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Details

Autor(en) / Beteiligte
Titel
Development of short forms for screening children's dental caries and urgent treatment needs using item response theory and machine learning methods
Ist Teil von
  • PloS one, 2024-03, Vol.19 (3), p.e0299947-e0299947
Ort / Verlag
United States: Public Library of Science
Erscheinungsjahr
2024
Quelle
Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
Beschreibungen/Notizen
  • Surveys can assist in screening oral diseases in populations to enhance the early detection of disease and intervention strategies for children in need. This paper aims to develop short forms of child-report and proxy-report survey screening instruments for active dental caries and urgent treatment needs in school-age children. This cross-sectional study recruited 497 distinct dyads of children aged 8-17 and their parents between 2015 to 2019 from 14 dental clinics and private practices in Los Angeles County. We evaluated responses to 88 child-reported and 64 proxy-reported oral health questions to select and calibrate short forms using Item Response Theory. Seven classical Machine Learning algorithms were employed to predict children's active caries and urgent treatment needs using the short forms together with family demographic variables. The candidate algorithms include CatBoost, Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes, Neural Network, Random Forest, and Support Vector Machine. Predictive performance was assessed using repeated 5-fold nested cross-validations. We developed and calibrated four ten-item short forms. Naïve Bayes outperformed other algorithms with the highest median of cross-validated area under the ROC curve. The means of best testing sensitivities and specificities using both child-reported and proxy-reported responses were 0.84 and 0.30 for active caries, and 0.81 and 0.31 for urgent treatment needs respectively. Models incorporating both response types showed a slightly higher predictive accuracy than those relying on either child-reported or proxy-reported responses. The combination of Item Response Theory and Machine Learning algorithms yielded potentially useful screening instruments for both active caries and urgent treatment needs of children. The survey screening approach is relatively cost-effective and convenient when dealing with oral health assessment in large populations. Future studies are needed to further leverage the customize and refine the instruments based on the estimated item characteristics for specific subgroups of the populations to enhance predictive accuracy.
Sprache
Englisch
Identifikatoren
ISSN: 1932-6203
eISSN: 1932-6203
DOI: 10.1371/journal.pone.0299947
Titel-ID: cdi_doaj_primary_oai_doaj_org_article_0ff1138172f344f79a6a1a7a5b0f4508

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