University Student's Mental Stress Detection Using Machine Learning

Mental stress among university students is a growing concern, significantly affecting their academic performance and overall well-being. This study proposes a machine learning-based system to detect mental stress through multiple parameters including facial recognition, behavior analysis, grade analysis, and question-answer-based analysis. The system employs advanced algorithms to analyze facial expressions, academic records, and behavioral data, offering a comprehensive assessment of a student's mental health. The proposed model aims to provide timely interventions, promoting better mental health support within educational institutions. Keywords: Mental Stress Detection, Machine Learning, Facial Recognition, Behavior Analysis, Grade Analysis, Student Well-being.