Cost-Effective Predictive Modeling for Student Mental Health Using Readily-Available Data

The mental health of post-secondary students is a critical public health issue, with alarming rates of psychological distress, suicidal thoughts, and behaviors on university and college campuses. Predictive modeling can be utilized for the analysis of student mental health to better understand current students' mental state. These predictions are often made using large surveys collected from students or participants to use the survey questions as features and then predict based on a target question related to mental health. The expensive nature of collecting data this way can be prohibitive for some institutions, and due to the scale and potential data processing required, the predictions made using those data could be too late for any proactive approaches to tackle the mental health of students. To address this, it is worth investigating the predictive performance of readily available data to predict student mental health as a means of accurately representing an institution's student body. In this paper, we show that readily-available data can be used to predict mental health with competitive accuracy compared to other experiments done in the literature that utilize more expensively collected data with neural network models.