- Title
- Data-analytically derived flexible HbA1c thresholds for type 2 diabetes mellitus diagnostic
- Creator
- Stranieri, Andrew; Yatsko, Andrew; Jelinek, Herbert; Venkatraman, Sitalakshmi
- Date
- 2015
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/162835
- Identifier
- vital:12735
- Identifier
-
https://doi.org/10.5430/air.v5n1p111
- Identifier
- ISBN:1927-6974
- Abstract
- Glycated haemoglobin (HbA1c) is now more commonly used as an alternative test to the fasting plasma glucose and oral glucose tolerance tests for the identification of Type 2 Diabetes Mellitus (T2DM) because it is easily obtained using the point-of-care technology and represents long-term blood sugar levels. According to WHO guidelines, HbA1c values of 6.5% or above are required for a diagnosis of T2DM. However outcomes of a large number of trials with HbA1c have been inconsistent across the clinical spectrum and further research is required to determine the efficacy of HbA1c testing in identification of T2DM. Medical records from a diabetes screening program in Australia illustrate that many patients could be classified as diabetics if other clinical indicators are included, even though the HbA1c result does not exceed 6.5%. This suggests that a cutoff for the general population of 6.5% may be too simple and miss individuals at risk or with already overt, undiagnosed diabetes. In this study, data mining algorithms have been applied to identify markers that can be used with HbA1c. The results indicate that T2DM is best classified by HbA1c at 6.2% - a cutoff level lower than the currently recommended one, which can be even less, having assumed the threshold flexibility, if additionally to HbA1c being high the rule is conditioned on oxidative stress or inflammation being present, atherogenicity or adiposity being high, or hypertension being diagnosed, etc.
- Publisher
- Sciedu Press
- Relation
- Artificial Intelligence Research Vol. 5, no. 1 (2015), p. 111-134
- Rights
- https://creativecommons.org/licenses/by/3.0
- Rights
- Copyright © The Authors
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; Decision tree; Incomplete data; Diabetes Mellitus; Diagnostic
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