University students experience disproportionately high rates of common mental health conditions, such as depression, which can impair learning, social functioning, and overall well-being.
Within this context, symptoms of amotivation (i.e. loss of motivational drive) and anhedonia (i.e. diminished interest or pleasure) are particularly debilitating, yet they frequently go undetected.
Developing new approaches to identify students with prominent amotivation and anhedonia could enable earlier and more targeted intervention.
Machine learning (ML) methods have increasingly been used to classify individuals according to symptom severity.
However, these ML models often suffer from class imbalance, where the majority of cases fall in the low-symptom group and relatively few in the high-symptom group.
This imbalance can reduce model accuracy and bias predictions.
To address this, studies commonly employ the popular oversampling strategy SMOTE.
However, SMOTE has a notable limitation: it may generate invalid values for nominal variables.
In this paper, we introduce a novel and effective oversampling method that addresses this shortcoming.
Our approach leverages a predictive model to generate nominal variables, rather than interpolating them.
We validate our method on a large-scale GPS location dataset collected from university students and demonstrate that it is significantly better than existing oversampling approaches in predicting elevated symptoms of amotivation and anhedonia.