  Previous research on EMA data of mental disorders was mainly focused on
multivariate regression-based approaches modeling each individual separately.
This paper goes a step further towards exploring the use of non-linear
interpretable machine learning (ML) models in classification problems. ML
models can enhance the ability to accurately predict the occurrence of
different behaviors by recognizing complicated patterns between variables in
data. To evaluate this, the performance of various ensembles of trees are
compared to linear models using imbalanced synthetic and real-world datasets.
After examining the distributions of AUC scores in all cases, non-linear models
appear to be superior to baseline linear models. Moreover, apart from
personalized approaches, group-level prediction models are also likely to offer
an enhanced performance. According to this, two different nomothetic approaches
to integrate data of more than one individuals are examined, one using directly
all data during training and one based on knowledge distillation.
Interestingly, it is observed that in one of the two real-world datasets,
knowledge distillation method achieves improved AUC scores (mean relative
change of +17\% compared to personalized) showing how it can benefit EMA data
classification and performance.
