Hybrid Approach for Enhanced Depression Detection using Learning Techniques

Abstract

According to the World Health Organization (WHO), depression affects over 350 million people worldwide, making it the most common health problem. Depression has numerous causes, including fluctuations in business, social life, the economy, and personal relationships. Depression is one of the leading contributors to mental illness in people, which also has an impact on a person's thoughts, behavior, emotions, and general wellbeing. This study aids in the clinical understanding of patients' mental health with depression. The primary objective of research is to examine learning strategies to enhance the effectiveness of depression detection. The proposed work includes ‘Extended- Distress Analysis Interview corpus’ (E-DAIC) label dataset description and proposed methodology. The membership function applies to the Patients Health Questionnaire (PHQ8_Score) for Mamdani Fuzzy depression detection levels, in addition to the study of the hybrid approach. It also reviews the proposed techniques used for depression detection to improve the performance of the system. Finally, we developed the Ensemble- LSRG (Logistic classifier, Support Vector classifier, Random Forest Classifier, Gradient boosting classifier) model, which gives 98.21% accuracy, precision of 99%, recall of 99%, F1 score of 99%, mean squared error of 1.78%, mean absolute error of 1.78%, and R2 of 94.23.