A Significance Assessment of Diabetes Diagnostic Biomarkers Using Machine Learning



: Cui Ran, Daskalaki Elena, Hossain Md Zakir, Lenskiy Artem, Nolan Christopher J, Suominen Hanna

: Michelle Honey, Charlene Ronquillo, Ting-Ting Lee, Lucy Westbrooke

: International Congress in Nursing Informatics

: 2021

: Studies in Health Technology and Informatics

: Nurses and Midwives in the Digital Age: Selected Papers, Posters and Panels from the 15th International Congress in Nursing Informatics

: Studies in health technology and informatics

: Stud Health Technol Inform

: Studies in Health Technology and Informatics

: 284

: 36

: 38

: 0926-9630

: 1879-8365

DOI: https://doi.org/10.3233/SHTI210657

: 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.

Last updated on 2024-26-11 at 22:34