Early Detection of Parkinson’s Disease Using Deep Learning and Machine Learning
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionEnvironmental Statistics Group
Statistics Program
Date
2020Permanent link to this record
http://hdl.handle.net/10754/664603
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Accurately detecting Parkinson’s disease (PD) at an early stage is certainly indispensable for slowing down its progress and providing patients the possibility of accessing to disease-modifying therapy. Towards this end, the premotor stage in PD should be carefully monitored. An innovative deep-learning technique is introduced to early uncover whether an individual is affected with PD or not based on premotor features. Specifically, to uncover PD at an early stage, several indicators have been considered in this study, including Rapid Eye Movement and olfactory loss, Cerebrospinal fluid data, and dopaminergic imaging markers. A comparison between the proposed deep learning model and twelve machine learning and ensemble learning methods based on relatively small data including 183 healthy individuals and 401 early PD patients shows the superior detection performance of the designed model, which achieves the highest accuracy, 96.45% on average. Besides detecting the PD, we also provide the feature importance on the PD detection process based on the Boosting method.Citation
Wang, W., Lee, J., Harrou, F., & Sun, Y. (2020). Early Detection of Parkinson’s Disease Using Deep Learning and Machine Learning. IEEE Access, 1–1. doi:10.1109/access.2020.3016062Publisher
IEEEJournal
IEEE AccessAdditional Links
https://ieeexplore.ieee.org/document/9165732/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9165732
ae974a485f413a2113503eed53cd6c53
10.1109/ACCESS.2020.3016062
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