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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 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 languageEnglish (US)
Title of host publicationBioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings
EditorsFrancisco Ortuno, Ignacio Rojas
PublisherSpringer Verlag
Pages71-81
Number of pages11
ISBN (Print)9783319317434
DOIs
StatePublished - Jan 1 2016
Event4th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016 - Granada, Spain
Duration: Apr 20 2016Apr 22 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9656
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016
CountrySpain
CityGranada
Period4/20/164/22/16

Keywords

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

Fingerprint Dive into the research topics of 'A feature selection scheme for accurate identification of Alzheimer’s disease'. Together they form a unique fingerprint.

  • Cite this

    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