Developing a multivariate model for the prediction of concussion recovery in sportspeople: a machine learning approach
Yates, Louise C Yates, Elliot Li, Xuanxuan Lu, Yiping Yakoub, Kamal Davies, David Belli, Antonio Sawlani, Vijay
Yates, Louise C
Yates, Elliot
Li, Xuanxuan
Lu, Yiping
Yakoub, Kamal
Davies, David
Belli, Antonio
Sawlani, Vijay
Abstract
Background: Sportspeople suffering from mild traumatic brain injury (mTBI) who return prematurely to sport are at an increased risk of delayed recovery, repeat concussion events and, in the longer-term, the development of chronic traumatic encephalopathy. Therefore, determining the appropriate recovery time, without unnecessarily delaying return to sport, is paramount at a professional/semi-professional level, yet notoriously difficult to predict.
Objectives: To use machine learning to develop a multivariate model for the prediction of concussion recovery in sportspeople.
Methods: Demographics, injury history, Sport Concussion Assessment Tool fifth edition questionnaire and MRI head reports were collected for sportspeople who suffered mTBI and were referred to a tertiary university hospital in the West Midlands over 3 years. Random forest (RF) machine learning algorithms were trained and tuned on a 90% outcome-balanced corpus subset, with subsequent validation testing on the previously unseen 10% subset for binary prediction of greater than five missed sporting games. Confusion matrices and receiver operator curves were used to determine model discrimination.
Results: 375 sportspeople were included. A final composite model accuracy of 94.6% based on the unseen testing subset was obtained, yielding a sensitivity of 100% and specificity of 93.8% with a positive predictive value of 71.4% and a negative predictive value of 100%. The area under the curve was 96.3%.
Discussion: In this large single-centre cohort study, a composite RF machine learning algorithm demonstrated high performance in predicting sporting games missed post-mTBI injury. Validation of this novel model on larger external datasets is therefore warranted.
MIDER Authors
Date
2025-03-24
Type
Article
Collections
Citation
Yates LC, Yates E, Li X, Lu Y, Yakoub K, Davies D, Belli A, Sawlani V. Developing a multivariate model for the prediction of concussion recovery in sportspeople: a machine learning approach. BMJ Open Sport Exerc Med. 2025 Mar 24;11(1):e002090. doi: 10.1136/bmjsem-2024-002090
Journal / Source Title
BMJ Open Sport & Exercise Medicine
DOI
10.1136/bmjsem-2024-002090
PMID
40134506
Publisher
BMJ Publishing
Publisher’s URL
https://bmjopensem.bmj.com/