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Random forest classification of etiologies for an orphan disease
Author(s) -
Speiser Jaime Lynn,
Durkalski Valerie L.,
Lee William M.
Publication year - 2015
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.6351
Subject(s) - random forest , computer science , machine learning , artificial intelligence , field (mathematics) , statistical classification , disease , statistics , medicine , mathematics , pathology , pure mathematics
Classification of objects into pre‐defined groups based on known information is a fundamental problem in the field of statistics. Although approaches for solving this problem exist, finding an accurate classification method can be challenging in an orphan disease setting, where data are minimal and often not normally distributed. The purpose of this paper is to illustrate the application of the random forest (RF) classification procedure in a real clinical setting and discuss typical questions that arise in the general classification framework as well as offer interpretations of RF results. This paper includes methods for assessing predictive performance, importance of predictor variables, and observation‐specific information. Copyright © 2014 John Wiley & Sons, Ltd.