Interpretable Fuzzy Modeling for Decision Support in IgA Nephropathy

  • Marco Lucarelli
  • Ciro Castiello
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6857)


The aim of the work is to show the potential usefulness of interpretable fuzzy modeling for decision support in medical applications. For this pursuit, we present an approach for designing interpretable fuzzy systems concerning the prognosis prediction in Immunoglobulin A Nephropathy (IgAN). To deal with such a hard problem, prognosis has been granulated into three classes; then, a number of fuzzy rule based classifiers have been designed so that several interpretability constraints are satisfied. The resulting classifiers have been evaluated in terms of classification accuracy (also compared with a standard neural network), some of interpretability indexes, and in terms of unclassified samples. Experimental results show that such models are capable to provide both a first estimation of prognosis and a readable knowledge base that can be inspected by physicians for further analyses.


Renal Biopsy Fuzzy Rule Fuzzy Partition Fuzzy Decision Tree Standard Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alonso, J.M., Magdalena, L.: GUAJE - a java environment for generating understandable and accurate models. In: Peregrin, A. (ed.) XV Spanish Conference for Fuzzy Logic and Technology (ESTYLF), vol. 1, pp. 399–404. University of Huelva Press (2010)Google Scholar
  2. 2.
    Alonso, J.M., Magdalena, L.: HILK++: an interpretability-guided fuzzy modeling methodology for learning readable and comprehensible fuzzy rule-based classifiers. Soft Computing, 1–22 (2010)Google Scholar
  3. 3.
    Alonso, J.M., Magdalena, L., Guillaume, S.: KBCT: a knowledge extraction and representation tool for fuzzy logic based systems. In: Proceedings of the 2004 IEEE International Conference on Fuzzy Systems, vol. 2, pp. 989–994 (July 2004)Google Scholar
  4. 4.
    Bartosik, L.P., Lajoie, G., Sugar, L., Cattran, D.C.: Predicting progression in IgA nephropathy. American Journal of Kidney Diseases 38(4), 728–735 (2001)CrossRefGoogle Scholar
  5. 5.
    Beerman, I., Novak, J., Wyatt, R.J., Julian, B.A., Gharavi, A.G.: The genetics of IgA Nephropathy. Nature Clinical Practice Nephrology 3, 325–338 (2007)CrossRefGoogle Scholar
  6. 6.
    Coppo, R., D’Amico, G.: Factors predicting progression of IgA Nephropathies. Journal of Nephrology 18(5), 503–512 (2005)Google Scholar
  7. 7.
    Cordón, O., Herrera, F.: A three-stage evolutionary process for learning descriptive and approximate fuzzy-logic-controller knowledge bases from examples. International Journal of Approximate Reasoning 17(4), 369–407 (1997)CrossRefzbMATHGoogle Scholar
  8. 8.
    Ichihashi, H., Shirai, T., Nagasaka, K., Miyoshi, T.: Neuro-fuzzy ID3: A method of inducing fuzzy decision trees with linear programming for maximizing entropy and an algebraic method for incremental learning. Fuzzy Sets and Systems 81, 157–167 (1996)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Manno, C., Strippoli, G.F.M., D’Altri, C., Torres, D., Rossini, M., Schena, F.P.: A novel simpler histological classification for renal survival in IgA Nephropathy: A retrospective study. American Journal of Kidney Diseases 49(6), 763–775 (2007)CrossRefGoogle Scholar
  10. 10.
    Mencar, C., Castiello, C., Cannone, R., Fanelli, A.M.: Interpretability assessment of fuzzy knowledge bases: A cointension based approach. International Journal of Approximate Reasoning (2010) (in Press),,11.007

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marco Lucarelli
    • 1
  • Ciro Castiello
    • 1
  1. 1.Department of InformaticsUniversity of BariItaly

Personalised recommendations