Mapping Heterogeneity in Psychological Risk Among University Students Using Explainable Machine Learning

TL;DR

This paper introduces a new machine learning framework to identify distinct psychological risk subtypes among university students, aiming to improve mental health interventions.

Contribution

The novel 'predict-explain-discover' pipeline combines XAI and unsupervised learning to uncover heterogeneous risk mechanisms in student mental health.

Findings

Three distinct psychological risk subtypes were identified: academically-driven, socio-emotional, and internal regulatory risks.

Sensitivity analysis confirmed the structural stability of these subtypes based on core features.

The framework aligns with RDoC and supports precision interventions by bridging predictive accuracy with mechanistic understanding.

Abstract

In the post-pandemic era, student mental health challenges have emerged as a critical issue in higher education. However, conventional assessment approaches often treat at-risk populations as a monolithic entity, thereby limiting intervention effectiveness. This study proposes a novel computational framework that integrates explainable artificial intelligence (XAI) with unsupervised learning to decode the latent heterogeneity of psychological risk mechanisms. We developed a “predict-explain-discover” pipeline leveraging TreeSHAP and Gaussian Mixture Models to identify distinct risk subtypes based on a 2556-dimensional feature space encompassing lexical, linguistic, and affective indicators. Our approach identified three theoretically-grounded subtypes: academically-driven (28.46%), socio-emotional (43.85%), and internal regulatory (27.69%) risks. Sensitivity analysis using top-20 core features further validated the structural stability of these mechanisms, proving that the subtypes are anchored in the model’s primary decision drivers rather than high-dimensional noise. The framework demonstrates how black-box classifiers can be transformed into diagnostic tools, bridging the gap between predictive accuracy and mechanistic understanding. Our findings align with the Research Domain Criteria (RDoC) and establish a foundation for precision interventions targeting specific risk drivers. This work advances computational mental health research through methodological innovations in mechanism-based subtyping and practical strategies for personalized student support.

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