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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 37 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 Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     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 Fri, 20 Apr 2012, 11:30:26 EST Mon, 23 Apr 2012, 12:16:23 EST Mon, 23 Apr 2012, 12:19:33 EST Tue, 01 May 2012, 14:18:49 EST Filtered Full Citation counts: Cited 4 times in Scopus Search Google Scholar Access Statistics: 36 Abstract Views, 37 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.