Lung-Hao Lee, Chang-Hao Chen, Wan-Chen Chang, Po-Lei Lee, Kuo-Kai Shyu, Mu-Hong Chen, Ju-Wei Hsu, Ya-Mei Bai, Tung-Ping Su, and Pei-Chi Tu*.
European Psychiatry (Eur. Psychiatry), 65(1), E1.
Abstract
Support vector machines (SVMs) based on brain-wise functional connectivity (FC) have been widely adopted for single-subject prediction of patients with schizophrenia, but most of them had small sample size. This study aimed to evaluate the performance of SVMs based on a large single-site dataset and investigate the effects of demographic homogeneity and training sample size on classification accuracy.