biostudies-literatureOgwo CCary Kleinman Oral Health Sciences Research FundDelta Dental of Iowa FoundationNIDCR NIH HHSWefel award from the University of Iowa College of DentistryNCRR NIH HHSRoy J. Carver Charitable TrustPost-Comprehensive Graduate Research award from the University of Iowa Graduate CollegeNational Institute of Dental and Craniofacial Research529https://www.ebi.ac.uk/biostudies/studies/S-EPMC11069237biostudies-literatureUnknown24(1)<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 D₂₊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 D₂₊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 (R²) 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.BMC oral healthPredicting dental caries outcomes in young adults using machine learning approach.PMC11069237UL1 RR024979R01-DE09551, R01-DE12101, M01-RR00059, UL1-RR024979M01 RR000059R01 DE012101R01 DE009551Ogwo CCaplan DLevy SBrown GWarren JfalsePredicting dental caries outcomes in young adults using machine learning approach.<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 D₂₊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 D₂₊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 (R²) 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.2024-01-01T00:00:00Z2024 May2026-03-17T16:12:43.295Z2025-08-18T09:53:16.379ZS-EPMC110692373870263910.1186/s12903-024-04294-7