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

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Optimization and Data Analysis in Biomedical Informatics

Part of the book series: Fields Institute Communications ((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.

Mathematics Subject Classification (2010): Primary 90-08, 90C06, 90C11, 90C90, Secondary 92C99

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Authors and Affiliations

  1. Center for Operations Research in Medicine and HealthCare, NSF I/UCRC Center for Health Organization Transformation, and School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0205, USA

    Eva K. Lee & Tsung-Lin Wu

  2. Center for Neurodegenerative Disease and Alzheimer’s Disease Center, and Department of Neurology, Emory University, Atlanta, GA, 30322, USA

    Felicia Goldstein & Allan Levey

Authors
  1. Eva K. Lee
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  2. Tsung-Lin Wu
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  3. Felicia Goldstein
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  4. Allan Levey
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Corresponding author

Correspondence to Eva K. Lee .

Editor information

Editors and Affiliations

  1. , Department of Industrial & Systems Engin, University of Florida, Weil Hall 401, Gainesville, 32611, Florida, USA

    Panos M. Pardalos

  2. , Department of Mathematics, University of Waterloo, University Avenue West 200, Waterloo, N2L 3G1, Ontario, Canada

    Thomas F. Coleman

  3. , Department of Industrial Engineering, University of Central Florida, Central Florida Blvd 4000, Orlando, 32816, Florida, USA

    Petros Xanthopoulos

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Lee, E.K., Wu, TL., Goldstein, F., Levey, A. (2013). Predictive Model for Early Detection of Mild Cognitive Impairment and Alzheimer’s Disease. In: Pardalos, P., Coleman, T., Xanthopoulos, P. (eds) Optimization and Data Analysis in Biomedical Informatics. Fields Institute Communications, vol 63. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4133-5_4

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