Evolutionary algorithms for multiobjective and multimodal optimization of diagnostic schemes.

Francisco de Toro; Eduardo Ros; Sonia Mota; Julio Ortega

doi:10.1109/tbme.2005.862539

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Evolutionary algorithms for multiobjective and multimodal optimization of diagnostic schemes.

Francisco de Toro·Eduardo Ros·Sonia Mota·Julio Ortega

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Abstract

This paper addresses the optimization of noninvasive diagnostic schemes using evolutionary algorithms in medical applications based on the interpretation of biosignals. A general diagnostic methodology using a set of definable characteristics extracted from the biosignal source followed by the specific diagnostic scheme is presented. In this framework, multiobjective evolutionary algorithms are used to meet not only classification accuracy but also other objectives of medical interest, which can be conflicting. Furthermore, the use of both multimodal and multiobjective evolutionary optimization algorithms provides the medical specialist with different alternatives for configuring the diagnostic scheme. Some application examples of this methodology are described in the diagnosis of a specific cardiac disorder-paroxysmal atrial fibrillation.

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Algorithms Artificial Intelligence Atrial Fibrillation Computer Simulation Decision Support Systems, Clinical Decision Support Techniques Diagnosis, Computer-Assisted Electrocardiography Factor Analysis, Statistical Humans Models, Biological Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity Evaluation Study Journal Article Research Support, Non-U.S. Gov't

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Francisco de Toro, Eduardo Ros, Sonia Mota, Julio Ortega. 2006. Evolutionary algorithms for multiobjective and multimodal optimization of diagnostic schemes.. https://doi.org/10.1109/tbme.2005.862539

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