Analyzing the Mental Health of Engineering Students using Classification and Regression

In this paper, we describe a data mining study of the mental health of undergraduate Engineering students in a large Canadian university. We created a survey based on guidelines from the Canadian Mental Health Association, and applied classification and regression algorithms to the collected data. Our results reveal interesting relationships between various aspects of mental health and year of study (first and final year students have lower mental health scores than second-year students), academic program (students in competitive programs have lower overall mental health but higher self-actualization, whereas students in a program with a flexible curriculum had higher overall scores), and gender (female Engineering students tend to have lower scores).