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Abstract

Parkinson's Disease, a neurodegenerative disorder, is one of the major chronic health issues in the world. It causes a severe disorder that affects majorly muscle control but it can also be the reason of affecting senses, cognitive ability and cognitive health. Approximately, 90% of the Parkinson affected people face speech difficulty. Conventional diagnosis methods may be biased which may result in wrong diagnosis of Parkinson's Disease because symptoms are usually elusive. This study aims to assess how well different machine learning algorithms can predict Parkinson's disease using vowel phonation data, with the goal of early detection and more accurate patient assessments. Different machine learning algorithms, like Random Forest, Logistic Regression, Decision Tree, Support Vector Machine, and Boosting algorithms (Gradient, Extreme Gradient, Light Gradient, and Categorical), are evaluated for their prediction ability. Based on vowel phonation data, Random Forest achieved the highest accuracy of 98.4% among the evaluated classifiers in predicting Parkinson's disease. It highlights the prominence of machine learning application for early detection of Parkinson's disease accurately. This research helps create a better way to assess patients' risk of Parkinson's disease, leading to a clearer understanding and supporting future studies in this area.

Keywords

Detection, Efficacy, Machine Learning, Parkinson, Prediction

Subject Area

Computer Science

Article Type

Article

First Page

2438

Last Page

2457

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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