ISSN : 2663-2187

Enhancing Parkinson’s Disease Prediction and Further Research Using Machine Learning Techniques

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J.JAMUNA, DR.K.KASTURI
» doi: 10.48047/AFJBS.6.12.2024.3393-3402

Abstract

The nerve cells that are found inside the human body are permanently damaged by a neuron-degenerative illness. Parkinson's and Alzheimer's, on nerve cells. In Parkinson's disease, the loss of dopamine neurons in the brain leads to disruptions in smooth coordination and communication with other nerve cells, resulting in characteristic motor symptoms such as tremors, stiffness, and impaired balance. This loss of coordination underscores the importance of dopamine in regulating movement and highlights one of the key pathological features of Parkinson's disease.Dopamine neuron deficit has an impact on both motor and non-motor symptoms. Physicians and other healthcare professionals still manually check patients' symptoms to diagnose Parkinson's disease. Numerous methods have been presented by researchers to identify Parkinson's disease. These methods make use of a variety of modalities, including voice signals, handwriting traces, PET and SPECT scans, MRIs, and finger tapping tests. These methods are discussed in this publication along with the difficulties that researchers are currently facing in diagnosing Parkinson's disease. Although a cure for Parkinson's disease is still a long way off, advances in recent years have substantially increased our knowledge of the illness's processes as well as its paraclinical and re-motor early signs. Although a proven disease-modifying medication has not yet been found, afflicted patients now have better options for symptomatic care. This includes invasive methods like continuous device-aided medication delivery and deep brain stimulation for patients with motor difficulties. The various aspects of non-motor issues that patients encounter have now been thoroughly identified and are the focus of non-pharmacological methods as well as therapy trials.

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