Minimal Hepatic Encephalopathy Classification Based on Discriminative Subgraph Reconstruction
TU Liyang1, DU Junqiang1, JIE Biao1,2, ZHANG Daoqiang1
1.College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics,.Nanjing 211106.2.School of Mathematics and Computer Science, Anhui Normal University, Wuhu 241000
Abstract Minimal hepatic encephalopathy (MHE) is related to the abnormality of subnetworks, but searching related subnetworks is still a challenging task. To solve this problem, a method based on discriminative subgraph reconstruction is proposed to search subnetworks related to MHE and the subnetworks are used for MHE classification. Firstly, frequent subgraphs are mined from the functional connectivity networks of MHE and non-MHE (NMHE), respectively. Next, the discriminative subgraphs are selected from the frequent subgraphs for the original networks reconstruction and the combination of discriminative networks is conducted to reconstruct the original networks. Finally, the graph kernel is applied to compute the similarity between pairwise reconstructed networks and the kernel SVM is adopted for MHE classification. On the dataset of 77 patients with hepatic cirrhosis, the high accuracy of the proposed algorithm is achieved and the effectiveness of the proposed method is demonstrated.
About author:: TU Liyang, born in 1992, master student. His research inte-rests include pattern recognition and medical image processing.DU Junqiang, born in 1992, master student. His research interests include pattern recognition and medical image proce-ssing.JIE Biao, born in 1976, Ph.D.,associate professor. His research interests include machine learning, pattern recognition and data mining.ZHANG Daoqiang(Corresponding author), born in 1978, Ph.D., professor. His research interests include machine lear-ning, pattern recognition, data mining and medical image processing.)
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2016, Vol. 29 
