TY - JOUR
T1 - Application of attention network test and demographic information to detect mild cognitive impairment via combining feature selection with support vector machine
AU - Lv, Shipin
AU - Wang, Xiukun
AU - Cui, Yifen
AU - Jin, Jue
AU - Sun, Yan
AU - Tang, Yiyuan
AU - Bai, Ying
AU - Wang, Yan
AU - Zhou, Li
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (no. 30670699) and Ministry of Education (NCET-06-0277). The authors would like to thank the members of the department of neurology of Dalian University affiliated Xinhua hospital for their invaluable help with this study. We would like to thank the anonymous reviewers for their insightful comments and constructive suggestions.
PY - 2010/1
Y1 - 2010/1
N2 - Mild cognitive impairment (MCI) is now thought as the prodromal phase of Alzheimer's disease (AD), and the usual method for diagnosing the disease would be a battery of neuropsychological assessment. The present study proposes to integrate a feature selection scheme with support vector machine (SVM) to identify patients with MCI by using attention network test (ANT) and demographic data. Forty-two patients with MCI and forty-five normal individuals underwent ANT recording, and the reaction time and accuracy of ANT and demographics (age, gender, and educational level) were selected as original features. To select features, we first introduced some random variables as probe features in the original data, then ranked all the features according to their influence on the support vector machine decision function, and finally selected those features that had an influence higher than that of the probes. Initially 18 different features were reduced to only four features by our method. SVM classifier created by using these four features gave an 85% classification accuracy with a sensitivity of 85% and a specificity of 86%. And the area under the curve obtained by receiver operating characteristics analysis was 0.918. The experimental results demonstrate that the proposed method is a good potential use to assist identifying patients with MCI objectively and efficiently.
AB - Mild cognitive impairment (MCI) is now thought as the prodromal phase of Alzheimer's disease (AD), and the usual method for diagnosing the disease would be a battery of neuropsychological assessment. The present study proposes to integrate a feature selection scheme with support vector machine (SVM) to identify patients with MCI by using attention network test (ANT) and demographic data. Forty-two patients with MCI and forty-five normal individuals underwent ANT recording, and the reaction time and accuracy of ANT and demographics (age, gender, and educational level) were selected as original features. To select features, we first introduced some random variables as probe features in the original data, then ranked all the features according to their influence on the support vector machine decision function, and finally selected those features that had an influence higher than that of the probes. Initially 18 different features were reduced to only four features by our method. SVM classifier created by using these four features gave an 85% classification accuracy with a sensitivity of 85% and a specificity of 86%. And the area under the curve obtained by receiver operating characteristics analysis was 0.918. The experimental results demonstrate that the proposed method is a good potential use to assist identifying patients with MCI objectively and efficiently.
KW - Attention
KW - Feature selection
KW - Mild cognitive impairment
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=73449111269&partnerID=8YFLogxK
U2 - 10.1016/j.cmpb.2009.05.003
DO - 10.1016/j.cmpb.2009.05.003
M3 - Article
C2 - 19500873
AN - SCOPUS:73449111269
SN - 0169-2607
VL - 97
SP - 11
EP - 18
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
IS - 1
ER -