Automated Machine Learning Classifier valuable for Early Childhood Caries: Study
An Automated Machine Learning Classifier for Early Childhood Caries can be valuable, according to a study published in the American Academy of Pediatric Dentistry.
A group of researchers from the U.S.A conducted a study to develop and evaluate an automated machine learning algorithm (AutoML) for children's classification according to early childhood caries (ECC) status.
Clinical, demographic, behavioural, and parent-reported oral health status information for a sample of 6,404 three- to five-year-old children (mean age equals 54 months) participating in an epidemiologic study of early childhood oral health in North Carolina was used.
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.
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