Voice Feature based Detection of Parkinson’s Disease using Machine Learning Approach

At present Parkinson’s disease (PD) is the second largest neuro effected disease. Its symptoms resemble daily life activities, which are not identified by the patient. There is no solely technique or measures for detection and confirmation of PD in patients. In this study, it employs that machine learning algorithms plays a major role in identification of PD and in analysis of complex PD representing data types. KNN algorithm is used for this case study to contribute to the growing field of Voice-based analysis that incorporate key features like shimmer, Jitter, frequency to understand the evolution of PD symptoms. This paper presents a comprehensive calculated regression analysis of PD in affected patients. By this study and experimental results, the proposed methodology helps to effectively detect the PD and incorporate into health care for diagnosis purpose