Prognostic Value of Histology and Lymph Node Status in Bilharziasis-Bladder Cancer: Outcome Prediction Using Neural Networks

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.O% in this study.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA412467

Entities

People

  • D. Petrovic
  • E. Gaura
  • M. A. Ghoneim
  • R. N. Naguib
  • W. Ji

Organizations

  • Coventry University

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Adenocarcinoma
  • Bladder Cancer
  • Cancer
  • Carcinoma
  • Data Mining
  • Data Sets
  • Disease Attributes
  • Diseases And Disorders
  • Experimental Data
  • Feature Selection
  • Histology
  • Information Science
  • Lymph Nodes
  • Lymphatic System
  • Neoplasms
  • Neural Networks

Readers

  • Neural Network Machine Learning.
  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks