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

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 identifying in...

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Bibliographic Details
Main Authors: Akbarzadeh, M. (Author), Alipour, N. (Author), Azizi, F. (Author), Daneshpour, M.S (Author), Hosseini-Esfahani, F. (Author), Lanjanian, H. (Author), Moheimani, H. (Author), Zahedi, A.S (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2022
Subjects:
Adaptor Proteins, Signal Transducing
algorithm
Algorithms
Decision tree
Discriminant analysis
GCKR protein, human
genetics
human
Humans
Iran
Logistic Models
Logistic Regression
machine learning
Machine Learning
Metabolic syndrome
Metabolic Syndrome
metabolic syndrome X
Random Forest
signal transducing adaptor protein
statistical model
support vector machine
Support Vector Machine
Support vector machines
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