Advertisement

Predictive Model for Early Detection of Mild Cognitive Impairment and Alzheimer’s Disease

  • Eva K. LeeEmail author
  • Tsung-Lin Wu
  • Felicia Goldstein
  • Allan Levey
Chapter
First Online:
Part of the Fields Institute Communications book series (FIC, volume 63)

Abstract

The number of people affected by Alzheimer’s disease is growing at a rapid rate, and the consequent increase in costs will have significant impacts on the world’s economies and health care systems. Therefore, there is an urgent need to identify mechanisms that can provide early detection of the disease to allow for timely intervention. Neuropsychological tests are inexpensive, non-invasive, and can be incorporated within an annual physical examination. Thus they can serve as a baseline for early cognitive impairment or Alzheimer’s disease risk prediction. In this paper, we describe a PSO-DAMIP machine-learning framework for early detection of mild cognitive impairment and Alzheimer’s disease. Using two trials of patients with Alzheimer’s disease (AD), mild cognitive impairment (MCI), and control groups, we show that one can successfully develop a classification rule based on data from neuropsychological tests to predict AD, MCI, and normal subjects where the blind prediction accuracy is over 90%. Further, our study strongly suggests that raw data of neuropsychological tests have higher potential to predict subjects from AD, MCI, and control groups than pre-processed subtotal score-like features. The classification approach and the results discussed herein offer the potential for development of a clinical decision making tool. Further study must be conducted to validate its clinical significance and its predictive accuracy among various demographic groups and across multiple sites.

Keywords

Particle Swarm Optimization Mild Cognitive Impairment Neuropsychological Test Classification Rule Mild Cognitive Impairment Patient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

References

  1. 1.
    J.A. Anderson, Constrained discrimination between k populations. J. Roy. Stat. Soc. B (Methodological) 31(1), 123–139 (1969)Google Scholar
  2. 2.
    M.W. Bondi, A.J. Jak, L. Delano-Wood, M.W. Jacobson, D.C. Delis, D.P. Salmon, Neuropsychological contributions to the early identification of Alzheimer’s disease. Neuropsychol. Rev. 18(1), 73–90 (2008)CrossRefGoogle Scholar
  3. 3.
    J.P. Brooks, E.K. Lee, Analysis of the consistency of a mixed integer programming-based multi-category constrained discriminant model. Ann. Oper. Res. 174(1), 147–168 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    J.P. Brooks, E.K. Lee, Solving a mixed integer programming multi-category classification model with misclassification constraints. INFORMS J. Comput. (2011, accepted)Google Scholar
  5. 5.
    M. Brys, E. Pirraglia, K. Rich, S. Rolstad, L. Mosconi, R. Switalski, L. Glodzik-Sobanska, S. De Santi, R. Zinkowski, P. Mehta et al., Prediction and longitudinal study of CSF biomarkers in mild cognitive impairment. Neurobiol. Aging 30(5), 682–690 (2009)CrossRefGoogle Scholar
  6. 6.
    R. Chaves, J. Ramírez, J.M. Górriz, M. López, D. Salas-Gonzalez, I. Álvarez, F. Segovia, SVM-based computer-aided diagnosis of the Alzheimer’s disease using t-test NMSE feature selection with feature correlation weighting. Neurosci. Lett. 461(3), 293–297 (2009)CrossRefGoogle Scholar
  7. 7.
    F.A. Feltus, E.K. Lee, J.F. Costello, C. Plass, P.M. Vertino, Predicting aberrant CpG island methylation. Proc. Natl. Acad. Sci. 100(21), 12253–12258 (2003)CrossRefGoogle Scholar
  8. 8.
    R.J. Gallagher, E.K. Lee, D.A. Patterson, in An Optimization Model for Constrained Discriminant Analysis and Numerical Experiments with Iris, Thyroid, and Heart Disease Datasets. Proceedings of the AMIA Annual Fall Symposium (American Medical Informatics Association, 1996), pp. 209–213Google Scholar
  9. 9.
    R.J. Gallagher, E.K. Lee, D.A. Patterson, Constrained discriminant analysis via 0/1 mixed integer programming. Ann. Oper. Res. 74, 65–88 (1997)zbMATHCrossRefGoogle Scholar
  10. 10.
    J. Kennedy, R. Eberhart, in Particle Swarm Optimization. IEEE International Conference on Neural Networks, 1995. Proceedings, vol. 4 (IEEE, NY, 1995), pp. 1942–1948Google Scholar
  11. 11.
    A. Kluger, S.H. Ferris, J. Golomb, M.S. Mittelman, B. Reisberg, Neuropsychological prediction of decline to dementia in nondemented elderly. J. Geriatric Psychiatr. Neurol. 12(4), 168–179 (1999)CrossRefGoogle Scholar
  12. 12.
    E.K. Lee, Large-scale optimization-based classification models in medicine and biology. Ann. Biomed. Eng. 35(6), 1095–1109 (2007)CrossRefGoogle Scholar
  13. 13.
    E.K. Lee, in Machine Learning Framework for Classification in Medicine and Biology. Integration of artificial intelligence and operations research techniques in constraint programming for combinatorial optimization problems. CPAIOR 2009, vol. 5547, pp. 1–7 (2009)Google Scholar
  14. 14.
    E.K. Lee, T.L. Wu, Classification and Disease Prediction via Mathematical Programming. Handbook of Optimization in Medicine, pp. 1–50 (2009)Google Scholar
  15. 15.
    E.K. Lee, A.Y.C. Fung, J.P. Brooks, M. Zaider, Automated planning volume definition in soft-tissue sarcoma adjuvant brachytherapy. Phys. Med. Biol. 47, 1891–1910 (2002)CrossRefGoogle Scholar
  16. 16.
    E.K. Lee, R.J. Gallagher, D.A. Patterson, A linear programming approach to discriminant analysis with a reserved-judgment region. INFORMS J. Comput. 15(1), 23–41 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    E.K. Lee, R.J. Gallagher, A.M. Campbell, M.R. Prausnitz, Prediction of ultrasound-mediated disruption of cell membranes using machine learning techniques and statistical analysis of acoustic spectra. IEEE Trans. Biomed. Eng. 51(1), 82–89 (2004)CrossRefGoogle Scholar
  18. 18.
    M.M. López, J. Ramírez, J.M. Górriz, I. Álvarez, D. Salas-Gonzalez, F. Segovia, R. Chaves, SVM-based CAD system for early detection of the Alzheimer’s disease using kernel PCA and LDA. Neurosci. Lett. 464(3), 233–238 (2009)CrossRefGoogle Scholar
  19. 19.
    O.L. Lopez, J.T. Becker, W.J. Jagust, A. Fitzpatrick, M.C. Carlson, S.T. DeKosky, J. Breitner, C.G. Lyketsos, B. Jones, C. Kawas et al., Neuropsychological characteristics of mild cognitive impairment subgroups. J. Neurol. Neurosurg. Psychiatr. 77(2), 159–165 (2006)CrossRefGoogle Scholar
  20. 20.
    M.T. McCabe, E.K. Lee, P.M. Vertino, A multifactorial signature of DNA sequence and polycomb binding predicts aberrant CpG island methylation. Cancer Res. 69(1), 282–291 (2009)CrossRefGoogle Scholar
  21. 21.
    L.K. McEvoy, C. Fennema-Notestine, J.C. Roddey, D.J. Hagler, D. Holland, D.S. Karow, C.J. Pung, J.B. Brewer, A.M. Dale, Alzheimer disease: Quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment. Radiology 251(1), 195–205 (2009)CrossRefGoogle Scholar
  22. 22.
    C. Misra, Y. Fan, C. Davatzikos, 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(4), 1415–1422 (2009)CrossRefGoogle Scholar
  23. 23.
    H.I. Nakaya, J. Wrammert, E.K. Lee, L. Racioppi, S. Marie-Kunze, W.N. Haining, A.R. Means, S.P. Kasturi, N. Khan, G.M. Li et al., Systems biology of vaccination for seasonal influenza in humans. Nat. Immunol. 12(8), 786–795 (2011)CrossRefGoogle Scholar
  24. 24.
    A.P. Nelson, M.G. O’Connor, Mild cognitive impairment: A neuropsychological perspective. CNS Spectrums 13(1), 56–64 (2008)Google Scholar
  25. 25.
    S.E. O’Bryant, G. Xiao, R. Barber, J. Reisch, R. Doody, T. Fairchild, P. Adams, S. Waring, R. Diaz-Arrastia, A serum protein-based algorithm for the detection of Alzheimer disease. Arch. Neurol. 67(9), 1077–1081 (2010)CrossRefGoogle Scholar
  26. 26.
    S.E. O’Bryant, G. Xiao, R. Barber, J. Reisch, J. Hall, C.M. Cullum, R. Doody, T. Fairchild, P. Adams, K. Wilhelmsen et al., A blood-based algorithm for the detection of Alzheimer’s disease. Dement. Geriatr. Cognit. Disord. 32(1), 55–62 (2011)CrossRefGoogle Scholar
  27. 27.
    R. Poli, J. Kennedy, T. Blackwell, Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)Google Scholar
  28. 28.
    T.D. Querec, R.S. Akondy, E.K. Lee, W. Cao, H.I. Nakaya, D. Teuwen, A. Pirani, K. Gernert, J. Deng, B. Marzolf et al., Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat. Immunol. 10(1), 116–125 (2008)CrossRefGoogle Scholar
  29. 29.
    D.T. Stuss, R.L. Trites, Classification of neurological status using multiple discriminant function analysis of neuropsychological test scores. J. Consult. Clin. Psychol. 45(1), 145 (1977)Google Scholar
  30. 30.
    M.H. Tabert, J.J. Manly, X. Liu, G.H. Pelton, S. Rosenblum, M. Jacobs, D. Zamora, M. Goodkind, K. Bell, Y. Stern, D.P. Devanand, Neuropsychological prediction of conversion to Alzheimer disease in patients with mild cognitive impairment. Arch. Gen. Psychiatr. 63, 916–924 (2006)CrossRefGoogle Scholar
  31. 31.
    T.L. Wu, Classification Models for Disease Diagnosis and Outcome Analysis. PhD thesis, Georgia Institute of Technology (2011)Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Eva K. Lee
    • 1
    Email author
  • Tsung-Lin Wu
    • 1
  • Felicia Goldstein
    • 2
  • Allan Levey
    • 2
  1. 1.Center for Operations Research in Medicine and HealthCareNSF I/UCRC Center for Health Organization Transformation, and School of Industrial and Systems Engineering, Georgia Institute of TechnologyAtlantaUSA
  2. 2.Center for Neurodegenerative Disease and Alzheimer’s Disease Center, and Department of NeurologyEmory UniversityAtlantaUSA

Personalised recommendations

Cite chapter

Buy options