Automatically Predicting Severity of Parkinson's Disease Using Model Based on XGBoost from Speech
Paper
Paper/Presentation Title | Automatically Predicting Severity of Parkinson's Disease Using Model Based on XGBoost from Speech |
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Presentation Type | Paper |
Authors | Zhu, Xuchen, Fang, Yong and Wen, Peng |
Journal or Proceedings Title | Proceedings of 2019 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) |
Journal Citation | pp. 1-5 |
Number of Pages | 5 |
Year | 2019 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | China |
ISBN | 9781728117089 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICSPCC46631.2019.8960722 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/abstract/document/8960722 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/8949349/proceeding |
Conference/Event | 2019 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) |
Event Details | 2019 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Parent IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Delivery In person Event Date 20 to end of 22 Sep 2019 Event Location Dalian, China |
Abstract | Self-service assessment model for measuring severity of Parkinson's disease (PD) has recently received considerable attention due to the inconvenience and cost of physical visits to the medical clinic for PD patients. Previous works, however, mostly focus on the feature extraction and classifier design without considering notable intrinsic differences between patients with Parkinsonism (PWP), which has an adverse impact on generalization of model detecting PD. This paper introduces a novel PD-detected model based on Extreme Gradient Boost (XGBoost), where prior knowledge including gender and age are used to predict the severity of PD through unified Parkinson's disease rating scale (UPDRS). Firstly, using gender and age as a prior knowledge decomposes the prediction model of UPDRS to get new sub-model. Secondly, we obtain a regressor trained by XGBoost on the respective sub-model. Finally, we can get the final prediction about UPDRS using trained model. The experimental results show that our method significantly improves performance on the remote Parkinson dataset. |
Keywords | extreme gradient boosting; age partition; gender partition; unified Parkinson's disease rating scale |
ANZSRC Field of Research 2020 | 400399. Biomedical engineering not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Shanghai University, China |
University of Southern Queensland | |
Library Services |
https://research.usq.edu.au/item/w3v03/automatically-predicting-severity-of-parkinson-s-disease-using-model-based-on-xgboost-from-speech
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