research-article

Evolved bayesian networks as a versatile alternative to partin tables for prostate cancer management

Authors:

Frank Herrmann,

Eng OngAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1547 - 1554

https://doi.org/10.1145/1389095.1389392

Published: 12 July 2008 Publication History

Abstract

In this paper, we report on work done evolving Bayesian Networks with Genetic Algorithms. We use a Chain Model GA [19] to induce a Bayesian network model for the real world problem of Prostate Cancer management. Bayesian networks can and have been used in a wide range of complex domains, notably in medicine. In fact, they have shown powerful capabilities in representing and dealing with the uncertainties generally inherent in the clinical practice. In this study, we investigate those capabilities by testing the evolved model's predictive power and exploring its potential use as a more versatile alternative to the widely used Partin tables for prostate cancer pathology staging.

References

[1]

R. R. Bouckaert. Probabilistic network construction using the minimum description length principle. Lecture Notes in Computer Science, 747:41--48, 1993.

Digital Library

[2]

W. Buntine. Operations for learning with graphical models. Journal of Artificial Intelligence Research, 2:159--225, 1994.

Digital Library

[3]

CancerResearchUK. http://www.cancerresearchuk.org.

[4]

D. Chickering, D. Heckerman, and C. Meek. Large-sample learning of Bayesian networks is NP-Hard. J. Mach. Learn. Res., 5:1287--1330, 2004.

[5]

D. M. Chickering. Learning equivalence classes of Bayesian-network structures. J. Mach. Learn. Res., 2:445--498, February 2002.

Digital Library

[6]

C. Chow and C. Liu. Approximating discrete probability distributions with dependence trees. IEEE transactions on Information Theory, 14:462--467, 1968.

[7]

G. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309--347, 1992.

[8]

R. G. Cowell, S. L. Lauritzen, A. P. David, and D. J. Spiegelhalter. Probabilistic Networks and Expert Systems. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 1999.

Digital Library

[9]

E. Crawford, E. Gamito, C. O'Donnell, A. Errejon, D. Raben, M. Han, A. Partin, and A. Tewari. Artificial neural network model to predict risk of non-organ-confined disease and risk of lymph node spread in men with clinically localized prostate cancer. Journal of Urology, pages 165--233, 2001.

[10]

L. de Campos and J. Huete. On the use of independence relationships for learning simplified belief networks. International Journal of Intelligent Systems, 12:495--522, 1997.

[11]

B. Djavan, M. Remzi, A. Zlotta, C. Seitz, P. Snow, and M. Marberger. Novel artificial neural network for early detection of prostate cancer. Journal of Clinical Oncology, 20(4):921--929, February 2002.

[12]

N. Friedman and M. Goldszmidt. Discretizing continuous attributes while learning Bayesian networks. In Proceedings of the International Conference on Machine Learning, pages 157--165, 1996.

[13]

N. Friedman and M. Goldszmidt. Learning Bayesian networks with local structure. In Proceedings of the 12th Conference on Uncertainty in AI, pages 252--262, 1996.

Digital Library

[14]

E. J. Gamito, E. D. Crawford, and A. Errejon. Artificial neural networks for predictive modeling in prostate cancer., chapter Handbook of Prostate Cancer: Biology, Epidem. and Therapeutic Modalities. 2002.

[15]

GeNIe. GeNIe structural modelling tool http://genie.sis.pitt.edu/.

[16]

J. Habrant. Structure learning of Bayesian networks from databases by genetic algorithms-application to time series prediction in finance. In ICEIS, pages 225--231, 1999.

[17]

D. Heckerman. An empirical comparison of three inference methods. In Proceedings of the 4th Conference on Uncertainty in AI, pages 283--302, 1990.

Digital Library

[18]

D. Heckerman, D. Geiger, and D. M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. In KDD Workshop, pages 85--96, 1994.

[19]

R. Kabli, F. Herrmann, and J. McCall. A chain-model genetic algorithm for bayesian network structure learning. In GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 1264--1271, New York, NY, USA, 2007. ACM.

Digital Library

[20]

R. S. Kirby, M. K. Brawer, and L. J. Denis. Fast Facts: Prostate Cancer. Health Press, third edition, 2001.

[21]

M. Koivisto and K. Sood. Exact Bayesian structure discovery in Bayesian networks. J. Mach. Learn. Res., 5:549--573, 2004.

Digital Library

[22]

P. Larrañaga, C. Kuijpers, and R. Murga. Learning Bayesian network structures by searching for the best ordering with genetic algorithms. IEEE Transactions on System, Man and Cybernetics, 26:487--493, 1996.

Digital Library

[23]

Netica. Netica Bayesian network software from Norsys http://www.norsys.com.

[24]

A. W. Partin, M. Kattan, E. Subong, P. Walsh, K. Wojno, J. Oesterling, P. Scardino, and J. Pearson. Combination of prostate-specific antigen, clinical stage, and gleason score to predict pathological stage of localized prostate cancer. A multi-institutional update. Jama, (277):1445--51, 1997.

[25]

A. W. Partin, J. Yoo, H. B. Carter, J. D. Pearson, D. W. Chan, J. I. Epstein, and P. C. Walsh. The use of prostate specific antigen, clinical stage and gleason score to predict pathological stage in men with localized prostate cancer. Journal of Urology, (150):110--4, 1993.

[26]

J. Pearl and T. Verma. Equivalence and synthesis of causal models. In Proceedings of the 6th Conference on Uncertainty in AI, pages 220--227, 1990.

Digital Library

[27]

R. Robinson. Counting labeled acyclic digraphs. New Directions in the Theory of Graphs, pages 239--273, 1973.

[28]

L. H. Sobin and C. Wittekind. TNM classification of malignant tumours. Wiley-Liss, 6th edition edition, 2002.

[29]

P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction and Search. Lecture Notes in Statistics, New York: Springer Verlag, 81, 1993.

[30]

S. van Dijk, D. Thierens, and L. C. van der Gaag. Building a GA from design principles for learning Bayesian networks. In GECCO'03, pages 886--897, 2003.

Digital Library

[31]

M. L. Wong, S. Y. Lee, and K. S. Leung. A hybrid data mining approach to discover Bayesian networks using evolutionary programming. In GECCO '02: Proceedings of the Genetic and Evolutionary Computation Conference, pages 214--222, San Francisco, CA, USA, 2002. Morgan Kaufmann Publishers Inc.

Digital Library

[32]

H. Zhang, Z. Zhang, and A. Partin. Neural network based systems for prostate cancer stage prediction. In Proceedings of the IEEE-INNS-ENNS Conference, 2000.

Digital Library

Cited By

LarrañAga PKarshenas HBielza CSantana R(2013)A review on evolutionary algorithms in Bayesian network learning and inference tasksInformation Sciences: an International Journal10.1016/j.ins.2012.12.051233(109-125)Online publication date: 1-Jun-2013
https://dl.acm.org/doi/10.1016/j.ins.2012.12.051
Regnier-Coudert OMcCall J(2012)An Island Model Genetic Algorithm for Bayesian network structure learning2012 IEEE Congress on Evolutionary Computation10.1109/CEC.2012.6252982(1-8)Online publication date: Jun-2012
https://doi.org/10.1109/CEC.2012.6252982
Lam TRegnier-Coudert OMcCall JMcClinton S(2012)Development and Validation of a UK-Specific Prostate Cancer Staging Predictive Model: UK Prostate Cancer TablesBritish Journal of Medical and Surgical Urology10.1016/j.bjmsu.2011.12.0055:5(224-235)Online publication date: 1-Sep-2012
https://doi.org/10.1016/j.bjmsu.2011.12.005
Show More Cited By

Index Terms

Evolved bayesian networks as a versatile alternative to partin tables for prostate cancer management
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
  2. Machine learning

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cover image ACM Conferences

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

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Cited By

LarrañAga PKarshenas HBielza CSantana R(2013)A review on evolutionary algorithms in Bayesian network learning and inference tasksInformation Sciences: an International Journal10.1016/j.ins.2012.12.051233(109-125)Online publication date: 1-Jun-2013
https://dl.acm.org/doi/10.1016/j.ins.2012.12.051
Regnier-Coudert OMcCall J(2012)An Island Model Genetic Algorithm for Bayesian network structure learning2012 IEEE Congress on Evolutionary Computation10.1109/CEC.2012.6252982(1-8)Online publication date: Jun-2012
https://doi.org/10.1109/CEC.2012.6252982
Lam TRegnier-Coudert OMcCall JMcClinton S(2012)Development and Validation of a UK-Specific Prostate Cancer Staging Predictive Model: UK Prostate Cancer TablesBritish Journal of Medical and Surgical Urology10.1016/j.bjmsu.2011.12.0055:5(224-235)Online publication date: 1-Sep-2012
https://doi.org/10.1016/j.bjmsu.2011.12.005
Fournier FWu YMcCall JPetrovski ABarclay P(2010)Application of evolutionary algorithms to learning evolved Bayesian Network models of rig operations in the Gulf of Mexico2010 UK Workshop on Computational Intelligence (UKCI)10.1109/UKCI.2010.5625588(1-6)Online publication date: Sep-2010
https://doi.org/10.1109/UKCI.2010.5625588
Ciolko ELu FJoshi A(2010)Intelligent Clinical Decision Support Systems based on SNOMED CT2010 Annual International Conference of the IEEE Engineering in Medicine and Biology10.1109/IEMBS.2010.5625982(6781-6784)Online publication date: Aug-2010
https://doi.org/10.1109/IEMBS.2010.5625982
Fournier FMcCall JPetrovski ABarclay P(2010)Evolved Bayesian Network models of rig operations in the gulf of MexicoIEEE Congress on Evolutionary Computation10.1109/CEC.2010.5586021(1-7)Online publication date: Jul-2010
https://doi.org/10.1109/CEC.2010.5586021

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