Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review

Vertigo is a sensation of movement that results from disorders of the inner ear balance organs and their central connections, with aetiologies that are often benign and sometimes serious. An individual who develops vertigo can be effectively treated only after a correct diagnosis of the underlying v...

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Autores principales:	Varad Kabade, Ritika Hooda, Chahat Raj, Zainab Awan, Allison S. Young, Miriam S. Welgampola, Mukesh Prasad
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence vertigo dizziness machine learning feature extraction Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/f3f2a1673de7455db50f8b6d957e49bb
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spelling	oai:doaj.org-article:f3f2a1673de7455db50f8b6d957e49bb2021-11-25T18:57:29ZMachine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review10.3390/s212275651424-8220https://doaj.org/article/f3f2a1673de7455db50f8b6d957e49bb2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7565https://doaj.org/toc/1424-8220Vertigo is a sensation of movement that results from disorders of the inner ear balance organs and their central connections, with aetiologies that are often benign and sometimes serious. An individual who develops vertigo can be effectively treated only after a correct diagnosis of the underlying vestibular disorder is reached. Recent advances in artificial intelligence promise novel strategies for the diagnosis and treatment of patients with this common symptom. Human analysts may experience difficulties manually extracting patterns from large clinical datasets. Machine learning techniques can be used to visualize, understand, and classify clinical data to create a computerized, faster, and more accurate evaluation of vertiginous disorders. Practitioners can also use them as a teaching tool to gain knowledge and valuable insights from medical data. This paper provides a review of the literatures from 1999 to 2021 using various feature extraction and machine learning techniques to diagnose vertigo disorders. This paper aims to provide a better understanding of the work done thus far and to provide future directions for research into the use of machine learning in vertigo diagnosis.Varad KabadeRitika HoodaChahat RajZainab AwanAllison S. YoungMiriam S. WelgampolaMukesh PrasadMDPI AGarticleartificial intelligencevertigodizzinessmachine learningfeature extractionChemical technologyTP1-1185ENSensors, Vol 21, Iss 7565, p 7565 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence vertigo dizziness machine learning feature extraction Chemical technology TP1-1185
spellingShingle	artificial intelligence vertigo dizziness machine learning feature extraction Chemical technology TP1-1185 Varad Kabade Ritika Hooda Chahat Raj Zainab Awan Allison S. Young Miriam S. Welgampola Mukesh Prasad Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review
description	Vertigo is a sensation of movement that results from disorders of the inner ear balance organs and their central connections, with aetiologies that are often benign and sometimes serious. An individual who develops vertigo can be effectively treated only after a correct diagnosis of the underlying vestibular disorder is reached. Recent advances in artificial intelligence promise novel strategies for the diagnosis and treatment of patients with this common symptom. Human analysts may experience difficulties manually extracting patterns from large clinical datasets. Machine learning techniques can be used to visualize, understand, and classify clinical data to create a computerized, faster, and more accurate evaluation of vertiginous disorders. Practitioners can also use them as a teaching tool to gain knowledge and valuable insights from medical data. This paper provides a review of the literatures from 1999 to 2021 using various feature extraction and machine learning techniques to diagnose vertigo disorders. This paper aims to provide a better understanding of the work done thus far and to provide future directions for research into the use of machine learning in vertigo diagnosis.
format	article
author	Varad Kabade Ritika Hooda Chahat Raj Zainab Awan Allison S. Young Miriam S. Welgampola Mukesh Prasad
author_facet	Varad Kabade Ritika Hooda Chahat Raj Zainab Awan Allison S. Young Miriam S. Welgampola Mukesh Prasad
author_sort	Varad Kabade
title	Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review
title_short	Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review
title_full	Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review
title_fullStr	Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review
title_full_unstemmed	Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review
title_sort	machine learning techniques for differential diagnosis of vertigo and dizziness: a review
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f3f2a1673de7455db50f8b6d957e49bb
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Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review

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