Development and use of a clinical decision support system for the diagnosis of social anxiety disorder.

BACKGROUND

Mental disorders, according to the definition of World Health Organization, consist of a wide range of signs, which are generally specified by a combination of unusual thoughts, feelings, behavior, and relationships with others. Social anxiety disorder (SAD) is one of the most prevalent mental disorders, described as permanent and severe fear or feeling of embarrassment in social situations. Considering the imprecise nature of SAD symptoms, the main objective of this study was to generate an intelligent decision support system for SAD diagnosis, using Adaptive neuro-fuzzy inference system (ANFIS) technique and to conduct an evaluation method, using sensitivity, specificity and accuracy metrics.

METHOD

In this study, a real-world dataset with the sample size of 214 was selected and used to generate the model. The method comprised a multi-stage procedure named preprocessing, classification, and evaluation. The preprocessing stage, itself, consists of three steps called normalization, feature selection, and anomaly detection, using the Self-Organizing Map (SOM) clustering method. The ANFIS technique with 5-fold cross-validation was used for the classification of social anxiety disorder.

RESULTS AND CONCLUSION

The preprocessed dataset with seven input features were used to train the ANFIS model. The hybrid optimization learning algorithm and 41 epochs were used as optimal learning parameters. The accuracy, sensitivity, and specificity metrics were reported 98.67%, 97.14%, and 100%, respectively. The results revealed that the proposed model was quite appropriate for SAD diagnosis and in line with findings of other studies. Further research study addressing the design of a decision support system for diagnosing the severity of SAD is recommended.