Ӧ�ø���ͳ�� 2015, 31(3) 309-319 DOI:      ISSN: 1001-4268 CN: 31-1256

����Ŀ¼ | ����Ŀ¼ | ������� | �߼�����                                                            [��ӡ��ҳ]   [�ر�]
Supporting info
Email Alert
��ά, ��ˮϼ

��63���������֢���ߺ�57�������˵�4005������������Ϊԭʼ�����ռ�, �����ò�ͬ�Ľ�ά����,
��ͬ��������ģ����Ѱ�����ŷ���ģ��. �������:
��Mann-Whitney U����ѡȡ�����������˲�������������Ϊ����,
��Elman������ģ���������Ч�����, ��ȷ��94.17%, �û�����p<0.001, ���ж�92.06%,
�����96.49%. ���ڵõ���߷�����ȷ�ʵ�������ģ��,
�����ҳ���34���Է���ȡ����������õĹ�ʶ��������, ���������26������,
��26��������������������Ӧ�Ĺ������ӱ������, ����ǿ۴��غͶ�Ҷ.

�ؼ����� ������   ����   ��������   �������֢.  
Discriminant Analysis in Schizophrenia based on Neural Network
Zhao Wei, Guo Shuixia
Mathematics and Computer Science College, Hunan Normal University

In this paper, we use neural network to classify schizophrenia
patients and healthy control subjects. Based on 4005 high dimensions feature space consist
of functional connectivity about 63 schizophrenic patients and 57 healthy control as the
original data, attempting to try different dimensionality reduction methods, different
neural network model to find the optimal classification model. The results show that using
the Mann-Whitney U test to select the more discrimination features as input and using
Elman neural network model for classification to get the best results, can reach a highest
accuracy of 94.17%, with the sensitivity being 92.06% and the specificity being 96.49%.
For the best classification neural network model, we identified 34 consensus functional
connectivities that exhibit high discriminative power in classification, which includes 26
brain regions, particularly in the thalamus regions corresponding to the maximum number of
functional connectivity edges, followed by the cingulate gyrus and frontal region.

�ո�����  �޻�����  ����淢������  

ͨѶ����: ��ά

1�����IJ�.�Ƽ�����С��ҵ������������������ϵ�Ĺ���----������������������ģ�͵�ʵ֤�о�[J]. Ӧ�ø���ͳ��, 2013,29(6): 666-671

Copyright by Ӧ�ø���ͳ��