Abstract
Parkinson disease is a neurodegenerative disorder that affects nervous system and the root cause of it is falling rates of dopamine levels in the forebrain. It is a chronic degenerative disease with progressive illness, which means it develops new symptoms over time.This happens with progressive neuronal loss in the substantia nigra of brain. People with PD cannot do their works as a normal human. Though clinical assessments considered ample amount of data that include various features, sometimes it is hard to decide whether a person is suffering from PD or not based on the type of data, feature selection methods help to solve this issue. Various methods are developed, proposed, and analyzed to detect the Parkinson disease, given the required data. This paper is a survey of predicting Parkinson disease using machine learning algorithms, various new technologies applied, and their accuracies achieved.
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