Michael F Bergeron1, Sara Landset2, Todd A Maugans3, Vernon B Williams4, Christy L Collins5, Erin B Wasserman5, Taghi M Khoshgoftaar2. 1. SIVOTEC Analytics, Boca Raton, FL. 2. Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL. 3. Division of Neurosurgery, Nemours Children's Hospital, Orlando, FL. 4. Kerlan-Jobe Center for Sports Neurology, Los Angeles, CA. 5. Datalys Center for Sports Injury Research and Prevention, Inc., Indianapolis, IN.
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.
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.
Authors: Michael F Bergeron; Sara Landset; Xianbo Zhou; Tao Ding; Taghi M Khoshgoftaar; Feng Zhao; Bo Du; Xinjie Chen; Xuan Wang; Lianmei Zhong; Xiaolei Liu; J Wesson Ashford Journal: J Alzheimers Dis Date: 2020 Impact factor: 4.472