Predicting patients with Parkinson's disease using Machine Learning and ensemble voting technique
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-16881-x.pdf
Reference85 articles.
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2. Kour N, Gupta S, Arora S (2023) Sensor technology with gait as a diagnostic tool for assessment of Parkinson’s disease: a survey. Multimed Tools Appl 82:10211–10247. https://doi.org/10.1007/s11042-022-13398-7
3. Montemurro N, Aliaga N, Graff P et al (2022) New targets and new Technologies in the Treatment of Parkinson’s disease: a narrative review. IJERPH 19:8799. https://doi.org/10.3390/ijerph19148799
4. Alexander GE (2004) Biology of Parkinson’s disease: pathogenesis and pathophysiology of a multisystem neurodegenerative disorder. Dialogues Clin NeuroSci 6:259–280. https://doi.org/10.31887/DCNS.2004.6.3/galexander
5. Poewe W (2006) The natural history of Parkinson’s disease. J Neurol 253:vii2–vii6. https://doi.org/10.1007/s00415-006-7002-7
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