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Parkinson’s Disease Prediction using Machine Learning

Preethi S.*, Priyadharshini K. S. **, Kavitha A. ***

*-*** Department of Information Technology, Bannari Amman Institute of Technology, Erode, Tamil Nadu, India.

Periodicity:January - March'2021
DOI : https://doi.org/10.26634/jse.15.3.18133

Abstract

Parkinson's disease is a disorder which is identified with loss of neurons and neurologic function. It is a condition that arises when fifty to seventy five percent of the neuronal cells are affected. The symptoms include muscle rigidity, tremors and change in the speech and gait. The genetic factor also increases the risk of Parkinson's disease in a person. Some researchers also suggest that Parkinson's disease is also caused by environmental factors and excessive medications. With the advancement of deep learning and machine learning technologies, disease prediction has received additional attention from big data researchers, and numerous studies have been conducted with a choice of different mechanisms. Studies have shown that about 90% of patients with this disease suffer from certain degree of speech impairment. Therefore, we have chosen voice data as an input for our model. The proposed methodology presents how algorithm works best for identification of disease with high accuracy by splitting the dataset. XGBoost algorithm has been applied on the dataset in order to get accuracy expected out of the model.

Keywords

Prediction, Data Analysis, Model Planning, Extreme Gradient Boosting (XGBoost), Parkinson's Disease.

How to Cite this Article?

Preethi, S., Priyadharshini, K. S., and Kavitha, A. (2021). Parkinson's Disease Prediction using Machine Learning. i-manager's Journal on Software Engineering, 15(3), 37-41. https://doi.org/10.26634/jse.15.3.18133

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