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Empirical Investigation of Decision Tree Ensembles for Monitoring Cardiac Complications of Diabetes

Empirical Investigation of Decision Tree Ensembles for Monitoring Cardiac Complications of Diabetes

Andrei V. Kelarev, Jemal Abawajy, Andrew Stranieri, Herbert F. Jelinek
Copyright: © 2013 |Volume: 9 |Issue: 4 |Pages: 18
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781466635883|DOI: 10.4018/ijdwm.2013100101
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MLA

Kelarev, Andrei V., et al. "Empirical Investigation of Decision Tree Ensembles for Monitoring Cardiac Complications of Diabetes." IJDWM vol.9, no.4 2013: pp.1-18. http://doi.org/10.4018/ijdwm.2013100101

APA

Kelarev, A. V., Abawajy, J., Stranieri, A., & Jelinek, H. F. (2013). Empirical Investigation of Decision Tree Ensembles for Monitoring Cardiac Complications of Diabetes. International Journal of Data Warehousing and Mining (IJDWM), 9(4), 1-18. http://doi.org/10.4018/ijdwm.2013100101

Chicago

Kelarev, Andrei V., et al. "Empirical Investigation of Decision Tree Ensembles for Monitoring Cardiac Complications of Diabetes," International Journal of Data Warehousing and Mining (IJDWM) 9, no.4: 1-18. http://doi.org/10.4018/ijdwm.2013100101

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Abstract

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.

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