Medical Xpress December 10, 2025
dA new article published in the Journal of Dental Research explores the development of an integrated data-cleaning and subtype discovery pipeline using unsupervised machine learning for comprehensive analysis and visualization of data patterns in the National Health and Nutrition Examination Survey (NHANES) database.
Authored by Alena Orlenko, Cedars-Sinai Medical Center, Los Angeles, the study “Uncovering Dental Caries Heterogeneity in NHANES Using Machine Learning” addresses the limitations of the NHANES, one of the largest curated repositories of nationally representative population-level health-related indicators, by establishing a data-cleaning pipeline with a novel outlier detection algorithm and unsupervised machine learning to identify phenotype subtypes within NHANES dental caries data.
“By bringing the power of machine learning to a large national data set, the authors...
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