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 - http://www.scopus.com/inward/record.url?scp=85031111297&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85031111297
T3 - 30th International Conference on Computer Applications in Industry and Engineering, CAINE 2017
SP - 169
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 -
