You are not logged in.

Multi-layer attribute selection and classification algorithm for the diagnosis of cardiac autonomic neuropathy based on HRV attributes

Jelinek, Herbert F., Abawajy, Jemal H., Cornforth, David J., Kowalczyk, Adam, Negnevitsky, Michael, Chowdhury, Morshed U., Krones, Robert and Kelarev, Andrei V. 2015, Multi-layer attribute selection and classification algorithm for the diagnosis of cardiac autonomic neuropathy based on HRV attributes, AIMS medical science, vol. 2, no. 4, pp. 396-409, doi: 10.3934/medsci.2015.4.396.

Attached Files
Name Description MIMEType Size Downloads

Title Multi-layer attribute selection and classification algorithm for the diagnosis of cardiac autonomic neuropathy based on HRV attributes
Author(s) Jelinek, Herbert F.
Abawajy, Jemal H.
Cornforth, David J.
Kowalczyk, Adam
Negnevitsky, Michael
Chowdhury, Morshed U.
Krones, Robert
Kelarev, Andrei V.
Journal name AIMS medical science
Volume number 2
Issue number 4
Start page 396
End page 409
Total pages 14
Publisher AIMS Press
Place of publication Springfield, Mo.
Publication date 2015-12-02
ISSN 2375-1576
Keyword(s) Diabetes
Cardiac autonomic neuropathy
Neurology
Heart rate variability
Data mining
Knowledge discovery
Renyi entropy
Summary Cardiac autonomic neuropathy (CAN) poses an important clinical problem, which often remains undetected due difficulty of conducting the current tests and their lack of sensitivity. CAN has been associated with growth in the risk of unexpected death in cardiac patients with diabetes mellitus. Heart rate variability (HRV) attributes have been actively investigated, since they are important for diagnostics in diabetes, Parkinson's disease, cardiac and renal disease. Due to the adverse effects of CAN it is important to obtain a robust and highly accurate diagnostic tool for identification of early CAN, when treatment has the best outcome. Use of HRV attributes to enhance the effectiveness of diagnosis of CAN progression may provide such a tool. In the present paper we propose a new machine learning algorithm, the Multi-Layer Attribute Selection and Classification (MLASC), for the diagnosis of CAN progression based on HRV attributes. It incorporates our new automated attribute selection procedure, Double Wrapper Subset Evaluator with Particle Swarm Optimization (DWSE-PSO). We present the results of experiments, which compare MLASC with other simpler versions and counterpart methods. The experiments used our large and well-known diabetes complications database. The results of experiments demonstrate that MLASC has significantly outperformed other simpler techniques.
Language eng
DOI 10.3934/medsci.2015.4.396
Field of Research 080109 Pattern Recognition and Data Mining
110302 Clinical Chemistry (Diagnostics)
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, AIMS Press
Persistent URL http://hdl.handle.net/10536/DRO/DU:30084650

Document type: Journal Article
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 84 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Thu, 07 Jul 2016, 14:21:11 EST

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.