Random Forest Classification to Predict Response to High-Definition Transcranial Direct Current Stimulation for Tinnitus Relief: A Preliminary Feasibility Study

Transcranial direct current stimulation (tDCS) of the right dorsolateral prefrontal cortex has been hypothesized to reduce tinnitus severity by modifying cortical activity in brain regions associated with the perception of tinnitus. However, individual response to tDCS has proven to be variable. We investigated the feasibility of using random forest classification to predict the response to high-definition (HD) tDCS for tinnitus relief. DESIGN: A retrospective analysis was performed on a dataset consisting of 99 patients with subjective tinnitus receiving six consecutive sessions of HD-tDCS at the Antwerp University Hospital. A baseline assessment consisted of pure-tone audiometry and a set of questionnaires including the Tinnitus Functional Index (TFI), Hospital Anxiety and Depression Scale, and Edinburgh Handedness Inventory. Random forest classification was applied to predict, based on baseline questionnaire scores and hearing levels, whether each individual responded positively to the treatment (defined as a decrease of at least 13 points on the TFI). Further testing of the model was performed on an independent cohort of 32 patients obtained from the tinnitus center at the University of Regensburg. RESULTS: Twenty-four participants responded positively to the HD-tDCS treatment. The random forest classifier predicted treatment response with an accuracy of 85.71% (100% sensitivity, 81.48% specificity), significantly outperforming a more traditional logistic regression approach. Performance of the classifier on an independent cohort was slightly but not significantly above chance level (71.88% accuracy, 66.67% sensitivity, 73.08% specificity). Feature importance analyses revealed that baseline tinnitus severity, co-occurrence of depressive symptoms and handedness were the most important predictors of treatment response. Baseline TFI scores were significantly higher in responders than in nonresponders. CONCLUSIONS: The proposed random forest classifier predicted treatment response with a high accuracy, significantly outperforming a more traditional statistical approach. Machine learning methods to predict treatment response might ultimately be used in a clinical setting to guide targeted treatment recommendations for individual tinnitus patients.