Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks

Autor: Lucijano Berus, Simon Klancnik, Miran Brezocnik, Mirko Ficko
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: Sensors, Vol 19, Iss 1, p 16 (2018)
Druh dokumentu: article
ISSN: 1424-8220
67815715
DOI: 10.3390/s19010016
Popis: In recent years, neural networks have become very popular in all kinds of prediction problems. In this paper, multiple feed-forward artificial neural networks (ANNs) with various configurations are used in the prediction of Parkinson’s disease (PD) of tested individuals, based on extracted features from 26 different voice samples per individual. Results are validated via the leave-one-subject-out (LOSO) scheme. Few feature selection procedures based on Pearson’s correlation coefficient, Kendall’s correlation coefficient, principal component analysis, and self-organizing maps, have been used for boosting the performance of algorithms and for data reduction. The best test accuracy result has been achieved with Kendall’s correlation coefficient-based feature selection, and the most relevant voice samples are recognized. Multiple ANNs have proven to be the best classification technique for diagnosis of PD without usage of the feature selection procedure (on raw data). Finally, a neural network is fine-tuned, and a test accuracy of 86.47% was achieved.
Databáze: Directory of Open Access Journals
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