International Journal for Research in Applied Science and Engineering Technology
MindTrack: Predicting Student Mental Health Risk Using Machine Learning
Student mental health has a direct bearing on academic performance and overall well-being, yet problems such as stress, anxiety and depression usually go unnoticed until they have already affected a student's grades or attendance. Counsellors and academic staff typically rely on manual observation, self-reported questionnaires, or referrals to identify students who may be struggling, an approach that is reactive rather than preventive and does not scale well across large student populations. This paper presents Min …