Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

A Machine Learning Framework for Assessment of Cognitive and Functional Impairments in Alzheimer’s Disease: Data Preprocessing and Analysis

Abstract

The neuropsychological scores and Functional Activities Questionnaire (FAQ) are significant to measure the cognitive and functional domain of the patients affected by the Alzheimer’s Disease. Further, there are standardized dataset available today that are curated from several centers across the globe that aid in development of Computer Aided Diagnosis tools. However, there are numerous clinical tests to measure these scores that lead to a challenging task for their assessment in diagnosis. Also, the datasets suffer from common missing and imbalanced data issues. In this paper, we propose a machine learning based framework to overcome these issues. Empirical results demonstrate that improved performance of Genetic Algorithm is obtained for the neuropsychological scores after Miss Forest Imputation and for FAQ scores is obtained after subjecting it to the Synthetic Minority Oversampling Technique.

This is a preview of subscription content, log in to check access.

Figure 1
Figure 2
Figure 3

References

  1. 1.

    G. Emilien, C. Durlach, K. L. Minaker, B. Winblad, S. Gauthier, and J.M. Maloteaux, «Alzheimer Disease,» Neuropsychology and Pharmacology, 2012.

  2. 2.

    S. T. McCutcheon, D. Han, J. Troncoso, V. E. Koliatsos, M. Albert, C. G. Lyketsos, and J.-M. S. Leoutsakos, «Clinicopathological correlates of depression in early Alzheimer’s disease in the NACC,» International Journal of Geriatric Psychiatry, 2016.

  3. 3.

    B. E. Gavett, L. Ashendorf, and A. S. Gurnani, «Reliable Change on Neuropsychological Tests in the Uniform Data Set,» Journal of the International Neuropsychological Society, vol. 21, no. 07, pp. 558–567, 2015.

  4. 4.

    J. Hassenstab, S. E. Monsell, C. Mock, C. M. Roe, N. J. Cairns, J. C. Morris, and W. Kukull, «Neuropsychological Markers of Cognitive Decline in Persons with Alzheimer Disease Neuropathology,» Journal of Neuropathology & Experimental Neurology, vol. 74, no. 11, pp. 1086–1092, 2015.

  5. 5.

    L. M. Besser, I. Litvan, S. E. Monsell, C. Mock, S. Weintraub, X.-H. Zhou, and W. Kukull, «Mild Cognitive Impairment in Parkinson’s Disease versus Alzheimer’s Disease,» Parkinsonism Related Disorders, vol. 27, pp. 54–60, 2016.

  6. 6.

    J. Barnes, B. C. Dickerson, C. Frost, L. C. Jiskoot, D. Wolk, and W. M. van der Flier, «Alzheimer’s Disease First Symptoms are Age Dependent: Evidence from the NACC Dataset,» Alzheimer’s Dementia, vol. 11, no. 11, pp. 1349–1357, 2015.

  7. 7.

    Battista, Petronilla, Christian Salvatore, and Isabella Castiglioni, «Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study,» Behavioural Neurology, 2017.

  8. 8.

    Machulda, M.M., Lundt, E.S., Albertson, S.M., Kremers, W.K., Mielke, M.M. et al. “Neuropsychological Subtypes of Incident Mild Cognitive Impairment in the Mayo Clinic Study of Aging». Alzheimer’s & Dementia, vol. 15, pp. 878–887, 2019.

  9. 9.

    Zhu, Yingying, Minjeong Kim, Xiaofeng Zhu, Jin Yan, Daniel Kaufer, and Guorong Wu. «Personalized Diagnosis for Alzheimer’s Disease.» In International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp. 205–213, 2017.

  10. 10.

    Moradi, Elaheh, Ilona Hallikainen, Tuomo Hänninen, Jussi Tohka, and Alzheimer’s Disease Neuroimaging Initiative. «Rey’s Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer’s disease.» NeuroImage: Clinical, vol. 13, pp. 415–427, 2017.

  11. 11.

    C. Green and S. Zhang, «Predicting the Progression of Alzheimer’s Disease Dementia: A Multidomain Health Policy Model,» Alzheimer’s & Dementia, vol. 12, no. 7, pp. 776–785, 2016.

  12. 12.

    S. E. John, A. S. Gurnani, C. Bussell, J. L. Saurman, J. W. Griffin, and B. E. Gavett, «The Effectiveness and Unique Contribution of Neuropsychological Tests and the Latent Phenotype in the Differential Diagnosis of Dementia in the Uniform Data Set,» Neuropsychology, vol. 30, no. 8, 2016.

  13. 13.

    Kielb, Stephanie, Emily Rogalski, Sandra Weintraub, and Alfred Rademaker, «Objective Features of Subjective Cognitive Decline in a United States National Database,» Alzheimer’s and Dementia vol. 13, no. 12, pp. 1337–1344, 2017.

  14. 14.

    Musa, Gada, Fernando Henríquez, Carlos Muñoz-Neira, Carolina Delgado, Patricia Lillo, and Andrea Slachevsky. «Utility of the Neuropsychiatric Inventory Questionnaire (NPI-Q) in the Assessment of a Sample of Patients with Alzheimer’s Disease in Chile,» Dementia and Neuropsychologia, vol. 11, no. 2, pp. 129–136, 2017.

  15. 15.

    Chen, Yingjia, Katherine G. Denny, Danielle Harvey, Sarah Tomaszewski Farias, Dan Mungas, Charles DeCarli, and Laurel Beckett. «Progression from Normal Cognition to Mild Cognitive Impairment in a Diverse Clinic-based and Community-based Elderly Cohort,» Alzheimer’s and Dementia, vol. 13, no. 4, pp. 399–405, 2017.

  16. 16.

    Zhu, Yingying, Xiaofeng Zhu, Minjeong Kim, Dinggang Shen, and Guorong Wu. «Early Diagnosis of Alzheimer’s Disease by Joint Feature Selection and Classification on Temporally Structured Support Vector Machine.» In International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp. 264–272, 2016.

  17. 17.

    Sørensen, Lauge, Christian Igel, Akshay Pai, Ioana Balas, Cecilie Anker, Martin Lillholm, Mads Nielsen, and Alzheimer’s Disease Neuroimaging Initiative. «Differential Diagnosis of Mild Cognitive Impairment and Alzheimer’s Disease using Structural MRI Cortical Thickness, Hippocampal Shape, Hippocampal Texture, and Volumetry.» NeuroImage: Clinical, vol. 13, pp. 470–482, 2017.

  18. 18.

    Beheshti, Iman, Hasan Demirel, Hiroshi Matsuda, and Alzheimer’s Disease Neuroimaging Initiative. «Classification of Alzheimer’s Disease and Prediction of Mild Cognitive Impairment-to-Alzheimer’s Conversion from Structural Magnetic Resource Imaging using Feature Ranking and a Genetic Algorithm.» Computers in Biology and Medicine, vol. 83 pp. 109–119, 2017.

  19. 19.

    K. Steenland, J. Macneil, S. Bartell, and J. Lah, “Analyses of Diagnostic Patterns at 30 Alzheimer’s Disease Centers in the US,” Neuroepidemiology, vol. 35, no. 1, pp. 19–27, 2010.

  20. 20.

    Stekhoven, Daniel J., and Peter Bühlmann. «MissForest Non-Parametric Missing Value Imputation for Mixed-Type Data.» Bioinformatics, vol. 28, no. 1, 2011.

  21. 21.

    Johnson, Piers, Luke Vandewater, William Wilson, Paul Maruff, Greg Savage, Petra Graham, Lance S. Macaulay et al. «Genetic algorithm with logistic regression for prediction of progression to Alzheimer’s disease,» BMC bioinformatics vol. 15, no. 16, 2014.

  22. 22.

    Hosmer Jr, David W., Stanley Lemeshow, and Rodney X. Sturdivant. Applied logistic regression. Vol. 398. John Wiley & Sons, 2013.

  23. 23.

    Vinutha N, Sonu Sharma, P Deepa Shenoy, Venugopal K R., “Optimization of Neuropsychological Scores at the Baseline Visit Using Evolutionary Technique.” In the Proceedings of Third IEEE International Women in Engineering Conference on Electrical and Computer Engineering (WIECON-ECE-2017), e-ISBN: 978-1-5386-2621-4, p-ISBN: 978-1-5386-2622-1, pp.55–59, December 18–19, 2017.

Download references

Acknowledgement

We thank the curators of NACC database for providing the data for our research work.

Author information

Correspondence to N. Vinutha.

Ethics declarations

Conflict of interest: All the authors declare no funding received from any source and there is no conflict of interest.

Ethical standards: This work was conducted in accordance with the guidelines provided by the Bangalore University.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Vinutha, N., Pattar, S., Sharma, S. et al. A Machine Learning Framework for Assessment of Cognitive and Functional Impairments in Alzheimer’s Disease: Data Preprocessing and Analysis. J Prev Alzheimers Dis (2020). https://doi.org/10.14283/jpad.2020.7

Download citation

Key words

  • Alzheimer’s disease
  • functional activities questionnaire
  • genetic algorithm
  • logistic regression
  • imputation
  • missforest
  • neuropsychological scores
  • synthetic minority oversampling technique