Genetic-Evolved Bayesian Networks in a Biomedical Application

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 20)


This study presents genetic algorithm (GA) for discovering Bayesian network structure. The algorithm is applied to a medical datasets for vertebral column. Data set containing values for six biomechanical features is used to classify patients into three categories: disk hernia (DH), spondylolisthesis (SL), and normal (NO) or two categories: abnormal (AB), and NO. On ten-fold cross-validation run, the average AUC (the area under the ROC curve) measures of 0.874 and 0.923 for two and three categories are obtained, respectively. Results indicate that GA is relatively effective algorithm. Consequently, the GA-evolved BN is powerful tool for knowledge representation and inference because the causality relationship can be observed.


Bayesian network Genetic algorithm Medicine Classification 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Francisco (1988)Google Scholar
  2. 2.
    Guo, S., Xu, G., Zhang, H., Li, C.: A real-time flood updating model based on the Bayesian method. Methodology in Hydrology 311, 210–215 (2007)Google Scholar
  3. 3.
    Verron, S., Li, J., Tiplica, T.: Fault detection and isolation of faults in a multivariate process with Bayesian network. Journal of Process Control 20, 902–911 (2010)CrossRefGoogle Scholar
  4. 4.
    Balov, N.: A Gaussian mixed model for learning discrete Bayesian networks. Statistics and Probability Letters 81, 220–230 (2011)MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    Inza, I., Larranaga, P., Sierra, B.: Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms. International Journal of Approximate Reasoning 27(2), 143–164 (2001)MATHCrossRefGoogle Scholar
  6. 6.
    Chickering, M., Geiger, D., Heckerman, D.: Learning Bayesian Networks is NP-hard, Technical Report MSR-TR-94-17, Microsoft Research, Redmond (1994)Google Scholar
  7. 7.
    Larranaga, P., Kuijpers, C.M.H., Murga, R.H., Yurramendi, Y.: Learning Bayesian network structures by searching for the best ordering with genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics-Part A 26(4), 487–493 (1996)CrossRefGoogle Scholar
  8. 8.
    Wong, M.L., Lam, W., Leung, K.S.: Using evolutionary programming and minimum description length principle for data mining of Bayesian networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(2), 174–178 (1999)CrossRefGoogle Scholar
  9. 9.
    Zheng, B., Chang, Y.H., Wang, X.H., Good, W.F., Gur, D.: Feature selection for computerized mass detection in digitized mammograms by using a genetic algorithm. Academic Radiology 6(6), 327–332 (1999)CrossRefGoogle Scholar
  10. 10.
    Moussa, A., El-Gammal, M., Abdallah, E.N., Attia, A.I.: A genetic based algorithm for loss reduction in distribution systems. IEEE Transactions on Power Delivery 4(2), 447–453 (2000)Google Scholar
  11. 11.
    Aiyar, R.S., Gagneur, J., Steinmetz, L.M.: Identification of mitochondrial disease genes through integrative analysis of multiple datasets. Methods 46(4), 248–255 (2008)CrossRefGoogle Scholar
  12. 12.
    Bouckaert, R.R., Frank, E., Hall, M., Kirkby, R., Reutemann, P., Seewald, A., Scuse, D.: WEKA Manual for Version 3-7-3. University of Waikato, New Zealand (2010)Google Scholar
  13. 13.
    da Rocha Neto, A.R., Sousa, R., de Barreto, G.A., Cardoso, J.S.: Diagnostic of Pathology on the Vertebral Column with Embedded Reject Option. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669, pp. 588–595. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Jensen, F.V.: Introduction to Bayesian Networks. Springer, Berlin (1996)Google Scholar
  15. 15.
    Zhu, W.: Using Bayesian network on network tomography. Computer Communications 26, 155–163 (2003)CrossRefGoogle Scholar
  16. 16.
    Sierra, B., Serrano, N., Larranaga, P., Plasencia, E.J., Inza, I., Jimenez, J.J., Revuelta, P., Mora, M.L.: Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data. Artificial Intelligence in Medicine 22(3), 233–248 (2001)Google Scholar
  17. 17.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  18. 18.
    Hluck, G.: Genetic algorithms. In: Liebowitz, J. (ed.) The Handbook of Applied Expert Systems. CRC, Boca Raton (1997)Google Scholar
  19. 19.
    Frank, A., Asuncion, A.: UCI Machine Learning Repository University of California, School of Information and Computer Science, Irvine, CA (2010),
  20. 20.
    Berthonnaud, E., Dimnet, J., Roussouly, P., Labelle, H.: Analysis of the sagittal balance of the spine and pelvis using shape and orientation parameters. Journal of Spinal Disorders & Techniques 18(1), 40–47 (2005)CrossRefGoogle Scholar
  21. 21.
    Han, U.K., Kim, Y.H.: Determination of Class II and Class III skeletal patterns: receiver operating characteristic (ROC) analysis on various cephalometric measurements. American Journal of Orthodontics and Dentofacial Orthopedics 113(5), 538–545 (1998)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Zou, K.H.: Comparison of correlated receiver operating characteristic curves derived from repeated diagnostic test data. Academic Radiology 8(3), 225–233 (2001)CrossRefGoogle Scholar
  23. 23.
    Metz, C.E.: Receiver operating characteristic analysis: A tool for the quantitative evaluation of observer performance and imaging systems. Journal of the American College of Radiology 3(6), 413–422 (2006)CrossRefGoogle Scholar
  24. 24.
    Daya, S.: Diagnostic test: Receiver operating characteristic (ROC) curve. Evidence-based Obstetrics and Gynecology 8, 3–4 (2006)CrossRefGoogle Scholar
  25. 25.
    Swets, J.A.: Measuring the accuracy of diagnostic systems. Science 240(4857), 1285–1293 (1988)MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Digit Fashion DesignToko UniversityPu-Tzu CityTaiwan

Personalised recommendations