A Study on Diagnosis of Parkinson’s Disease from Voice Dysphonias

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Kemal Akyol 1,*

1. Department of Computer Engineering, Kastamonu University, Kastamonu, 37100, Turkey

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2018.06.04

Received: 12 Feb. 2018 / Revised: 11 Apr. 2018 / Accepted: 7 May 2018 / Published: 8 Jun. 2018

Index Terms

Importance of feature, parkinson disease, recursive feature elimination, voice dysphonias


Parkinson disease that occurs at older ages is a neurological disorder that is one of the most painful, dangerous and non-curable diseases. One symptom that a person may have Parkinson’s disease is trouble that can occur in the voice of a person which is so-called dysphonia. In this study, an application based on assessing the importance of features was carried out by using multiple types of sound recordings dataset for diagnosis of Parkinson disease from voice disorders. The sub-datasets, which were obtained from these records and were divided into 70-30% training and testing data respectively, include the important features. According to the experimental results, the Random Forest and Logistic Regression algorithms were found successful in general. Besides, one or two of these algorithms were found to be more successful for each sound. For example, the Logistic Regression algorithm is more successful for the ‘a’ voice. The Artificial Neural Networks algorithm is more successful for the ‘o’ voice.

Cite This Paper

Kemal Akyol, "A Study on Diagnosis of Parkinson’s Disease from Voice Dysphonias", International Journal of Information Technology and Computer Science(IJITCS), Vol.10, No.6, pp.36-43, 2018. DOI:10.5815/ijitcs.2018.06.04


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