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

Citation

Jelinek, HF and Abawajy, JH and Cornforth, DJ and Kowalczyk, A and Negnevitsky, M and Chowdhury, MU and Krones, R and Kelarev, AV, Multi-layer attribute selection and classification algorithm for the diagnosis of cardiac autonomic neuropathy based on HRV attributes, AIMS Medical Science, 2, (4) pp. 396-409. ISSN 2375-1576 (2015) [Refereed Article]


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Copyright Statement

© 2015 Herbert F. Jelinek et al., licensee AIMS Press. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) http://creativecommons.org/licenses/by/4.0/

DOI: doi:10.3934/medsci.2015.4.396

Abstract

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.

Item Details

Item Type:Refereed Article
Keywords:diabetes, cardiac autonomic neuropathy, neurology, heart rate variability, data mining, knowledge discovery, Rényi entropy
Research Division:Engineering
Research Group:Electrical and Electronic Engineering
Research Field:Power and Energy Systems Engineering (excl. Renewable Power)
Objective Division:Energy
Objective Group:Energy Storage, Distribution and Supply
Objective Field:Energy Services and Utilities
Author:Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:105766
Year Published:2015
Web of Science® Times Cited:1
Deposited By:Engineering
Deposited On:2016-01-14
Last Modified:2016-04-11
Downloads:18 View Download Statistics

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