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NUMERICAL METHODS AND DATA ANALYSIS
Biometric data and machine learning methods in the diagnosis and monitoring of neurodegenerative diseases: a review
I. A. Hodashinsky, K. S. Sarin, M. B. Bardamova, M. O. Svetlakov, A. O. Slezkin, N. P. Koryshev Tomsk State University of Control Systems and Radioelectronics
Abstract:
A review of noninvasive biometric methods for detecting and predicting neurodegenerative diseases is presented. An analysis of various modalities used to diagnose and monitor diseases is given. Such modalities as handwritten data, electroencephalography, speech, gait, eye movement, as well as the use of compositions of these modalities are considered. A detailed analysis of modern methods and solutions based on machine learning is conducted. Data sets, preprocessing methods, machine learning models, and accuracy estimates for disease diagnosis are presented. In the conclusion current open problems and future prospects of research in this direction are considered.
Keywords:
non-invasive diagnostic methods, neurodegenerative diseases, biometric signal processing, machine learning
Received: 26.03.2022 Accepted: 30.08.2022
Citation:
I. A. Hodashinsky, K. S. Sarin, M. B. Bardamova, M. O. Svetlakov, A. O. Slezkin, N. P. Koryshev, “Biometric data and machine learning methods in the diagnosis and monitoring of neurodegenerative diseases: a review”, Computer Optics, 46:6 (2022), 988–1019
Linking options:
https://www.mathnet.ru/eng/co1095 https://www.mathnet.ru/eng/co/v46/i6/p988
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Abstract page: | 39 | Full-text PDF : | 46 |
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