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Empirical investigation of decision tree ensembles for monitoring cardiac complications of diabetes

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journal contribution
posted on 2013-01-01, 00:00 authored by Andrei Kelarev, Jemal AbawajyJemal Abawajy, A Stranieri, H Jelinek
Cardiac complications of diabetes require continuous monitoring since they may lead to increased morbidity or sudden death of patients. In order to monitor clinical complications of diabetes using wearable sensors, a small set of features have to be identified and effective algorithms for their processing need to be investigated. This article focuses on detecting and monitoring cardiac autonomic neuropathy (CAN) in diabetes patients. The authors investigate and compare the effectiveness of classifiers based on the following decision trees: ADTree, J48, NBTree, RandomTree, REPTree, and SimpleCart. The authors perform a thorough study comparing these decision trees as well as several decision tree ensembles created by applying the following ensemble methods: AdaBoost, Bagging, Dagging, Decorate, Grading, MultiBoost, Stacking, and two multi-level combinations of AdaBoost and MultiBoost with Bagging for the processing of data from diabetes patients for pervasive health monitoring of CAN. This paper concentrates on the particular task of applying decision tree ensembles for the detection and monitoring of cardiac autonomic neuropathy using these features. Experimental outcomes presented here show that the authors' application of the decision tree ensembles for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the results obtained previously in the literature.

History

Journal

International journal of data warehousing and mining

Volume

9

Issue

4

Pagination

1 - 18

Publisher

IGI Global

Location

Hershey, PA

ISSN

1548-3924

eISSN

1548-3932

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2013, IGI Global