A Multi-Model Ensemble Approach for Proactive Student Mental Health Assessment

In the present proposed system, an investigation is conducted on the utilization of the predictive modelling capabilities of machine learning with the objective of identifying students who exhibit a higher susceptibility to experiencing mental health issues. This identification is based on an analysis of their demographic and academic characteristics. The adoption of a machine learning approach allows for a comprehensive exploration of the intricate interplay of various factors that contribute to the development of mental health challenges among students enrolled in universities. This exploration is made possible through the utilization of a diverse array of data, which encompasses not only demographic attributes but also academic performance and potentially correlated social indicators. The overarching goal of this research endeavor is to provide educational institutions with the necessary tools to proactively identify students who may be particularly vulnerable and to subsequently implement targeted support mechanisms that aim to cultivate a healthier and more supportive learning environment. Through the implementation of such mechanisms, the research ultimately strives to foster both the success and well-being of students. As a result of this research, the development of accurate and reliable predictive models will come to fruition.