Predicting and Understanding College Student Mental Health with Interpretable Machine Learning
Journal:
arXiv
Published Date:
Mar 11, 2025
Abstract
Mental health issues among college students have reached critical levels,
significantly impacting academic performance and overall wellbeing. Predicting
and understanding mental health status among college students is challenging
due to three main factors: the necessity for large-scale longitudinal datasets,
the prevalence of black-box machine learning models lacking transparency, and
the tendency of existing approaches to provide aggregated insights at the
population level rather than individualized understanding.
To tackle these challenges, this paper presents I-HOPE, the first
Interpretable Hierarchical mOdel for Personalized mEntal health prediction.
I-HOPE is a two-stage hierarchical model, validated on the College Experience
Study, the longest longitudinal mobile sensing dataset. This dataset spans five
years and captures data from both pre-pandemic periods and the COVID-19
pandemic. I-HOPE connects raw behavioral features to mental health status
through five defined behavioral categories as interaction labels. This approach
achieves a prediction accuracy of 91%, significantly surpassing the 60-70%
accuracy of baseline methods. In addition, our model distills complex patterns
into interpretable and individualized insights, enabling the future development
of tailored interventions and improving mental health support. The code is
available at https://github.com/roycmeghna/I-HOPE.
