Advertisement

Neural Network Ensemble Based on Feature Selection for Non-Invasive Recognition of Liver Fibrosis Stage

Conference paper
First Online:
  • 1.1k Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 223)

Abstract

Contemporary medicine concentrates on providing high quality diagnostic services, yet it should not be forgotten that the comfort of the patient during the examination is also of high importance. Therefore non-invasive methods that allows to precisely predict the state of the disease are currently one of the key issues in the medical business. The paper presents a novel ensemble of neural networks applied to recognition of liver fibrosis stage from indirect examination method. Several neural network models are build on the basis of outputs of different feature selection algorithms. Then an ensemble pruning procedure with the usage of diversity measures is conducted in order to eliminate redundant predictors from the pool. Finally the weights of classifiers in the fusion process are assessed to establish their influence on the output of the whole ensemble. Proposed method is compared with several state-of-the-art ensemble methods. Extensive experimental investigations, carried out on a dataset collected by authors, show that the proposed method achieve a satisfactory level of the fibrosis level recognition, outperforming other machine learning algorithms and thus may be used as a real-time medical decision support system for this task.

Keywords

machine learning multiple classifier system neural network feature selection trained fuser diversity medical informatics liver fibrosis. 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Alpaydin, E.: Combined 5 x 2 cv f test for comparing supervised classification learning algorithms. Neural Comput. 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. Int. J. Approximate Reasoning 53(4), 584–607 (2012)MathSciNetCrossRefGoogle Scholar
  4. 4.
  5. 5.
    Guyon, I., Gunn, S., Nikravesh, M., Zadeh L. (eds.): Feature Extraction, Foundations and Applications. Springer, Heidelberg (2006)Google Scholar
  6. 6.
    Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001 (1990)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.
    Kittler, J., Alkoot, F.M.: Sum versus vote fusion in multiple classifier systems. IEEE Trans. Pattern Anal. Mach. Intell. 25(1), 110–115 (2003)CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    Krawczyk, B., Woźniak, M., Orczyk, T., Porwik, P., Musialik, J., Błońska-Fajfrowska, B.: Classification techniques for non-invasive recognition of liver fibrosis stage. J. Med. Inform. Technol. 20, 121–127 (2012)Google Scholar
  11. 11.
    Krawczyk, B., Woźniak, M.: Combining diverse one-class classifiers. In: Corchado, E., Snasel, V., Abraham, A., Wozniak, M., Grana, M., Cho, S-B. (eds.) Hybrid Artificial Intelligent Systems, volume 7209 of Lecture Notes in Computer Science, pp. 590–601. Springer, Berlin (2012)Google Scholar
  12. 12.
  13. 13.
    Orczyk, T., Pałys, M., Porwik, P., Musialik, J., Błońska-Fajfrowska, B.: Simple and non-invasive liver fibrosis stage prediction method. J. Med. Inform. Technol. 17, 227–232 (2011)Google Scholar
  14. 14.
    Rokach, L.: Pattern Classification Using Ensemble Methods. Series in Machine Perception and Artificial Intelligence. World Scientific Publishing, Singapore (2010)Google Scholar
  15. 15.
    R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008)Google Scholar
  16. 16.
    Ting, Kai, Wells, Jonathan, Tan, Swee, Teng, Shyh, Webb, Geoffrey: Feature-subspace aggregating: ensembles for stable and unstable learners. Mach. Learn. 82, 375–397 (2011)MathSciNetCrossRefGoogle Scholar
  17. 17.
    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
  18. 18.
    Wolpert, DH.: The supervised learning no-free-lunch theorems. In: Proceedings of the 6th Online World Conference on Soft Computing in Industrial Applications, pp. 25–42 (2001)Google Scholar
  19. 19.
    Michal Wozniak. Experiments with trained and untrained fusers. In Emilio Corchado, Juan Corchado, and Ajith Abraham, editors, Innovations in Hybrid Intelligent Systems, volume 44 of Advances in Soft Computing, pages 144–150. Springer Berlin / Heidelberg, 2007.Google Scholar
  20. 20.
    Wozniak, Michal, Zmyslony, Marcin: Combining classifiers using trained fuser—analytical and experimental results. Neural Netw. World 13(7), 925–934 (2010)Google Scholar
  21. 21.
    Yu, L., Liu, H.: Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of the Twentieth International Conference on Machine Learning, vol. 2, pp. 856–863 (2003)Google Scholar
  22. 22.
    Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learning Res. 5, 1205–1224 (2004)zbMATHGoogle Scholar
  23. 23.
    Zmyslony, M., Krawczyk, B., Wozniak, M.: Combined classifiers with neural fuser for spam detection. In: Herrero, A., Snasel, V., Abraham, A., Zelinka, I., Baruque, B., Quintin, H, Calvo, JL., Sedano, J., Corchado, E. (eds.) International Joint Conference CISIS12-ICEUTE12-SOCO12 Special Sessions, volume 189 of Advances in Intelligent Systems and Computing, pp. 245–252. Springer, Heidelberg (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland
  2. 2.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland
  3. 3.Department of Gastroenterology and HepatologyMedical University of SilesiaKatowicePoland
  4. 4.Department of Basic Biomedical ScienceMedical University of SilesiaSosnowiecPoland

Personalised recommendations

Cite paper

Buy options