RESEARCH ON BRAIN NETWORK CLASSIFICATION METHOD BASED ON INTEGRATED MODEL

Zhang- sensen

Abstract


mild cognitive impairment (MCI) is a condition between healthy elderly people and alzheimer's disease (AD). At
present, brain network analysis based on machine learning methods can help diagnose MCI. In this paper, the brain
network is divided into several subnets based on the shortest path, and the feature vectors of each subnet are extracted
and classified. In order to make full use of subnet information, this paper adopts integrated classification model for
classification. Each base classification model can predict the classification of a subnet, and the classification results of all
subnets are calculated as the classification results of brain network. In order to verify the effectiveness of this method, a
brain network of 66 people was constructed and a comparative experiment was carried out. The experimental results
show that the classification accuracy of the integrated classification model proposed in this paper is 19% higher than that
of SVM, which effectively improves the classification accuracy.


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References


Gong Li, Fang min. (2017),“early recognition and screening of cognitive impairment.” Shanghai pharmaceutical ,38 (17): 3-7 + 55.

Jiebiao, Zhang Daoqiang.(2016), “New graph kernel for brain network and its application in MCI classification .” Journal of computer science, 39 (08): 1667-1680.

Zhang D,Huang J,Jie B,et al.(2018),“Ordinal pattern: a new descriptor for brain connectivity networks.”IEEE transactions on medical imaging, 37(7):1711-1722.

Liu J,Li M,Lan W,et al.(2016),“Classification of alzheimer's disease using whole brain hierarchical network.”IEEE/ACM transactions on computational biology and bioinformatics, 15(2): 624-632.


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