DocumentCode
395142
Title
Prognostic systems for NPC: a comparison of the multi layer perceptron model and the recurrent model
Author
Abdul-Kareem, Sameem ; Baba, Sapiyan ; Zubairi, Yong Zulina ; Prasad, U. ; Ibrahim, Mohd ; Wahid, A.
Author_Institution
Fac. of Comput. Sci. & Inf. Technol., Malaya Univ., Kuala Lumpur, Malaysia
Volume
1
fYear
2002
fDate
18-22 Nov. 2002
Firstpage
271
Abstract
Artificial neural networks are considered good alternatives to conventional statistical methods for the prediction of survival. Neural networks have been used in medicine since the late 1980s, first, as an aid to diagnosis and treatment and then, recently, as a tool to study medical prognosis of a variety of diseases. Survival predictions at the individual level can help patients make informed decisions with regards to the quality of life and finance. We describe our research in the use of neural network to predict the prognosis of nasopharyngeal carcinoma. Two prognostic models for nasopharyngeal carcinoma were developed, namely the multi-layer perceptron model and the recurrent model and their performance compared.
Keywords
cancer; medical diagnostic computing; multilayer perceptrons; recurrent neural nets; NPC; artificial neural network; back propagation; informed decisions; medical prognosis; medicine; multi layer perceptron model; multi-layer perceptron model; nasopharyngeal carcinoma; patients; prognostic systems; recurrent model; statistical methods; survival analysis; survival prediction; survival predictions; Artificial neural networks; Cancer; Data analysis; Diseases; Information technology; Mathematical model; Medical diagnostic imaging; Neural networks; Predictive models; Statistical analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN
981-04-7524-1
Type
conf
DOI
10.1109/ICONIP.2002.1202176
Filename
1202176
Link To Document