Impact of Bayesian Network Model Structure on the Accuracy of Medical Diagnostic Systems

While Bayesian network models may contain a handful of numerical parameters that are important for their quality, several empirical studies have confirmed that overall precision of their probabilities is not crucial. In this paper, we study the impact of the structure of a Bayesian network on the precision of medical diagnostic systems. We show that also the structure is not that important – diagnostic accuracy of several medical diagnostic models changes minimally when we subject their structures to such transformations as arc removal and arc reversal.

[1]  J. Czerniak,et al.  Application of rough sets in the presumptive diagnosis of urinary system diseases , 2003 .

[2]  I. Ebert‐Uphoff.  Measuring Connection Strengths and Link Strengths in Discrete Bayesian Networks , 2007 .

[3]  Eric Horvitz,et al.  Decision Analysis and Expert Systems , 1991, AI Mag..

[4]  Hiroshi Motoda,et al.  PRICAI’98: Topics in Artificial Intelligence , 1998, Lecture Notes in Computer Science.

[5]  Igor Kononenko,et al.  Inductive and Bayesian learning in medical diagnosis , 1993, Appl. Artif. Intell..

[6]  Yuichiro Kanazawa,et al.  Hellinger distance and Akaike's information criterion for the histogram , 1993 .

[7]  Marek J. Druzdzel,et al.  An Experimental Comparison of Methods for Handling Incomplete Data in Learning Parameters of Bayesian Networks , 2002, Intelligent Information Systems.

[8]  A. Onisko.  Effect of Imprecision in Probabilities on Bayesian Network Models : An Empirical Study , 2003 .

[9]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.

[10]  Marek J. Druzdzel,et al.  The impact of overconfidence bias on practical accuracy of Bayesian network models: an empirical study , 2008, BMA.

[11]  Gregory M. Provan,et al.  The Sensitivity of Belief Networks to Imprecise Probabilities: An Experimental Investigation , 1996, Artif. Intell..

[12]  Daniel Kahneman,et al.  Probabilistic reasoning , 1993 .

[13]  Ann E. Nicholson,et al.  Using Mutual Information to Determine Relevance in Bayesian Networks , 1998, PRICAI.

[14]  Hanna Wasyluk,et al.  Extension of the HEPAR II Model to Multiple-Disorder Diagnosis , 2000, Intelligent Information Systems.

[15]  Carmen Lacave,et al.  A review of explanation methods for heuristic expert systems , 2004, The Knowledge Engineering Review.

[16]  Imme Ebert-Uphoff,et al.  Tutorial on How to Measure Link Strengths in Discrete Bayesian Networks , 2009 .

[17]  Andrew W. Moore,et al.  Efficient Algorithms for Minimizing Cross Validation Error , 1994, ICML.

[18]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[19]  Marek J. Druzdzel,et al.  Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems , 2013, Artif. Intell. Medicine.