A feature selection scheme for accurate identification of Alzheimer’s disease

Hao Shen; Wen Zhang; Peng Chen; Jun Zhang; Aiqin Fang; Bing Wang

doi:10.1007/978-3-319-31744-1_7

A feature selection scheme for accurate identification of Alzheimer’s disease

Hao Shen, Wen Zhang, Peng Chen, Jun Zhang, Aiqin Fang, Bing Wang

Bioproducts and Biosystems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Bioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings
Editors	Francisco Ortuno, Ignacio Rojas
Publisher	Springer Verlag
Pages	71-81
Number of pages	11
ISBN (Print)	9783319317434
DOIs	https://doi.org/10.1007/978-3-319-31744-1_7
State	Published - 2016
Event	4th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016 - Granada, Spain Duration: Apr 20 2016 → Apr 22 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9656
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	4th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016
Country/Territory	Spain
City	Granada
Period	4/20/16 → 4/22/16

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China (Nos. 61300058, 61472282 and 61374181), Anhui Provincial Natural Science Foundation (No. 1508085MF129). The authors give special thanks to Professor D.Q. Zhang in Nanjing University of Aeronautics and Astronautics for his work in data preprocessing, and the data support from ADNI.

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

Keywords

Alzheimer’s disease (AD)
Feature selection (FS)
Mild cognitive impairment (MCI)
Support vector machine (SVM)

Access

10.1007/978-3-319-31744-1_7

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Shen, H., Zhang, W., Chen, P., Zhang, J., Fang, A., & Wang, B. (2016). A feature selection scheme for accurate identification of Alzheimer’s disease. In F. Ortuno, & I. Rojas (Eds.), Bioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings (pp. 71-81). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9656). Springer Verlag. https://doi.org/10.1007/978-3-319-31744-1_7

A feature selection scheme for accurate identification of Alzheimer’s disease. / Shen, Hao; Zhang, Wen; Chen, Peng et al.
Bioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings. ed. / Francisco Ortuno; Ignacio Rojas. Springer Verlag, 2016. p. 71-81 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9656).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Shen, H, Zhang, W, Chen, P, Zhang, J, Fang, A & Wang, B 2016, A feature selection scheme for accurate identification of Alzheimer’s disease. in F Ortuno & I Rojas (eds), Bioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9656, Springer Verlag, pp. 71-81, 4th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016, Granada, Spain, 4/20/16. https://doi.org/10.1007/978-3-319-31744-1_7

Shen H, Zhang W, Chen P, Zhang J, Fang A, Wang B. A feature selection scheme for accurate identification of Alzheimer’s disease. In Ortuno F, Rojas I, editors, Bioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings. Springer Verlag. 2016. p. 71-81. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-31744-1_7

Shen, Hao ; Zhang, Wen ; Chen, Peng et al. / A feature selection scheme for accurate identification of Alzheimer’s disease. Bioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings. editor / Francisco Ortuno ; Ignacio Rojas. Springer Verlag, 2016. pp. 71-81 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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A feature selection scheme for accurate identification of Alzheimer’s disease

Abstract

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Keywords

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