Applying evolutionary algorithms to discover knowledge from medical databases

Man Leung WONG, Wai LAM, Kwong Sak LEUNG, C. Y., Jack CHENG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

2 Citations (Scopus)


Data mining has become an important research topic. The increasing use of computer results in an explosion of information. These data can be best used if the knowledge hidden can be uncovered. Thus there is a need for a way to automatically discover knowledge from data. In this paper, new approaches for knowledge discovery from two medical databases are investigated. Two different kinds of knowledge, namely rules and causal structures, are learned. Rules capture interesting patterns and regularities in the databases. Causal structures represented by Bayesian networks capture the causality relationships among the attributes. We employ advanced evolutionary algorithms for these discovery tasks. In particular, Generic Genetic Programming is employed as rule learning algorithm. Our approach for discovering causality relationships is based on Evolutionary Programming which learns Bayesian network structures.
Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Systems, Man and Cybernetics
PublisherIEEE, United States
Number of pages6
Publication statusPublished - 1 Jan 1999
EventProceedings of International Workshop on Evolutionary Computation - China, China
Duration: 1 Jan 20001 Jan 2000

Publication series

NameIEEE International Conference on Systems, Man, and Cybernetics
ISSN (Print)1062-922X


ConferenceProceedings of International Workshop on Evolutionary Computation
OtherInternational Workshop on Evolutionary Computation


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