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Sensors 2017, 17(7), 1486; doi:10.3390/s17071486

Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network

Computer Science Department, University of Quebec at Chicoutimi, Chicoutimi, QC G7H 2B1, Canada
Computer Science Department, University of Quebec at Montreal, Montreal, QC H2L 2C4, Canada
Author to whom correspondence should be addressed.
Academic Editor: Francisco Javier Falcone Lanas
Received: 1 May 2017 / Revised: 13 June 2017 / Accepted: 14 June 2017 / Published: 23 June 2017
(This article belongs to the Special Issue Context Aware Environments and Applications)
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In the last three decades, researchers have examined extensively how context-aware systems can assist people, specifically those suffering from incurable diseases, to help them cope with their medical illness. Over the years, a huge number of studies on Chronic Obstructive Pulmonary Disease (COPD) have been published. However, how to derive relevant attributes and early detection of COPD exacerbations remains a challenge. In this research work, we will use an efficient algorithm to select relevant attributes where there is no proper approach in this domain. Such algorithm predicts exacerbations with high accuracy by adding discretization process, and organizes the pertinent attributes in priority order based on their impact to facilitate the emergency medical treatment. In this paper, we propose an extension of our existing Helper Context-Aware Engine System (HCES) for COPD. This project uses Bayesian network algorithm to depict the dependency between the COPD symptoms (attributes) in order to overcome the insufficiency and the independency hypothesis of naïve Bayesian. In addition, the dependency in Bayesian network is realized using TAN algorithm rather than consulting pneumologists. All these combined algorithms (discretization, selection, dependency, and the ordering of the relevant attributes) constitute an effective prediction model, comparing to effective ones. Moreover, an investigation and comparison of different scenarios of these algorithms are also done to verify which sequence of steps of prediction model gives more accurate results. Finally, we designed and validated a computer-aided support application to integrate different steps of this model. The findings of our system HCES has shown promising results using Area Under Receiver Operating Characteristic (AUC = 81.5%). View Full-Text
Keywords: context-aware applications; health care system; Bayesian Belief Network; ubiquitous and ambient computing; chronic pulmonary disease context-aware applications; health care system; Bayesian Belief Network; ubiquitous and ambient computing; chronic pulmonary disease

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Mcheick, H.; Saleh, L.; Ajami, H.; Mili, H. Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network. Sensors 2017, 17, 1486.

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