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2018 | OriginalPaper | Buchkapitel

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

verfasst von : Henry Jhoán Areiza-Laverde, Andrés Eduardo Castro-Ospina, Diego Hernán Peluffo-Ordóñez

Erschienen in: Applied Computer Sciences in Engineering

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Feature Group Selection Using MKL Penalized with -norm and SVM as Base Learner

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Titel: Voice Pathology Detection Using Artificial Neural Networks and Support Vector Machines Powered by a Multicriteria Optimization Algorithm
verfasst von: Henry Jhoán Areiza-Laverde
Andrés Eduardo Castro-Ospina
Diego Hernán Peluffo-Ordóñez
Verlag: Springer International Publishing
Buch: Applied Computer Sciences in Engineering
Print ISBN: 978-3-030-00349-4

Electronic ISBN: 978-3-030-00350-0

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-00350-0_13

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