Ovarian cancer prognosis by hemostasis and complementary learning

Authors:
T. Z. Tan;G. S. Ng;C. Quek;Stephen C. L. Koh
Affiliations:
Center for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore;Center for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore;Center for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore;Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

Citing 2
Cited 1

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Neural Networks - 2005 Special issue: IJCNN 2005

Ovarian cancer diagnosis with complementary learning fuzzy neural network

Artificial Intelligence in Medicine

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Abstract

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.