Prognostic value of histology and lymph node status in bilharziasis-bladder cancer: outcome prediction using neural networks

Author

Ji, W. ; Naguib, R.N.G. ; Petrovic, D. ; Gaura, E. ; Ghoneim, M.A.

Author_Institution

Sch. of Math. & Inf. Sci., Coventry Univ., UK

Volume

4

fYear

2001

fDate

2001

Firstpage

3870

Abstract

In this paper, the evaluation of two features in predicting the outcomes of patients with bilharziasis bladder cancer has been investigated using an RBF neural network. Prior to prediction, the feature subsets were extracted from the whole set of features for the purpose of providing high performance of the network Throughout the analysis of the prognostic feature combinations, two features, histological type and lymph node status, have been identified as important indicators for outcome prediction of this type of cancer. The highest predictive accuracy reached 85.0% in this study.

Keywords

biological tissues; cancer; feature extraction; medical diagnostic computing; radial basis function networks; RBF neural network; bilharziasis bladder cancer prognosis; data set partition; epidemiology; feature extraction; feature subset extraction; histology; lymph node status; outcome prediction; pathological markers; predictive accuracy; prognostic feature combinations; schistosomiasis; survival analysis; Accuracy; Bladder; Cancer; Diseases; Feature extraction; History; Lymph nodes; Neural networks; Pathology; Tumors;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE

ISSN

1094-687X

Print_ISBN

0-7803-7211-5

Type

conf

DOI

10.1109/IEMBS.2001.1019685

Filename

1019685