Predictive analysis on depression among university students in Bangladesh

Abstract

The identification of depression is done by medical practitioners based on mental status questionnaires and the patient’s self-reporting. Apart from the methods being highly dependent on the patient’s current mood, people who go through mental disorders seek mental help reluctantly. Universities always promise scholars a promising career in their domains. However, the academic competition, peer pressure, isolation and many other factors could put a student in a state of depression. In this research, we propose a big data analytics template to detect depression among university students. Asserting again, since isolation and separation are believed to have the most dramatic effect on the pupils, the framework also models the correlation between these factors and depression. To conclude, the journal evaluates the performance of the proposed framework on a massive real dataset collected from different university students of Bangladesh and proves that the accuracy of the machine learning models outperforms traditional techniques for detecting depression in universities.