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Voice Pathology Detection Using Artificial Neural Networks and Support Vector Machines Powered by a Multicriteria Optimization Algorithm

  • Henry Jhoán Areiza-Laverde
  • Andrés Eduardo Castro-Ospina
  • Diego Hernán Peluffo-Ordóñez
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 915)

Abstract

Computer-aided diagnosis (CAD) systems have allowed to enhance the performance of conventional, medical diagnosis procedures in different scenarios. Particularly, in the context of voice pathology detection, the use of machine learning algorithms has proved to be a promising and suitable alternative. This work proposes the implementation of two well known classification algorithms, namely artificial neural networks (ANN) and support vector machines (SVM), optimized by particle swarm optimization (PSO) algorithm, aimed at classifying voice signals between healthy and pathologic ones. Three different configurations of the Saarbrucken voice database (SVD) are used. The effect of using balanced and unbalanced versions of this dataset is proved as well as the usefulness of the considered optimization algorithm to improve the final performance outcomes. Also, proposed approach is comparable with state-of-the-art methods.

Keywords

Voice pathology Computer-aided diagnosis Optimization Classification 

Notes

Acknowledgements

This work was partially supported by the grants provided by Programa Nacional de Jóvenes Investigadores e Innovadores – COLCIENCIAS – Announcement 775 of 2017 and the support for Instituto Tecnológico Metropolitano from Medellin-Colombia.

Also, authors specially thank the support given by the SDAS Research Group.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Grupo de Investigación Automática, Electrónica y Ciencias ComputacionalesInstituto Tecnológico MetropolitanoMedellínColombia
  2. 2.SDAS Research GroupYachay TechUrcuquíEcuador

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