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

Kelarev, Andrei V, Abawajy, Jemal, Stranieri, Andrew and Jelinek, Herbert F 2013, Empirical investigation of decision tree ensembles for monitoring cardiac complications of diabetes, International journal of data warehousing and mining, vol. 9, no. 4, pp. 1-18.

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Title Empirical investigation of decision tree ensembles for monitoring cardiac complications of diabetes
Author(s) Kelarev, Andrei V
Abawajy, Jemal
Stranieri, Andrew
Jelinek, Herbert F
Journal name International journal of data warehousing and mining
Volume number 9
Issue number 4
Start page 1
End page 18
Total pages 18
Publisher IGI Global
Place of publication Hershey, PA
Publication date 2013
ISSN 1548-3924
1548-3932
Keyword(s) cardiac autonomic neuropathy (CAN)
decision tree ensembles
decision trees
diabetes
receiver operating characteristic (ROC) area
Summary 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.
Language eng
Field of Research 080501 Distributed and Grid Systems
Socio Economic Objective 890103 Mobile Data Networks and Services
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30062557

Document type: Journal Article
Collection: School of Information Technology
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