Evaluating Depression, Anxiety, and Stress in College Students: Combining DASS-21, BAI, BDI, and AI for Preventive Support

https://doi.org/10.5281/zenodo.17539033

Abstract

Undergraduate students are increasingly concerning mental health challenges like stress, anxiety, and depression, which frequently stay concealed until they intensify. The DASS-21 Scale (Depression Anxiety Stress Scales— 21 items- solely the stress question is employed), BDI (Beck Depression Inventory) for depression questions, and BAI (Beck Anxiety Inventory) for anxiety questions are recognized instruments utilized in this investigation to evaluate these conditions. Data was collected from two student questionaries—one centered on general wellness and the other regarding depression. We analyzed the information to categorize scores based on intensity and examine patterns in signs like exhaustion and hopelessness following its handling through conventional techniques such as tackling absent data. Subsequently, the study evaluated artificial intelligence through the utilization of three machine learning models to identify their efficacy in identifying these conditions. Relying on our findings, a Random Forest model showed outstanding effectiveness in evaluating degrees of stress, anxiety, and depression, attaining an accuracy level of up to 98.8 demonstrates that merging contemporary. Al with classical psychological evaluations can result in enhanced, more anticipatory assistance for student psychological well-being

Key Terms: College students, depression, anxiety, tension, DASS-21, BDI, BAI, machine learning, psychological well-being,