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

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

Biomedical Engineering & Biotechnology

Deakin University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 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 language	British English
Title of host publication	30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017
Editors	Takaaki Goto, Gongzhu Hu
Pages	169-175
Number of pages	7
ISBN (Electronic)	9781943436088
State	Published - 2017
Event	30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017 - San Diego, United States Duration: 2 Oct 2017 → 4 Oct 2017

Publication series

Name	30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017

Conference

Conference	30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017
Country/Territory	United States
City	San Diego
Period	2/10/17 → 4/10/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Cardiac autonomic neuropathy
Heart rate variability
Meta ensemble technique
Metaclassifiers

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Abdalrada, A. S., Abawajy, J., Chowdhury, M., Rajasegarar, S., Al-Quraishi, T., & Jelinek, H. F. (2017). Meta learning ensemble technique for diagnosis of cardiac autonomic neuropathy based on heart rate variability features. In T. Goto, & G. Hu (Eds.), 30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017 (pp. 169-175). (30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017).

Abdalrada, Ahmad Shaker ; Abawajy, Jemal ; Chowdhury, Morshed et al. / Meta learning ensemble technique for diagnosis of cardiac autonomic neuropathy based on heart rate variability features. 30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017. editor / Takaaki Goto ; Gongzhu Hu. 2017. pp. 169-175 (30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017).

@inproceedings{5f99c8925a054a239fff129d0ccce754,

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

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.",

keywords = "Cardiac autonomic neuropathy, Heart rate variability, Meta ensemble technique, Metaclassifiers",

author = "Abdalrada, \{Ahmad Shaker\} and Jemal Abawajy and Morshed Chowdhury and Sutharshan Rajasegarar and Tahsien Al-Quraishi and Jelinek, \{Herbert F.\}",

year = "2017",

language = "British English",

series = "30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017",

pages = "169--175",

editor = "Takaaki Goto and Gongzhu Hu",

booktitle = "30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017",

note = "30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017 ; Conference date: 02-10-2017 Through 04-10-2017",

}

Abdalrada, AS, Abawajy, J, Chowdhury, M, Rajasegarar, S, Al-Quraishi, T & Jelinek, HF 2017, Meta learning ensemble technique for diagnosis of cardiac autonomic neuropathy based on heart rate variability features. in T Goto & G Hu (eds), 30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017. 30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017, pp. 169-175, 30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017, San Diego, United States, 2/10/17.

Meta learning ensemble technique for diagnosis of cardiac autonomic neuropathy based on heart rate variability features. / Abdalrada, Ahmad Shaker; Abawajy, Jemal; Chowdhury, Morshed et al.
30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017. ed. / Takaaki Goto; Gongzhu Hu. 2017. p. 169-175 (30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Meta learning ensemble technique for diagnosis of cardiac autonomic neuropathy based on heart rate variability features

AU - Abdalrada, Ahmad Shaker

AU - Abawajy, Jemal

AU - Chowdhury, Morshed

AU - Rajasegarar, Sutharshan

AU - Al-Quraishi, Tahsien

AU - Jelinek, Herbert F.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Cardiac autonomic neuropathy

KW - Heart rate variability

KW - Meta ensemble technique

KW - Metaclassifiers

UR - https://www.scopus.com/pages/publications/85031111297

M3 - Conference contribution

AN - SCOPUS:85031111297

T3 - 30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017

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EP - 175

BT - 30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017

A2 - Goto, Takaaki

A2 - Hu, Gongzhu

T2 - 30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017

Y2 - 2 October 2017 through 4 October 2017

ER -

Abdalrada AS, Abawajy J, Chowdhury M, Rajasegarar S, Al-Quraishi T, Jelinek HF. Meta learning ensemble technique for diagnosis of cardiac autonomic neuropathy based on heart rate variability features. In Goto T, Hu G, editors, 30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017. 2017. p. 169-175. (30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017).

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

Abstract

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UN SDGs

Keywords

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