Detection of Parkinson Disease using Machine Learning
J. Divya, P. Radhakrishnan, G Pavithra, Anandbabu Gopatoti, D. Baburao, R. Krishnamoorthy
Abstract
Parkinson's disease (PD) is a degenerative ailment that affects the central nervous system and causes a variety of movement problems. The symptoms vary from case to case. However, the illness also often causes stiffness and a slowdown of movement. Tremors are a typical symptom, but these other symptoms are less prevalent. It is simple to apply an algorithm for machine learning to assess the fluctuations in a person's vocal pattern to determine whether or not they have Parkinson's disease. Because our algorithm regularly making use of parameter extracts and XGboost classifiers, the audio featured dataset that is available in the UCI dataset database is one of the outputs that may be obtained as a consequence. When it comes to determining whether or not the palladium patient is in good health, the algorithm has achieved a result that is far more accurate. The highest possible level of precision was achieved with XGBoost.