Machine Learning-Based Clustering And Prediction Of Students Mental Health Care Needs: Evidence From Hanoi National University Of Education

Authors: Hoang Thi Lam, Pham Minh Phuong, Pham Quoc Thang

Abstract: This study extends an undergraduate thesis dataset on mental health care needs among 300 students at Hanoi National University of Education by adding machine learning analyses. Three need dimensions, namely cognitive/informational needs, emotional support needs, and access-to-service needs, were used for K-means clustering, while demographic variables, DASS-21 scores, and contextual factors were used to train Decision Tree and Random Forest models. Results showed a relatively high overall need level (M = 3.93/5). K-means with k = 3 identified low-need (25.7%), moderate-need (44.0%), and high-need (30.3%) groups. Random Forest outperformed Decision Tree, reaching 78.9% accuracy in three-class prediction and 85.6% accuracy in detecting high-need students. Family, learning-environment, socio-cultural, and personal factors were the strongest predictors. The findings suggest that machine learning can complement traditional statistics for early screening and targeted student support.

DOI: http://doi.org/10.5281/zenodo.20838284