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- W3092328829 abstract "This research proposes deep learning methods to approach the diagnosis of Parkinson’s Disease (PD) with more accurate results than those of existing methods. This automation approach also reduce the diagnosis expenses and make it possible to be applied in developing countries. In order to find a more accurate and cheaper solution to the problem, I have employed and contrasted three different experimental algorithms—Kmeans clustering, Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The best performance, with accuracy of 100%, can be achieved by the SVM model and convolutional neural network model." @default.
- W3092328829 created "2020-10-15" @default.
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- W3092328829 date "2020-08-01" @default.
- W3092328829 modified "2023-10-16" @default.
- W3092328829 title "Efficient Pre-diagnosis Approach for Rarkinson’s Disease with Machine Learning" @default.
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- W3092328829 doi "https://doi.org/10.1109/aeeca49918.2020.9213472" @default.
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