A REVIEW ON DIAGNOSTIC SYSTEM FOR EARLY DETECTION OF PARKINSON’S DISEASES USING MACHINE LEARNING ALGORITHMS

M. Rajendiran; Dr. K. P. Sanal Kumar; Dr. S. Anu H. Nair

doi:10.17605/OSF.IO/ZY634

Authors

M. Rajendiran Research Scholar Department of Computer and Information Sciences, Annamalai University, Chidambaram, India
Dr. K. P. Sanal Kumar Assistant professor PG Department of Computer Science, R.V Government Arts College, Chengalpattu, India
Dr. S. Anu H. Nair Assistant professor Department of CSE, Annamalai University, Chidambaram, India [Deputed to WPT, Chennai

DOI:

https://doi.org/10.17605/OSF.IO/ZY634

Keywords:

Parkinson’s disease (PD), Electroencephalogram (EEG), Graphical User Interface (GUI), Machine Learning, Electromyogram (EMG), Neurodegenerative Disorder.

Abstract

Medical observations and assessments of clinical signals, such as the characterisation of a variety of motor symptoms, are widely used to diagnose Parkinson's disease (PD). Traditional diagnostic procedures, on the other hand, may be vulnerable to subjectivity because they rely on the interpretation of motions that are sometimes subtle to human sight and hence difficult to define, potentially leading to misdiagnosis. Meanwhile, early non-motor symptoms of Parkinson's disease might be moderate and be caused by a variety of diseases. As a result, these signs and symptoms are frequently missed, making early PD diagnosis difficult. Machine learning algorithms have been applied for the classification of PD and healthy controls or patients with comparable clinical presentations to solve these issues and to refine the diagnosis and assessment procedures for PD (e.g., movement disorders or other Parkinsonian syndromes). The term "early detection" refers to a method of detecting a problem early enough to start treatment. The study's goal is to provide insight into Biosignals diagnostic parameters for detecting brain and muscle abnormalities in Parkinson's disease (PD) patients. Because Parkinson's disease is caused by a decrease in dopamine synthesis in the substantia nigra of the brain, electroencephalogram and electromyogram-based GUI models would be an effective tool and true rationale for early identification of the disease. Biophysical recording device was used to gather EEG and EMG from early stage PD and healthy patients. Artificial Neural Networks were used to extract and categorise EEG and EMG features. A unique strategy to distinguishing PD from non-PD and tracking illness development is the designed model. There are a variety of models available, however the work described here combines biosignals interpretations with other factors such as those of radiological techniques to aid in disease diagnosis

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