The effect of data balancing approaches on the prediction of metabolic syndrome using non-invasive parameters based on random forest

The effect of data balancing approaches on the prediction of metabolic syndrome using non-invasive parameters based on random forest

Abstract Background Metabolic syndrome (MetS) is a cluster of metabolic abnormalities (including obesity, insulin resistance, hypertension, and dyslipidemia), which can be used to identify at-risk populations for diabetes and cardiovascular diseases, the main causes of morbidity and mortality worldw...

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Bibliographic Details
Main Authors: Sahar Mohseni-Takalloo, Hadis Mohseni, Hassan Mozaffari-Khosravi, Masoud Mirzaei, Mahdieh Hosseinzadeh
Format: Article
Language:English
Published: BMC 2024-01-01
Series:BMC Bioinformatics
Subjects:
Metabolic syndrome
Machine learning
Random forest
Data balancing
SMOTE
SplitBal
Online Access:https://doi.org/10.1186/s12859-024-05633-9

Internet

https://doi.org/10.1186/s12859-024-05633-9

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