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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Vydané v:	Antioxidants Ročník 10; číslo 7; s. 1132
Hlavní autori:	Kim, Youjin, Kim, Yunsoo, Hwang, Jiyoung, van den Broek, Tim J., Oh, Bumjo, Kim, Ji Yeon, Wopereis, Suzan, Bouwman, Jildau, Kwon, Oran
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 16.07.2021 MDPI
Predmet:	adults Algorithms Artificial intelligence Blood Chronic illnesses composite biomarker data collection Datasets diagnostic model Disease elastic net regularized generalized linear model Fines & penalties Generalized linear models Hemoglobin Korean Peninsula Learning algorithms Machine learning medical facilities Metabolic syndrome nutrition Nutrition research Oxidative stress Principal components analysis Statistical analysis Variables Korean Peninsula
ISSN:	2076-3921, 2076-3921
On-line prístup:	Získať plný text
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