Empirical investigation of multi-tier ensembles for the detection of cardiac autonomic neuropathy using subsets of the Ewing Features

Abawajy, J., Kelarev, A.V., Stranieri, A. and Jelinek, H.F. 2012, Empirical investigation of multi-tier ensembles for the detection of cardiac autonomic neuropathy using subsets of the Ewing Features, in CIHealth 2012- Proceedings of the Workshop on New Trends of Computational Intelligence in Health Applciations, CEURS-WS, Sydney, N.S.W., pp. 1-11.

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Title Empirical investigation of multi-tier ensembles for the detection of cardiac autonomic neuropathy using subsets of the Ewing Features
Author(s) Abawajy, J.
Kelarev, A.V.
Stranieri, A.
Jelinek, H.F.
Conference name New Trends of Computational Intelligence in Health Applications. Workshop (2012 : Sydney, N.S.W.)
Conference location Sydney, N.S.W.
Conference dates 4 Dec. 2012
Title of proceedings CIHealth 2012- Proceedings of the Workshop on New Trends of Computational Intelligence in Health Applciations
Editor(s) Al-Jumaily, Adel
Bennamoun, Mohammed
Al-Ani, Ahmed
Publication date 2012
Conference series New Trends of Computational Intelligence in Health Applications Workshop
Start page 1
End page 11
Total pages 11
Publisher CEURS-WS
Place of publication Sydney, N.S.W.
Summary This article is devoted to an empirical investigation of per- formance of several new large multi-tier ensembles for the detection of cardiac autonomic neuropathy (CAN) in diabetes patients using subsets of the Ewing features. We used new data collected by the diabetes screening research initiative (DiScRi) project, which is more than ten times larger than the data set originally used by Ewing in the investigation of CAN. The results show that new multi-tier ensembles achieved better performance compared with the outcomes published in the literature previously. The best accuracy 97.74% of the detection of CAN has been achieved by the novel multi-tier combination of AdaBoost and Bagging, where AdaBoost is used at the top tier and Bagging is used at the middle tier, for the set consisting of the following four Ewing features: the deep breathing heart rate change, the Valsalva manoeuvre heart rate change, the hand grip blood pressure change and the lying to standing blood pressure change.
Language eng
Field of Research 080501 Distributed and Grid Systems
080109 Pattern Recognition and Data Mining
080702 Health Informatics
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2012, CEURS-WS
Persistent URL http://hdl.handle.net/10536/DRO/DU:30051359

Document type: Conference Paper
Collection: School of Information Technology
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