Predicting distal tibia fracture type using demographic, vehicle, and crash factors via a random forest classification algorithm.

Journal: Traffic injury prevention
Published Date: Jul 31, 2025

Abstract

OBJECTIVE: Distal tibia fractures occur in approximately 4% of police-reported crashes where at least 1 vehicle was towed in the U.S., and frequently result in complications like infection, nonunion, and osteoarthritis. This study used real-world crash data to train a random forest algorithm to predict distal tibia fracture types, identifying key demographic, vehicle, and crash factors contributing to the model predictions.

Authors

  • L Garrett Bangert
    School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, Virginia.
  • William Armstrong
    School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina.
  • Edward Shangin
    School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, Virginia.
  • Joel D Stitzel
    Wake Forest University, Winston-Salem, NC, USA.
  • R Shayn Martin
    School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina.
  • Anna N Miller
    Dartmouth Hitchcock Medical Center and Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
  • Luke E Riexinger
    Insurance Institute for Highway Safety, Arlington, Virginia.
  • Ashley A Weaver
    Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, North Carolina.
  • Caitlyn J Collins
    Institute for Biomechanics, ETH Zurich, Zurich, Switzerland; Virginia Tech, Department of Biomedical Engineering and Mechanics, Blacksburg, United States. Electronic address: caitlyn.collins@hest.ethz.ch.

Keywords

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