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.
H.J. Areiza-Laverde—This work is carried out under grants provided by Programa Nacional de Jóvenes Investigadores e Innovadores – COLCIENCIAS – Announcement 775 of 2017.
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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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Areiza-Laverde, H.J., Castro-Ospina, A.E., Peluffo-Ordóñez, D.H. (2018). Voice Pathology Detection Using Artificial Neural Networks and Support Vector Machines Powered by a Multicriteria Optimization Algorithm. In: Figueroa-García, J., López-Santana, E., Rodriguez-Molano, J. (eds) Applied Computer Sciences in Engineering. WEA 2018. Communications in Computer and Information Science, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-00350-0_13
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