Rana, Arti and Dumka, Ankur and Singh, Rajesh and Panda, Manoj Kumar and Priyadarshi, Neeraj and Twala, Bhekisipho UNSPECIFIED (2022) Imperative Role of Machine Learning Algorithm for Detection of Parkinson’s Disease: Review, Challenges and Recommendations. Diagnostics, 12 (8). p. 2003. ISSN 2075-4418
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Abstract
Parkinson’s disease (PD) is a neurodegenerative disease that affects the neural, behavioral, and physiological systems of the brain. This disease is also known as tremor. The common symptoms of this disease are a slowness of movement known as ‘bradykinesia’, loss of automatic movements, speech/writing changes, and difficulty with walking at early stages. To solve these issues and to enhance the diagnostic process of PD, machine learning (ML) algorithms have been implemented for the categorization of subjective disease and healthy controls (HC) with comparable medical appearances. To provide a far-reaching outline of data modalities and artificial intelligence techniques that have been utilized in the analysis and diagnosis of PD, we conducted a literature analysis of research papers published up until 2022. A total of 112 research papers were included in this study, with an examination of their targets, data sources and different types of datasets, ML algorithms, and associated outcomes. The results showed that ML approaches and new biomarkers have a lot of promise for being used in clinical decision-making, resulting in a more systematic and informed diagnosis of PD. In this study, some major challenges were addressed along with a future recommendation
| Item Type: | Article |
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| Uncontrolled Keywords: | Parkinson’s disease; machine learning; artificial neural network; logistic regression; support vector machine; classification |
| Subjects: | Subjects > Engineering |
| Divisions: | Ibero-american International University > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 08 Sep 2022 08:31 |
| Last Modified: | 08 Sep 2022 08:31 |
| URI: | http://repositorio.funiber.org/id/eprint/3543 |
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