A Machine Learning Algorithm for Quantitatively Diagnosing Oxidative Stress Risks in Healthy Adult Individuals Based on Health Space Methodology: A Proof-of-Concept Study Using Korean Cross-Sectional Cohort Data

A Machine Learning Algorithm for Quantitatively Diagnosing Oxidative Stress Risks in Healthy Adult Individuals Based on Health Space Methodology: A Proof-of-Concept Study Using Korean Cross-Sectional Cohort Data

Oxidative stress aggravates the progression of lifestyle-related chronic diseases. However, knowledge and practices that enable quantifying oxidative stress are still lacking. Here, we performed a proof-of-concept study to predict the oxidative stress status in a healthy population using retrospecti...

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Bibliographic Details
Main Authors: Youjin Kim, Yunsoo Kim, Jiyoung Hwang, Tim J. van den Broek, Bumjo Oh, Ji Yeon Kim, Suzan Wopereis, Jildau Bouwman, Oran Kwon
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Antioxidants
Subjects:
elastic net regularized generalized linear model
diagnostic model
oxidative stress
composite biomarker
Online Access:https://www.mdpi.com/2076-3921/10/7/1132

Internet

https://www.mdpi.com/2076-3921/10/7/1132

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