Predicting Depression Among Community Residing Older Adults: A Use of Machine Learning Approch.

Journal: Studies in health technology and informatics
Published Date: Jan 1, 2018

Abstract

The study demonstrated an application of machine learning techniques in building a depression prediction model. We used the NSHAP II data (3,377 subjects and 261 variables) and built the models using a logistic regression with and without L1 regularization. Depression prediction rates ranged 58.33% to 90.48% and 83.33% to 90.44% in the model with and without L1 regularization, respectively. The moderate to high prediction rates imply that the machine learning algorithms built the prediction models successfully.

Authors

  • Jeungok Choi
    College of Nursing University of Massachusetts, Amherst, MA, USA.
  • Jeeyae Choi
    School of Nursing, University of North Carolina Wilmington, NC, USA.
  • Woo Jung Choi
    Department of Radiology, Hanyang University Hospital, Seoul, South Korea; Department of Radiology, University of Ulsan, Asan Medical Center, Seoul, South Korea.

Keywords

External Resources

Popular Topics
Recent Journals