Logistic regression and artificial neural network classification models: A methodology review

Stephan Dreiseitl, Lucila Ohno-Machado

Research output: Contribution to journalArticlepeer-review

831 Citations (Scopus)

Abstract

Logistic regression and artificial neural networks are the models of choice in many medical data classification tasks. In this review, we summarize the differences and similarities of these models from a technical point of view, and compare them with other machine learning algorithms. We provide considerations useful for critically assessing the quality of the models and the results based on these models. Finally, we summarize our findings on how quality criteria for logistic regression and artificial neural network models are met in a sample of papers from the medical literature.

Original languageEnglish
Pages (from-to)352-359
Number of pages8
JournalJournal of Biomedical Informatics
Volume35
Issue number5-6
DOIs
Publication statusPublished - Oct 2002

Keywords

  • Artificial neural networks
  • Classification
  • Logistic regression
  • Medical data analysis
  • Model comparison
  • Model evaluation
  • Models, Theoretical
  • Regression Analysis
  • Nerve Net
  • Logistic Models
  • Organization and Administration

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