@article{ref1,
title="Machine learning in modeling high school sport concussion symptom resolve",
journal="Medicine and science in sports and exercise",
year="2019",
author="Bergeron, Michael F. and Landset, Sara and Maugans, Todd A. and Williams, Vernon B. and Collins, Christy L. and Wasserman, Erin B. and Khoshgoftaar, Taghi M.",
volume="51",
number="7",
pages="1362-1371",
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. Clear, valid insight to the anticipated resolution time could assist in planning treatment intervention. <br><br>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. <br><br>METHODS: We examined the efficacy of 10 classification algorithms using machine learning for prediction of symptom resolution time (within seven, fourteen, or twenty-eight days), with a dataset representing three years of concussions suffered by high school student-athletes in football (most concussion incidents) and other contact sports. <br><br>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 ANOVAs revealed statistically significant performance differences across the ten classification models for all learners at a 95% confidence level (P=0.000). Naïve Bayes and Random Forest with either 100 or 500 trees were the top-performing learners with an area under the ROC curve performance ranging between 0.666 and 0.742 (0.0-1.0 scale). <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="0195-9131",
doi="10.1249/MSS.0000000000001903",
url="http://dx.doi.org/10.1249/MSS.0000000000001903"
}