Title :
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
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;
Conference_Titel :
Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE
Print_ISBN :
0-7803-7211-5
DOI :
10.1109/IEMBS.2001.1019685