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A neurological condition that affects millions of people is Parkinson's disease globally. The effects of Parkinson's disease (PD) sixty percent of persons over fifty. It is challenging for people Having Parkinson's illness in order to get to treatment and monitoring appointments since they have difficulty speaking and moving. It is feasible for Parkinson's disease (PD) sufferers to have normal lives with treatment. The necessity for precise, early, and remote PD identification is highlighted by the aging global population. The early identification and detection of Parkinson's disease has shown great promise in recent years thanks to machine learning methods. We describe a novel approach for the diagnosis of Parkinson's illness in this work using exception architecture and machine learning approaches. Specifically, we focus on the Parkinson's disease diagnosis illness.
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Copyright © 2024, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: D. Dileep Kumar, dadidileep08@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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