Prediction Model by Using Bayesian and Cognition-driven Techniques: A Study in the Context of Obstructive Sleep Apnea

This research proposes a mechanism for cost-effective medical diagnostic support for relatively new physical ailments or diseases where there are incomplete data sets available and hence, common parameters are forced to be used for drawing a- priori inferences. We propose a simple but powerful predi...

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Main Authors: Sim, Doreen Ying Ying, Chee, Siong Teh, Probir Kumar, Banerjee
Format: Article
Language:English
Published: Elsevier Ltd. 2013
Subjects:
Online Access:http://ir.unimas.my/id/eprint/17677/
http://ir.unimas.my/id/eprint/17677/1/Doreen.pdf
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author Sim, Doreen Ying Ying
Chee, Siong Teh
Probir Kumar, Banerjee
author_facet Sim, Doreen Ying Ying
Chee, Siong Teh
Probir Kumar, Banerjee
author_sort Sim, Doreen Ying Ying
building UNIMAS Institutional Repository
collection Online Access
description This research proposes a mechanism for cost-effective medical diagnostic support for relatively new physical ailments or diseases where there are incomplete data sets available and hence, common parameters are forced to be used for drawing a- priori inferences. We propose a simple but powerful prediction model that combines the advantages of the Bayesian Approaches and Cognition-Driven Techniques such as Expert Reasoning (ER) and Cognitive Reasoning (CR) using Markov Chain analyses. Then, we demonstrate the effectiveness of our approach in predicting Obstructive Sleep Apnea (OSA).
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institution Universiti Malaysia Sarawak
institution_category Local University
language English
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publishDate 2013
publisher Elsevier Ltd.
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spelling unimas-176772021-06-30T17:00:00Z http://ir.unimas.my/id/eprint/17677/ Prediction Model by Using Bayesian and Cognition-driven Techniques: A Study in the Context of Obstructive Sleep Apnea Sim, Doreen Ying Ying Chee, Siong Teh Probir Kumar, Banerjee H Social Sciences (General) This research proposes a mechanism for cost-effective medical diagnostic support for relatively new physical ailments or diseases where there are incomplete data sets available and hence, common parameters are forced to be used for drawing a- priori inferences. We propose a simple but powerful prediction model that combines the advantages of the Bayesian Approaches and Cognition-Driven Techniques such as Expert Reasoning (ER) and Cognitive Reasoning (CR) using Markov Chain analyses. Then, we demonstrate the effectiveness of our approach in predicting Obstructive Sleep Apnea (OSA). Elsevier Ltd. 2013 Article PeerReviewed text en http://ir.unimas.my/id/eprint/17677/1/Doreen.pdf Sim, Doreen Ying Ying and Chee, Siong Teh and Probir Kumar, Banerjee (2013) Prediction Model by Using Bayesian and Cognition-driven Techniques: A Study in the Context of Obstructive Sleep Apnea. Procedia - Social and Behavioral Sciences, 97. pp. 528-537. ISSN 1877-0428 http://www.sciencedirect.com/science/article/pii/S1877042813037142 doi : 10.1016/j.sbspro.2013.10.269
spellingShingle H Social Sciences (General)
Sim, Doreen Ying Ying
Chee, Siong Teh
Probir Kumar, Banerjee
Prediction Model by Using Bayesian and Cognition-driven Techniques: A Study in the Context of Obstructive Sleep Apnea
title Prediction Model by Using Bayesian and Cognition-driven Techniques: A Study in the Context of Obstructive Sleep Apnea
title_full Prediction Model by Using Bayesian and Cognition-driven Techniques: A Study in the Context of Obstructive Sleep Apnea
title_fullStr Prediction Model by Using Bayesian and Cognition-driven Techniques: A Study in the Context of Obstructive Sleep Apnea
title_full_unstemmed Prediction Model by Using Bayesian and Cognition-driven Techniques: A Study in the Context of Obstructive Sleep Apnea
title_short Prediction Model by Using Bayesian and Cognition-driven Techniques: A Study in the Context of Obstructive Sleep Apnea
title_sort prediction model by using bayesian and cognition-driven techniques: a study in the context of obstructive sleep apnea
topic H Social Sciences (General)
url http://ir.unimas.my/id/eprint/17677/
http://ir.unimas.my/id/eprint/17677/
http://ir.unimas.my/id/eprint/17677/
http://ir.unimas.my/id/eprint/17677/1/Doreen.pdf