TY - GEN
T1 - A feature selection scheme for accurate identification of Alzheimer’s disease
AU - Shen, Hao
AU - Zhang, Wen
AU - Chen, Peng
AU - Zhang, Jun
AU - Fang, Aiqin
AU - Wang, Bing
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Effective biomarkers play important roles for accurate diagnosis of Alzheimer’s Disease (AD), including its intermediate stage (i.e. mild cognitive impairment, MCI). In this paper, a new feature selection scheme was proposed to improve the identification AD and MCI from healthy controls (HC) by a support vector machine (SVM) based-classifier with recursive feature addition. Our method can find the significant features automatically, and the experiments in this work demonstrates that our scheme can achieve better classification performance based on a dataset with 103 subjects where three biomarkers, i.e., structural MR imaging (MRI), functional imaging PET, and cerebrospinal fluid(CSF), had been used. Our proposed method demonstrated its effectiveness in identifying AD from HC with an accuracy of 95.0%, while only 89.3% for the classifier without the step of feature selection. In addition, some features selected in this work had shown strong relation with AD by other previous studies, which can provide the support for the significance of our results.
AB - Effective biomarkers play important roles for accurate diagnosis of Alzheimer’s Disease (AD), including its intermediate stage (i.e. mild cognitive impairment, MCI). In this paper, a new feature selection scheme was proposed to improve the identification AD and MCI from healthy controls (HC) by a support vector machine (SVM) based-classifier with recursive feature addition. Our method can find the significant features automatically, and the experiments in this work demonstrates that our scheme can achieve better classification performance based on a dataset with 103 subjects where three biomarkers, i.e., structural MR imaging (MRI), functional imaging PET, and cerebrospinal fluid(CSF), had been used. Our proposed method demonstrated its effectiveness in identifying AD from HC with an accuracy of 95.0%, while only 89.3% for the classifier without the step of feature selection. In addition, some features selected in this work had shown strong relation with AD by other previous studies, which can provide the support for the significance of our results.
KW - Alzheimer’s disease (AD)
KW - Feature selection (FS)
KW - Mild cognitive impairment (MCI)
KW - Support vector machine (SVM)
UR - http://www.scopus.com/inward/record.url?scp=84973861661&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84973861661&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-31744-1_7
DO - 10.1007/978-3-319-31744-1_7
M3 - Conference contribution
AN - SCOPUS:84973861661
SN - 9783319317434
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 71
EP - 81
BT - Bioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings
A2 - Ortuno, Francisco
A2 - Rojas, Ignacio
PB - Springer Verlag
T2 - 4th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016
Y2 - 20 April 2016 through 22 April 2016
ER -