Beyond Accuracy: Machine Learning Models for Predicting the Presence of Permanent Molar Caries in U.S. Children and Adolescents With Fairness Consideration.

INTRODUCTION: Although predictors of dental caries have been previously explored, a comprehensive understanding of factors influencing permanent-molar decay in U.S. children and adolescents, especially with respect to racial and ethnic biases, remains limited. This study aims to develop and evaluate machine learning models incorporating algorithmic fairness to predict caries in permanent molars.

METHODS: Data from the National Health and Nutrition Examination Survey were analyzed using the 2011-2014 cycles for training and validation and the 2015-2016 cycle for testing. The primary outcome was decayed, missing, and filled teeth in at least 1 permanent molar, dichotomized to represent the presence or absence of caries. Candidate predictors included demographic characteristics, dietary factors, laboratory and examination measures, behaviors, and lifestyle indicators. Three machine learning models (logistic regression, random forest, and XGBoost) were implemented and compared using the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Fairness was quantified by comparing group-specific true-positive rates and false-positive rates across race/ethnicity over 10 cross-validation folds, using Shapiro-Wilk tests and paired tests to assess subgroup gaps and pre/postmitigation changes. To mitigate disparities, an in-processing equalized-odds constraint was applied through an exponentiated-gradient reduction framework. Sensitivity analyses using National Health and Nutrition Examination Survey weights were also conducted.

RESULTS: Among 5,710 participants aged 6-19 years, 37.30% had caries in at least 1 permanent molar. Logistic regression achieved the highest premitigation area under the receiver operating characteristic curve (78.50%). SHAP (SHapley Additive exPlanations) analyses identified age, family income, BMI, and household education as key contributors. Fairness evaluation indicated minimal equal-opportunity gaps but showed evidence of equalized-odds violations across race/ethnicity. After mitigation, race/ethnicity-related disparities decreased across models, with varying performance trade-offs. XGBoost and random forest maintained stable area under the receiver operating characteristic curve values in both unweighted (∼77%-78%) and survey-weighted (∼75%-77%) evaluations. In contrast, logistic regression showed a substantial decline in unweighted area under the receiver operating characteristic curve (78.50%-65.53%) and a marked reduction in recall and F1 score, whereas changes under weighted evaluation were more modest (77.91%-76.54%).

CONCLUSIONS: Machine learning models effectively predicted dental caries in children and adolescents using National Health and Nutrition Examination Survey data. Applying equalized-odds constraints reduces disparities across models, although with varying fairness-performance trade-offs. These findings highlight the importance of tailoring bias mitigation strategies to clinical priorities, balancing equitable outcomes and predictive utility, and they can be used to inform policy and better design-targeted interventions to improve oral health.