Explainable AI for Mental Health Screening in Bangladesh: A Socio-Behavioral and Psychometric Dataset

This dataset contains anonymized survey responses from 9,984 adults in Bangladesh, collected to investigate the socio-behavioral and psychometric factors associated with mental health. The data was gathered as part of the research paper titled, "Explainable AI for Mental Health Screening in Bangladesh: A Measurement-Aware, Fairness-Audited, and Reproducible Framework." The primary goal of this data collection was to build and validate a machine learning framework capable of predicting depression severity, thereby addressing the challenges of mental health screening (e.g., cultural stigma, lack of structured data) in low- and middle-income countries. The dataset comprises a wide range of variables, including: Sociodemographic Information: Age, gender, marital status, educational attainment, employment status, geographic location (division), and financial indicators. Mental Health & Well-being Scales: Complete responses to validated psychometric questionnaires, including the Patient Health Questionnaire-9 (PHQ-9) for depression, the Generalized Anxiety Disorder-7 (GAD-7) for anxiety, and the Perceived Stress Scale-10 (PSS-10). Adverse Childhood Experiences (ACEs): Data related to experiences of abuse, neglect, and household dysfunction. Behavioral Factors: Self-reported data on internet usage patterns (based on the Internet Addiction Test - IAT), sleep duration, physical activity levels, and daily screen time. The data is provided in a single Comma-Separated Values (.csv) file. This resource is intended for researchers, data scientists, and public health professionals interested in the social determinants of mental health, the development of predictive models, and the auditing of algorithmic fairness in healthcare.