A comparative and comprehensive study of prediction of Parkinson's disease

Abstract

Parkinson's disease (PD) is a form of neurodegenerative disease that is caused the progressive weakening of dopaminergic nerve cells that affects a large number of people around the world. The recent treatment methods principally depend upon the experimental data resulting from assessment balances and patients' journals that take varied boundaries with reference to legitimacy, inter-rater inconsistency, and incessant monitoring. Nowadays various computational Intelligence techniques are utilized in predicting an accuracy of PD and these techniques are widely applied to form the acceptable decision accurately. In this paper an in-depth review was administered on various techniques proposed by numerous researchers. A replacement system must be proposed which uses DL techniques and considers other attributes of paralysis agitans which can improve the prediction and be an advancement within the medical field. It has been observed that many researches have been done in identifying the PD yet there is a need of suitable method or algorithm to improve the prediction of PD which helps the clinical management. In order to increase the precision approaches involving movements, facial expression and other attributes also be considered for evaluation, since most of the methods have used speech as a major attribute.