Support vector machine based classification of smokers and nonsmokers using diffusion tensor imaging
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ORIGINAL RESEARCH
Support vector machine based classification of smokers and nonsmokers using diffusion tensor imaging Meng Zhao 1,2 & Jingjing Liu 1,2 & Wanye Cai 1,2 & Jun Li 1 & Xueling Zhu 3 & Dahua Yu 4 & Kai Yuan 1,2,4
# Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract Despite significant progress in treatments for smoking cessation, smoking continues to be a significant public health concern, especially in young adulthood. Thus, developing a predictive model that can classify and characterize the brain-based biomarkers predicting smoking status would be imperative to improving treatment development. In this study, we applied a support vector machine-based classification method to discriminate 70 young male smokers and 70 matched nonsmokers using their diffusion tensor imaging (DTI) data. The classification procedure achieved an average accuracy of 88.6% and an average area under the curve of 0.95. The most discriminative features that contributed to the classification were primarily located in the sagittal stratum (SS), external capsule (EC), superior longitudinal fasciculus (SLF), anterior corona radiata (ACR) and inferior front-occipital fasciculus (IFOF). The following regression analysis showed a significant negatively correlation between the average RD values of the left ACR (r = −0.247, p = 0.039) and FTND. The average MD values in the right EC (r = −0.254, p = 0.034) and RD values in the right IFOF (r = −0.240, p = 0.046) were inversely associated with pack-years. Our findings indicate that the discriminative white matter (WM) features as brain biomarkers provide great predictive power for smoking status and suggest that machine learning techniques can reveal underlying smoking-related neurobiology. Keywords Smoking . Machine learning . White matter . Support vector machine . Diffusion tensor imaging
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11682-019-00176-7) contains supplementary material, which is available to authorized users. * Xueling Zhu [email protected] * Dahua Yu [email protected] * Kai Yuan [email protected] 1
School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710071, People’s Republic of China
2
Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi’an, People’s Republic of China
3
Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
4
Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, People’s Republic of China
Introduction Cigarette smoking is one of the biggest public health threats, which kills more than 7 million people a year (Reitsma et al. 2017). Smoking during adolescence and young adulthood produces neurophysiological and brain structural changes that may promote nicotine dependence later in life (Yuan et al. 2015). Despite these serious consequences, t
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