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 (Deakin University, Burwood, VIC, Australia), Jemal Abawajy (School of Information Technology, Deakin University, Burwood, VIC, Australia), Andrew Stranieri (School of Science, Information Technology and Engineering, University of Ballarat, Ballarat, VIC, Australia) and Herbert F. Jelinek (Khalifa University, Abu Dhabi, UAE & Charles Sturt University, Albury, NSW, Australia)
Copyright: © 2013 |Pages: 18
DOI: 10.4018/ijdwm.2013100101


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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Cardiac complications of diabetes may lead to increased morbidity and a higher probability of sudden death of the patients. Many patients suffering from diabetes develop complications that require continuous cardiac monitoring that can be performed by state of the art mobile phones and commercial-off-the-shelf wearable bio-sensors (Tayebi, Krishnaswamy, Waluyo, Sinha & Gaber, 2011). In order to be able to monitor cardiac complications of diabetes using wearable sensors, a small set of features needs to be determined and effective algorithms for their processing have to be investigated.

The investigation and development of algorithms for pervasive healthcare systems has attracted serious attention in the literature and has been growing. Many novel advanced techniques for the use in new data acquisition systems have been studied. These advances in research relying on wireless communication and sensor technologies have opened up a new paradigm in the healthcare industry. Examples of recent results obtained in this field include a flexible, efficient and lightweight Wireless Body Area Network (WBAN) Middleware. Song, Xiao, Waluyo, Chen and Wu (2008) investigated a service-specific middleware architecture bridging the gap between the application development and the underlying network sensor devices. The proposed middleware is able to provide functions including network initialization and registration, service announcement, sensor actuation and control for flexible multiple modalities data acquisition, and real-time network service management. The middleware has been implemented and tested with a healthcare monitoring test-bed using Imperial College sensor nodes. An extension of the middleware overcame the constraints on the application development in WBAN placed by the limited network resources and bridges the communication gap between sensor nodes and a mobile device. The middleware shielded the underlying sensor and OS/protocol stack away from the WBAN application layer (developed by Chen, Waluyo, Pek, & Yeoh, 2010). The middleware was implemented as a lightweight dynamic link library, which allows an application developer to simply incorporate the middleware resource dynamic link library into their application to call the required functions.

Within the context of individual healthcare, an architectural framework for the wellness management was implemented by Biswas, Jayachandran, Shue, Xiao, and Yap (2007). The key benefit that this framework brought was in enabling incremental incorporation of new sensors and sensing modalities as well as other hardware devices. Software modules, such as signal processing algorithms and self-help oriented user interfaces may be added easily, and the responses can be personalized and customized to suit the needs of a patient, caregiver or doctor. The extensibility and personalization are particularly valuable for home based healthcare and wellness management. Sensor technology and continuous monitoring increases the amount of data that needs to be efficiently sorted for point-of-care decision making. This has led to developments in the application of data warehousing including analysis of the defining features of a clinical data warehouse (Nealon, Rahayu, & Pardede, 2009). The analysis centered on the opportunities for and threats to the optimization of individual performance solutions based on the structure and merits of the data within the data warehouse to obtain the most optimal configuration. This focus was motivated by the large periods of time commonly required to process query results in the data warehousing and online analytical processing (OLAP) as crucial elements of decision support in healthcare. A windowing data structure architecture which manages a collection of popular windows was introduced for increasing the performance of OLAP queries on a clinical data warehouse.

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