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

Clinical Information and Decision Making (1915) Cardiovascular Disease Prevention (206) Artificial Intelligence (2129) Machine Learning (2076) Cardiac Risk and Cardiac Risk Calculators (102) Prevention and Health Promotion (1362) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (87) Clinical Informatics (1325) Cardiology (224)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67525, first published .
Development of a Predictive Model for Metabolic Syndrome Using Noninvasive Data and its Cardiovascular Disease Risk Assessments: Multicohort Validation Study

Development of a Predictive Model for Metabolic Syndrome Using Noninvasive Data and its Cardiovascular Disease Risk Assessments: Multicohort Validation Study

Development of a Predictive Model for Metabolic Syndrome Using Noninvasive Data and its Cardiovascular Disease Risk Assessments: Multicohort Validation Study

Authors of this article:

Jin-Hyun Park1 Author Orcid Image ;   Inyong Jeong1 Author Orcid Image ;   Gang-Jee Ko2 Author Orcid Image ;   Seogsong Jeong1 Author Orcid Image ;   Hwamin Lee1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Mirzaei N, Mostafaei S, Izadi N, Najafi F, Darbandi M, Pasdar Y. Machine learning-driven predictions of metabolic syndrome in adults: evidence from a Kurdish cohort in Iran. European Journal of Medical Research 2025;30(1) View
  2. Song Y, Wang T, Su M, Wang X, Zhang Y, Pan J, Wang Y, Yang J. FTO rs9939609 and rs17817449 polymorphisms contribute to metabolic syndrome risk by increasing triglyceride and glucose levels. Frontiers in Genetics 2025;16 View