Ovarian Cancer Prognosis by Hemostasis and Complementary Learning
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.
KeywordsOvarian Cancer Fuzzy Rule Lateral Inhibition Linguistic Term Clinical Decision Support System
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