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Genetic Analysis with Feature Reduction to Predict the Onset of Parkinson’s disease

Parkinson is a disease which directly affects the
brain cells and certain movement, voice and other disabilities.
Hence curable medication is not available in market. The best
solution is the early diagnosis to relieve the symptoms of
Parkinson’s disease affected people. One major concern effecting
public is Parkinson's disease (PD). This paper studies the bias of
various traditional algorithms on the voice-based data that has
various parameters recorded from Parkinson patients and healthy
patients. A brief survey of techniques are mentioned for the
prediction of Parkinson's diseases is presented. To accomplish
this task, identifying the best feature reduction approach was the
primary focus. This paper further applies feature reduction
techniques using a genetic algorithm for efficient prediction of
Parkinson's disease along with machine learning-based
approaches. The proposed method also presents higher accuracy
in prediction by using this optimal feature reduction technique.