Discovering knowledge from medical databases using evolutionary algorithms : learning rules and causal structures for capturing patterns and causality relationships
Document Type
Journal article
Source Publication
IEEE Engineering in Medicine and Biology Magazine
Publication Date
7-20-2000
Volume
19
Issue
4
First Page
45
Last Page
55
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
Print ISSN
07395175
E-ISSN
21542317
Publisher Statement
Copyright © 2000 IEEE
Access to external full text or publisher's version may require subscription.
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
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