Parkinson’s disease diagnosis using machine learning and voice

Name: 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.

İsim	Parkinson’s disease diagnosis using machine learning and voice
Yazar	Wroge, T. J., Özkanca, Yasin Serdar, Demiroğlu, Cenk, Si, D., Atkins, D. C., Ghomi, R. H.
Basım Tarihi:	2018
Basım Yeri	- IEEE
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-153865916-8
Kayıt Numarası	ba8c41e6-e6de-4dde-8dcb-794dfdf082f0
Lokasyon	Electrical & Electronics Engineering
Tarih	2018
Notlar	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
Örnek Metin	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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Parkinson’s disease diagnosis using machine learning and voice

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

Basım Tarihi 2018

Basım Yeri - IEEE

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-153865916-8

Kayıt Numarası ba8c41e6-e6de-4dde-8dcb-794dfdf082f0

Lokasyon Electrical & Electronics Engineering

Tarih 2018

Notlar 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

Örnek Metin 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