The Use of Evolutionary and Fuzzy Models for Oncological Prognosis

  • A. A. Odusanya
  • M. O. Odetayo
  • D. Petrovic
  • R. N. G. Naguib
Part of the Advances in Soft Computing book series (AINSC, volume 9)


Evolutionary and fuzzy models have been used increasingly in many decision support, optimization, and control tasks. Oncological (cancer) data and are largely numerical in representation for analysis, however some are images or smears. The focus here is on the numerical nature of large samples. Lack of precise knowledge characterizes a lot of numerical samples for analysis in oncological decision making. The work here explores evolutionary and fuzzy models that are suitable for determining prognostic outcome.


Genetic Algorithm Fuzzy Logic Evolutionary Algorithm Fuzzy System Fuzzy Model 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • A. A. Odusanya
    • 1
  • M. O. Odetayo
    • 1
  • D. Petrovic
    • 1
  • R. N. G. Naguib
    • 1
  1. 1.Biomedical Computing Research Group (BIOCORE) School of Mathematical and Information SciencesCoventry UniversityCoventryUK

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