Openly accessible

Bayesian networks and decision trees in the diagnosis of female urinary incontinence

Hunt, Miranda, von Konsky, Brian, Venkatesh, Svetha and Petros, Peter 2000, Bayesian networks and decision trees in the diagnosis of female urinary incontinence, in Proceedings of the 22nd Annual EMBS International Conference, IEEE, Washington, D. C., pp. 551-554.

Attached Files
Name Description MIMEType Size Downloads
venkatesh-bayesiannetworks-2000.pdf Published version application/pdf 393.56KB 42

Title Bayesian networks and decision trees in the diagnosis of female urinary incontinence
Author(s) Hunt, Miranda
von Konsky, Brian
Venkatesh, Svetha
Petros, Peter
Conference name International Conference of the IEEE Engineering in Medicine and Biology Society (22nd : 2000 : Chicago, Ill.)
Conference location Chicago, Ill.
Conference dates 23-28 Jul. 2000
Title of proceedings Proceedings of the 22nd Annual EMBS International Conference
Editor(s) Enderle, J. D.
Publication date 2000
Conference series International Conference of the IEEE Engineering in Medicine and Biology Society
Start page 551
End page 554
Total pages 4
Publisher IEEE
Place of publication Washington, D. C.
Keyword(s) bayesian network
decision tree
expert system
urinary incontinence
Summary This study compares the effectiveness of Bayesian networks versus Decision Trees in modeling the Integral Theory of Female Urinary Incontinence diagnostic algorithm. Bayesian networks and Decision Trees were developed and trained using data from 58 adult women presenting with urinary incontinence symptoms. A Bayesian Network was developed in collaboration with an expert specialist who regularly utilizes a non-automated diagnostic algorithm in clinical practice. The original Bayesian network was later refined using a more connected approach. Diagnoses determined from all automated approaches were compared with the diagnoses of a single human expert. In most cases, Bayesian networks were found to be at least as accurate as the Decision Tree approach. The refined Connected Bayesian Network was found to be more accurate than the Original Bayesian Network accurately discriminated between diagnoses despite the small sample size. In contrast, the Connected and Decision Tree approaches were less able to discriminate between diagnoses. The Original Bayesian Network was found to provide an excellent basis for graphically communicating the correlation between symptoms and laxity defects in a given anatomical zone. Performance measures in both networks indicate that Bayesian networks could provide a potentially useful tool in the management of female pelvic floor dysfunction. Before the technique can be utilized in practice, well-established learning algorithms should be applied to improve network structure. A larger training data set should also improve network accuracy, sensitivity, and specificity.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 0780364651
Language eng
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2000, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044534

Document type: Conference Paper
Collections: School of Information Technology
Open Access Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 4 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 46 Abstract Views, 42 File Downloads  -  Detailed Statistics
Created: Fri, 20 Apr 2012, 11:30:26 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.