A hybrid intelligent system for the prediction of Parkinson's Disease progression using machine learning techniques

2018 ◽  
Vol 38 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Mehrbakhsh Nilashi ◽  
Othman Ibrahim ◽  
Hossein Ahmadi ◽  
Leila Shahmoradi ◽  
Mohammadreza Farahmand
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Progression ◽  
Intelligent System ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Hybrid Intelligent System

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

2021 ◽  
Vol 309 ◽  
pp. 01008
Author(s):  
P. Mounika ◽  
S. Govinda Rao
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Early Stage ◽  
Signs And Symptoms ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Machine Learning Methods ◽  
Learning Techniques

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.

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