A dual cognitive control model for the coexistence of depression and anxiety disorders: A machine learning-based prediction of symptom severity

The diagnostic methods used for the combination of traditional depression and anxiety disorders involve simply adding the symptoms of each disorder together, without taking into account the dynamic interactions and heterogeneity between these symptoms. The study utilized the AX Continuous Operations Test along with machine learning techniques. Fifty individuals from the combined disorder group, fifty from the depression group, fifty from the anxiety disorder group, and fifty from a healthy control group were used as subjects. The study aimed to investigate the dual cognitive control characteristics of individuals with combined disorders, and to develop predictive models for the severity of symptoms. The results showed that: (1) individuals in the combined disorder group had significantly longer reaction times during the AY trials, and their accuracy rates during the BX trials were significantly lower than those in the healthy control group. Additionally, their active control index was higher than…