Risk analysis of Thalassemia using knowledge representation model: Diagnostic Bayesian Networks
Bayesian Networks (BNs) is one of the most effective theoretical models applied to make medical diagnostic decisions. In particular, it has been applied to Thalassemia, which is one of the most common genetic disorders in the world. The main problems of diagnosing this disease are the complex proces...
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| Main Authors: | , |
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| Format: | Conference Proceeding |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84864193787&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/51612 |
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| Institution: | Chiang Mai University |
| Summary: | Bayesian Networks (BNs) is one of the most effective theoretical models applied to make medical diagnostic decisions. In particular, it has been applied to Thalassemia, which is one of the most common genetic disorders in the world. The main problems of diagnosing this disease are the complex processes for diagnosing the several types of Thalassemia which occur in Thailand. Moreover, diagnostic methods are slow and rely on expert knowledge and experience as well as expensive equipment. The advantage of BNs is that they are used to represent the diagnostic domain in the form of graphical statistical models. The propose of this paper is to construct a Diagnostic Bayesian Networks for risk analysis of Thalassemia using polychromatic model for screening each type of Thalassemia, including related variables. The model will be used to elicit and calculate the probabilities of each type of Thalassemia in future research. © 2012 IEEE. |
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