Abstract: Parkinson's disease is a neurodegenerative disorder affecting 60% of people over the age of 50 years. Patients with Parkinson's face mobility challenges and speech difficulties, making physical visits for treatment and monitoring a hurdle. Parkinson’s Disease can be treated through early detection, thus enabling patients to lead a normal life.
This project highlights the use of Machine Learning techniques in telemedicine to detect Parkinson’s Disease in its early stages. Researches evolved in recent years use Machine Learning and Deep Learning approaches for finding early stages of Parkinson’s Disease. Through the use of XGBoost algorithm we are aiming at achieving high accuracy in the detection compared to other Machine Learning techniques
Keywords: Keywords for Precision Monitoring for Parkinson’s Disease using Machine Learning include early detection, Parkinson’s disease, audio signals, Machine Learning, classifier, gradient boosting, XGBoost, hyperparameter, accuracy and precesion.
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DOI:
10.17148/IJARCCE.2024.134149
