Predicting Parkinson’s Disease Using Machine Learning Model

Abstract

This research work discusses the steps involved in developing a machine learning program
for the early detection of Parkinson's disease (PD) using a variety of clinical and behavioral
data. By utilizing highlights extracted from persistent data, including engine and non-motor
side effects, the demonstration employs administered learning procedures to identify patterns indicative of Parkinson's disease (PD). We assess the performance of various
calculations, including back vector machines and neural systems, to determine the most
effective method for accurate forecasts. The results demonstrate the model's potential to
enhance early diagnosis and personalized treatment strategies for Parkinson's infection.
Parkinson's disease (PD) is a dynamic neurodegenerative disorder characterized by engine
side effects such as tremors, inflexibility, and bradykinesia, as well as non-motor side effects
including cognitive disability and autonomic brokenness. Early and precise diagnosis is
essential for effective management and treatment of the infection. In later years, machine
learning (ML) has risen as an effective device in the field of therapeutic diagnostics,
advertising potential changes in the early location and observation of Parkinson's malady.