Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7669
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dc.contributor.authorShun Ngan, Poen_US
dc.contributor.authorLeung Wong, Manen_US
dc.contributor.authorLam, Waien_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorCheng, Jack C.Y.en_US
dc.date.accessioned2023-03-30T03:15:59Z-
dc.date.available2023-03-30T03:15:59Z-
dc.date.issued1999-
dc.identifier.citationArtificial Intelligence in Medicine, 1999, Vol. 16 (1), pp. 73 - 96en_US
dc.identifier.issn09333657-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7669-
dc.description.abstractIn this paper, we introduce a system for discovering medical knowledge by learning Bayesian networks and rules. Evolutionary computation is used as the search algorithm. The Bayesian networks can provide an overall structure of the relationships among the attributes. The rules can capture detailed and interesting patterns in the database. The system is applied to real-life medical databases for limb fracture and scoliosis. The knowledge discovered provides insights to and allows better understanding of these two medical domains. In this paper, we introduce a system for discovering medical knowledge by learning Bayesian networks and rules. Evolutionary computation is used as the search algorithm. The Bayesian networks can provide an overall structure of the relationships among the attributes. The rules can capture detailed and interesting patterns in the database. The system is applied to real-life medical databases for limb fracture and scoliosis. The knowledge discovered provides insights to and allows better understanding of these two medical domains.en_US
dc.language.isoenen_US
dc.publisherElsevier Science B.V.en_US
dc.relation.ispartofArtificial Intelligence in Medicineen_US
dc.titleMedical data mining using evolutionary computationen_US
dc.typePeer Reviewed Journal Articleen_US
dc.identifier.doi10.1016/S0933-3657(98)00065-7-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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