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Parkinson's Disease Detection with Gait Recognition using Soft Computing Techniques
Abstract
Parkinson's disease is a degenerative disorder of the central nervous system which occurs as a result of dopamine loss, a chemical mediator that is responsible for body's ability to control the movements. It's a very common disease among elder population effecting approx 6.3 million people worldwide across all genders, races and cultures. In this chapter, authors have proposed an automated classification system based on Artificial Neural Network using Feed Forward Back-propagation Algorithm for Parkinson's disease diagnosis by analyzing gait of a person. The system is trained, tested and validated by a gait dataset consisting data of Parkinson's disease patients and healthy persons. The system is evaluated based on several measuring parameters like sensitivity, specificity, and classification accuracy. For the proposed system observed classification accuracy is 97.11% using 19 features of gait, and 95.55% using 10 prominent features of gait selected by Genetic Algorithm.
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