Review on Parkinson's Disease Detection Methods: Traditional Machine Learning Models vs. Deep Learning Models

Millions of people throughout the world suffer with Parkinson's disease (PD), severely reducing their quality life. With symptoms when we detect Parkinson automatically, it could provide insights to disease's early stages development, enhancing patients' projected clinical results through correctly focused therapies. This potential has prompted numerous academics explore ways for measuring and quantifying existence PD using commercially available sensors. In this paper, offer an overview some recent scientific articles on several machine learning techniques that assist physiologists in detecting early. addition, a comparative study between traditional (TML) algorithms deep (DL) architectures based scope appropriate usage classifying effectively been discussed. Based comparison from previous works, paper concludes models are more efficacious than algorithms.