Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICBPE.2006.348614
Title: A prognosis tool based on hemostasis and genetic complementary learning
Authors: Tan, T.Z.
Ng, G.S.
Quek, C.
Koh, S.C.L. 
Keywords: Complementary learning
Genetic algorithm
Hemostasis
Prognosis
Issue Date: 2006
Source: Tan, T.Z., Ng, G.S., Quek, C., Koh, S.C.L. (2006). A prognosis tool based on hemostasis and genetic complementary learning. ICBPE 2006 - Proceedings of the 2006 International Conference on Biomedical and Pharmaceutical Engineering : 362-367. ScholarBank@NUS Repository. https://doi.org/10.1109/ICBPE.2006.348614
Abstract: Hemostatic parameters or parameters related to blood clotting are useful for diagnosis and prognosis. This is because abnormalities in hemostatic parameters are observed in various diseases. However, these parameters vary among localities and individuals. Thus, computational intelligent tool is required to aid the diagnosis and prognosis using hemostasis. Genetic complementary learning (GCL) is a biological-inspired method that outperform conventional computational intelligent tool in classification performance, and hence, is adopted as clinical decision support tool for ovarian cancer diagnosis and prognosis. The hemostasis-GCL confluence exhibits a promising approach for diagnosis and prognosis. © 2006 Research Publishing Services.
Source Title: ICBPE 2006 - Proceedings of the 2006 International Conference on Biomedical and Pharmaceutical Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/129828
ISBN: 8190426249
DOI: 10.1109/ICBPE.2006.348614
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