- Title
- Empirical study of decision trees and ensemble classifiers for monitoring of diabetes patients in pervasive healthcare
- Creator
- Kelarev, Andrei; Stranieri, Andrew; Yearwood, John; Jelinek, Herbert
- Date
- 2012
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/35452
- Identifier
- vital:4850
- Identifier
-
https://doi.org/10.1109/NBiS.2012.20
- Abstract
- Diabetes is a condition requiring continuous everyday monitoring of health related tests. To monitor specific clinical complications one has to find a small set of features to be collected from the sensors and efficient resource-aware algorithms for their processing. This article is concerned with the detection and monitoring of cardiovascular autonomic neuropathy, CAN, in diabetes patients. Using a small set of features identified previously, we carry out an empirical investigation and comparison of several ensemble methods based on decision trees for a novel application of the processing of sensor data from diabetes patients for pervasive health monitoring of CAN. Our experiments relied on an extensive database collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University and concentrated on the particular task of the detection and monitoring of cardiovascular autonomic neuropathy. Most of the features in the database can now be collected using wearable sensors. Our experiments included several essential ensemble methods, a few more advanced and recent techniques, and a novel consensus function. The results show that our novel application of the decision trees in ensemble classifiers for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the outcomes obtained previously in the literature. © 2012 IEEE.
- Publisher
- Melbourne, VIC IEEE Computer Society, Conference Publishing Services (CPS)
- Rights
- Copyright 2012 The Institute of Electrical and Electronics Engineers, Inc.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Charles sturt universities; Clinical complications; Consensus functions; Diabetes patients; Empirical investigation; Empirical studies; Ensemble classifiers; Ensemble methods; Health monitoring; Novel applications; Performance parameters; Pervasive healthcare; Research initiatives; Resource aware; Sensor data; Wearable sensors; Data handling; Forestry; Health care; Information systems; Sensors
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