Comparative Analysis for Feature Selection Approaches for Parkinson’s Disease Prediction

A neurological illness known as Parkinson's disease (PD) worsens over time and affects both the motor and non-motor systems. Therefore, it's crucial to find PD beforehand in order to treat patients effectively and slow down the disease's progression. An early PD detection method based on machine learning is presented and several voice factors are considered for this purpose. This study compares and contrasts various feature selection methods for selecting the better factors that predict PD with the high accuracy. This provides the most important feature for detecting PD, which are independent features selected from the dependent ones.