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

Formative Evaluation of Digital Health Interventions (2410) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (832) Cardiovascular Disease Prevention (137) Cardiac Risk and Cardiac Risk Calculators (45) Machine Learning (1465) Machine Learning (49) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (36)

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

Journals

  1. Jeong S, Choi Y. Investigating the Influence of Heavy Metals and Environmental Factors on Metabolic Syndrome Risk Based on Nutrient Intake: Machine Learning Analysis of Data from the Eighth Korea National Health and Nutrition Examination Survey (KNHANES). Nutrients 2024;16(5):724 View