Exploring Machine Learning Methods for Developing a Predictive System for Parkinson's Disease

Abstract

ABSTRACT: The Integration of Machine Learning (ML) techniques holds significant promise in addressing challenges across various sectors, particularly within healthcare and biomedical fields. In this study, we focus on leveraging ML methodologies to address the longstanding issues surrounding the prediction and treatment of Parkinson's Disease (PD). PD prediction has historically suffered from inaccuracies and inconsistent treatments. Our research aims to mitigate these challenges by developing a predictive system tailored specifically to PD datasets. To achieve this, we systematically explore various ML algorithms for binary classification tasks, comparing their efficacy in predicting PD. By analyzing and comparing the performance of these algorithms, we aim to establish a robust pathway for accurately examining and diagnosing PD, thereby reducing discrepancies and associated risks. Our findings underscore the importance of employing ML techniques in developing effective decision support systems for PD prediction. By synthesizing results from multiple algorithms, our study not only contributes to filling existing research gaps but also provides actionable insights for the development of advanced medical applications. Overall, this research offers a comprehensive evaluation of ML approaches in the context of PD prediction, highlighting their potential to revolutionize diagnostic processes and improve patient outcomes. Our work not only enhances our understanding of PD but also underscores the transformative impact of ML in addressing complex medical challenges.