Early Prediction of Mental Health Disorder Among Higher Education Students Using Machine Learning

Abstract

In spite of the fact that mental health illnesses are quite common among students in higher education, early detection continues to be a difficult task. This study seeks to determine the use of machine learning to forecast the occurrence of mental health issues in this group. Various machine learning methods were explored to analyze the data collected from higher education students and to identify potential risk factors associated with mental health issues. Through the development of a model that is capable of accurately predicting the risk of mental health illnesses, the project intends to facilitate early intervention and improve the overall well-being of their student population.