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 Jelinek

Research output: Book chapter/Published conference paperConference paper

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 essential 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 languageEnglish
Title of host publicationProceedings of the 30th International Conference on Computer Applications in Industry and Engineering (CAINE 2017)
EditorsGongzhu Hu, Takaaki Goto
Place of PublicationNew York, USA
PublisherInternational Society for Computers and Their Applications (ISCA)
Pages169-176
Number of pages7
ISBN (Print)9781510847835
Publication statusPublished - 2017
Event30th International Conference on Computer Applications in Industry and Engineering: CAINE 2017 - Hilton San Diego/Harbor Island, San Diego, United States
Duration: 02 Oct 201704 Oct 2017
http://www.caine-conf.org/2017/

Conference

Conference30th International Conference on Computer Applications in Industry and Engineering
CountryUnited States
CitySan Diego
Period02/10/1704/10/17
Internet address

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  • Cite this

    Abdalrada, A. S., Abawajy, J., Chowdhury, M., Rajasegarar, S., Al-Quraishi, T., & Jelinek, H. (2017). Meta Learning Ensemble Technique for Diagnosis of Cardiac Autonomic Neuropathy Based on Heart Rate Variability Features. In G. Hu, & T. Goto (Eds.), Proceedings of the 30th International Conference on Computer Applications in Industry and Engineering (CAINE 2017) (pp. 169-176). International Society for Computers and Their Applications (ISCA).