Abstract: “The aim of this paper is to study the potential use of an intelligent/automatic classification system for Parkinson's Disease (PD), using magnetic resonance images (MRI). For feature extraction of MRI Discrete Wavelet Transform has been used, followed by minimum Redundancy Maximum Relevance critrium (mRMR) for feature selection and Principal Component Analysis (PCA) for feature reduction. We then applied Support Vector Machine (SVM) for classification and Genetic Algorithms (GA) for optimization. To discover which slices and regions of the MRI are the most relevant in the brain for identification of Parkinson's Disease region, optimization was carried out using a multi-objetive genetic algorithm. The slices obtained in this optimization process were consistent with those recommended by medical experts. The methodology presented outperformed most of the research available in the bibliography, achieving accuracies of 95% in classification of subjects. This suggests that the proposed work”

Keywords: Parkinson Disease (PD), Magnetic Resonance Image (MRI), Discrete Wavelet Transform (DWT), minimum Redundancy Maximum Relevance (mRMR), Support Vector Machine (SVM), Genetic Algorithms (GA).