Abstract:
Parkinson's disease (PD) is a neurodegenerative disorder impacting millions worldwide. This study aims to leverage machine learning algorithms to improve the diagnosis and
understanding of PD. Building upon existing research that utilizes classifiers, feature
extraction, data partitioning, and feature selection, we explore the potential of various
feature selection algorithms in maximizing classification accuracy on publicly available
PD datasets. The investigation will compare and contrast the performance of feature
selection technique, ultimately identifying the method that yields the highest accuracy in
PD classification. This research contributes to the growing body of knowledge
surrounding the application of machine learning in PD diagnosis and paves the way for
further exploration of specific feature sets and classification models to enhance clinical
practice and patient outcomes. Here we will work on different selecting algorithms. Such
as:PCA. For the result we will use several popular ML techniques. Including: K-NN, Random Forest, Decision Tree Algorithm,XG Boost,ANN.After our study of parkinson
disease classification with the feature selectionwith correlation method we got accuracy
of 89% on the Random Forest and the result after applying the PCA classifier the
accuracy increased to 92%.
