Parkinson Disease Detection Using Various Machine Learning Algorithms

Abstract

Parkinson disease is a neural disease. It prompts shaking of the hands, difficulty to walk, balance with coordination. No medical treatment is available in the high-level stage. X-ray, CT scan and blood tests report are not sufficiently results available in the early stage. About two trillion community are alive in Parkinson's disease (PD) in the U.K., which is the highest number of people affected. are pinpointed to have different sclerosis, solid dystrophy and Lou Gehrig's illness. This is relied upon to ascend to 1.5 million by 2040. Around the 75,000 Americans are diagnosis PD with every year. It is very important to predict Parkinson's disease early so that important treatment can be done. The purpose of the proposed work is to detect Parkinson disease, where we aimed to identify disease in early prediction using clinical imaging that incorporate the use of Machine learning techniques. A comparative analysis done with various Machine Learning classifier algorithms like XGBoost, Random Forest, KNN, SVM are the best model is proposed which is used to make predictions and find accuracy. We are observed that Random Forest provides better performance with an accuracy 90%. Automatic detection with more accuracy will make screening for Parkinson disease as cost effective and efficient manner facilitates to use appropriate and fast solutions.