Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population

Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population

The prevalence of metabolic syndrome (MS) in the nonobese population is not low. However, the identification and risk mitigation of MS are not easy in this population. We aimed to develop an MS prediction model using genetic and clinical factors of nonobese Koreans through machine learning methods....

Full description

Saved in:
Bibliographic Details
Main Authors: Eun Kyung Choe, Hwanseok Rhee, Seungjae Lee, Eunsoon Shin, Seung-Won Oh, Jong-Eun Lee, Seung Ho Choi
Format: Article
Language:English
Published: BioMed Central 2018-12-01
Series:Genomics & Informatics
Subjects:
genetic polymorphism
machine learning
metabolic syndrome
Online Access:http://genominfo.org/upload/pdf/gi-2018-16-4-e31.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://genominfo.org/upload/pdf/gi-2018-16-4-e31.pdf

Similar Items