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 a 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 the important indicators for outcome prediction of this type of cancer. The highest predictive accuracy reached 85.0% in this study.
Original language | English |
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Title of host publication | Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001 |
Publisher | IEEE |
Pages | 3870-3873 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 7 Nov 2002 |
Event | Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Istanbul, Turkey Duration: 25 Oct 2001 → 28 Oct 2001 Conference number: 23 |
Conference
Conference | Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
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Country/Territory | Turkey |
City | Istanbul |
Period | 25/10/01 → 28/10/01 |
Keywords
- Lymph nodes
- Cancer
- Bladder
- Neural networks
- Accuracy
- Feature extraction
- Pathology
- History
- Tumors
- feature extraction
- cancer
- biological tissues
- medical diagnostic computing
- radial basis function networks
- data set partition
- bilharziasis bladder cancer prognosis
- outcome prediction
- RBF neural network
- survival analysis
- epidemiology
- schistosomiasis
- histology
- lymph node status
- feature subset extraction
- prognostic feature combinations
- predictive accuracy
- pathological markers