Abstract
Parkinson’s disease is one of the common chronic and progressive neurodegenerative diseases across the globe. Speech parameters are the most important indicators that can be used to detect the disease at its early stage. In this article, an efficient approach using Convolutional Neural Network (CNN) is used to predict Parkinson’s disease by using speech parameters that are extracted from the voice recordings. CNN is the most emerging technology that is used for many computer vision tasks. The performance of the approach is discussed and evaluated with the dataset available in the UCI machine learning repository. The dataset will contain the attributes of voice recordings from 80 individuals out of which 40 individuals are Parkinson affected. Three recordings of each individual are used and 44 features are extracted from each recording of a subject. The experimental setup of the proposed approach on the benchmark data has achieved the best testing accuracy of 87.76% when compared with the available ground truth of the dataset.
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Krishna, A., Sahu, S.p., Janghel, R.R., Singh, B.K. (2021). Speech Parameter and Deep Learning Based Approach for the Detection of Parkinson’s Disease. In: Pandian, A., Fernando, X., Islam, S.M.S. (eds) Computer Networks, Big Data and IoT. Lecture Notes on Data Engineering and Communications Technologies, vol 66. Springer, Singapore. https://doi.org/10.1007/978-981-16-0965-7_40
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DOI: https://doi.org/10.1007/978-981-16-0965-7_40
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