Assessment of bilharziasis history in outcome prediction of bladder cancer using a radial basis function neural network

被引：2
|
作者：
Ji, W [1 ]
Naguib, RNG [1 ]
Ghoneim, M [1 ]
机构：
[1] Coventry Univ, BIOCORE, Coventry, W Midlands, England
来源：
2000 IEEE EMBS INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY APPLICATIONS IN BIOMEDICINE, PROCEEDINGS | 2000年
关键词：
schistosomiasis; predictive analysis; outcome prediction; feature selection;
D O I：
10.1109/ITAB.2000.892399
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper we investigate the potential value of bilharziasis history in predicting the outcome progress of patients with bladder cancer using the 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 5 years of diagnosis. The total number of patients is 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.
引用
收藏
页码：268 / 271
页数：4
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