Machine Learning Combined with PHQ-9 for Analyzing Depression in Bangladeshi Metropolitan Areas

Depression has become a severe problem with grave consequences for Bangladesh's social and economic sectors. Fearing societal stigmas and misconceptions regarding depression, most individuals in our nation put off getting the medical care they need, which causes disaster in their lives. This is why it is difficult to gather accurate information on a depressed person, making it sometimes impossible to offer assistance. Social media can be a valuable source of information for recognizing depression among Bangladeshis due to its extensive use. We used a variety of natural language processing (NLP) techniques and machine learning algorithms (including LR, SVM, RF, GBDT, and XGBoost) to analyze postings from 1778 Facebook users (Female 25%, Male 60%, Other 15%) in this study to test the viability of determining the level of depression among locals of Chittagong and Dhaka over the previous four months. We found the RF depression classification model with the highest accuracy (0.7454) and F1-score (0.60) for detecting depression levels. To investigate the situation of depression at the local level, we also employed the medically approved Patient Health Questionnaire-9 (PHQ9) assessment on residents of Chittagong and Dhaka (both online and offline). By utilizing their comparison, we could determine the depression levels among individuals of various ages and sexual orientations in Dhaka and Chittagong.