Next-Generation Parkinson’s Disease Management: the Perform of Machine Learning in Early Diagnosis and Tailored Treatments

Parkinson's Disease (PD) is a progressive neurodegenerative disorder that significantly impacts the quality of life of affected individuals. Early detection and diagnosis are necessary for Actual management and treatment. This literature survey focuses on machine learning (ML) approaches for Parkinson's Disease diagnosis and prediction. Various ML techniques, such as supervised learning, unsupervised learning, deep learning, and ensemble methods, have been explored to analyze clinical, neuroimaging, and biosignal data. The paper reviews the effectiveness of these approaches in detecting PD at various stages, including the use of speech signals, gait analysis, and electroencephalogram (EEG) data. Additionally, the challenges related to data quality, feature extraction, and model interpretability are discussed. Finally, the paper outlines future research directions, emphasizing the need for integrated multi-modal data analysis and real-time monitoring systems for more accurate and timely diagnosis of Parkinson's Disease.