  In this paper, machine learning models are used to predict outcomes for
patients with persistent post-concussion syndrome (PCS). Patients had sustained
a concussion at an average of two to three months before the study. By
utilizing assessed data, the machine learning models aimed to predict whether
or not a patient would continue to have PCS after four to five months. The
random forest classifier achieved the highest performance with an 85% accuracy
and an area under the receiver operating characteristic curve (AUC) of 0.94.
Factors found to be predictive of PCS outcome were Post-Traumatic Stress
Disorder (PTSD), perceived injustice, self-rated prognosis, and symptom
severity post-injury. The results of this study demonstrate that machine
learning models can predict PCS outcomes with high accuracy. With further
research, machine learning models may be implemented in healthcare settings to
help patients with persistent PCS.
