David Kahan, Thomas L McKenzie
Energy expenditure estimates during school physical education: Potential vs. reality?
Preventive medicine
predict dental caries outcomes young adults set longitudinallyobtained predictor variables identify important predictors using machine learning techniques study conducted using iowa fluoride study dataset predictor variables sex mothers education family income composite socioeconomic status ses caries experience ages 9 13 17 cumulative estimates risk protective factors including fluoride dietary behavioral variables ages 59 913 1317 1723 used predict age 23 d2mfs count following machine learning models lasso regression generalized boosting machines gbm negative binomial negglm extreme gradient boosting models xgboost compared 5fold cross validation nested resampling techniques prevalence cavitated level caries experience age 23 mean d2mfs count 475 predictive analysis found lasso best performing model compared gbm negglm xgboost root mean square error rmse 070 coefficient determination r2 044 dichotomization predicted observed values lasso regression classification results showed accuracy precision recall roc auc 837 859 931 682 respectively previous caries experience age 13 age 17 sugarsweetened beverages intakes age 13 age 17 found four important predictors cavitated caries count age 23 machine learning model showed high accuracy precision prediction caries young adults longitudinallyobtained predictor variables model could future development validation diverse population data used public health specialists policymakers screening tool identify risk caries young adults apply targeted interventions however data diverse population needed improve quality generalizability caries prediction