A predictive system for Parkinson Disease using Generative Adversarial network (GAN).
Loading...
Date
2023-12-16
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Published by Federal University of Wukari (FUW)
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.
Description
Keywords
Citation
Babatunde et. al., 2023