Performance evaluation of machine learning algorithms in the classification of parkinson disease using voice attributes

Nerve cells, the building blocks of the nervous system in the
brain don’t reproduce when damaged. On damage, the
dopamine produced by these nerve cells are not produced
which hinders motor skills and speech. Voice undergoes
changes at an earlier stage before the brain cells are affected [1]. Voice changes helps to identify Parkinson disease at an initial stage thereby preventing damage to the brain cells which would result in reduced coordination and movement. The idea of this paper is to evaluate the performance of various data mining classification techniques used in the identification of Parkinson disease.