The neurological condition known as Parkinson's disease (PD) is the second most common, resulting in significant impairment, no cure, and a decrease in a patient's quality of life. It has been determined that Parkinson's disease (PD) is a challenging condition that can provide patients with a computerized classification of PD and is healthy for normal people. In this region of the brain, nerve cells produce the neurotransmitter dopamine. Dopamine is a neurotransmitter that acts as a messenger between the brain and parts of the nervous system to help regulate and coordinate body actions. It becomes more challenging to speak, write, walk or complete other straightforward tasks as dopamine levels in the brain decrease. Speech difficulties affect roughly 90% of Parkinson's disease patients. There is no cure for Parkinson's disease at this time, but research is ongoing, and medications or surgery can sometimes significantly alleviate movement symptoms. even results in death. Consequently, early detection may aid in the prevention or relief of symptoms. The classification techniques of machine learning are used to ascertain whether or not a person has Parkinson's disease. A non-linear support vector machine (SVM) is used in this study to diagnose PD based on voice data. SVM, on the other hand, is known to be one of the fastest and most accurate learning methods. Choosing relevant feature elements from the PD dataset can help improve SVM's classification performance. From speech recordings, the GA-SVM identifies the presence of a vocal disorder with greater precision, enabling a quicker diagnosis.
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