Enhancing predictive accuracy of cardiac autonomic neuropathy using blood biochemistry features and iterative multitier ensembles

Abawajy, Jemal, Kelarev, Andrei, Chowdhury, Morshed and Jelinek, Herbert F. 2016, Enhancing predictive accuracy of cardiac autonomic neuropathy using blood biochemistry features and iterative multitier ensembles, IEEE journal of biomedical and health informatics, vol. 20, no. 1, pp. 408-415, doi: 10.1109/JBHI.2014.2363177.

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Title Enhancing predictive accuracy of cardiac autonomic neuropathy using blood biochemistry features and iterative multitier ensembles
Author(s) Abawajy, JemalORCID iD for Abawajy, Jemal orcid.org/0000-0001-8962-1222
Kelarev, Andrei
Chowdhury, MorshedORCID iD for Chowdhury, Morshed orcid.org/0000-0002-2866-4955
Jelinek, Herbert F.
Journal name IEEE journal of biomedical and health informatics
Volume number 20
Issue number 1
Start page 408
End page 415
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2016-01
ISSN 2168-2194
Keyword(s) blood biochemistry
cardiac autonomic neuropathy (CAN)
ensemble classifiers
iterative multitier ensembles
Science & Technology
Life Sciences & Biomedicine
Computer Science, Information Systems
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Medical Informatics
Computer Science
Blood biochemistry attributes
Summary Blood biochemistry attributes form an important class of tests, routinely collected several times per year for many patients with diabetes. The objective of this study is to investigate the role of blood biochemistry for improving the predictive accuracy of the diagnosis of cardiac autonomic neuropathy (CAN) progression. Blood biochemistry contributes to CAN, and so it is a causative factor that can provide additional power for the diagnosis of CAN especially in the absence of a complete set of Ewing tests. We introduce automated iterative multitier ensembles (AIME) and investigate their performance in comparison to base classifiers and standard ensemble classifiers for blood biochemistry attributes. AIME incorporate diverse ensembles into several tiers simultaneously and combine them into one automatically generated integrated system so that one ensemble acts as an integral part of another ensemble. We carried out extensive experimental analysis using large datasets from the diabetes screening research initiative (DiScRi) project. The results of our experiments show that several blood biochemistry attributes can be used to supplement the Ewing battery for the detection of CAN in situations where one or more of the Ewing tests cannot be completed because of the individual difficulties faced by each patient in performing the tests. The results show that AIME provide higher accuracy as a multitier CAN classification paradigm. The best predictive accuracy of 99.57% has been obtained by the AIME combining decorate on top tier with bagging on middle tier based on random forest. Practitioners can use these findings to increase the accuracy of CAN diagnosis.
Language eng
DOI 10.1109/JBHI.2014.2363177
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083423

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