Abstract
A long term neuro degenerative genetic and sporadic based Parkinson disorder affects the central nervous system of homosapiens. Most of the existing approaches have detected the Parkinson Disorder using more number of voice features such as MDVP:Fo(Hz), jitter, shimmer, and so on, which causes complexity O(mxn) because of inclusion of unrequired extra features in the field n. In order to reduce the processing time, the feature subset is identified using Correlation based Feature Selector, Principal Component Analysis and Genetic Algorithm. In this paper supervised approach named Neural Network based Genetic Algorithm is used to construct the probabilistic model for existing data set. This model is compared with existing approach. The experiment reached a performance improvement of 95.38%.
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Anusha, B., Geetha, P. (2018). Biomedical Voice Based Parkinson Disorder Identification for Homosapiens. In: Hemanth, D., Smys, S. (eds) Computational Vision and Bio Inspired Computing . Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-71767-8_56
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DOI: https://doi.org/10.1007/978-3-319-71767-8_56
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