A hybrid manifold learning algorithm for the diagnosis and prognostication of Alzheimer's disease.

Journal: AMIA ... Annual Symposium proceedings. AMIA Symposium
Published Date: Nov 5, 2015

Abstract

The diagnosis of Alzheimer's disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. Such data are difficult to compare, visualize, and analyze due to the heterogeneous nature of medical tests. We present a hybrid manifold learning framework, which embeds the feature vectors in a subspace preserving the underlying pairwise similarity structure, i.e. similar/dissimilar pairs. Evaluation tests are carried out using the neuroimaging and biological data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in a three-class (normal, mild cognitive impairment, and AD) classification task using support vector machine (SVM). Furthermore, we make extensive comparison with standard manifold learning algorithms, such as Principal Component Analysis (PCA), Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and isometric feature mapping (Isomap). Experimental results show that our proposed algorithm yields an overall accuracy of 85.33% in the three-class task.

Authors

  • Peng Dai
    Key Laboratory of Agricultural Products Processing and Quality Control of Nanchang City/College of Food Science and Engineering, Jiangxi Agricultural University, Nanchang 330045, China.
  • Femida Gwadry-Sridhar
    Department of Computer Science, The University of Western Ontario, London, ON, Canada; Lawson Health Research Institute, St. Josephs Healthcare, London, ON, Canada.
  • Michael Bauer
    Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus Medical Faculty, Technische Universität Dresden, Dresden, Germany.
  • Michael Borrie
    Division of Geriatric Medicine, University of Western Ontario, London, ON, Canada.

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