Adaptive Splitting and Selection Method for Noninvasive Recognition of Liver Fibrosis Stage

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7803)


Therapy of patients suffer form liver diseases strongly depends on the liver fibrosis progression. Unfortunately, to asses it the liver biopsy has been usually used which is an invasive and raging medical procedure which could lead to serious health complications. Additionally even when experienced medical experts perform liver biopsy and read the findings, up to a 20% error rate in liver fibrosis staging has been reported. Nowadays a few noninvasive commercial tests based on the blood examinations are available for the mentioned above problem. Unfortunately they are quite expensive and usually they are not refundable by the health insurance in Poland. Thus, the cross-disciplinary team, which includes researches form the Polish medical and technical universities has started work on new noninvasive method of liver fibrosis stage classification. This paper presents a starting point of the project where several traditional classification methods are compared with the originally developed classifier ensembles based on local specialization of the classifiers in given feature space partitions. The experiment was carried out on the basis of originally acquired database about patients with the different stages of liver fibrosis. The preliminary results are very promising, because they confirmed the possibility of outperforming the noninvasive commercial tests.


machine learning multiple classifier system clustering and selection evolutionary algorithm feature selection medical informatics liver fibrosis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alpaydin, E.: Combined 5 x 2 cv f test for comparing supervised classification learning algorithms. Neural Computation 11(8), 1885–1892 (1999)CrossRefGoogle Scholar
  2. 2.
    Bedossa, P., Poynard, T.: An algorithm for the grading of activity in chronic hepatitis c. the metavir cooperative study group. Hepatology 24, 289–293 (1996)CrossRefGoogle Scholar
  3. 3.
    Bi, Y.: The impact of diversity on the accuracy of evidential classifier ensembles. International Journal of Approximate Reasoning 53(4), 584–607 (2012)MathSciNetCrossRefGoogle Scholar
  4. 4.
  5. 5.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)zbMATHGoogle Scholar
  6. 6.
    Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20, 832–844 (1998)CrossRefGoogle Scholar
  7. 7.
    Ishak, K., Baptista, A., Bianchi, L., Callea, F., De Groote, J., Gudat, F., Denk, H., Desmet, V., Korb, G., MacSween, R.N., et al.: Histological grading and staging of chronic hepatitis. Hepatology 22, 696–699 (1995)CrossRefGoogle Scholar
  8. 8.
    Jackowski, K., Krawczyk, B., Woźniak, M.: Cost-Sensitive Splitting and Selection Method for Medical Decision Support System. In: Yin, H., Costa, J.A.F., Barreto, G. (eds.) IDEAL 2012. LNCS, vol. 7435, pp. 850–857. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Jackowski, K., Woźniak, M.: Algorithm of designing compound recognition system on the basis of combining classifiers with simultaneous splitting feature space into competence areas. Pattern Analysis and Applications 12(4), 415–425 (2009)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Jackowski, K., Woźniak, M.: Method of classifier selection using the genetic approach. Expert Systems 27(2), 114–128 (2010)CrossRefGoogle Scholar
  11. 11.
    Knodell, R.G., Ishak, K.G., Black, W.C., Chen, T.S., Craig, R., Kaplowitz, N., Kiernan, T.W., Wollman, J.: Formulation and application of a numerical scoring system for assessing histological activity in asymptomatic chronic active hepatitis. Hepatology 1, 431–435 (1981)CrossRefGoogle Scholar
  12. 12.
    Krawczyk, B., Woźniak, M.: Designing Cost-Sensitive Ensemble – Genetic Approach. In: Choraś, R.S. (ed.) Image Processing and Communications Challenges 3. AISC, vol. 102, pp. 227–234. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  13. 13.
    Krawczyk, B., Woźniak, M.: Analysis of Diversity Assurance Methods for Combined Classifiers. In: Choraś, R.S. (ed.) Image Processing and Communications Challenges 4. AISC, vol. 184, pp. 177–184. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Krawczyk, B., Woźniak, M., Orczyk, T., Porwik, P., Musialik, J., Błońska-Fajfrowska, B.: Classification techniques for non-invasive recognition of liver fibrosis stage. Journal of Medical Informatics & Technologies 20, 121–127 (2012)Google Scholar
  15. 15.
    Kuncheva, L.I.: Clustering-and-selection model for classifier combination. In: Proceedings of the Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, vol. 1, pp. 185–188 (2000)Google Scholar
  16. 16.
    Liebowitz, J.: The handbook of applied expert systems. CRC Press, Boca Raton (1998)zbMATHGoogle Scholar
  17. 17.
  18. 18.
    Michalewicz, Z.: Genetic algorithms + data structures = evolution programs, 3rd edn. Springer, London (1996)zbMATHCrossRefGoogle Scholar
  19. 19.
    Orczyk, T., Pałys, M., Porwik, P., Musialik, J., Błońska-Fajfrowska, B.: Simple and non-invasive liver fibrosis stage prediction method. Journal of Medical Informatics & Technologies 17, 227–232 (2011)Google Scholar
  20. 20.
    Polikar, R.: Ensemble based systems in decision making. IEEE Circuits and Systems Magazine 6(3), 21–45 (2006)CrossRefGoogle Scholar
  21. 21.
    R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008)Google Scholar
  22. 22.
    Wai, C.T., Greenson, J.K., Fontana, R.J., Kalbfleisch, J.D., Marrero, J.A., Conjeevaram, H.S., Lok, A.S.: A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis c. Hepatology 38, 518–526 (2003)CrossRefGoogle Scholar
  23. 23.
    Woźniak, M., Krawczyk, B.: Combined classifier based on feature space partitioning. Journal of Applied Mathematics and Computer Science 22(4) (2012) (in press) (to appear)Google Scholar
  24. 24.
    Woźniak, M., Zmyslony, M.: Designing combining classifier with trained fuser - analytical and experimental evaluation. Neural Network World 20(7), 925–934 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland
  2. 2.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland

Personalised recommendations