A Review of Multiple Prognosticate Techniques for Parkinson's Disease

Abstract

Parkinson's disease (PD) is a common neurodegenerative disorder that affects millions of people worldwide. Parkinson's disease is driven by low amounts of the neurotransmitter serotonin and it is developed by the subtle degradation of dopamine-producing cells within the brain. Still, there is no real treatment for Parkinson's disease, although the symptoms can be constrained as allowing it to be delayed. Research shows that "diagnosis" is vital for symptom management. However, PD diagnosis is complex, particularly in the early stages of the illness. This study mentions variety of approaches for detecting Parkinson's disease (PD), like deep learning (DL), transfer learning, convolutional neural networks (CNN), machine learning (ML), and the Internet of things (IOT), but it also displays the results of multiple studies conducted by other scientists using different techniques.