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Intelligent Hybrid Approach for Computer-Aided Diagnosis of Mild Cognitive Impairment

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Advances in Computing (CCC 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 885))

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Abstract

Mild Cognitive Impairment (MCI) is a paramount nosological entity. The concept was introduced to define the clinical state of decline or the loss of cognitive abilities which implies an initial stage to severe dementia disorders. However, diagnosis of such impairment is a challenging task due to difficulties in costs, time, as well as finding qualified experts on this topic. In this paper, a hybrid intelligent approach based on symbolic and sub-symbolic machine learning techniques is proposed. It allows to analyze the results of different cognitive tests to support decisions-making by health service staff regarding the mental state of patients. The results show that the proposed approach has a high degree of effectiveness in computer-aided diagnosis of Mild Cognitive Impairment.

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Notes

  1. 1.

    Data used in preparation of this paper was obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the researchers within ADNI contributed to the design and implementation of ADNI and nor provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

References

  1. Bramer, M. (ed.): Artificial Intelligence an International Perspective. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03226-4

    Book  Google Scholar 

  2. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)

    MathSciNet  MATH  Google Scholar 

  3. Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4), 198–211 (2007)

    Article  Google Scholar 

  4. Flicker, C., Ferris, S.H.: Mild cognitive impairment in the elderly: predictors of dementia. Neurology 41(7), 449–450 (1991)

    Article  Google Scholar 

  5. Folstein, M.F., Folstein, S.E., McHugh, P.R.: “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12(3), 189–198 (1975)

    Article  Google Scholar 

  6. Gauthier, S., et al.: Mild cognitive impairment. Lancet 367(9518), 1262–1270 (2006)

    Article  Google Scholar 

  7. Hassanien, A.-E., Abraham, A. (eds.): Computational Intelligence in Multimedia Processing: Recent Advances. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-76827-2. Chap. 2

    Book  Google Scholar 

  8. Hsu, K.W.: Hybrid ensembles of decision trees and artificial neural networks. In: IEEE International Conference on Computational Intelligence and Cybernetics (CyberneticsCom), pp. 25–29 (2012)

    Google Scholar 

  9. Hughes, C.P., Berg, L., Danziger, W.L., Coben, L.A., Martin, R.L.: A new clinical scale for the staging of dementia. Br. J. Psychiatry 140, 566–572 (1982)

    Article  Google Scholar 

  10. Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization (2015)

    Google Scholar 

  11. Kingsford, C., Salzberg, S.L.: What are decision trees? Nat. Biotechnol. 26(9), 1011–1013 (2008)

    Article  Google Scholar 

  12. Krenker, A., Bester, J., Kos, A.: Introduction to the artificial neural networks. Artificial Neural Networks - Methodological Advances and Biomedical Applications. InTech, The Hague (2011)

    Google Scholar 

  13. Langa, K.M., Levine, D.A.: The diagnosis and management of mild cognitive impairment: a clinical review. JAMA 312(23), 2551–2561 (2014)

    Article  Google Scholar 

  14. Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., Feng, D.: Early diagnosis of Alzheimer’s disease with deep learning. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 1015–1018 (2014)

    Google Scholar 

  15. Liu, S., et al.: Multifold Bayesian kernelization in Alzheimer’s diagnosis. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 303–310. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_38

    Chapter  Google Scholar 

  16. Louppe, G.,Wehenkel, L., Sutera, A., Geurts, P.: Understanding variable importances in forests of randomized trees. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 26. Neural Information Processing Systems Foundation Inc. (2013)

    Google Scholar 

  17. Mariani, E., Monastero, R., Meccoci, P.: Mild cognitive impairment: a systematic review. J. Alzheimer’s Dis. 12(1), 23–25 (2007)

    Article  Google Scholar 

  18. McDade, E.M., Petersen, R.C.: Mild cognitive impairment: epidemiology, pathology, and clinical assessment (2015)

    Google Scholar 

  19. Nasreddine, Z.S., et al.: The montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment. J. Geriatr. Soc. 53(4), 695–699 (2005)

    Article  Google Scholar 

  20. University of Illinois at Chicago: When Healthcare and Computer Science Collide (2014)

    Google Scholar 

  21. Petersen, R.C., Morris, J.C.: Mild cognitive impairment as a clinical entity and treatment target. Arch. Neurol. 62(7), 1160–1163 (2004)

    Article  Google Scholar 

  22. Petersen, R.C., et al.: Apolipoprotein E status as a predictor of the development of Alzheimer’s disease in memory-impaired individuals. JAMA 273(16), 1274–1278 (1995)

    Article  Google Scholar 

  23. Roy, E.: Cognitive impairment. In: Gellman, M.D., Turner, J.R. (eds.) Encyclopedia of Behavioral Medicine, pp. 449–451. Springer, New York (2013). https://doi.org/10.1007/978-1-4419-1005-9_1118

    Chapter  Google Scholar 

  24. Stoub, T.R., et al.: MRI predictors of risk of incident Alzheimer disease: a longitudinal study. Neurology 64(9), 1520–1524 (2005)

    Article  Google Scholar 

  25. Suk, H.-I., Shen, D.: Deep learning-based feature representation for AD/MCI classification. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 583–590. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_72

    Chapter  Google Scholar 

  26. Sun, Y., Tang, Y., Ding, S., Cui, Y.: Diagnose the mild cognitive impairment by constructing Bayesian network with missing data. Expert Syst. Appl. 38, 442–449 (2011)

    Article  Google Scholar 

  27. Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Education Ltd., London (2006). Chap. 4

    Google Scholar 

  28. Umer, R.: Machine learning approaches for the computer aided diagnosis and prediction of Alzheimer’s disease based on clinical data. Master’s thesis, University of Georgia (2011)

    Google Scholar 

  29. Voisin, T., Touchon, J., Vellas, B.: Mild cognitive impairment: a nosological entity? Curr. Opin. Neurol. 16, S43–S45 (2003)

    Article  Google Scholar 

  30. Williams, J.A., Weakley, A. Cook, D.J., Schmitter-Edgecombe, M.: Machine learning techniques for diagnostic differentiation of mild cognitive impairment and dementia. In: Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence, pp. 71–76 (2013)

    Google Scholar 

  31. Xekardaki, A., et al.: Arterial spin labeling may contribute to the prediction of cognitive deterioration in healthy elderly individuals. Radiology 274(2), 490–499 (2015)

    Article  Google Scholar 

  32. Yesavage, J.A., et al.: Development and validation of a geriatric depression screening scale: a preliminary report. J. Psychiatr. Res. 17(1), 37–49 (1982)

    Article  Google Scholar 

  33. Yin, Z., Zhao, Y., Xudong, L., Duan, H.: A hybrid intelligent diagnosis approach for quick screening of Alzheimer’s disease based on multiple neuropsychological rating scales. Comput. Math. Methods Med. 27–40, 2015 (2015)

    Google Scholar 

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Authors and Affiliations

  1. Universidad Autónoma de Manizales, Caldas, Colombia

    Juan Camilo Flórez, Santiago Murillo Rendón, Francia Restrepo de Mejía & Belarmino Segura Giraldo

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  1. Juan Camilo Flórez
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  2. Santiago Murillo Rendón
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  3. Francia Restrepo de Mejía
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  4. Belarmino Segura Giraldo
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for The Alzheimer’s Disease Neuroimaging Initiative

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Correspondence to Santiago Murillo Rendón .

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Editors and Affiliations

  1. Universidad Tecnológica de Bolívar, Cartagena, Colombia

    Jairo E. Serrano C.

  2. Universidad Tecnológica de Bolívar, Cartagena, Colombia

    Juan Carlos Martínez-Santos

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Flórez, J.C., Murillo Rendón, S., Restrepo de Mejía, F., Segura Giraldo, B., for The Alzheimer’s Disease Neuroimaging Initiative. (2018). Intelligent Hybrid Approach for Computer-Aided Diagnosis of Mild Cognitive Impairment. In: Serrano C., J., Martínez-Santos, J. (eds) Advances in Computing. CCC 2018. Communications in Computer and Information Science, vol 885. Springer, Cham. https://doi.org/10.1007/978-3-319-98998-3_38

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  • DOI: https://doi.org/10.1007/978-3-319-98998-3_38

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