A Study On Deep Learning And Machine Learning Techniques On Detection Of Parkinson’s Disease

P Mounika,S Govinda Rao,S Tummala,S Singh,P Bobba,S Kosaraju

doi:10.1051/e3sconf/202130901008

Abstract

Parkinson’s disease (PD) is a sophisticated anxiety malady that impairs movement. Symptoms emerge gradually, initiating with a slight tremor in only one hand occasionally. Tremors are prevalent, although the condition is sometimes associated with stiffness or slowed mobility. In the early degrees of PD, your face can also additionally display very little expression. Your fingers won’t swing while you walk. Your speech can also additionally grow to be gentle or slurred. PD signs and symptoms get worse as your circumstance progresses over time. The goal of this study is to test the efficiency of deep learning and machine learning approaches in order to identify the most accurate strategy for sensing Parkinson’s disease at an early stage. In order to measure the average performance most accurately, we compared deep learning and machine learning methods.

Highlights

Parkinson's disease (PD) was first described by Dr Parkinson's as "trembling paralysis"
The results showed that when compared to machine learning models, developed deep learning has a better detection performance in distinguishing normal persons from Parkinson's disease patients
The six specific varieties of function choice techniques which are as compared of their studies to get the preferred effects are minimum-redundancy, maximum-relevancy(MRMR), Bhattacharyya, records gain, relief, t-test, and SVMtechniques primarily based totally on recursive function elimination (SVM-RFE).Experiments conducted shown that 95.13% classification accuracy is obtained from Support Vector Machine (SVM)-RFE for Parkinson’s disease dataset

Summary

Introduction

Parkinson's disease (PD) was first described by Dr Parkinson's as "trembling paralysis". The results showed that when compared to machine learning models, developed deep learning has a better detection performance in distinguishing normal persons from Parkinson's disease patients.

Objectives

Results

Conclusion