Skip to main content
SpringerLink
Log in
Book cover

International Conference on Bioinformatics and Biomedical Engineering

IWBBIO 2016: Bioinformatics and Biomedical Engineering pp 71–81Cite as

A Feature Selection Scheme for Accurate Identification of Alzheimer’s Disease

  • Conference paper
  • First Online:

  • 1887 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9656))

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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Ramaroson, H., Helmer, C., Barberger-Gateau, P., Letenneur, L., Dartigues, J.F.: Prevalence of dementia and Alzheimer’s disease among subjects aged 75 years or over: updated results of the PAQUID cohort. Rev. Neurol. (Paris) 159, 405–411 (2003)

    Google Scholar 

  2. Brookmeyer, R., Johnson, E., Ziegler-Graham, K., Arrighi, H.M.: Forecasting the global burden of Alzheimer’s disease. Alzheimer’s Dement. 3, 186–191 (2007)

    Article  Google Scholar 

  3. De Toledo-Morrell, L., Stoub, T.R., Bulgakova, M., Wilson, R.S., Bennett, D.A., Leurgans, S., et al.: MRI-derived entorhinal volume is a good predictor of conversion from MCI to AD. Neurobiol. Aging 25, 1197–1203 (2004)

    Article  Google Scholar 

  4. Nestor, P.J., Scheltens, P., Hodges, J.R.: Advances in the early detection of Alzheimer’s disease. Nat. Med. 10(suppl.), S34–S41 (2004). (Review)

    Google Scholar 

  5. Jack Jr., C.R., Shiung, M.M., Weigand, S.D., O’Brien, P.C., Gunter, J.L., Boeve, B.F., et al.: Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI. Neurology 65, 1227–1231 (2005)

    Article  Google Scholar 

  6. Ramírez, J., Górriz, J.M., Salas-Gonzalez, D., Romero, A., López, M., Álvarez, I., et al.: Computer-aided diagnosis of Alzheimer’s type dementia combining support vector machines and discriminant set of features. Inf. Sci. 237, 59–72 (2013)

    Article  Google Scholar 

  7. Hoffman, J.M., Welsh-Bohmer, K.A., Hanson, M., Crain, B., Hulette, C., Earl, N., et al.: FDG PET imaging in patients with pathologically verified dementia. J. Nucl. Med. 41, 1920–1928 (2000)

    Google Scholar 

  8. Ishii, K., Sasaki, H., Kono, A.K., Miyamoto, N., Fukuda, T., Mori, E.: Comparison of gray matter and metabolic reduction in mild Alzheimer’s disease using FDG-PET and voxel-based morphometric MR studies. Eur. J. Nucl. Med. Mol. Imaging 32, 959–963 (2005)

    Article  Google Scholar 

  9. McEvoy, L.K., Fennema-Notestine, C., Roddey, J.C., Hagler Jr., D.J., Holland, D., Karow, D.S., Pung, C.J., Brewer, J.B., Dale, A.M.: Alzheimer disease: quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment. Radiology 251, 195–205 (2009)

    Article  Google Scholar 

  10. Klunk, W.E., Engler, H., Nordberg, A., Wang, Y., Blomqvist, G., Holt, D.P., et al.: Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann. Neurol. 55, 306–319 (2004)

    Article  Google Scholar 

  11. Ji, Y., Permanne, B., Sigurdsson, E.M., Holtzman, D.M., Wisniewski, T.: Amyloid beta40/42 clearance across the blood-brain barrier following intra-ventricular injections in wild-type, apoE knock-out and human apoE3 or E4 expressing transgenic mice. J. Alzheimers Dis. 3, 23–30 (2001)

    Google Scholar 

  12. Bouwman, F.H., Schoonenboom, S.N., van der Flier, W.M., van Elk, E.J., Kok, A., Barkhof, F., et al.: CSF biomarkers and medial temporal lobe atrophy predict dementia in mild cognitive impairment. Neurobiol. Aging 28, 1070–1074 (2007)

    Article  Google Scholar 

  13. Fjell, A.M., Walhovd, K.B., Fennema-Notestine, C., McEvoy, L.K., Hagler, D.J., Holland, D., et al.: CSF biomarkers in prediction of cerebral and clinical change in mild cognitive impairment and Alzheimer’s disease. J. Neurosci. 30, 2088–2101 (2010)

    Article  Google Scholar 

  14. Orru, G., Pettersson-Yeo, W., Marquand, A.F., Sartori, G., Mechelli, A.: Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci. Biobehav. Rev. 36, 1140–1152 (2012)

    Article  Google Scholar 

  15. Vemuri, P., Gunter, J.L., Senjem, M.L., Whitwell, J.L., Kantarci, K., Knopman, D.S., et al.: Alzheimer’s disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 39, 1186–1197 (2008)

    Article  Google Scholar 

  16. Magnin, B., Mesrob, L., Kinkingnehun, S., Pelegrini-Issac, M., Colliot, O., Sarazin, M., et al.: Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI. Neuroradiology 51, 73–83 (2009)

    Article  Google Scholar 

  17. Kohannim, O., Hua, X., Hibar, D.P., Lee, S., Chou, Y.Y., Toga, A.W., et al.: Boosting power for clinical trials using classifiers based on multiple biomarkers. Neurobiol. Aging 31, 1429–1442 (2010)

    Article  Google Scholar 

  18. Westman, E., Muehlboeck, J.S., Simmons, A.: Combining MRI and CSF measures for classification of Alzheimer’s disease and prediction of mild cognitive impairment conversion. Neuroimage 62, 229–238 (2012)

    Article  Google Scholar 

  19. Walhovd, K.B., Fjell, A.M., Brewer, J., McEvoy, L.K., Fennema-Notestine, C., Hagler Jr., D.J., et al.: Combining MR imaging, positron-emission tomography, and CSF biomarkers in the diagnosis and prognosis of Alzheimer disease. AJNR Am. J. Neuroradiol. 31, 347–354 (2010)

    Article  Google Scholar 

  20. Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D.: Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55, 856–867 (2011)

    Article  Google Scholar 

  21. Hinrichs, C., Singh, V., Xu, G., Johnson, S.C.: Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. Neuroimage 55, 574–589 (2011)

    Article  Google Scholar 

  22. McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., Stadlan, E.M.: Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA work group under the auspices of department of health and human services task force on Alzheimer’s disease. Neurology 34, 939–944 (1984)

    Article  Google Scholar 

  23. McKhann, G.M., Knopman, D.S., Chertkow, H., Hyman, B.T., Jack Jr., C.R., Kawas, C.H., et al.: The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 263–269 (2011)

    Article  Google Scholar 

  24. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1999)

    MATH  Google Scholar 

  25. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:27–27:27 (2011)

    Article  Google Scholar 

  26. Chu, C., Hsu, A.L., Chou, K.H., Bandettini, P., Lin, C.: Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. Neuroimage 60, 59–70 (2012)

    Article  Google Scholar 

  27. Walhovd, K.B., Fjell, A.M., Dale, A.M., McEvoy, L.K., Brewer, J., Karow, D.S., et al.: Multi-modal imaging predicts memory performance in normal aging and cognitive decline. Neurobiol. Aging 31, 1107–1121 (2010)

    Article  Google Scholar 

  28. Fan, Y., Resnick, S.M., Wu, X., Davatzikos, C.: Structural and functional biomarkers of prodromal Alzheimer’s disease: a high-dimensional pattern classification study. Neuroimage 41, 277–285 (2008)

    Article  Google Scholar 

  29. Fan, Y., Rao, H., Hurt, H., Giannetta, J., Korczykowski, M., Shera, D., et al.: Multivariate examination of brain abnormality using both structural and functional MRI. Neuroimage 36, 1189–1199 (2007)

    Article  Google Scholar 

  30. Costafreda, S.G., Chu, C., Ashburner, J., Fu, C.H.: Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS ONE 4, e6353 (2009)

    Article  Google Scholar 

  31. Chetelat, G., Desgranges, B., De La Sayette, V., Viader, F., Eustache, F., Baron, J.-C.: Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment. NeuroReport 13, 1939–1943 (2002)

    Article  Google Scholar 

  32. Misra, C., Fan, Y., Davatzikos, C.: Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. Neuroimage 44, 1415–1422 (2009)

    Article  Google Scholar 

  33. Hampel, H., Burger, K., Teipel, S.J., Bokde, A.L., Zetterberg, H., Blennow, K.: Core candidate neurochemical and imaging biomarkers of Alzheimer’s disease. Alzheimers Dement. 4, 38–48 (2008)

    Article  Google Scholar 

  34. Jack Jr., C.R., Petersen, R.C., Xu, Y.C., O’Brien, P.C., Smith, G.E., Ivnik, R.J., et al.: Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology 52, 1397–1403 (1999)

    Article  Google Scholar 

  35. Barnes, J., Scahill, R.I., Boyes, R.G., Frost, C., Lewis, E.B., Rossor, C.L., et al.: Differentiating AD from aging using semiautomated measurement of hippocampal atrophy rates. Neuroimage 23, 574–581 (2004)

    Article  Google Scholar 

  36. Edward, E.S., Stephen, M.K.: Cognitive Psychology: Mind and Brain, pp. 21, 194–199, 349. Prentice Hall, New Jersey (2007)

    Google Scholar 

  37. Arnold, S.E., Hyman, B.T., Van Hoesen, G.W.: Neuropathologic changes of the temporal pole in Alzheimer’s disease and Pick’s disease. Arch. Neurol. 51, 145–150 (1994)

    Article  Google Scholar 

  38. Yang, J., Pan, P., Song, W., Huang, R., Li, J., Chen, K., et al.: Voxelwise meta-analysis of gray matter anomalies in Alzheimer’s disease and mild cognitive impairment using anatomic likelihood estimation. J. Neurol. Sci. 316, 21–29 (2012)

    Article  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, Shanghai, China

    Hao Shen, Wen Zhang & Bing Wang

  2. The Advanced Research Institute of Intelligent Sensing Network, Tongji University, Shanghai, China

    Hao Shen, Wen Zhang & Bing Wang

  3. The Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai, China

    Hao Shen, Wen Zhang & Bing Wang

  4. Institute of Health Sciences, Anhui University, Hefei, Anhui, China

    Peng Chen

  5. College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, China

    Jun Zhang

  6. Department of Chemistry, University of Louisville, Louisville, KY, 40292, USA

    Aiqin Fang

Authors
  1. Hao Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Aiqin Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bing Wang .

Editor information

Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Francisco Ortuño

  2. Universidad de Granada, Granada, Spain

    Ignacio Rojas

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

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: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-31744-1_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31743-4

  • Online ISBN: 978-3-319-31744-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation