A Comparison of Multiple Regression and a Neural Network for Predicting a Medical Diagnosis.

Abstract

Regression and neural network prediction methods were compared using artificial data generated to simulate three types of predictor-criterion relationships: linear, polynomial, and interactive. Analyses of linear data indicated that both methods were comparable on large data sets. On small data sets the neural network tended to overfit the initial data and thus did not generalize as well as the regression equation. Analysis of data with a non- linear component demonstrated the ability of the neural network to fit either a polynomial or interactive term without the user having to model such terms. However, when these effects were modeled, the regression equation permored well. The implications of these results for the development of predictive algorithms were discussed. Algorithms, regression equations, predictive algorithms, diagnostic algorithms, neural network.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1991
Accession Number
ADA258366

Entities

People

  • David H. Ryman
  • William Pugh

Organizations

  • Naval Health Research Center

Tags

Communities of Interest

  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Biomedical Research
  • Computational Science
  • Computers
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Diseases And Disorders
  • Equations
  • Information Science
  • Neural Networks
  • Pain
  • Polynomials
  • Signs And Symptoms
  • Standards

Fields of Study

  • Psychology

Readers

  • Neural Network Machine Learning.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks