A machine learning approach to predict rtms therapy response in major depressive disorder




Shams, Mohammad

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Machine learning techniques have been utilized to predict the outcome of repetitive transcranial magnetic stimulation (rTMS) treatment in depression, e.g., through classifying the responders (R) and non-responders (NR) to rTMS treatment for major depression disorder (MDD) patients. MDD is among the leading causes of disability in the world with affecting more than 260 million people, and a major contributor to the overall global burden of disease. In this study, the outputs of the Local Subset Feature Selection (LSFS) method were used by an SVM classifier to evaluate the capability of the proposed method in the prediction of rTMS treatment response in depression cases. A Leave-One-Out cross-validation method is applied to the input data to evaluate the performance of the response classification. The achieved accuracy, sensitivity, and specificity were 89.5%, 90%, and 87%, respectively. The main restriction of this study that would limit its usage in clinical applications is the small sample size.



Major depressive disorder, Repetitive transcranial magnetic stimulation