The feature selection process is integrated into the cross-validation procedure to prevent overfitting, given the substantial disparity between the number of features and participants. In each cross-validation step of the model training process, feature selection based on F scores was conducted by using all features to train a classifier with the training dataset. ReHo values were ranked based on their F scores, which were calculated using an F-test to compare participant groups (i.e., AUD and HCs) in the training dataset. The SVM model was constructed by selecting the top N% of ReHo with the highest weights, which underwent training and testing using separate datasets. This resulted in classification accuracy (or correlation coefficient) for this cross-validation step. In this study, we tested a range of N% values (i.e., from 10% to 100% in increments of 10%) and calculated the classification accuracy or correlation coefficient for each value. The highest accuracy or correlation coefficient among ten cross-validation steps was used to determine the performance for each N%, and the final accuracy or correlation coefficient was obtained by averaging across all cross-validation steps.
