This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2491) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (847) Cardiovascular Disease Prevention (142) Cardiac Risk and Cardiac Risk Calculators (47) Machine Learning (1501) Machine Learning (55) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (39)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42756, first published .
Identification of Risk Groups for and Factors Affecting Metabolic Syndrome in South Korean Single-Person Households Using Latent Class Analysis and Machine Learning Techniques: Secondary Analysis Study

Identification of Risk Groups for and Factors Affecting Metabolic Syndrome in South Korean Single-Person Households Using Latent Class Analysis and Machine Learning Techniques: Secondary Analysis Study

Identification of Risk Groups for and Factors Affecting Metabolic Syndrome in South Korean Single-Person Households Using Latent Class Analysis and Machine Learning Techniques: Secondary Analysis Study

Authors of this article:

Ji-Soo Lee1 Author Orcid Image ;   Soo-Kyoung Lee2 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

Ji-Soo Lee   1 * , RN, PhD ;   Soo-Kyoung Lee   2 * , RN, PhD

1 Department of Nursing, Gimcheon University, Gimcheon-si, Republic of Korea

2 Big Data Convergence and Open Sharing System, Seoul National University, Seoul, Republic of Korea

*all authors contributed equally

Corresponding Author:

  • Soo-Kyoung Lee, RN, PhD
  • Big Data Convergence and Open Sharing System
  • Seoul National University
  • 1 Gwanak-ro
  • Gwanak-gu
  • Seoul, 08826
  • Republic of Korea
  • Phone: 82 2 889 5710
  • Fax: 82 2 889 5711
  • Email: soo1005s@gmail.com