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Article
Discovering knowledge from medical databases using evolutionary algorithms : learning rules and causal structures for capturing patterns and causality relationships
IEEE Engineering in Medicine and Biology Magazine
  • Man Leung WONG, Lingnan University, Hong Kong
  • Wai LAM, Chinese University of Hong Kong
  • Kwong Sak LEUNG, Chinese University of Hong Kong
  • Po Shun NGAN, Chinese University of Hong Kong
  • C. Y., Jack CHENG, Chinese University of Hong Kong
Document Type
Journal article
Publication Date
7-20-2000
Abstract
Data mining, referred to as knowledge discovery in databases (KDD), is the nontrivial process of identifying valid, novel and potentially useful patterns in data. Evolutionary algorithms are employed for representing knowledge in rules and causal structures determined by Bayesian networks. Two medical databases are used to learn the rules for representing the patterns of data in addition to the use of Bayesian networks as causality relationship models among the attributes. Advanced evolutionary algorithms such as generic genetic programming, evolutionary programming and genetic algorithms are used to conduct the learning task.
DOI
10.1109/51.853481
E-ISSN
21542317
Publisher Statement

Copyright © 2000 IEEE

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Citation Information
Wong, M. L., Lam, W., Leung, K. S., Ngan, P. S., & Cheng, J. C. Y. (2000). Discovering knowledge from medical databases using evolutionary algorithms: Learning rules and causal structures for capturing patterns and causality relationships. IEEE Engineering in Medicine and Biology Magazine, 19(4), 45-55. doi: 10.1109/51.853481