Meta learning ensemble technique for diagnosis of cardiac autonomic neuropathy based on heart rate variability features

Ahmad Shaker Abdalrada, Jemal Abawajy, Morshed Chowdhury, Sutharshan Rajasegarar, Tahsien Al-Quraishi, Herbert F. Jelinek

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Heart Rate Variability (HRV) attributes form an important set of tests, usually collected for patients with different kinds of pathology such as diabetes, kidney disease and cardiovascular disease. The aim of this study was to examine the role of HRV attributes for improving the diagnosis of Cardiac Autonomic Neuropathy (CAN). We investigated the performance of various base classifiers for the most essentials features for CAN combined with the HRV attributes. To get the optimal subset of features, we used a feature selection method based on mean decrease accuracy (MDA), which is implemented in the Random Forest classifier. Random Forest consistently outperformed all other base classifiers. A number of ensemble classifiers have also been investigated using Random Forest to enhance the diagnosis of CAN when Ewing battery tests were combined with HRV attributes. The results improved classification accuracy compared to existing classifiers with the best results obtained by AdaBoostM and MultBoost ensembles.

Original languageBritish English
Title of host publication30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017
EditorsTakaaki Goto, Gongzhu Hu
Pages169-175
Number of pages7
ISBN (Electronic)9781943436088
StatePublished - 2017
Event30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017 - San Diego, United States
Duration: 2 Oct 20174 Oct 2017

Publication series

Name30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017

Conference

Conference30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017
Country/TerritoryUnited States
CitySan Diego
Period2/10/174/10/17

Keywords

  • Cardiac autonomic neuropathy
  • Heart rate variability
  • Meta ensemble technique
  • Metaclassifiers

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