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Genetic Algorithm applied to Paroxysmal Atrial Fibrillation Prediction

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Artificial Neural Nets Problem Solving Methods (IWANN 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2687))

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Abstract

Paroxysmal Atrial Fibrillation (PAF) prediction viability is an open research line. The definition of new valid parameters for this task can be based on very heterogeneous features. Genetic Algorithms (GAs) automatically find a set of parameters to maximize the diagnosis capabilities of a scheme based on the K-nearest neighbours algorithm. This is an efficient way of generating a number of possible solutions for the problem of PAF prediction. The present paper illustrates how GAs, rather than a statistical study of the database can be used to select the parameters giving the best classification rates.

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Author information

Authors and Affiliations

  1. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Spain

    Sonia Mota, Eduardo Ros & Julio Ortega

  2. Departamento de Ingeniería Electrónica, Sistemas Informáticos y Automática, Universidad de Huelva, Spain

    Francisco de Toro

Authors
  1. Sonia Mota
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  2. Eduardo Ros
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  3. Francisco de Toro
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  4. Julio Ortega
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Editor information

Editors and Affiliations

  1. E.T.S. de Ingeniería Informática Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia, Juan del Rosal, 16, 28040, Madrid, Spain

    José Mira  & José R. Álvarez  & 

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© 2003 Springer-Verlag Berlin Heidelberg

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Mota, S., Ros, E., de Toro, F., Ortega, J. (2003). Genetic Algorithm applied to Paroxysmal Atrial Fibrillation Prediction. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_44

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  • DOI: https://doi.org/10.1007/3-540-44869-1_44

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40211-4

  • Online ISBN: 978-3-540-44869-3

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