title: Random forest classification of etiologies for an orphan disease.
abstract: 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.
authors.affiliation: Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, U.S.A.
authors.forename: Jaime Lynn
authors.initials: JL
authors.lastname: Speiser
authors.forename: Valerie L
authors.initials: VL
authors.lastname: Durkalski
authors.forename: William M
authors.initials: WM
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D056808:Biostatistics; D003663:Decision Trees; D006801:Humans; D017114:Liver Failure, Acute / Q000145:classification / Q000209:etiology; D000069550:Machine Learning; D015233:Models, Statistical; D035583:Rare Diseases / Q000145:classification* / Q000209:etiology; D012042:Registries / Q000706:statistics & numerical data
keywords: acute liver failure; etiology; random forest; statistical classification