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Understanding neural networks using regression trees: an application to multiple myeloma survival data
Author(s) -
Faraggi David,
LeBlanc Michael,
Crowley John
Publication year - 2001
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.912
Subject(s) - covariate , artificial neural network , computer science , regression , context (archaeology) , artificial intelligence , machine learning , regression analysis , focus (optics) , multiple myeloma , data mining , statistics , medicine , mathematics , biology , paleontology , physics , optics
Abstract Neural networks are becoming very popular tools for analysing data. It is however quite difficult to understand the neural network output in terms of the original covariates or input variables. In this paper we provide, using readily available software, an easy way of understanding the output of the neural network using regression trees. We focus on the problem in the context of censored survival data for patients with multiple myeloma, where identifying groups of patients with different prognosis is an important aspect of clinical studies. The use of regression trees to help understand neural networks can be easily applied to uncensored situations. Copyright © 2001 John Wiley & Sons, Ltd.