ADHD-200 classification based on social network method

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Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common diseases in school aged children. In this study, we proposed a method based on social network to extract the features of the ADHD-200 resting state fMRI data between ADHD conditioned and control subjects. And the classification is done by using the support vector machine. The innovation of this paper lies in that: firstly, in the social network, the edge is defined by correlation between two voxels, where the threshold is proposed based on the optimal properties of small world; secondly, in the procedure of feature extraction, besides the traditional network features, we also exploit the new features such as assortative mixing and synchronization. We obtain an average accuracy of 63.75%, which is better than the average best imaging-based diagnostic performance 61.54% achieved in the ADHD-200 global competition. Compared with the proposed method, the result of the method based on traditional features is 61.04%, which verified that the proposed method based on new features is better than traditional one. © 2014 Springer International Publishing Switzerland.

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Guo, X., An, X., Kuang, D., Zhao, Y., & He, L. (2014). ADHD-200 classification based on social network method. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8590 LNBI, pp. 233–240). Springer Verlag. https://doi.org/10.1007/978-3-319-09330-7_28

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