Volume : IX, Issue : I, January - 2020

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, ain network analysis based on machine learning methods can help diagnose MCI. In this paper, the ain 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 ain network. In order to verify the effectiveness of this method, a ain 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.

Keywords :

brain network   subnets   integrated model   MCI  

Article: Download PDF    DOI : https://www.doi.org/10.36106/paripex/9308196  

Cite This Article:

RESEARCH ON BRAIN NETWORK CLASSIFICATION METHOD BASED ON INTEGRATED MODEL, Zhang-Sensen PARIPEX-INDIAN JOURNAL OF RESEARCH : Volume-9 | Issue-1 | January-2020


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