Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging

Figure 2

The flowchart of the optimal value threshold selection and M3 method used in our study. We used leave-one-out crossvalidation (LOOCV) to estimate the performance of our classifier. All features in the training and test sets were standardized by -score, and then, two-sample two-tailed -tests with threshold from 0.001 to 0.05 with a 0.001 interval (50 iterations) were performed to select discriminative features. We used MLDA with five category features (ALFF, ReHo, DC, VMHC, and GMD) to obtain five base classifiers, and then, we combined five classifiers into one classifier by weighted voting. Subsequently, we evaluate the performance of the classifier and obtained 50 classification accuracies, and the threshold with the highest classification accuracy was defined as the optimal threshold.