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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Veröffentlicht in:Antioxidants Jg. 10; H. 7; S. 1132
Hauptverfasser: Kim, Youjin, Kim, Yunsoo, Hwang, Jiyoung, van den Broek, Tim J., Oh, Bumjo, Kim, Ji Yeon, Wopereis, Suzan, Bouwman, Jildau, Kwon, Oran
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 16.07.2021
MDPI
Schlagworte:
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
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Zusammenfassung: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 retrospective cohort data from Boramae medical center in Korea (n = 1328). To obtain binary performance measures, we selected healthy controls versus oxidative disease cases based on the “health space” statistical methodology. We then developed a machine learning algorithm for discrimination of oxidative stress status using least absolute shrinkage and selection operator (LASSO)/elastic net regression with 10-fold cross-validation. A proposed fine-tune model included 16 features out of the full spectrum of diverse and complex data. The predictive performance was externally evaluated by generating receiver operating characteristic curves with area under the curve of 0.949 (CI 0.925 to 0.974), sensitivity of 0.923 (CI 0.879 to 0.967), and specificity of 0.855 (CI 0.795 to 0.915). Moreover, the discrimination power was confirmed by applying the proposed diagnostic model to the full dataset consisting of subjects with various degrees of oxidative stress. The results provide a feasible approach for stratifying the oxidative stress risks in the healthy population and selecting appropriate strategies for individual subjects toward implementing data-driven precision nutrition.
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These authors contributed equally to this work.
ISSN:2076-3921
2076-3921
DOI:10.3390/antiox10071132