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Citation
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Mihalik JR, Leeds DD, Kroshus E, Kerr ZY, Knight K, D'Lauro C, Lynall RC, Ahmed T, Hagiwara Y, Broglio SP, McCrea MA, McAllister T, Schmidt JD. Med. Sci. Sports Exerc. 2022; ePub(ePub): ePub. |
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Abstract
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PURPOSE: There is limited understanding of factors impacting concussion diagnosis status utilizing large sample sizes. The study objective was to identify factors that can accurately classify previous concussion diagnosis status among collegiate student-athletes and service academy cadets with concussion history.
METHODS: This retrospective study utilized Support Vector Machine, Gaussian Naïve Bayes, and Decision Tree machine learning techniques to identify individual (e.g. sex) and institutional (e.g. academic caliber) factors that accurately classify previous concussion diagnosis status (all diagnosed versus 1+ undiagnosed) among Concussion Assessment, Research, and Education (CARE) Consortium participants with concussion histories (n = 7714).
RESULTS: Across all classifiers, the factors examined enable >50% classification between prior diagnosed and undiagnosed concussion histories. However, across twenty folds of cross validation, ROC-AUC accuracy averaged between 56% and 65% using all factors. Similar performance is achieved considering individual risk factors alone. In contrast, classifications with institutional risk factors typically did not distinguish between those with all concussions diagnosed versus 1+ undiagnosed; average performances using only institutional risk factors was almost always <58%, including confidence intervals for many groups <50%. Participants with more extensive concussion histories were more commonly classified as having one or more of those prior concussions undiagnosed.
CONCLUSIONS: While the current study provides preliminary evidence about factors to help classify concussion diagnosis status, more work is needed given the tested models' accuracy. Future work should include a broader set of theoretically indicated factors, at levels ranging from individual behavioral determinants, to features of the setting in which the individual was injured.
Language: en |