Optimizing Concussion Care Seeking: Identification of Factors Predicting Previous Concussion Diagnosis Status

Purpose There is limited understanding of factors affecting concussion diagnosis status using 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 used 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 vs 1+ undiagnosed) among Concussion Assessment, Research, and Education Consortium participants with concussion histories (n = 7714). Results Across all classifiers, the factors examined enable>50% classification between previous diagnosed and undiagnosed concussion histories. However, across 20-fold cross validation, ROC-AUC accuracy averaged between 56% and 65% using all factors. Similar performance is achieved considering individual risk factors alone. By 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 were almost always
Source: Medicine and Science in Sports and Exercise - Category: Sports Medicine Tags: BASIC SCIENCES Source Type: research