{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ogwo C"],"funding":["Cary Kleinman Oral Health Sciences Research Fund","Delta Dental of Iowa Foundation","NIDCR NIH HHS","Wefel award from the University of Iowa College of Dentistry","NCRR NIH HHS","Roy J. Carver Charitable Trust","Post-Comprehensive Graduate Research award from the University of Iowa Graduate College","National Institute of Dental and Craniofacial Research"],"pagination":["529"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11069237"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(1)"],"pubmed_abstract":["<h4>Objectives</h4>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.<h4>Methods</h4>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<sub>2+</sub>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.<h4>Results</h4>The prevalence of cavitated level caries experience at age 23 (mean D<sub>2+</sub>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<sup>2</sup>) 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.<h4>Conclusion</h4>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."],"journal":["BMC oral health"],"pubmed_title":["Predicting dental caries outcomes in young adults using machine learning approach."],"pmcid":["PMC11069237"],"funding_grant_id":["UL1 RR024979","R01-DE09551, R01-DE12101, M01-RR00059, UL1-RR024979","M01 RR000059","R01 DE012101","R01 DE009551"],"pubmed_authors":["Ogwo C","Caplan D","Levy S","Brown G","Warren J"],"additional_accession":[]},"is_claimable":false,"name":"Predicting dental caries outcomes in young adults using machine learning approach.","description":"<h4>Objectives</h4>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.<h4>Methods</h4>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<sub>2+</sub>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.<h4>Results</h4>The prevalence of cavitated level caries experience at age 23 (mean D<sub>2+</sub>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<sup>2</sup>) 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.<h4>Conclusion</h4>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.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 May","modification":"2026-03-17T16:12:43.295Z","creation":"2025-08-18T09:53:16.379Z"},"accession":"S-EPMC11069237","cross_references":{"pubmed":["38702639"],"doi":["10.1186/s12903-024-04294-7"]}}