Other publication
A Significance Assessment of Diabetes Diagnostic Biomarkers Using Machine Learning
Authors: Cui Ran, Daskalaki Elena, Hossain Md Zakir, Lenskiy Artem, Nolan Christopher J, Suominen Hanna
Editors: Michelle Honey, Charlene Ronquillo, Ting-Ting Lee, Lucy Westbrooke
Conference name: International Congress in Nursing Informatics
Publication year: 2021
Journal: Studies in Health Technology and Informatics
Book title : Nurses and Midwives in the Digital Age: Selected Papers, Posters and Panels from the 15th International Congress in Nursing Informatics
Journal name in source: Studies in health technology and informatics
Journal acronym: Stud Health Technol Inform
Series title: Studies in Health Technology and Informatics
Volume: 284
First page : 36
Last page: 38
ISSN: 0926-9630
eISSN: 1879-8365
DOI: https://doi.org/10.3233/SHTI210657
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/176265088
Diabetes can be diagnosed by either Fasting Plasma Glucose or Hemoglobin A1c. The aim of our study was to explore the differences between the two criteria through the development of a machine learning based diabetes diagnostic algorithm and analysing the predictive contribution of each input biomarker. Our study concludes that fasting insulin is predictive of diabetes defined by FPG, but not by HbA1c. Besides, 28 other fasting blood biomarkers were not significant predictors of diabetes.
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