Effective Detection of Parkinson’s Disease at Different Stages using Measurements of Dysphonia

Elmehdi BENMALEK, Jamal Elmhamdi, Abdelilah Jilbab


This paper addressees the problem of multiclass of Parkinson’s disease by the characteristic features of person’s voice. So we computed 22 dysphonia measures from 375 voice samples of healthy and people suffer from PD. We used the particle swarm optimization (PSO) feature selection method, with random forest and the linear discriminant analysis (LDA) along with the 4-fold cross validation analysis to classify the subjects in 4 classes according to the severity of symptoms. With a classification accuracy score of 95.2%. Promisingly, the proposed diagnosis system might serve as a powerful tool for diagnosing PD, and could also extended for other voice pathologies.


Parkinson’s disease; Dysphonia measures; Multiclass classification; PSO; Random forest; LDA.

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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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