Prediction of Parkinson's Disease using Machine Learning
A. Naresh, N. Anusha, P Joharika, N. Jenny Jerusha, Pentyala Tanuja
Abstract
The disruption of the brain cells that create the chemical dopamine, which enables brain cells to communicate with one another, causes Parkinson's illness. Dopamine-producing cells in the brain are what give movements control, adaptability, and fluidity. One of the most deadly and progressive nervous system illnesses that impact movement is Parkinson's. It is the second most prevalent neurological illness that impairs function, shortens lifespan, and currently lacks a treatment. Speech problems, tremor, rthymic shaking, and delayed movement impact over 90% of those who have this disease (bradykinesia). When considering the word "Parkinson's," speech traits are the major idea. Parkinson's disease can be predicted using a variety of machine learning approaches, including SVM, KNN, RF, and LR Models. The dataset is constructed using user input as well as algorithmic input. They employ the KNN Model in the current system to predict PD. The Proposed System makes use of the RF Model. Prediction is crucial, and the proposed system has a higher accuracy rate for recovering patients in the early phases. Machine learning can be used to complete this procedure.