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: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
Elsevier Ltd.
2013
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/17677/1/Doreen.pdf http://ir.unimas.my/id/eprint/17677/ http://www.sciencedirect.com/science/article/pii/S1877042813037142 |
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| Summary: | 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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