Machine Learning Techniques for Parkinson's Disease Prediction and Progression: A Comprehensive Review

Parkinson's disease (PD) presents a major healthcare obstacle globally, requiring efficient methods for early identification and precise prognosis to enhance patient results. Recently, machine learning (ML) approaches have become promising tools for meeting these requirements. This review study thoroughly examines the use of machine learning approaches in forecasting the development and advancement of Parkinson's disease. We present an overview of the current state of machine learning-based methods for predicting and modeling the course of Parkinson's disease by examining various studies, methodologies, and results. We focus on using a variety of data sources such as clinical assessments, genetic profiles, neuroimaging scans, and wearable sensor data to create prediction models. We will address the problems, constraints, and future directions in this fast-advancing field to offer insights that can guide the creation of more precise, resilient, and clinically relevant machine learning models for managing Parkinson's disease.