  Early screening for anxiety and appropriate interventions are essential to
reduce the incidence of self-harm and suicide in patients. Due to limited
medical resources, traditional methods that overly rely on physician expertise
and specialized equipment cannot simultaneously meet the needs for high
accuracy and model interpretability. Multimodal data can provide more objective
evidence for anxiety screening to improve the accuracy of models. The large
amount of noise in multimodal data and the unbalanced nature of the data make
the model prone to overfitting. However, it is a non-differentiable problem
when high-dimensional and multimodal feature combinations are used as model
inputs and incorporated into model training. This causes existing anxiety
screening methods based on machine learning and deep learning to be
inapplicable. Therefore, we propose a multimodal data-driven anxiety screening
framework, namely MMD-AS, and conduct experiments on the collected health data
of over 200 seafarers by smartphones. The proposed framework's feature
extraction, dimension reduction, feature selection, and anxiety inference are
jointly trained to improve the model's performance. In the feature selection
step, a feature selection method based on the Improved Fireworks Algorithm is
used to solve the non-differentiable problem of feature combination to remove
redundant features and search for the ideal feature subset. The experimental
results show that our framework outperforms the comparison methods.
