Classification of Parkinson's Disease Based on Acoustic Characteristics Using Feature Selection

Parkinson's Disease (PD) is the degeneration of the nervous system. Patient suffering with PD is going through many issues and among the population most of them are elderly peoples. Detection of this disease at it's emergencee is complex and time consuming. With the aid of rapid development in Artificial intelligence and using the different characteristics it is possible to foresee PD. To anticipate this problem, a robust machine learning model, support vector machine with regularization is proposed to effectively select the features to predict and classify the PD patients using acoustic characteristics. The proposed machine learning approach predicts the PD affected people from healthy people on the bases of their voice. This approach assures better accuracy rate in prediction of PD effected people and can be seamlessly integrated into healthcare systems.