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)

    Abstract

    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
    Pages936-941
    Number of pages6
    Volume5
    DOIs
    Publication statusPublished - 1 Jan 1999

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    Bayesian networks
    Evolutionary algorithms
    Data mining
    Genetic programming
    Learning algorithms
    Explosions

    Cite this

    WONG, M. L., LAM, W., LEUNG, K. S., & CHENG, C. Y. . J. (1999). Applying evolutionary algorithms to discover knowledge from medical databases. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (Vol. 5, pp. 936-941). IEEE, United States. https://doi.org/10.1109/ICSMC.1999.815680
    WONG, Man Leung ; LAM, Wai ; LEUNG, Kwong Sak ; CHENG, C. Y., Jack. / Applying evolutionary algorithms to discover knowledge from medical databases. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Vol. 5 IEEE, United States, 1999. pp. 936-941
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    title = "Applying evolutionary algorithms to discover knowledge from medical databases",
    abstract = "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.",
    author = "WONG, {Man Leung} and Wai LAM and LEUNG, {Kwong Sak} and CHENG, {C. Y., Jack}",
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    WONG, ML, LAM, W, LEUNG, KS & CHENG, CYJ 1999, Applying evolutionary algorithms to discover knowledge from medical databases. in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. vol. 5, IEEE, United States, pp. 936-941. https://doi.org/10.1109/ICSMC.1999.815680

    Applying evolutionary algorithms to discover knowledge from medical databases. / WONG, Man Leung; LAM, Wai; LEUNG, Kwong Sak; CHENG, C. Y., Jack.

    Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Vol. 5 IEEE, United States, 1999. p. 936-941.

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

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    AB - 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.

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    WONG ML, LAM W, LEUNG KS, CHENG CYJ. Applying evolutionary algorithms to discover knowledge from medical databases. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Vol. 5. IEEE, United States. 1999. p. 936-941 https://doi.org/10.1109/ICSMC.1999.815680