Tag: XGBoost
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Prevalence, risk factors, and ML-driven prediction of depression, anxiety, and stress among Bangladeshi university students
Overview Mental health is a cornerstone of overall well-being, yet university students in low- and middle-income countries face rising rates of depression, anxiety, and stress. This cross-sectional study from Bangladesh combines traditional epidemiology with machine learning (ML) to assess prevalence, identify associated factors, and predict mental health risk among students from two public universities. Key…
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Prevalence, Predictors, and ML-Based Prediction of Depression, Anxiety, and Stress Among Bangladeshi University Students
Introduction: Mental Health in Bangladeshi Universities Mental health is a cornerstone of overall well-being, impacting students’ academic success, social connections, and long-term trajectories. This study from Bangladesh investigates how common depression, anxiety, and stress are among university students and identifies their key risk factors. It also evaluates how well several machine learning (ML) models can…