Genetic Algorithm applied to Paroxysmal Atrial Fibrillation Prediction

Authors:
Sonia Mota;Eduardo Ros;Francisco 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:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

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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.