A medical data mining application based on evolutionary computation

Man Leung WONG, Wai LAM, Kwong Sak LEUNG

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Abstract

In this chapter, we will present data mining techniques for discovering knowledge from Scoliosis database in the medical domain. Two kinds of knowledge, namely causal structures and rule knowledge, are learned. We employ Bayesian networks to represent causal structures. These networks are capable of depicting the causality relationships among the attributes. Rule knowledge captures interesting patterns and regularities in the database. We develop discovery methods based on Evolutionary Algarithms. Evolutionary Algorithms simulate the natural evolution to perform function optimization and machine learning. In particular, our approach for discovering causality relationships is based on Evolutionary Programming which learns Bayesian network structures. For rule learning, we apply Generic Genetic Programming to discover the rule knowledge. We have discovered new knowledge about the classification of Scoliosis and about the treatment. We demonstrate that the data mining process helps clinicians make decisions and enhance professional training.
Original languageEnglish
Title of host publicationMedical data mining and knowledge discovery
EditorsKrzysztof J. CIOS
Place of PublicationUnited States
PublisherSpringer-Verlag GmbH and Co. KG
Chapter10
Pages281-317
Number of pages37
ISBN (Print)9783790813401
Publication statusPublished - 2001

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    WONG, M. L., LAM, W., & LEUNG, K. S. (2001). A medical data mining application based on evolutionary computation. In K. J. CIOS (Ed.), Medical data mining and knowledge discovery (pp. 281-317). Springer-Verlag GmbH and Co. KG.