Predicting dental caries outcomes in young adults using machine learning approach

作者：Chukwuebuka Ogwo, Grant Brown, John J. Warren, Daniel J. Caplan, Steven M. Levy · 发表于：BMC Oral Health · 年份：2024 · DOI：10.1186/s12903-024-04294-7 · 被引用次数：10 · 研究领域：Dental Health and Care Utilization、Oral microbiology and periodontitis research、Dental Radiography and Imaging

Abstract Objectives 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. Methods 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 2+ 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. Results The prevalence of cavitated level caries experience at age 23 (mean D 2+ 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 2 ) 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 f...