Rule Evolving System for Knee Lesion Prognosis from Medical Isokinetic Curves

  • Jorge Couchet
  • José María Font
  • Daniel Manrique
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5602)


This paper proposes a system for applying data mining to a set of time series with medical information. The series represent an isokinetic curve that is obtained from a group of patients performing a knee exercise on an isokinetic machine. This system has two steps: the first one is to analyze the input time series in order to generate a simplified model of an isokinetic curve; the second step applies a grammar-guided genetic program including an evolutionary gradient operator and an entropy-based fitness function to obtain a set of rules for a knowledge-based system. This system performs medical prognosis for knee injury detection. The results achieved have been statistically compared to another evolutionary approach that generates fuzzy rule-based systems.


Isokinetic curves medical time series grammar-guided genetic programming data-mining evolutionary gradient 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alonso, F., López-Illescas, A., Martínez, L., Montes, C., Caraca-Valente, J.: Analysis on strength data based on expert knowledge. In: Crespo, J.L., Maojo, V., Martin, F. (eds.) ISMDA 2001. LNCS, vol. 2199, pp. 35–41. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  2. 2.
    Alonso, F., Caraca-Valente, J., González, A., Montes, C.: Combining expert knowledge and data mining in a medical diagnosis domain. Expert Systems with Applications 23, 367–375 (2002)CrossRefGoogle Scholar
  3. 3.
    Podgorelec, V., Kokol, P., Stiglic, M., Hericko, M., Rozman, I.: Knowledge discovery with classification rules in a cardiovascular dataset. Computer Methods and Programs in Biomedicine 80, S39–S49 (2005)Google Scholar
  4. 4.
    Ohsaki, M., Yokoi, H., Abe, M., Tsumoto, S., Yamaguchi, T.: Proposal of medical kdd support user interface utilizing rule interestingness measures. In: Proc. of Sixth IEEE International Conference on Data Mining, pp. 759–764 (2006)Google Scholar
  5. 5.
    Panait, L., Luke, S.: Alternative bloat control methods. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 630–641. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Couchet, J., Font, J.M., Manrique, D.: Using evolved fuzzy neural networks for injury detection from isokinetic curves. In: Proc. of the Twenty-Eighth SGAI International Conference, AI 2008, pp. 225–238 (2008)Google Scholar
  7. 7.
    Koza, J.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  8. 8.
    Whigham, P.: Grammatically-based genetic programming. In: Proc. of the Workshop on Genetic Programming: From Theory to Real-World Apps, pp. 33–41 (1995)Google Scholar
  9. 9.
    Couchet, J., Manrique, D., Ríos, J., Rodríguez-Patón, A.: Crossover and mutation operators for grammar-guided genetic programming. Soft Computing - A Fusion of Foundations, Methodologies and Applications 11(10), 943–955 (2007)Google Scholar
  10. 10.
    García-Arnau, M., Manrique, D., Ríos, J., Rodríguez-Patón, A.: Initialization method for grammar-guided genetic programming. Knowledge-Based Systems 20 (2), 127–133 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jorge Couchet
    • 1
  • José María Font
    • 1
  • Daniel Manrique
    • 1
  1. 1.Departamento de Inteligencia Artificial, Facultad de InformáticaUniversidad Politécnica de MadridMadridSpain

Personalised recommendations