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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MDPI AG
2021-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/22/7565 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:05:34Z |
| format | Article |
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| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:05:34Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-f3f2a1673de7455db50f8b6d957e49bb2023-11-23T01:25:44ZengMDPI AGSensors1424-82202021-11-012122756510.3390/s21227565Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A ReviewVarad Kabade0Ritika Hooda1Chahat Raj2Zainab Awan3Allison S. Young4Miriam S. Welgampola5Mukesh Prasad6Department of Textile Technology, Indian Institute of Technology Delhi, New Delhi 110016, IndiaDepartment of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi 110016, IndiaSchool of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, AustraliaSchool of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, AustraliaCentral Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney 2006, AustraliaCentral Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney 2006, AustraliaSchool of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, AustraliaVertigo 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.https://www.mdpi.com/1424-8220/21/22/7565artificial intelligencevertigodizzinessmachine learningfeature extraction |
| spellingShingle | 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 Sensors artificial intelligence vertigo dizziness machine learning feature extraction |
| title | 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_short | 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 |
| topic | artificial intelligence vertigo dizziness machine learning feature extraction |
| url | https://www.mdpi.com/1424-8220/21/22/7565 |
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