Random forest classification of etiologies for an orphan disease

Jaime Lynn Speiser, Valerie L. Durkalski, William M. Lee

Research output: Contribution to journalArticle

10 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)887-899
Number of pages13
JournalStatistics in Medicine
Volume34
Issue number5
DOIs
StatePublished - Feb 28 2014

Keywords

  • Acute liver failure
  • Etiology
  • Random forest
  • Statistical classification

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Fingerprint Dive into the research topics of 'Random forest classification of etiologies for an orphan disease'. Together they form a unique fingerprint.

  • Cite this