Predicting and Understanding College Student Mental Health with Interpretable Machine Learning

Mental health issues among college students have reached critical levels, affecting both academic performance and overall wellbeing. Predicting and understanding mental health status among college students is challenging due to three key barriers: the lack of large-scale longitudinal datasets, the prevalence of black-box machine learning models that offer little transparency, and a reliance on population-level analysis rather than personalized 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 that connects raw behavioral features to mental health status through five defined behavioral categories as interaction labels. We evaluate I-HOPE 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 achieves a prediction accuracy of 91%, significantly surpassing the 60-70% accuracy of baseline methods. In addition, I-HOPE distills complex patterns into interpretable and individualized insights, enabling the future development of tailored interventions and improving mental health support.CCS Concepts• Computing methodologies → Machine learning approaches; • Human-centered computing → Empirical studies in ubiquitous and mobile computing; • Applied computing → Health informatics.