Predicting Parkinson’s disease using filter feature selection method

Abstract

This A type of neurodegenerative condition called Parkinson's Disease (PD) is defined by the loss of dopamine-producing brain cells. Parkinson's disease is brought on by damage to the brain cells that produce dopamine, a neurotransmitter that promotes communication between brain cells. Dopamine-producing brain cells regulate all aspects of movement, including control, adaptation, and speed. To stop the progression of the disease, researchers have been looking for ways to quickly identify non-motor symptoms that manifest early in the illness. The suggested detection method makes use of feature selection and classification algorithms. The feature selection strategy described in this article reduces data noise well. In particular, Pearson's Correlation Coefficient (PCC) is used to assess data redundancy. The efficacy of these features is assessed using the Support Vector Machine (SVM) classification approach. The proposed approach has an accuracy of up to 94.9 %.