A machine learning model for orthodontic extraction/non-extraction decision in a racially and ethnically diverse patient population.

Journal: International orthodontics
Published Date: May 15, 2023

Abstract

INTRODUCTION: The purpose of the present study was to create a machine learning (ML) algorithm with the ability to predict the extraction/non-extraction decision in a racially and ethnically diverse sample.

Authors

  • Taylor Mason
    Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN, US.
  • Kynnedy M Kelly
    Indiana University School of Dentistry, Indianapolis, IN, US.
  • George Eckert
    Department of Biostatistics and Health Data Science, Indianapolis, Indiana University School of Medicine, IN, US.
  • Jeffrey A Dean
    Department of Pediatric Dentistry, Indiana University School of Dentistry, Indianapolis, IN, US.
  • M Murat Dundar
    Department of Computer and Information Sciences, Indiana University, Purdue University, Indianapolis, IN, 46202, USA. mdundar@iupui.edu.
  • Hakan Turkkahraman
    Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, 1121 West Michigan Street, 46202, Indianapolis, IN, USA. haturk@iu.edu.

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