Ovarian Cancer Prognosis by Hemostasis and Complementary Learning

  • T. Z. Tan
  • G. S. Ng
  • C. Quek
  • Stephen C. L. Koh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


Ovarian cancer is a major cause of deaths worldwide. As a result, women are not diagnosed until the cancer has advanced to later stages. Accurate prognosis is required to determine the suitable therapeutic decision. Since abnormalities of hemostasis and increased risk of thrombosis are observed in cancer patient, assay involving hemostatic parameters can be potential prognosis tool. Thus a biological brain-inspired Complementary Learning Fuzzy Neural Network (CLFNN) is proposed, to complement the hemostasis in ovarian cancer prognosis. Experimental results that demonstrate the confluence of hemostasis and CLFNN offers a promising prognosis tool. Apart from superior performance, CLFNN provides interpretable rules to facilitate validation and justification of the system. Besides, CLFNN can be used as a concept validation tool for ovarian cancer prognosis.


Ovarian Cancer Fuzzy Rule Lateral Inhibition Linguistic Term Clinical Decision Support System 
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 2006

Authors and Affiliations

  • T. Z. Tan
    • 1
  • G. S. Ng
    • 1
  • C. Quek
    • 1
  • Stephen C. L. Koh
    • 2
  1. 1.Center for Computational Intelligence, School of Computer EngineeringTechnological UniversityNanyangSingapore
  2. 2.Department of Obstetrics and Gynaecology, Yong Loo Lin School of MedicineNational University of SingaporeSingapore

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