Stacked ensemble machine learning approach for electroencephalography based major depressive disorder classification using temporal statistics

Major depressive disorder (MDD) is a serious and widespread mental health condition that remains challenging to diagnose accurately. Traditional psychological assessments, which can be subjective and sometimes unreliable, emphasize the need for more objective diagnostic tools. In this study, we present a machine learning (ML) model designed to diagnose depression by analysing statistical time-domain features extracted from Electroencephalography (EEG) data. The model is built using a stacked ensemble ML approach, incorporating nine-base estimators with various meta-classifiers. Through multiple trials, the model achieved an accuracy of 98.01%, with precision and recall rates of 97.78% and 96.61%, respectively with Adaptive Boosting (AdaBoost) as the meta-classifer. We also investigated the effects of data sampling and the number of base classifiers on the model's performance. The findings demonstrate that the stacked ensemble approach significantly enhances the accuracy of diagnosing MDD and that the proposed model outperforms the methods used in previous studies. This model offers a promising tool for psychologists and medical professionals to diagnose depression more reliably, potentially leading to better treatment outcomes for those affected by the disorder.