Deakin University

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

conference contribution
posted on 2012-01-01, 00:00 authored by Andrei Kelarev, A Stranieri, John YearwoodJohn Yearwood, Jemal AbawajyJemal Abawajy, H Jelinek
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.



Australasian Data Mining. Conference (10th : 2012 : Sydney, N.S.W.)


Conferences in Research and Practice in Information Technology; vol. 134


93 - 101


Australian Computer Society


Sydney, N.S.W.

Place of publication

[Sydney, N.S.W.]

Start date


End date




Publication classification

E1 Full written paper - refereed

Copyright notice

2012, Australian Computer Society


Y Zhao, J Li, P Kennedy, P Christen

Title of proceedings

AusDM 2012 : Proceedings of the 10th Australasian Data Mining Conference