Algorithm for Constructing a Classifier Team Using a Modified PCA (Principal Component Analysis) in the Task of Diagnosis of Acute Lymphocytic Leukaemia Type B-CLL

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11734)


Systems of data recognition and data classification are getting more and more developed. There appear newer algorithms that solve more difficult and complex decision problems. Very good results are obtained using sets of classifiers. The authors in their research focused on certain data characteristics. The characteristics concerns recognition of classes of objects whose features can be grouped. Clusters created in this manner can contribute to better recognition of certain decision classes. One such example is a diagnosis of forecast in the case of acute lymphocytic chronic leukaemia B-CLL type. In this document, the authors present a modified selection method of features of the PCA object. The modification concerns the rotation of objects in relation to decision classes. In addition to grouping similar features using Varimax rotation, a procedure for grouping patients in these PCA groups was developed. Within each PCA, two classifiers - strong and weak ones were built. In the research part, the developed method was compared to the one-stage recognition algorithms known from the literature. The obtained results have a significant contribution to medical diagnostics. They allow to develop a procedure for treatment of B-CLL lymphocytic leukaemia. Making an appropriate diagnosis allows to increase a patient’s survival chance by implementing appropriate treatment.


Analysis of major components Classifiers Lymphocytic leukaemia 



This work was supported by the statutory funds of the Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Science and Technology.


  1. 1.
    Burduk, R.: Integration base classifiers based on their decision boundary. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10246, pp. 13–20. Springer, Cham (2017). Scholar
  2. 2.
    Woźniak, M., Ksieniewicz, P., Cyganek, B., Kasprzak, A., Walkowiak, K.: Active learning classification of drifted streaming data. Procedia Comput. Sci. 80, 1724–1733 (2014)CrossRefGoogle Scholar
  3. 3.
    Krawczyk, B., Ksieniewicz, P., Woźniak, M.: Hyperspectral image analysis based on color channels and ensemble classifier. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS (LNAI), vol. 8480, pp. 274–284. Springer, Cham (2014). Scholar
  4. 4.
    Zyblewski, P., Ksieniewicz, P., Woźniak, M.: Classifier selection for highly imbalanced data streams with Minority Driven Ensemble. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2019. LNCS (LNAI), vol. 11508, pp. 626–635. Springer, Cham (2019). Scholar
  5. 5.
    Kay, N., Hamblin, T., Jelinek, D., et al.: Chronic lymphocytic leukemia. American Society of Hematology, Hematology, pp. 193–213 (2002)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Dmoszyńska, A., Robak, T.: Podstawy hematologii. Wydawnictwo Czelej, Lublin, wyd 2 (2008)Google Scholar
  7. 7.
    Hallek, M., Cheson, B., Catovsky, D., Caligaris-Cappio, F., Dighiero, G.: Guidelines for the diagnosis and treatment of chronic lymphocytic leukemia: a report from the International Workshop on Chronic Lymphocytic Leukemia (IWCLL) updating the National Cancer Institute-Working Group (NCI-WG) 1996 guidelines, vol. 111, pp. 5446–5456 (2008)Google Scholar
  8. 8.
    Monserrat, E., Gine, E., Bosch, F.: Redefining prognostic elements in chronic lymphocytic leukemia. Hematol J. 4(suppl. 3), 180–182 (2003)Google Scholar
  9. 9.
    Hamblin, T.J.: CLL: How many diseases? Hematol J. 4(suppl. 3), 183–186 (2003)Google Scholar
  10. 10.
    Rai, K.R., Chiorazzi, N.: Determining the clinical course and outcome in chronic lymphocytic leukemia. N. Engl. J. Med. 348, 1797–1799 (2003)CrossRefGoogle Scholar
  11. 11.
    Bosch, F., Villamor, N.: ZAP-70 expression in chronic lymphocytic leukemia: a new parameter for an old disease. Hematologica 88, 724–726 (2003)Google Scholar
  12. 12.
    Brugiatelli, M., Mannina, D., Neri, S., et al.: Recent update of prognosis and staging of chronic lymphocytic leukemia. Hematol J. 88(suppl. 10), 30–31 (2003)Google Scholar
  13. 13.
    Grabiński, T.: Metody taksonometrii. Akademia Ekonomiczna, Kraków (1992)Google Scholar
  14. 14.
    Stanisz, A.: Przystępny kurs statystyki z zastosowaniem Statistica PL na przykładach z medycyny. T. 3: Analizy wielowymiarowe. StatSoft, Kraków (2007)Google Scholar
  15. 15.
    Fix, E., Hodges, J.L.: Discriminatory analysis. Nonparametric discrimination: consistency properties, Report Number 4, Project Number 21–49-004, 1951, Reprinted in International Statistical Review, 57, pp. 238–247 (1989)zbMATHGoogle Scholar
  16. 16.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, New York (1990)zbMATHGoogle Scholar
  17. 17.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995). Scholar
  18. 18.
    Quinlan, J.R.: Discovering rules by induction from large collections of examples. In: Expert Systems in the Micro Electronic Age, pp. 168–201. Edinburgh University Press (1979)Google Scholar
  19. 19.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees, Wadsworth, Belmont (1984)Google Scholar
  20. 20.
    Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy K-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern. 15(4), 580–585 (1985)CrossRefGoogle Scholar
  21. 21.
    Devijver, P.A., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice-Hall, London (1982)zbMATHGoogle Scholar
  22. 22.
    Sutton, C., McCallum, A.: An introduction to conditional random fields for relational learning. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning, pp. 93–128. MIT Press, Cambridge (2006)Google Scholar
  23. 23.
    Zhang, J., Gong, S.: Action categorization with modified hidden conditional random field. Pattern Recogn. 43, 197–203 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Systems and Computer Networks, Faculty of ElectronicsWroclaw University of Science and TechnologyWroclawPoland
  2. 2.WSB UniversitiesWroclawPoland

Personalised recommendations