Next Article in Journal
An Exact Efficiency Formula for Holographic Heat Engines
Next Article in Special Issue
Operational Complexity of Supplier-Customer Systems Measured by Entropy—Case Studies
Previous Article in Journal
Second Law Analysis of Adiabatic and Non-Adiabatic Pipeline Flows of Unstable and Surfactant-Stabilized Emulsions
Previous Article in Special Issue
A Complexity-Based Approach for the Detection of Weak Signals in Ocean Ambient Noise
Article Menu

Export Article

Open AccessArticle
Entropy 2016, 18(4), 115; doi:10.3390/e18040115

A Comparison of Classification Methods for Telediagnosis of Parkinson’s Disease

1
Electrical Engineering Department, University of California, Los Angeles, CA 90095, USA
2
Department of Biomedical Engineering, Fatih Sultan Mehmet Vakıf University, Istanbul 34445, Turkey
Academic Editors: J.A. Tenreiro Machado and António M. Lopes
Received: 14 February 2016 / Revised: 13 March 2016 / Accepted: 24 March 2016 / Published: 30 March 2016
(This article belongs to the Special Issue Computational Complexity)
View Full-Text   |   Download PDF [3850 KB, uploaded 30 March 2016]   |  

Abstract

Parkinson’s disease (PD) is a progressive and chronic nervous system disease that impairs the ability of speech, gait, and complex muscle-and-nerve actions. Early diagnosis of PD is quite important for alleviating the symptoms. Cost effective and convenient telemedicine technology helps to distinguish the patients with PD from healthy people using variations of dysphonia, gait or motor skills. In this study, a novel telemedicine technology was developed to detect PD remotely using dysphonia features. Feature transformation and several machine learning (ML) methods with 2-, 5- and 10-fold cross-validations were implemented on the vocal features. It was observed that the combination of principal component analysis (PCA) as a feature transformation (FT) and k-nearest neighbor (k-NN) as a classifier with 10-fold cross-validation has the best accuracy as 99.1%. All ML processes were applied to the prerecorded PD dataset using a newly created program named ParkDet 2.0. Additionally, the blind test interface was created on the ParkDet so that users could detect new patients with PD in future. Clinicians or medical technicians, without any knowledge of ML, will be able to use the blind test interface to detect PD at a clinic or remote location utilizing internet as a telemedicine application. View Full-Text
Keywords: telemedicine; Parkinson’s disease; machine learning; feature transformation; principal component analysis; k-nearest neighbor telemedicine; Parkinson’s disease; machine learning; feature transformation; principal component analysis; k-nearest neighbor
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Ozkan, H. A Comparison of Classification Methods for Telediagnosis of Parkinson’s Disease. Entropy 2016, 18, 115.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top