New digital approach to managing student depression with focus on early detection through academic Pressure profiling.

Abstract

Managing student mental health remains a growing challenge for educational and healthcare systems, especially as academic pressure increasingly contributes to depressive symptoms. This study introduces a novel digital strategy to support early detection for student depression by analyzing how academic stressors shape mental health outcomes. Using a dataset of 5000 anonymized student records including psychological, behavioral, academic, and demographic indicators this research identifies key drivers of depressive symptoms in real-world educational contexts. Academic pressure emerged as the most influential factor, enabling the creation of a targeted risk profile. To support scalable and practical deployment, predictive models were developed to identify high-risk individuals with a high degree of accuracy (best model accuracy: 0.969). Rather than replacing clinical diagnosis, this approach serves as a decision-support tool for schools, counselors, and digital health systems. By translating complex data into early warnings, this framework enhances the capacity of educational institutions and care providers to proactively respond to mental health risks, reduce screening burdens, and improve access to timely support.