Machine Learning in Modeling High School Sport Concussion Symptom Resolve

Michael F. Bergeron, Sara Landset, Todd A. Maugans, Vernon B. Williams, Christy L. Collins, Erin B. Wasserman, Taghi M. Khoshgoftaar

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Introduction Concussion prevalence in sport is well recognized, so too is the challenge of clinical and return-to-play management for an injury with an inherent indeterminant time course of resolve. A clear, valid insight into the anticipated resolution time could assist in planning treatment intervention. Purpose This study implemented a supervised machine learning-based approach in modeling estimated symptom resolve time in high school athletes who incurred a concussion during sport activity. Methods We examined the efficacy of 10 classification algorithms using machine learning for the prediction of symptom resolution time (within 7, 14, or 28 d), with a data set representing 3 yr of concussions suffered by high school student-athletes in football (most concussion incidents) and other contact sports. Results The most prevalent sport-related concussion reported symptom was headache (94.9%), followed by dizziness (74.3%) and difficulty concentrating (61.1%). For all three category thresholds of predicted symptom resolution time, single-factor ANOVA revealed statistically significant performance differences across the 10 classification models for all learners at a 95% confidence interval (P = 0.000). Naïve Bayes and Random Forest with either 100 or 500 trees were the top-performing learners with an area under the receiver operating characteristic curve performance ranging between 0.656 and 0.742 (0.0-1.0 scale). Conclusions Considering the limitations of these data specific to symptom presentation and resolve, supervised machine learning demonstrated efficacy, while warranting further exploration, in developing symptom-based prediction models for practical estimation of sport-related concussion recovery in enhancing clinical decision support.

Original languageEnglish (US)
Pages (from-to)1362-1371
Number of pages10
JournalMedicine and Science in Sports and Exercise
Volume51
Issue number7
DOIs
StatePublished - Jul 1 2019
Externally publishedYes

Keywords

  • ADOLESCENT
  • AUGMENTED INTELLIGENCE
  • RECOVERY
  • SPORTS MEDICINE
  • TRAUMATIC BRAIN INJURY

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine
  • Physical Therapy, Sports Therapy and Rehabilitation

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