countryNão Informado
local.subject.cnpqCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
local.subject.cnpqCIENCIAS DA SAUDE::SAUDE COLETIVA::SAUDE PUBLICA
local.subject.cnpqCIENCIAS DA SAUDE
local.subject.cnpqENGENHARIAS::ENGENHARIA BIOMEDICA
local.subject.cnpqENGENHARIAS::ENGENHARIA BIOMEDICA::BIOENGENHARIA
local.subject.cnpqCIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::PROBABILIDADE E ESTATISTICA APLICADAS
local.typeArtigo Científico
publicationvolume.volumeNumber100
relation.isAuthorOfPublicationb10d272e-98b2-4953-8e51-37aea3fde20c
relation.isAuthorOfPublication.latestForDiscoveryb10d272e-98b2-4953-8e51-37aea3fde20c
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