Abstract
ABSTRACTA hybrid computational method based on the extreme learning machine (ELM) neural network for classification and the evolutionary genetic algorithms (GA) for feature selection is presented in this paper. The dimension of the feature space is reduced by the GA in this scheme and only the appointed features are selected. The data is then passed to an ELM neural network for the classification phase. An automated system for the diagnosis of Parkinson’s Disease (PD) based on gait data set is proposed by using the GA-ELM method. PD is a neurodegenerative disease that may cause change in the central nervous causing disturbance to the gait cycle duration. Our hybrid GA-ELM algorithm has produced an optimized diagnosis of PD from healthy subjects given in the gait dataset with accuracy 93.5%, and with five effective features that reduce the original dataset dimension.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
