Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study

Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study

Abstract Background Metabolic syndrome (MetS) is a prevalent multifactorial disorder that can increase the risk of developing diabetes, cardiovascular diseases, and cancer. We aimed to compare different machine learning classification methods in predicting metabolic syndrome status as well as identi...

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Bibliographic Details
Main Authors: Mahdi Akbarzadeh, Nadia Alipour, Hamed Moheimani, Asieh Sadat Zahedi, Firoozeh Hosseini-Esfahani, Hossein Lanjanian, Fereidoun Azizi, Maryam S. Daneshpour
Format: Article
Language:English
Published: BMC 2022-04-01
Series:Journal of Translational Medicine
Subjects:
Decision tree
Discriminant analysis
Logistic Regression
Metabolic syndrome
Random Forest
Support vector machines
Online Access:https://doi.org/10.1186/s12967-022-03349-z

Internet

https://doi.org/10.1186/s12967-022-03349-z

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