Predicting Student Mental Health Using Machine Learning Approaches

Abstract

Concern over mental health issues among students has in- creased in the last few years. Academic stress, fears about the future, and social constraints can all negatively impact students’ well-being. Early detection is necessary for effective support and intervention for mental health issues. Even though mental health is crucial for well-being, in countries like Bangladesh, where there are few community care facilities for psychiatric patients and the government only devotes 0.44% of the total health budget to mental health, it is not adequately re- searched and recognized as a serious public health concern. Furthermore, the fact that less than 0.11% of people have free access to psychotropic medicine suggests that there is a severe lack of mental health services. In this age of technology and data-driven decision-making, machine learning has emerged as a practical method for assessing and categorizing the mental health conditions of pupils. This research investigates the development of a predictive model that categorizes student mental health into three groups: ”At Risk,” ”Healthy,” and ”Distressed” By utilizing machine learning for student mental health assessments, educational institutions and healthcare practitioners may tackle these problems in a whole new way, which will benefit students’ overall wellbeing.