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Parkinson’s disease diagnosis using machine learning and voice

Title	Parkinson’s disease diagnosis using machine learning and voice
Author	Wroge, T. J., Özkanca, Yasin Serdar, Demiroğlu, Cenk, Si, D., Atkins, D. C., Ghomi, R. H.
Publication Date:	2018
Publication Place	- IEEE
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-153865916-8
Record ID	ba8c41e6-e6de-4dde-8dcb-794dfdf082f0
Library Location	Electrical & Electronics Engineering
Date	2018
Notes	Graduate Research Award from the Computing and Software Systems division of University of Washington Bothell ; United States Department of Health & Human Services National Institutes of Health (NIH) - USA ; VA Advanced Fellowship Program in Parkinson's Disease
Sample Text	Biomarkers derived from human voice can offer in-sight into neurological disorders, such as Parkinson's disease (PD), because of their underlying cognitive and neuromuscular function. PD is a progressive neurodegenerative disorder that affects about one million people in the the United States, with approximately sixty thousand new clinical diagnoses made each year [1]. Historically, PD has been difficult to quantity and doctors have tended to focus on some symptoms while ignoring others, relying primarily on subjective rating scales [2]. Due to the decrease in motor control that is the hallmark of the disease, voice can be used as a means to detect and diagnose PD. With advancements in technology and the prevalence of audio collecting devices in daily lives, reliable models that can translate this audio data into a diagnostic tool for healthcare professionals would potentially provide diagnoses that are cheaper and more accurate. We provide evidence to validate this concept here using a voice dataset collected from people with and without PD. This paper explores the effectiveness of using supervised classification algorithms, such as deep neural networks, to accurately diagnose individuals with the disease. Our peak accuracy of 85% provided by the machine learning models exceed the average clinical diagnosis accuracy of non-experts (73.8%) and average accuracy of movement disorder specialists (79.6% without follow-up, 83.9% after follow-up) with pathological post-mortem examination as ground truth [3].
DOI	10.1109/SPMB.2018.8615607

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Özyeğin University

Parkinson’s disease diagnosis using machine learning and voice

Author Wroge, T. J., Özkanca, Yasin Serdar, Demiroğlu, Cenk, Si, D., Atkins, D. C., Ghomi, R. H.

Publication Date 2018

Publication Place - IEEE

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-153865916-8

Record ID ba8c41e6-e6de-4dde-8dcb-794dfdf082f0

Library Location Electrical & Electronics Engineering

Date 2018

Notes Graduate Research Award from the Computing and Software Systems division of University of Washington Bothell ; United States Department of Health & Human Services National Institutes of Health (NIH) - USA ; VA Advanced Fellowship Program in Parkinson's Disease

Sample Text Biomarkers derived from human voice can offer in-sight into neurological disorders, such as Parkinson's disease (PD), because of their underlying cognitive and neuromuscular function. PD is a progressive neurodegenerative disorder that affects about one million people in the the United States, with approximately sixty thousand new clinical diagnoses made each year [1]. Historically, PD has been difficult to quantity and doctors have tended to focus on some symptoms while ignoring others, relying primarily on subjective rating scales [2]. Due to the decrease in motor control that is the hallmark of the disease, voice can be used as a means to detect and diagnose PD. With advancements in technology and the prevalence of audio collecting devices in daily lives, reliable models that can translate this audio data into a diagnostic tool for healthcare professionals would potentially provide diagnoses that are cheaper and more accurate. We provide evidence to validate this concept here using a voice dataset collected from people with and without PD. This paper explores the effectiveness of using supervised classification algorithms, such as deep neural networks, to accurately diagnose individuals with the disease. Our peak accuracy of 85% provided by the machine learning models exceed the average clinical diagnosis accuracy of non-experts (73.8%) and average accuracy of movement disorder specialists (79.6% without follow-up, 83.9% after follow-up) with pathological post-mortem examination as ground truth [3].

DOI 10.1109/SPMB.2018.8615607