O2 Muu julkaisu
A Significance Assessment of Diabetes Diagnostic Biomarkers Using Machine Learning
Tekijät: Cui Ran, Daskalaki Elena, Hossain Md Zakir, Lenskiy Artem, Nolan Christopher J, Suominen Hanna
Toimittaja: Michelle Honey, Charlene Ronquillo, Ting-Ting Lee, Lucy Westbrooke
Konferenssin vakiintunut nimi: International Congress in Nursing Informatics
Julkaisuvuosi: 2021
Journal: Studies in Health Technology and Informatics
Kokoomateoksen nimi: Nurses and Midwives in the Digital Age: Selected Papers, Posters and Panels from the 15th International Congress in Nursing Informatics
Tietokannassa oleva lehden nimi: Studies in health technology and informatics
Lehden akronyymi: Stud Health Technol Inform
Sarjan nimi: Studies in Health Technology and Informatics
Vuosikerta: 284
Aloitussivu: 36
Lopetussivu: 38
ISSN: 0926-9630
eISSN: 1879-8365
DOI: https://doi.org/10.3233/SHTI210657
Rinnakkaistallenteen osoite: 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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
