Machine and Deep Learning Applied to Predict Metabolic Syndrome without a Blood Screening

Machine and Deep Learning Applied to Predict Metabolic Syndrome without a Blood Screening

The exponential increase of metabolic syndrome and its association with the risk impact of morbidity and mortality has propitiated the development of tools to diagnose this syndrome early. This work presents a model that is based on prognostic variables to classify Mexicans with metabolic syndrome w...

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Bibliographic Details
Main Authors: Guadalupe O. Gutiérrez-Esparza, Tania A. Ramírez-delReal, Mireya Martínez-García, Oscar Infante Vázquez, Maite Vallejo, José Hernández-Torruco
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Applied Sciences
Subjects:
metabolic syndrome
features selection
health parameters
random forest
deep neural networks
C4.5
Online Access:https://www.mdpi.com/2076-3417/11/10/4334

Internet

https://www.mdpi.com/2076-3417/11/10/4334

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