Predicting dental caries outcomes in young adults using machine learning approach.
Artificial intelliegence
Caries
Dental
Longitudinal
Machine learning
Prediction
Journal
BMC oral health
ISSN: 1472-6831
Titre abrégé: BMC Oral Health
Pays: England
ID NLM: 101088684
Informations de publication
Date de publication:
03 May 2024
03 May 2024
Historique:
received:
27
09
2023
accepted:
24
04
2024
medline:
4
5
2024
pubmed:
4
5
2024
entrez:
3
5
2024
Statut:
epublish
Résumé
To predict the dental caries outcomes in young adults from a set of longitudinally-obtained predictor variables and identify the most important predictors using machine learning techniques. This study was conducted using the Iowa Fluoride Study dataset. The predictor variables - sex, mother's education, family income, composite socio-economic status (SES), caries experience at ages 9, 13, and 17, and the cumulative estimates of risk and protective factors, including fluoride, dietary, and behavioral variables from ages 5-9, 9-13, 13-17, and 17-23 were used to predict the age 23 D The prevalence of cavitated level caries experience at age 23 (mean D Our machine learning model showed high accuracy and precision in the prediction of caries in young adults from a longitudinally-obtained predictor variables. Our model could, in the future, after further development and validation with other diverse population data, be used by public health specialists and policy-makers as a screening tool to identify the risk of caries in young adults and apply more targeted interventions. However, data from a more diverse population are needed to improve the quality and generalizability of caries prediction.
Identifiants
pubmed: 38702639
doi: 10.1186/s12903-024-04294-7
pii: 10.1186/s12903-024-04294-7
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
529Subventions
Organisme : NIDCR NIH HHS
ID : R01-DE09551, R01-DE12101, M01-RR00059, UL1-RR024979
Pays : United States
Informations de copyright
© 2024. The Author(s).
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