A Comprehensive Study of Prediction of Parkinson’s Disease Using Machine Learning

Abstract

Parkinson’s sickness is caused due to a nervous breakdown of dopamine secreting cells due to which several techniques have been used to detect its severity and its existence. Therefore this research area has got enormous attention to work upon. Until now several procedures have been used by various researchers, some of them which include tracking through the UPDRS Scale, Using inbuilt sensors of the smartphones, using various physical tasks, etc. This paper gives a significance of all the above-said methods as well as introduce to some more methods, which take unique considerations and assumptions, which are applied via multiple machine learning models and displaying their inter-model comparisons. It also covers the advancements and challenges. We conclude with a thorough study and detailed impact on a basket full of methods concerning several publications, journals/venues, and subtopics.