An Efficient Feature Optimization Approach with Machine Learning for Detection of Major Depressive Disorder Using EEG Signal

Major Depressive Disorder (MDD) is a mental health condition marked by persistent feelings of sadness, diminished interest in once-enjoyable activities, and an array of physical and emotional symptoms that profoundly disrupt an individual's daily life and functioning. In this circumstance, detecting major depressive disorder in the early phase is required. Identifying Major Depressive Disorder (MDD) involves the extraction of multiple features from unprocessed EEG signals. This study proposed a machine learning approach to identify minimum possible features as 14 significant features by removing highly correlated features and applying a Genetic algorithm with several combinations of maximum features. The proposed method obtained an accuracy of 84.1% in AdaBoost, 80.1% in DecissionTree, 85.5% in RandomForest, 84.9% Gradient Boosting, 84.9% in XG Boosting. To assess performance, Subject-wise five-fold cross-validation is used. This performance is comparable to the performance of all features, which is roughly 1300 features, and as a result, utilizing fewer features maintains nearly the same performance while reducing the time necessary for MDD detection.