Information Methods for Predicting Risk and Outcome of Stroke

Chapter

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.

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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Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.School of Computing and Mathematical ScienceAuckland University of TechnologyAucklandNew Zealand
  2. 2.National Institute for Stroke and Applied NeurosciencesAUT UniversityAucklandNew Zealand
  3. 3.KEDRI – Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand
  4. 4.National Institute for Stroke and Applied NeurosciencesAUT UniversityNorthcoteNew Zealand

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