To predict addiction severity in the AUD group, we employed support vector regression (SVR) constructed using a linear support vector machine. The neuroimaging data (i.e., ReHo) from each participant were utilized as input features, while., AUDIT and MAST scores served as labels. To mitigate overfitting of the training set, a 10-fold cross-validation was conducted. A leave-one-fold-out cross-validation procedure was employed to calculate the average correlation coefficient across all folds. Regression analyses were conducted using e-SVR with a linear kernel and default SVR parameters.
