Prediction of Anger Expression of Individuals with Psychiatric Disorders using the Developed Computational Codes based on the Various Soft Computing Algorithms.

Abstract

Anger is defined as a psychobiological emotional state that consists of feelings varying in intensity from mild irritation or annoyance to intense fury and rage. Dysfunction in anger regulation is marker of most psychiatric disorders. The most important point about anger regulation by the individuals is how to express anger and control it. The purpose of the present study is to predict the anger expression from the anger experience in individuals with psychiatric disorder for assessment of how to express and control the anger. To this end, the number of 3,000 subjects of individuals with clinical disorders had filled in the State-Trait Anger Expression Inventory-II (STAXI-II). After removing the uncertain diagnoses (900 subjects), the number of 2,100 data was considered in the analysis. Then, the computational codes based on three soft computing algorithms, including Radial Basis Function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Decision Tree (DT) were developed to predict the scales of anger expression of the individuals with psychiatric disorders. The scales of anger experience were used as input data of the developed computational codes. Comparison between the results obtained from the DT, RBF and ANFIS algorithms show that all the developed soft computing algorithms forecast the anger expression scales with an acceptable accuracy. However, the accuracy of the DT algorithm is better than the other algorithms.