The MVPANI toolbox was utilized to perform MVPA on neuroimaging data (including ReHo, ALFF, fALFF, and DC) in order to distinguish patients with AUD from HCs [38]. The classification model, constructed using linear support vector machine (SVM) algorithm to construct a classification model (support vector classification [SVC], the SVC parameters: Kernel Function (linear), penalty coefficient (c) = 1, Gamma (g) = 0.1, and Degree (d) = 3, coefficient (r) = 0, nu (n) = 0.5, and epsilon in the loss function (p) = 0.1), outperformed other classification methods for our data, such as random forest, logistic regression, naive Bayes, linear discriminant analysis, K-nearest neighbor, and decision tree. This model was employed to identify a hyperplane between AUD and HCs that maximized the distance to the support vectors on each side. The training set was subjected to a 10-fold cross-validation procedure in order to mitigate the risk of overfitting. To obtain an average classification accuracy across all folds, a leave-one-fold-out cross-validation approach was employed.
