A Clinical-Oriented Non-Severe Depression Diagnosis Method Based on Cognitive Behavior of Emotional Conflict

To improve the diagnosis accuracy of non-severe depression (NSD), this article proposes a diagnosis method of NSD based on cognitive behavior of emotional conflict. First, the original classification features are constructed based on the cognitive behavior of emotional conflict and statistical distribution, and a classification normalization method is proposed to preprocess the feature data. Then, the relief algorithm and principal component analysis (PCA) are recruited for feature processing. Finally, four classifiers [k-nearest neighbor (KNN), support vector machine (SVM), kernel extreme learning machine (KELM), and random forest (RF)] are used to classify NSD patients and normal subjects. The test results show that among all the classifiers, RF achieves the highest classification sensitivity and specificity of 92% and 88%, respectively. Compared with the results of other NSD diagnosis methods in recent years, it has a better performance. The diagnostic method for NSD proposed in this article has obvious performance advantages and provides technical support for improving the accuracy of clinical depression diagnosis. Furthermore, it also provides a new idea and method for the diagnosis and screening of depression.