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Classification Models for Alzheimer’s Disease Detection

  • Conference paper
Engineering Applications of Neural Networks (EANN 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 384))

Abstract

This paper presents a classification fusion for Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) classification based on dataset acquired basically from an automated structural MRI image processing pipeline. The dataset includes eighty-one regional cortical volume and cortical thickness features produced by the automated pipeline, along with two demographic measurements and three manual volume measurements of the hippocampus. This high-dimensional pattern classification problem is tested in a large database that contains clinical tests from six medical centers in Europe. The assessment of the results has shown that with a careful selection of combined classifiers, subject classification in three classes (Normal Controls, patients with MCI or with AD) is fairly accurate and can be used as an assistive tool to clinical examinations.

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Author information

Authors and Affiliations

  1. Cultural Technology and Communication Dpt., University of the Aegean, 81100, Mytilene, Greece

    Christos-Nikolaos Anagnostopoulos & Ioannis Giannoukos

  2. Department of Clinical Science, Intervention and Technology, 14183, Stockholm, Sweden

    Christian Spenger

  3. King’s College London, UK

    Andrew Simmons & Simon Lovestone

  4. Section of Gerontology and Geriatrics, Department of Clinical and Experimental Medicine, University of Perugia, Perugia, Italy

    Patrizia Mecocci

  5. Department of Neurology, University of Eastern Finland, Finland

    Hikka Soininen

  6. Kuopio University Hospital, 21, Kuopio, Finland

    Hikka Soininen

  7. Department of Old Age Psychiatry & Psychotic Disorders, Medical University of Lodz, 92-17 216, Lodz, Poland

    Iwona Kłoszewska

  8. Department of Internal and Geriatrics Medicine, Hôpitaux de Toulouse, Toulouse, France

    Bruno Vellas

  9. Third Department of Neurology, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Magda Tsolaki

Authors
  1. Christos-Nikolaos Anagnostopoulos
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  2. Ioannis Giannoukos
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  3. Christian Spenger
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  4. Andrew Simmons
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  5. Patrizia Mecocci
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  6. Hikka Soininen
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  7. Iwona Kłoszewska
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  8. Bruno Vellas
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  9. Simon Lovestone
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  10. Magda Tsolaki
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Editor information

Editors and Affiliations

  1. Department of Forestry & Management of the Environment and Natural Resources, Democritus University of Thrace, GR-68200, Orestiada, Hellas

    Lazaros Iliadis

  2. Frederick University of Cyprus, Cyprus

    Harris Papadopoulos

  3. Faculty of Engineering and Computing, Coventry University, UK

    Chrisina Jayne

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Anagnostopoulos, CN. et al. (2013). Classification Models for Alzheimer’s Disease Detection. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41016-1_21

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  • DOI: https://doi.org/10.1007/978-3-642-41016-1_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41015-4

  • Online ISBN: 978-3-642-41016-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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