Transforming Mental Health Assessment: Machine Learning for Early Detection and Personalized Care Among College Students

Abstract

The growing global incidence of mental health disorders underlines the urgent need for improved tools to enable early diagnosis and intervention. This study investigates the potential of machine learning models to predict mental health issues among college students by utilizing a dataset that includes a variety of demographic and behavioural characteristics. This study employs several Machine learning models, including Logistic Regression, Random Forest, Decision Tree, and XGBoost, using a dataset comprising demographic, behavioural, and self-reported mental health information. Data preprocessing involved cleansing, normalization, and feature selection to optimize model performance. Models were trained and validated using cross-validation, and their performance was measured using metrics such as accuracy, precision, and ROC-AUC scores. Machine learning models, particularly Logistic Regression, show significant potential for improving mental health assessments by providing early, accurate, and scalable predictions. This study is significant in addressing the rising mental health challenges among college students by leveraging machine learning (ML) for early detection and personalized care. Traditional diagnostic methods, often time-consuming and subjective, are enhanced by ML’s ability to process large datasets for faster, accurate, and scalable assessments. The Logistic Regression model achieved an accuracy of 85% and a precision of 81%, demonstrating its reliability for general mental health predictions. By integrating demographic, behavioural, and physiological data, the study promotes tailored interventions while emphasizing ethical considerations like privacy and transparency. Its findings can guide institutions and policymakers in developing data-driven mental health programs, fostering healthier academic environments and advancing mental health care.

Keywords: Machine learning, Mental Health, Logistic Regression, Data Preprocessing

DOI: 10.54941/ahfe1005961