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Impact of Quality of Bayesian Network Parameters on Accuracy of Medical Diagnostic Systems

Onisko, Agnieszka and Druzdzel, Marek J. (2011) Impact of Quality of Bayesian Network Parameters on Accuracy of Medical Diagnostic Systems. In: AIME'11 Workshop on Probabilistic Problem Solving in Biomedicine (ProBioMed--11), in conjunction with the Thirteenth Conference on Artificial Intelligence in Medicine (AIME-2011), 02 July 2011 - 02 July 2011, Bled, Slovenia.

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Abstract

While most knowledge engineers believe that the quality of results obtained by means of Bayesian networks is not too sensitive to imprecision in probabilities, this remains a conjecture with only modest empirical support. We summarize the results of several previously presented experiments involving Hepar II model, in which we manipulated the quality of the model's numerical parameters and checked the impact of these manipulations on the model's accuracy. The chief contribution of this paper are results of replicating our experiments on several medical diagnostic models derived from data sets available at the Irvine Machine Learning Repository. We show that the results of our experiments are qualitatively identical to those obtained earlier with Hepar II.


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Details

Item Type: Conference or Workshop Item (Paper)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Onisko, Agnieszka
Druzdzel, Marek J.marek@sis.pitt.eduDRUZDZEL0000-0002-7598-2286
Date: 2 July 2011
Date Type: Publication
Page Range: 135 - 148
Event Title: AIME'11 Workshop on Probabilistic Problem Solving in Biomedicine (ProBioMed--11), in conjunction with the Thirteenth Conference on Artificial Intelligence in Medicine (AIME-2011)
Event Dates: 02 July 2011 - 02 July 2011
Event Type: Conference
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
Official URL: http://probiomed.cs.ru.nl/
Date Deposited: 07 Jul 2012 14:44
Last Modified: 05 Mar 2019 01:55
URI: http://d-scholarship.pitt.edu/id/eprint/12727

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