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Carver Charitable Trust","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001024","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Delta Dental of Iowa Foundation"},{"name":"Wefel award from the University of Iowa College of Dentistry","id":[{"id":"https:\/\/ror.org\/036jqmy94","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Post-Comprehensive Graduate Research award from the University of Iowa Graduate College"},{"name":"Cary Kleinman Oral Health Sciences Research Fund","id":[{"id":"https:\/\/ror.org\/01cgtm828","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Oral Health"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study was conducted using the Iowa Fluoride Study dataset. The predictor variables - sex, mother\u2019s 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\u20139, 9\u201313, 13\u201317, and 17\u201323 were used to predict the age 23 D\n                      <jats:sub>2+<\/jats:sub>\n                      MFS count. The following machine learning models (LASSO regression, generalized boosting machines (GBM), negative binomial (NegGLM), and extreme gradient boosting models (XGBOOST)) were compared under 5-fold cross validation with nested resampling techniques.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The prevalence of cavitated level caries experience at age 23 (mean D\n                      <jats:sub>2+<\/jats:sub>\n                      MFS count) was 4.75. The predictive analysis found LASSO to be the best performing model (compared to GBM, NegGLM, and XGBOOST), with a root mean square error (RMSE) of 0.70, and coefficient of determination (R\n                      <jats:sup>2<\/jats:sup>\n                      ) of 0.44. After dichotomization of the predicted and observed values of the LASSO regression, the classification results showed accuracy, precision, recall, and ROC AUC of 83.7%, 85.9%, 93.1%, 68.2%, respectively. Previous caries experience at age 13 and age 17 and sugar-sweetened beverages intakes at age 13 and age 17 were found to be the four most important predictors of cavitated caries count at age 23.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12903-024-04294-7","type":"journal-article","created":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T11:02:01Z","timestamp":1714734121000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Predicting dental caries outcomes in young adults using machine learning approach"],"prefix":"10.1186","volume":"24","author":[{"given":"Chukwuebuka","family":"Ogwo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Grant","family":"Brown","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Warren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Caplan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven","family":"Levy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,3]]},"reference":[{"issue":"10","key":"4294_CR1","first-page":"703","volume":"35","author":"JD Featherstone","year":"2007","unstructured":"Featherstone JD, Domejean-Orliaguet S, Jenson L, Wolff M, Young DA. 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Informed consent was obtained from the participants prior to the examinations and questionnaires during age 23 assessments, with assent obtained at ages 13 and 17. Informed Consent also was obtained from the participants\u2019 parents for all ages to children\u2019s age 17. All the methods included in this study are in accordance with the declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"529"}}