Title :
A Prognosis Tool based on Hemostasis and Genetic Complementary Learning
Author :
Tan, T.Z. ; Ng, G.S. ; Quek, Chai ; Koh, S C Lenny
Author_Institution :
Nanyang Technol. Univ., Singapore
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.
Keywords :
biological organs; cancer; decision support systems; fuzzy neural nets; genetic algorithms; gynaecology; haemodynamics; learning (artificial intelligence); medical diagnostic computing; patient diagnosis; tumours; biological-inspired method; blood clotting; clinical decision support tool; computational intelligent tool; disease; fuzzy neural network; genetic algorithm; genetic complementary learning; hemostasis-GCL; ovarian cancer diagnosis; prognosis;
Conference_Titel :
Biomedical and Pharmaceutical Engineering, 2006. ICBPE 2006. International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-981-05-79
Electronic_ISBN :
81-904262-1-4
