Regularized Machine Learning Models for Prediction of Metabolic Syndrome Using GCKR, APOA5, and BUD13 Gene Variants: Tehran Cardiometabolic Genetic Study

OBJECTIVE: Metabolic syndrome (MetS) is a complex multifactorial disorder that considerably burdens healthcare systems. We aim to classify MetS using regularized machine learning models in the presence of the risk variants of GCKR, BUD13 and APOA5, and environmental risk factors. MATERIALS AND METHO...

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Detalles Bibliográficos
Autores principales: Alipour, Nadia, Kazemnejad, Anoshirvan, Akbarzadeh, Mahdi, Eskandari, Farzad, Zahedi, Asiyeh Sadat, Daneshpour, Maryam S
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Royan Institute 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10542204/
https://www.ncbi.nlm.nih.gov/pubmed/37641415
http://dx.doi.org/10.22074/CELLJ.2023.2000864.1294

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10542204/
https://www.ncbi.nlm.nih.gov/pubmed/37641415
http://dx.doi.org/10.22074/CELLJ.2023.2000864.1294

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