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A Significance Assessment of Diabetes Diagnostic Biomarkers Using Machine Learning

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Date

2021

Authors

Cui, Ran
Daskalaki, Eleni
Hossain, Zakir
Nolan, Christopher
Lenskiy, Artem
Suominen, Hanna

Journal Title

Journal ISSN

Volume Title

Publisher

IOS Press Ebooks

Abstract

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.

Description

Keywords

Diabetes biomarkers, machine learning, feature importance

Citation

URI

http://hdl.handle.net/1885/307470

Collections

ANU Research Publications

Source

Nurses and Midwives in the Digital Age

Type

Conference paper

Book Title

Entity type

Access Statement

Open Access

License Rights

Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).

DOI

10.3233/SHTI210657

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SHTI-284-SHTI210657.pdf (154.6 KB)

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