A predictive system for Parkinson Disease using Generative Adversarial network (GAN)

Abstract

Parkinson's disease (PD) symptoms often overlap with those of other neurological disorders, making an early diagnosis difficult or even impossible. To tackle this problem, this study suggests a unique approach that makes use of Generative Adversarial Networks (GANs). Using a variety of datasets, GANs create synthetic medical data that includes PD-related clinical and demographic characteristics. The algorithm undergoes a rigorous training process to improve prediction accuracy. The generated data is assessed by a discriminator, which makes accurate PD predictions possible. Thorough measurements and statistical analysis verify the system's efficacy. The work demonstrates the revolutionary potential of GANs, especially in addressing data limitations for early Parkinson's disease diagnosis. The early PD detection efficacy of the technology is demonstrated by the very accurate predictive model. This study offers a sophisticated and useful method for early identification of Parkinson's disease (PD) by combining modern machine learning. It also promises improved patient outcomes by prompt neurology and healthcare interventions. Parkinson's disease, Generative Adversarial Networks (GANs), Early Diagnosis, Neurological Disorders