Skip to main content

Decision Trees for Decision Analysis (1,004 and 953 Patients)

  • Chapter
  • First Online:
Machine Learning in Medicine - Cookbook

Part of the book series: SpringerBriefs in Statistics ((BRIEFSSTATIST))

  • 4183 Accesses

Abstract

Decision trees are, so-called, non-metric or non-algorithmic methods adequate for fitting nominal and interval data. This chapter is to assess whether decision trees can be appropriately applied to predict health risks and improvements.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ton J. Cleophas .

Rights and permissions

Reprints and permissions

Copyright information

© 2014 The Author(s)

About this chapter

Cite this chapter

Cleophas, T.J., Zwinderman, A.H. (2014). Decision Trees for Decision Analysis (1,004 and 953 Patients). In: Machine Learning in Medicine - Cookbook. SpringerBriefs in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-04181-0_16

Download citation

Publish with us

Policies and ethics