Using Artificial Neural Networks to Predict Malignancy of Ovarian Tumors

Abstract

This paper discusses the application of artificial neural networks (ANNs) to preoperative discrimination between benign and malignant ovarian tumors. With the input variables selected by logistic regression analysis, two types of feed-forward neural networks were built: multi-layer perceptrons (MLPs) and generalized regression networks (GRNNs). We assess the performance of the models using the Receiver Operating Characteristic (ROC) curve, particularly the area under the ROC curves (AUC), and statistically compare the cross-validated estimate of the AUC of different models.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA412556

Entities

People

  • Chunsong Lu
  • D. Timmerman
  • I. Vergote
  • J. De Brabanter
  • S. Van Huffel

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bayesian Networks
  • Cancer
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Electrical Engineering
  • Engineering
  • European Communities
  • Factor Analysis
  • Information Science
  • Machine Learning
  • Models
  • Neural Networks
  • Probability
  • Statistical Analysis
  • Test Sets

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Oncology (Cancer Research).
  • Radar Systems Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks