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.
History
Event
Australasian Data Mining. Conference (10th : 2012 : Sydney, N.S.W.)
Series
Conferences in Research and Practice in Information Technology; vol. 134
Pagination
93 - 101
Publisher
Australian Computer Society
Location
Sydney, N.S.W.
Place of publication
[Sydney, N.S.W.]
Start date
2012-12-05
End date
2012-12-07
Language
eng
Publication classification
E1 Full written paper - refereed
Copyright notice
2012, Australian Computer Society
Editor/Contributor(s)
Y Zhao, J Li, P Kennedy, P Christen
Title of proceedings
AusDM 2012 : Proceedings of the 10th Australasian Data Mining Conference