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Empirical investigation of multi-tier ensembles for the detection of cardiac autonomic neuropathy using subsets of the Ewing Features
conference contribution
posted on 2012-01-01, 00:00 authored by Jemal AbawajyJemal Abawajy, Andrei Kelarev, A Stranieri, H JelinekThis 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.
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New Trends of Computational Intelligence in Health Applications. Workshop (2012 : Sydney, N.S.W.)Pagination
1 - 11Publisher
CEURS-WSLocation
Sydney, N.S.W.Place of publication
Sydney, N.S.W.Start date
2012-12-04Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2012, CEURS-WSEditor/Contributor(s)
A Al-Jumaily, M Bennamoun, A Al-AniTitle of proceedings
CIHealth 2012- Proceedings of the Workshop on New Trends of Computational Intelligence in Health ApplciationsUsage metrics
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