Title

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

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