Onboard Cloud-enabled Parkinson Disease Identification System Using Enhanced Grey Wolf Optimization

Abstract

Parkinson disease is a rigorous neurodegenerative disorder characterized by the cognitive behavior ending with disability problems. Especially, the elderly people should be given more care and spend more time duration to diagnose when they are at risk. It is more important to identify and diagnose Parkinson disease at an earlier stage rather than spending too much of cost later stages. Different ways of diagnosing the disease ranging from gene analysis to gait behavior, speech, writing test and olfactory models were used in the conventional testing process. In order to increase the patient’s quality of life and minimize the cost of healthcare utilization, an Onboard Cloud-Enabled Parkinson Disease Identification System (OCPDIS) is proposed. An enhanced grey wolf optimization is explored along with the differential evolution techniques to form an effective hybrid feature selection method. Using this feature selection method in the enhanced k-Nearest Neighbor (k-NN) classifier model could substantially improve the prediction time and prediction accuracy.