Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7669
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shun Ngan, Po | en_US |
dc.contributor.author | Leung Wong, Man | en_US |
dc.contributor.author | Lam, Wai | en_US |
dc.contributor.author | Prof. LEUNG Kwong Sak | en_US |
dc.contributor.author | Cheng, Jack C.Y. | en_US |
dc.date.accessioned | 2023-03-30T03:15:59Z | - |
dc.date.available | 2023-03-30T03:15:59Z | - |
dc.date.issued | 1999 | - |
dc.identifier.citation | Artificial Intelligence in Medicine, 1999, Vol. 16 (1), pp. 73 - 96 | en_US |
dc.identifier.issn | 09333657 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11861/7669 | - |
dc.description.abstract | 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. 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.iso | en | en_US |
dc.publisher | Elsevier Science B.V. | en_US |
dc.relation.ispartof | Artificial Intelligence in Medicine | en_US |
dc.title | Medical data mining using evolutionary computation | en_US |
dc.type | Peer Reviewed Journal Article | en_US |
dc.identifier.doi | 10.1016/S0933-3657(98)00065-7 | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Department of Applied Data Science | - |
Appears in Collections: | Applied Data Science - Publication |
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