Predicting Depression Status After Transcranial Direct Current Stimulation Treatment Using Machine Learning

Depression is a serious medical illness that adversely affects how a person feels, thinks, and behaves. This illness can be treated with the aid of Transcranial Direct Current Stimulation (tDCS), which can help to reduce the symptoms of depression. The level of illness is typically evaluated using the Hamilton Depression Rating Scale (HDRS). The focus of this paper is the prediction of the HDRS score after a tDCS course. By predicting the result of tDCS, psychiatrists can provide better counseling to the patients about their future conditions after the treatment and decide wisely about the treatment method. We used different kinds of demographic information, treatment information, and the HDRS score before the treatment as predictors and supervised Machine Learning (ML) algorithms for the prediction. The analysis is conducted on 169 patients with depression. Our preliminary results show that the accuracy can be up to 63% when predicting the value of HDRS after tDCS treatment sessions as a binary variable using Gradient Boosting. This is encouraging on such a small data set. Moreover, our results provide insight into the predictors pivotal to this outcome. They show that the HDRS score at baseline, the age, and the gender of the subject are the three main predictors. The results suggest this methodology may yield very interesting results.