Optimal Feature Identification and Major Depressive Disorder Prediction through Correlation-Based Machine Learning Approach

Major Depressive Disorder (MDD) is a mental health disorder characterized by continuous feelings of sadness, diminished interest in activities, and a range of physical and emotional symptoms that profoundly affect a person’s daily life and functioning. Early detection of MDD is crucial for improved treatment outcomes. The detection of Major Depressive Disorder (MDD) relies on extracting several features from raw EEG signals. This study presents a machine learning-based approach to identify MDD using minimal EEG signal features, employing a correlation analysis with various correlation coefficient values. The proposed method achieved an impressive accuracy of 84% with SVM, showing 91.5% sensitivity, 75.5% specificity, and 85.7% f1-score with best correlation coefficient value. To evaluate performance, 5-fold cross-validation with inter-subject variability is utilized. The study extracts several feature sets using different coefficient values and assesses their performance with diverse coefficient values, aiming to identify the most effective features for early diagnosis of MDD.