Efficient Model for Prediction of Parkinson's Disease Using Machine Learning Algorithms with Hybrid Feature Selection Methods
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-54547-4_15
Reference26 articles.
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2. Pfeiffer, R.F.: Non-motor symptoms in Parkinson’s disease. Parkinsonism Relat Disord 22, S119–S122 (2016). https://doi.org/10.1016/j.parkreldis.2015.09.004
3. Anitha, R., Nandhini, T.S.R.S., Nikitha, V.: Early detection of parkinson’s disease using machine learning 2, 505–511 (2020)
4. Raza, M., Awais, M., Singh, N., Imran, M., Hussain, S.: Intelligent IoT Framework for Indoor Healthcare Monitoring of Parkinson’s Disease Patient. IEEE J. Select. Areas in Commu. 39(2), 593–602 (2021). https://doi.org/10.1109/JSAC.2020.3021571
5. Hawi, S., et al.: Automatic Parkinson’s disease detection based on the combination of long-term acoustic features and Mel frequency cepstral coefficients (MFCC). Biomed Signal Process Control 78 (2022). https://doi.org/10.1016/j.bspc.2022.104013
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