Predictors of tooth loss: A machine learning approach

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dc.contributor.authorElani, Hawazin W.
dc.contributor.authorANDRE FILIPE DE MORAES BATISTA
dc.contributor.authorW. Murray Thomson
dc.contributor.authorKawachi, Ichiro
dc.contributor.authorChiavegatto Filho, Alexandre D. P.
dc.creatorElani, Hawazin W.
dc.creatorW. Murray Thomson
dc.creatorKawachi, Ichiro
dc.creatorChiavegatto Filho, Alexandre D. P.
dc.date.accessioned2024-11-21T23:08:33Z
dc.date.available2024-11-21T23:08:33Z
dc.date.issued2021
dc.description.abstractIntroduction Little is understood about the socioeconomic predictors of tooth loss, a condition that can negatively impact individual’s quality of life. The goal of this study is to develop a machine-learning algorithm to predict complete and incremental tooth loss among adults and to compare the predictive performance of these models. Methods We used data from the National Health and Nutrition Examination Survey from 2011 to 2014. We developed multiple machine-learning algorithms and assessed their predictive performances by examining the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values. Results The extreme gradient boosting trees presented the highest performance in the prediction of edentulism (AUC = 88.7%; 95%CI: 87.1, 90.2), the absence of a functional dentition (AUC = 88.3% 95%CI: 87.3,89.3) and for predicting missing any tooth (AUC = 83.2%; 95%CI, 82.0, 84.4). Although, as expected, age and routine dental care emerged as strong predictors of tooth loss, the machine learning approach identified additional predictors, including socioeconomic conditions. Indeed, the performance of models incorporating socioeconomic characteristics was better at predicting tooth loss than those relying on clinical dental indicators alone. Conclusions Future application of machine-learning algorithm, with longitudinal cohorts, for identification of individuals at risk for tooth loss could assist clinicians to prioritize interventions directed toward the prevention of tooth loss.en
dc.formatDigital
dc.format.extent14 p.
dc.identifier.doi10.1371/journal.pone.0252873
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/7235
dc.language.isoInglês
dc.relation.ispartofPLoS ONE
dc.titlePredictors of tooth loss: A machine learning approach
dc.typejournal article
dspace.entity.typePublication
local.identifier.sourceUrihttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0252873
local.publisher.countryNão Informado
local.subject.cnpqCIENCIAS DA SAUDE::ODONTOLOGIA
local.subject.cnpqCIENCIAS DA SAUDE::SAUDE COLETIVA
local.subject.cnpqCIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
local.subject.cnpqCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
local.subject.cnpqENGENHARIAS::ENGENHARIA BIOMEDICA
local.typeArtigo Científico
publicationissue.issueNumber6
publicationvolume.volumeNumber16
relation.isAuthorOfPublicationb10d272e-98b2-4953-8e51-37aea3fde20c
relation.isAuthorOfPublication.latestForDiscoveryb10d272e-98b2-4953-8e51-37aea3fde20c
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