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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