DocumentCode :
2737968
Title :
Assessment of bilharziasis history in outcome prediction of bladder cancer using a radial basis function neural network
Author :
Ji, W. ; Naguib, R.N.G. ; Ghoneim, M.
Author_Institution :
BIOCORE, Coventry Univ., UK
fYear :
2000
fDate :
2000
Firstpage :
268
Lastpage :
271
Abstract :
Investigates the potential value of bilharziasis history in predicting the outcome progress of patients with bladder cancer using a radial basis function (RBF) neural network. The data set is described by eight input features: histology, tumour grade, lymph nodes status, bilharziasis history, stage, DNA ploidy, sex, and age interval. Two outcomes are of interest: recurrence of disease and death within five years of diagnosis. The total number of patients was 321, of whom 83.5% had been confirmed with bilharziasis history. Different feature subsets have been examined to improve the predictive accuracy and to assess the effect of bilharziasis. The highest predictive accuracy is 74.07% from the RBF network. The analysis shows that bilharziasis history is an important prognostic marker in the prediction
Keywords :
cancer; diseases; medical computing; radial basis function networks; tumours; DNA ploidy; age interval; bilharziasis history; bladder cancer; disease stage; feature selection; feature subsets; histology; infection; input features; lymph nodes status; parasitic disease; patient outcome prediction; predictive accuracy; predictive analysis; prognostic marker; radial basis function neural network; schistosomiasis; sex; tumour grade; Accuracy; Bladder; Cancer; DNA; Diseases; History; Lymph nodes; Neural networks; Radial basis function networks; Tumors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Technology Applications in Biomedicine, 2000. Proceedings. 2000 IEEE EMBS International Conference on
Conference_Location :
Arlington, VA
Print_ISBN :
0-7803-6449-X
Type :
conf
DOI :
10.1109/ITAB.2000.892399
Filename :
892399
Link To Document :
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