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

Alipour, Nadia; Kazemnejad, Anoshirvan; Akbarzadeh, Mahdi; Eskandari, Farzad; Zahedi, Asiyeh Sadat; Daneshpour, Maryam S

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

Alipour, Nadia; Kazemnejad, Anoshirvan; Akbarzadeh, Mahdi; Eskandari, Farzad; Zahedi, Asiyeh Sadat; Daneshpour, Maryam S.

Afiliação

Alipour N; Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Kazemnejad A; Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. Email: kazem_an@modares.ac.ir.
Akbarzadeh M; Cellular and Molecular Endocrine Research Centre, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Eskandari F; Department of Statistics, Faculty of Statistics, Mathematics and Computer, Allameh Tabataba'i University, Tehran, Iran.
Zahedi AS; Cellular and Molecular Endocrine Research Centre, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Daneshpour MS; Cellular and Molecular Endocrine Research Centre, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Email: daneshpour@sbmu.ac.ir.

Cell J ; 25(8): 536-545, 2023 Aug 01.

Article em En | MEDLINE | ID: mdl-37641415

Palavras-chave

Classification; LASSO; Machine Learning; Metabolic syndrome; Penalized Regression

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Cell J Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Irã

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