Figure 7.

Group classification using random forest machine learning algorithm. (A) The receiver operating characteristics (ROC) results from the random forest supervised machine learning algorithm for classifying patients with paediatric mild traumatic brain injury (pmTBI) relative to HC. Two models compared diagnostic accuracy with current clinical gold standards [5P risk score (5P) and percentage of total possible score on the Post-Concussion Symptom Inventory (PCSA, %; solid line) or clinical gold standards plus significant dMRI findings (dashed line)]. dMRI variables included the weighted average of increased (pmTBI > HC) FA, decreased (HC > pmTBI) GM and increased WM V_iso, increased V_ic and increased ODI. All volume fraction estimates were obtained from the biologically informed MDT algorithm. Estimates for area under the curve (AUC), balanced accuracy (BA), sensitivity (Sen) and specificity (Spe) are provided for each model. (B) The variable importance for each metric in each model.