Parkinson’s Disease Analysis and Detection using Machine Learning Technique (International Journal of Communication Systems and Network Technologies, 2021)
This chapter presents Parkinson’s disease analysis and detection using machine learning techniques. The Parkinson’s information is tested with two different models in order to determine whether model provides the most accurate categorization. Logistic Regression is used in parametric modelling to organize the Parkinson’s information that has been collected. ML Algorithms are applied to organize the preparation and test information of Parkinson’s disease. These algorithms are derived from non-parametric showing. The order is determined by combining the results of the parametric and non-parametric models with the information acquired about Parkinson’s disease. This chapter advances on research and the discussion on their importance and efficiency in the efficient treatment and further diagnosis.
Keywords: Parkinson’s Disease; Machine Learning; Logistic Regression; Classification; Diagnosis