Hybrid Machine Learning Approaches for Predicting and Diagnosing Major Depressive Disorder

Abstract

Major Depressive Disorder (MDD) is common and debilitating, requiring accurate prediction and diagnosis. This study uses clinical, demographic, and EEG data to test hybrid machine learning methods for MDD prediction and diagnosis. EEG data reveals brain electrical activity and can identify MDD patterns and traits. The study aimed to enhance Major Depressive Disorder (MDD) prediction and diagnosis using hybrid machine learning methods, focusing on EEG data alongside clinical and demographic information. Employing various algorithms like CatBoost, Random Forest, XG Boost, XGB Random Forest, SVM with a linear kernel, and logistic regression with Elasticnet regularization, the study found that CatBoost achieved the highest accuracy of 93.1% in MDD prediction and diagnosis, surpassing other models. Additionally, the ensemble model combining XGBoost and Random Forest showed strong performance in ROC analysis, effectively discriminating between individuals with and without MDD. These findings underscore the potential of EEG data integration and hybrid machine learning techniques in accurately identifying and classifying MDD patients, paving the way for personalized interventions and targeted treatments in depressive disorders.