Random forest classification of etiologies for an orphan disease

• Published in: Statistics in medicine 2015-02, Vol.34 (5), p.887-899
• Main Authors: Speiser, Jaime Lynn, Durkalski, Valerie L., Lee, William M.
• Format: Article
• Language: English
• Subjects: acute liver failure; Algorithms; Biostatistics; Classification; Decision Trees; etiology; Humans; Liver Failure, Acute - classification; Liver Failure, Acute - etiology; Machine Learning; Medical statistics; Models, Statistical; Normal distribution; random forest; Rare Diseases - classification; Rare Diseases - etiology; Registries - statistics & numerical data; statistical classification
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.6351

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.