Prognostic Comparison of Statistical, Neural and Fuzzy Methods of Analysis of Breast Cancer Image Cytometric Data

Abstract

This paper aims to predict a breast cancer patient's prognosis and to determine the most important prognostic factors by means of logistic regression (LR) as a conventional statistical method, multilayer backpropagation neural network (MLBPNN) as a neural network method, fuzzy K-nearest neighbor algorithm (FK-NN) as a fuzzy logic method, a fuzzy measurement based on the FK-NN and the leave-one-out error method. The data used for breast cancer prognostic prediction were collected from 100 women who were clinically diagnosed with breast disease in the form of carcinoma or benign conditions.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA411710

Entities

People

  • C. Bartoli
  • D. Petrovic
  • H. Seker
  • M. Odetayo
  • R. N. Naguib

Organizations

  • Coventry University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Breast Cancer
  • Classification
  • Data Science
  • Data Sets
  • Diseases And Disorders
  • Factor Analysis
  • Fuzzy Logic
  • Fuzzy Sets
  • Information Science
  • Machine Learning
  • Mathematical Models
  • Models
  • Neoplasms
  • Neural Networks
  • Statistical Analysis

Readers

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

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks