Economic Strains and Mental Chains : A Machine Learning Exploration of Mental Health in Bangladesh

Abstract

Bangladesh, despite its commendable economic strides over the past decade faces a persistent challenge: the mental well-being of its economically disadvantaged populace. This study delves deep into this intersection of economic hardship and mental health, harnessing a dataset rich with 1,17,453 records. Through extensive surveys, the research captures a gamut of factors ranging from income and employment to familial support and traumatic experiences. The exploratory data analysis revealed stark correlations between economic stresses and feelings of hopelessness, with robust social support emerging as a silver lining. Delving further, Machine Learning models, including a standout Neural Network model with a 0.92 accuracy, illuminated the intricate patterns and relationships in the data. The findings underscore the urgent need for targeted interventions, offering a roadmap for NGOs, health professionals, and policymakers. This research, positioned at the nexus of data analytics and socio-economic study, aspires to not only highlight the challenges faced by Bangladesh’s vulnerable, but also pave the way for a more mentally healthy future.