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....

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Detalles Bibliográficos
Autores principales: Choe, Eun Kyung, Rhee, Hwanseok, Lee, Seungjae, Shin, Eunsoon, Oh, Seung-Won, Lee, Jong-Eun, Choi, Seung Ho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korea Genome Organization 2018
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6440667/
https://www.ncbi.nlm.nih.gov/pubmed/30602092
http://dx.doi.org/10.5808/GI.2018.16.4.e31

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6440667/
https://www.ncbi.nlm.nih.gov/pubmed/30602092
http://dx.doi.org/10.5808/GI.2018.16.4.e31

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