Machine Learning to Identify Behavioral Determinants of Oral Health in Inner City Older Hispanic Adults.

Journal: Studies in health technology and informatics
Published Date:

Abstract

We applied machine learning techniques to a community-based behavioral dataset to build prediction models to gain insights about minority dental health and population aging as the foundation for future interventions for urban Hispanics. Our application of machine learning techniques identified emotional and systemic factors such as chronic stress and health literacy as the strongest predictors of self-reported dental health among hundreds of possible variables. Application of machine learning algorithms was useful to build prediction models to gain insights about dental health and minority population aging.

Authors

  • Sunmoo Yoon
    School of Nursing, Columbia University Medical Center, New York, NY, USA.
  • Thomas Choi
    College of Dental Medicine, Columbia University, New York, NY, USA.
  • Michelle Odlum
    School of Nursing, Columbia University, New York, NY, USA.
  • Dennis A Mitchell
    College of Dental Medicine, Columbia University, New York, NY, USA.
  • Ian M Kronish
    Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA.
  • Karina W Davidson
    Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY, USA.
  • Joseph Finkelstein
    Department of Biomedical Informatics, School of Medicine, University of Utah, USA.