Ensemble machine learning regression model based predictive framework for Parkinson’s UPDRS motor score prediction from speech data
Author:
Funder
Vision Group on Science and Technology
Publisher
Springer Science and Business Media LLC
Subject
Computer Vision and Pattern Recognition,Linguistics and Language,Human-Computer Interaction,Language and Linguistics,Software
Link
https://link.springer.com/content/pdf/10.1007/s10772-023-10026-z.pdf
Reference31 articles.
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2. Ahmadi Rastegar, D., Ho, N., Halliday, G. M., et al. (2019). Parkinson’s progression prediction using machine learning and serum cytokines. NPJ Parkinson’s Disease, 5, 14. https://doi.org/10.1038/s41531-019-0086-4
3. Arias-Londoño, J. D., & Gómez-García, J. A. (2020). Predicting UPDRS scores in Parkinson’s disease using voice signals: A deep learning/transfer-learning-based approach. In J. I. Godino-Llorente (Ed.), Automatic assessment of parkinsonian speech (AAPS 2019). Communications in computer and information science. (Vol. 1295). Springer. https://doi.org/10.1007/978-3-030-65654-6_6
4. Batina, L., Gierlichs, B., Prouff, E., Rivain, M., Standaert, F., & Veyrat-Charvillon, N. (2011). Mutual information analysis: A comprehensive study. Journal of Cryptology, 24, 269–291. https://doi.org/10.1007/s00145-010-9084-8
5. Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16–28. https://doi.org/10.1016/j.compeleceng.2013.11.024.
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