Chunk #26 — Results — Demographic and clinical characteristics — Classifier evaluation and brain regions that contributed most to the SVC classification in data set 1
The SVC classification results are presented in Fig. 1, indicating that SVC outperformed the other five classification methods including random forest, logistic regression, naive Bayes, linear discriminate, K-nearest neighbor, and decision tree for our data. The highest classification accuracies achieved for each measure were as follows: ReHo had an accuracy of 98.57% (with a specificity of 91.67% and a sensitivity of 95.00%), ALFF had an accuracy of 95.17%, fALFF had an accuracy of 96.07%, DCpb had an accuracy of 90.36%, and DCpw had an accuracy of 87.32% (Fig. 1A, left). The corresponding AUC values for each measure were as follows: ReHo (0.99), ALFF (0.98), fALFF (0.99), DCpb (0.95), and DCpw (0.94) (Fig. 1A, right). In comparison, ReHo exhibited the highest classification accuracy and AUC in distinguishing AUD from HCs (Fig. 1A). The weight map of the top 10% selected features of ReHo is presented in Fig. 1B, C, while permutation tests confirmed that its classification accuracy was significantly higher than the chance level at P < 0.001.
