Paroxysmal atrial fibrillation prediction application using genetic algorithms

Authors:
Sonia Mota;Eduardo Ros;Francisco de Toro;Julio Ortega
Affiliations:
Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Spain;Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Spain;Departamento de Ingeniería Electrónica, Sistemas Informáticos y Automática, Universidad de Huelva, Spain;Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Spain
Venue:
ICCS'03 Proceedings of the 2003 international conference on Computational science
Year:
2003

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Abstract

Paroxysmal Atrial Fibrillation (PAF) prediction viability is a line of research currently being investigated. The definition of new valid parameters for this task may generate various 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.