Alcohol Expectancy Prediction Using Fuzzy C-Regression Based Structural Brain Imaging

Abstract

An Alcohol Use Disorder (AUD) can be easily predicted by using General Linear Model (GLM) which evaluates the interaction of alcohol expectancy and impulsivity associated with the reduced Gray Matter Volume (GMV) of the right posterior insula in women and the left thalamus in both women and men. However, this analysis does not support the detection of non-linear distribution of GMV at the human brain. Therefore, in this paper, detection of GMV reduction of a brain also known as Alcoholism Detection (AD) is improved by using a Fuzzy C-Regression (FCR) model that supports the non-linearity of gray matter in the brain. In this method, spatial information is also incorporated into the FCR model to remove the noise in the brain images. Moreover, the Alcohol Expectancy Test (AET) score is estimated based on the deviations of GMV that mediates the correlation between GMV and alcoholism. This AET score is mostly related to the reduced GMV of the left thalamus in women and men combined and in men alone. Thus, the GMV reduction in the right posterior insula and left thalamus indicates the relationship between AET score and alcoholism. Finally, the experimental outcomes demonstrate that the proposed FCRAD method achieves an accuracy of 86.06% which is 8.77% higher than the existing GLMAD method.