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

fYear

2006

fDate

11-14 Dec. 2006

Firstpage

362

Lastpage

367

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;

fLanguage

English

Publisher

ieee

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

Type

conf

Filename

4155923