Predictive Modeling for Parkinson's Disease Detection: A Systematic Review of Machine Learning Techniques

Abstract

This systematic review investigates the utilization of machine learning (ML) methodologies in predictive modeling for the identification of Parkinson's disease (PD). This review evaluates the efficacy and constraints of different machine learning algorithms, such as support vector machines, random forests, neural networks, and ensemble approaches, in detecting Parkinson's disease by a thorough examination and analysis of pertinent literature. The current environment is comprehensively examined by scrutinizing key features such as data types, feature selection methods, model performance indicators, and validation methodologies. The study discusses several problems, including variations in datasets, limited sample sizes, and the ability of models to apply to different situations. It also emphasizes positive results in specific studies, such as high accuracy and sensitivity in prediction. This study intends to use the synthesis of these findings to guide future research in order to develop more robust and practically useful predictive models. The ultimate goal is to improve early detection and management of Parkinson's disease.