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There has been a steady progress in creating expert systems capable of diagnosis; however, there exists no generic expert system structure capable of accurately representing medical knowledge and extracting diagnosis in a specific multi-disease medical discipline. This paper aims to justify a standard methodology that can be used in creating a generic expert system model for generic medical domains.
We investigated Bayesian network structure learning and probability estimation from mammographic feature data in order to classify breast lesions into different pathological categories. We compared the learned networks to naive Bayes classifiers, which are similar to the expert systems previously investigated for breast lesion classification. The learned network structures reflect the difference in...
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