Please use this identifier to cite or link to this item:
Title: A co-evolving memetic wrapper for prediction of patient outcomes in TCM informatics
Contributor(s): Detterer, Dion (author); Kwan, Paul H (author); Gondro, Cedric (author)orcid 
Publication Date: 2012
DOI: 10.1007/s11704-012-2959-0
Handle Link:
Abstract: Traditional Chinese medicine (TCM) relies on the combined effects of herbs within prescribed formulae. However, given the combinatorial explosion due to the vast number of herbs available for treatment, the study of these combined effects can become computationally intractable. Thus feature selection has become increasingly crucial as a pre-processing step prior to the study of combined effects in TCM informatics. In accord with this goal, a new feature selection algorithm known as a co-evolving memetic wrapper (COW) is proposed in this paper. COW takes advantage of recent research in genetic algorithms (GAs) and memetic algorithms (MAs) by evolving appropriate feature subsets for a given domain. Our empirical experiments have demonstrated that COW is capable of selecting subsets of herbs from a TCM insomnia dataset that shows signs of combined effects on the prediction of patient outcomes measured in terms of classification accuracy. We compare the proposed algorithm with results from statistical analysis including main effects and up to three way interaction terms and show that COW is capable of correctly identifying the herbs and herb by herb effects that are significantly associated to patient outcome prediction.
Publication Type: Journal Article
Source of Publication: Frontiers of Computer Science, 6(5), p. 621-629
Publisher: Springer
Place of Publication: United Kingdom
ISSN: 2095-2236
Field of Research (FOR): 110404 Traditional Chinese Medicine and Treatments
080108 Neural, Evolutionary and Fuzzy Computation
080109 Pattern Recognition and Data Mining
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Statistics to Oct 2018: Visitors: 266
Views: 386
Downloads: 0
Appears in Collections:Journal Article

Files in This Item:
2 files
File Description SizeFormat 
Show full item record

Page view(s)

checked on Mar 4, 2019
Google Media

Google ScholarTM







Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.