Automated Machine Learning Classifier valuable for Early Childhood Caries: Study

Early childhood caries (ECC) prevalence (decayed, missing, and filled primary teeth surfaces [dmfs] score greater than zero, using an International Caries Detection and Assessment System score greater than or equal to three caries lesion detection threshold) was 54 percent. Ten sets of ECC predictors were evaluated for ECC classification accuracy (i.e., area under the ROC curve [AUC], sensitivity [Se], and positive predictive value [PPV]) using an AutoML deployment on Google Cloud, followed by internal validation and external replication.

The results of the study are as follows:

A parsimonious model including two terms (i.e., children's age and parent-reported child oral health status: excellent/very good/good/fair/poor) had the highest AUC (0.74), Se (0.67), and PPV (0.64) scores and similar performance using an external National Health and Nutrition Examination Survey (NHANES) dataset (AUC equals 0.80, Se equals 0.73, PPV equals 0.49). Contrarily, a comprehensive model with 12 variables covering demographics (e.g., race/ethnicity, parental education), oral health behaviours, fluoride exposure, and dental home had worse performance.

Thus, the researchers concluded that parsimonious automated machine learning early childhood caries classifiers, including single-item self-reports, can be valuable for Early Childhood Caries screening. The classifier can accommodate biological information that can help improve its performance in the future.

Reference:

An Automated Machine Learning Classifier for Early Childhood Caries by Karhade D et. al published in the American Academy of Pediatric Dentistry