Understanding neural networks using regression trees: an application to multiple myeloma survival data.
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.
Faraggi
D
D
Department of Statistics, University of Haifa, Haifa, 31905, Israel.
LeBlanc
M
M
Crowley
J
J
D000367:Age Factors; D000368:Aged; D000418:Albumins / Q000032:analysis; D002118:Calcium / Q000097:blood; D003404:Creatinine / Q000652:urine; D003627:Data Interpretation, Statistical; D006801:Humans; D008875:Middle Aged; D009101:Multiple Myeloma / Q000401:mortality; D016571:Neural Networks, Computer; D012044:Regression Analysis; D016019:Survival Analysis; D001613:beta 2-Microglobulin / Q000097:blood
D000418:Albumins; D001613:beta 2-Microglobulin; D003404:Creatinine; D002118:Calcium