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A Significance Assessment of Diabetes Diagnostic Biomarkers Using Machine Learning

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dc.contributor.authorCui, Ran
dc.contributor.authorDaskalaki, Eleni
dc.contributor.authorHossain, Zakir
dc.contributor.authorNolan, Christopher
dc.contributor.authorLenskiy, Artem
dc.contributor.authorSuominen, Hanna
dc.contributor.editorHoney, Michelle
dc.contributor.editorRonquillo, Charlene
dc.contributor.editorLee, Ting-Ting
dc.contributor.editorWestbrooke, Lucy
dc.coverage.spatialvirtual event
dc.date.accessioned2023-11-28T00:36:41Z
dc.date.available2023-11-28T00:36:41Z
dc.date.created23 August to 2 September 2021
dc.date.issued2021
dc.date.updated2022-08-21T08:16:31Z
dc.description.abstractDiabetes 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.en_AU
dc.description.sponsorshipWe acknowledge the funding from the ANU School of Computing for the first author’s PhD studies.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.isbn978-1-64368-220-4en_AU
dc.identifier.urihttp://hdl.handle.net/1885/307470
dc.language.isoen_AUen_AU
dc.provenanceThis article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).en_AU
dc.publisherIOS Press Ebooksen_AU
dc.relation.ispartofseries15th International Congress on Nursing Informaticsen_AU
dc.rights© 2021 International Medical Informatics Association (IMIA) and IOS Press.en_AU
dc.rights.licenseCreative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).en_AU
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/en_AU
dc.sourceNurses and Midwives in the Digital Ageen_AU
dc.subjectDiabetes biomarkersen_AU
dc.subjectmachine learningen_AU
dc.subjectfeature importanceen_AU
dc.titleA Significance Assessment of Diabetes Diagnostic Biomarkers Using Machine Learningen_AU
dc.typeConference paperen_AU
dcterms.accessRightsOpen Accessen_AU
local.bibliographicCitation.lastpage38en_AU
local.bibliographicCitation.startpage36en_AU
local.contributor.affiliationCui, Ran, RSCH Research & Innovation Portfolio, ANUen_AU
local.contributor.affiliationDaskalaki, Eleni, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationHossain, Zakir, College of Science, ANUen_AU
local.contributor.affiliationNolan, Christopher, College of Health and Medicine, ANUen_AU
local.contributor.affiliationLenskiy, Artem, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationSuominen, Hanna, College of Engineering and Computer Science, ANUen_AU
local.contributor.authoruidCui, Ran, u5757796en_AU
local.contributor.authoruidDaskalaki, Eleni, u1085378en_AU
local.contributor.authoruidHossain, Zakir, u5710140en_AU
local.contributor.authoruidNolan, Christopher, u1820721en_AU
local.contributor.authoruidLenskiy, Artem, u1089996en_AU
local.contributor.authoruidSuominen, Hanna, u4872279en_AU
local.description.notesImported from ARIESen_AU
local.description.refereedYes
local.identifier.absfor460809 - Pervasive computingen_AU
local.identifier.absfor420605 - Preventative health careen_AU
local.identifier.ariespublicationa383154xPUB24118en_AU
local.identifier.doi10.3233/SHTI210657en_AU
local.identifier.scopusID2-s2.0-85122024835
local.publisher.urlhttps://ebooks.iospress.nl/en_AU
local.type.statusPublished Versionen_AU

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