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Information Methods for Predicting Risk and Outcome of Stroke

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Springer Handbook of Bio-/Neuroinformatics

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Abstract

Stroke is a major cause of disability and mortality in most economically developed countries. It is the second leading cause of death worldwide (after cancer and heart disease) [55.1,2] and a major cause of disability in adults in developed countries [55.3]. Personalized modeling is an emerging effective computational approach, which has been applied to various disciplines, such as in personalized drug design, ecology, business, and crime prevention; it has recently become more prominent in biomedical applications. Biomedical data on stroke risk factors and prognostic data are available in a large volume, but the data are complex and often difficult to apply to a specific person. Individualizing stroke risk prediction and prognosis will allow patients to focus on risk factors specific to them, thereby reducing their stroke risk and managing stroke outcomes more effectively. This chapter reviews various methods–conventional statistical methods and computational intelligent modeling methods for predicting risk and outcome of stroke.

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Abbreviations

ANN:

artificial neural network

CHS:

cardiovascular health study

KNN:

K nearest neighbor

NINDS:

National Institute of Neurological Disorders and Stroke

NN:

neural network

SF:

straight filament

SNN:

spiking neural network

STD:

spatio-temporal data

WHO:

World Health Organization

WKNN:

weighted nearest neighbor

WWKNN:

weighted distance and weighted variables K nearest neighbor

eSNN:

evolving spiking neural network

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Author information

Authors and Affiliations

  1. School of Computing and Mathematical Science, Auckland University of Technology, 120 Mayoral Drive, 1142, Auckland, New Zealand

    Wen Liang

  2. National Institute for Stroke and Applied Neurosciences, AUT University, 90 Akoranga Drive, 0627, Auckland, New Zealand

    Rita Krishnamurthi

  3. KEDRI – Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, 120 Mayoral Drive, Auckland, New Zealand

    Nikola Kasabov

  4. National Institute for Stroke and Applied Neurosciences, AUT University, 90 Akoranga Drive, 0627, Northcote, Auckland, New Zealand

    Valery Feigin

Authors
  1. Wen Liang
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  2. Rita Krishnamurthi
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  3. Nikola Kasabov
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  4. Valery Feigin
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Corresponding authors

Correspondence to Wen Liang , Rita Krishnamurthi , Nikola Kasabov or Valery Feigin .

Editor information

Editors and Affiliations

  1. KEDRI – Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, 120 Mayoral Drive, 1010, Auckland, New Zealand

    Nikola Kasabov

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Liang, W., Krishnamurthi, R., Kasabov, N., Feigin, V. (2014). Information Methods for Predicting Risk and Outcome of Stroke. In: Kasabov, N. (eds) Springer Handbook of Bio-/Neuroinformatics. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30574-0_55

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  • DOI: https://doi.org/10.1007/978-3-642-30574-0_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30573-3

  • Online ISBN: 978-3-642-30574-0

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