Authors - Halima Tuj Saydia, Partha Chakraborty Abstract - The mental health issues, such as stress and suicidal threats, have become a major public health concern for students and young adults. Early identification of such conditions is important for timely interventions and prevention. The study aims to develop a two-stage hierarchical framework to predict stress and suicide risk early. It is based on the questionnaire survey dataset of 1436 responses. The hierarchical method utilizes psychological and lifestyle characteristics gathered through surveys, thereby eliminating the need for physiological sensors. The first stage develops machine learning (ML) models, namely XGBoost, Random Forest (RF), and Support Vector Machine (SVM), to detect stress. These models have achieved an accuracy of 93%, 88%, and 83%, respectively. If the individual is detected as stressed, it moves to the second stage for suicide risk detection. Deep learning (DL) models, mainly Artificial Neural Network (ANN), Deep Neural Network (DNN), and Recurrent Neural Network (RNN), are developed in the second stage. They have achieved accuracy of 94%, 90%, and 89%, respectively. The study presents a scalable, data-driven framework that supports early mental health screening in resource-limited communities.
Open Zoom