Low-cost and scalable machine learning model for identifying children and adolescents with poor oral health using survey data: An empirical study in Portugal.

ABSTRACT: This empirical study assessed the potential of developing a machine-learning model to identify children and adolescents with poor oral health using only self-reported survey data. Such a model could enable scalable and cost-effective screening and targeted interventions, optimizing limited resources to improve oral health outcomes. To train and test the model, we used data from 2,133 students attending schools in a Portuguese municipality. Poor oral health (the dependent variable) was defined as having a Decayed, Missing, and Filled Teeth index for deciduous teeth (dmft) or permanent teeth (DMFT) above expert-defined thresholds (dmft/DMFT ≥ 3 or 4). The survey provided information about the students' oral health habits, knowledge, beliefs, and food and physical activity habits, which serve

SUBMITTER: Lavado S 

PROVIDER: S-EPMC11759376 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature