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Improving classifications for cardiac autonomic neuropathy using multi-level ensemble classifiers and feature selection based on random forest

Kelarev, A.V., Stranieri, A., Yearwood, J.L., Abawajy, J. and Jelinek, H.F. 2012, Improving classifications for cardiac autonomic neuropathy using multi-level ensemble classifiers and feature selection based on random forest, in AusDM 2012 : Proceedings of the 10th Australasian Data Mining Conference, Australian Computer Society, [Sydney, N.S.W.], pp. 93-101.

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Title Improving classifications for cardiac autonomic neuropathy using multi-level ensemble classifiers and feature selection based on random forest
Author(s) Kelarev, A.V.
Stranieri, A.
Yearwood, J.L.
Abawajy, J.
Jelinek, H.F.
Conference name Australasian Data Mining. Conference (10th : 2012 : Sydney, N.S.W.)
Conference location Sydney, N.S.W.
Conference dates 5-7 Dec. 2012
Title of proceedings AusDM 2012 : Proceedings of the 10th Australasian Data Mining Conference
Editor(s) Zhao, Yanchang
Li, Jiuyong
Kennedy, Paul J.
Christen, Peter
Publication date 2012
Series Conferences in Research and Practice in Information Technology; vol. 134
Conference series Australasian Data Mining Conference
Start page 93
End page 101
Total pages 9
Publisher Australian Computer Society
Place of publication [Sydney, N.S.W.]
Keyword(s) random forest
ensembles of ensembles
multi-level ensembles
meta classifiers
feature selection
cardiac autonomic neuropathy
Summary This paper is devoted to empirical investigation of novel multi-level ensemble meta classifiers for the detection and monitoring of progression of cardiac autonomic neuropathy, CAN, in diabetes patients. Our experiments relied on an extensive database and concentrated on ensembles of ensembles, or multi-level meta classifiers, for the classification of cardiac autonomic neuropathy progression. First, we carried out a thorough investigation comparing the performance of various base classifiers for several known sets of the most essential features in this database and determined that Random Forest significantly and consistently outperforms all other base classifiers in this new application. Second, we used feature selection and ranking implemented in Random Forest. It was able to identify a new set of features, which has turned out better than all other sets considered for this large and well-known database previously. Random Forest remained the very best classier for the new set of features too. Third, we investigated meta classifiers and new multi-level meta classifiers based on Random Forest, which have improved its performance. The results obtained show that novel multi-level meta classifiers achieved further improvement and obtained new outcomes that are significantly better compared with the outcomes published in the literature previously for cardiac autonomic neuropathy.
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, Australian Computer Society
Persistent URL http://hdl.handle.net/10536/DRO/DU:30051782

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.