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. 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). 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 <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 previous concussions undiagnosed. Although 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.