Classifying Delayed Dental Care Using Machine Learning: A National Health Interview Survey Analysis

Delayed dental care affects a substantial share of US adults and disproportionately impacts groups defined by age, race/ethnicity, education and insurance status. Interpretable machine learning models can complement traditional survey analyses by supporting population-level risk stratification. Policies that expand dental coverage, reduce financial barriers and target outreach to high-risk populations may mitigate inequities. Given the cross-sectional design, findings should be interpreted as predictive rather than causal. Future research should incorporate contextual determinants (eg, geographic access and provider availability) and longitudinal data to refine population targeting and evaluate intervention impact.