Multivariate Pattern Analysis of Lifelong Premature Ejaculation Based on Multiple Kernel Support Vector Machine - Geng, Bowen; Gao, Ming; Piao, Ruiqing; Liu, Chengxiang; Xu, Ke; Zhang, Shuming; Zeng, Xiao; Liu, Peng; Wang, Yanzhu (Frontiers in Psychiatry, 2022)
Objective: To develop an effective support vector machine (SVM) classifier based on the multi-modal data for detecting the main brain networks involved in group separation of premature ejaculation (PE). Methods: Fifty-two lifelong PE patients and 36 matched healthy controls were enrolled in this study. Structural magnetic resonance imaging (MRI) data, functional MRI data and diffusion tensor imaging (DTI) data were processed SPM12, DPABI4.5 and PANDA, respectively. A total of 12735 features were reduced by Mann–Whitney U test. The resilience nets method was further used to select features. Results: Finally, 36 features (3 structural MRI, 7 functional MRI and 26 DTI) were chosen in the training dataset. We got the best SVM model with an accuracy of 97.5% and an area under the curve (AUC) of 0.986 in the training dataset, and the accuracy of 91.4% and the AUC of 0.966 in the testing dataset. Conclusion: Our findings showed that the majority of the brain abnormalities for the classification was located within or across several networks. This study may contribute to neural mechanisms of PE and provide new insights into the pathophysiology of lifelong PE patients.