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

Youjin Kim, Yunsoo Kim, Jiyoung Hwang, Tim J. van den Broek, Bumjo Oh, Ji Yeon Kim, Suzan Wopereis, Jildau Bouwman, Oran Kwon

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Article number1132
JournalAntioxidants
Volume10
Issue number7
DOIs
StatePublished - Jul 2021

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Composite biomarker
  • Diagnostic model
  • Elastic net regularized generalized linear model
  • Oxidative stress

Fingerprint

Dive into the research topics of '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'. Together they form a unique fingerprint.

Cite this