Fusional Recognition for Depressive Tendency With Multi-Modal Feature

In recent years, people with depression tend to be younger and normalized. Although current findings can identify the depressive tendency, the effects of recognition are unsatisfactory. Therefore, this paper aims to integrate multi-modal features and build a more objective and accurate model to identify the depressive tendency effectively. First, we combine mental health self-examination data and eye tracking data to extract multi-modal features. Explicitly, we integrate a variety of functions, including eye movements, memory features, cognitive style features, and network behavior. Second, we innovatively propose a scanning stacking model to capture complex nonlinear relationships between features. Finally, the effectiveness of our proposed method is verified by experiments. The experimental results show that our multi-modal based scanning stacking method is superior to the state-of-the-art detection methods.