Adaption of Best Methodology by Comparing Different Algorithm to Detect Parkinson Disease

Abstract

Abstract: Early and accurate prediction of Parkinson's disease (PD) can play a crucial role in improving its management and outcome. The aim of this study was to identify the best methodology for PD prediction. A comprehensive review of various prediction methods, including machine learning algorithms such as Support Vector Machines, Random Forest, and Neural Networks, was conducted. The performance of these methods was evaluated using metrics such as accuracy, sensitivity, and specificity. The findings revealed that a combination of Random Forest and Neural Networks emerged as the best approach for PD prediction, providing high accuracy and robust performance. This highlights the significance of selecting the appropriate prediction method for PD and the advantages of using a combination of algorithms for improved prediction.