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A Review on Parkinson's Disease Diagnosis using Machine Learning Techniques

Chnachal*, Megha Mishra**, Vishnu Kumar Mishra***

*-*** Department of Computer Science, Shri Shankaracharya Engineering College, Shri Shankaracharya Group of Institutions, Chhattisgarh, India.

Periodicity:January - March'2023
DOI : https://doi.org/10.26634/jip.10.1.19381

Abstract

The decreased production of dopamine in the forebrain is believed to be the underlying cause of Parkinson's disease, a neurodegenerative disorder that affects the nervous system. Parkinson's disease is a chronic and progressive illness that may develop new symptoms over time (Nilashi et al., 2016). This occurs as neurons in the substantia nigra of the brain gradually die. People with Parkinson's disease may find it difficult to perform everyday tasks in the workplace. Although clinical evaluations consider a significant amount of data that includes various aspects, it is not always easy to determine whether a person has PD based on this data alone. Feature selection methods can help address this issue. Various techniques are being researched, developed, and evaluated for diagnosing Parkinson's disease, based on the relevant information. This study provides an overview of the use of machine learning algorithms to predict Parkinson's disease, as well as the various new technologies that have been developed and the accuracy that has been achieved.

Keywords

PD (Parkinson Disease), Dopamine, SVM (Support Vector Machine), KNN (K Nearest Neighbor), ANN (Artificial Neural Network).

How to Cite this Article?

Chnachal, Mishra, M., and Mishra, V. K. (2023). A Review on Parkinson's Disease Diagnosis using Machine Learning Techniques. i-manager’s Journal on Image Processing , 10(1), 21-28. https://doi.org/10.26634/jip.10.1.19381

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