A Novel Approach Using Learning Algorithm for Parkinson’s Disease Detection with Handwritten Sketches’
Anurag Shrivastava, Midhun Chakkaravarthy, Mohd Asif Shah
Abstract
The degenerative neurological condition known as Parkinson’s disease is distinguished by tremors, stiffness, and difficulties with walking, balance, and motor coordination. Parkinson’s disease is a progressive condition. Identifying whether this guy suffers from Parkinson’s disease is the purpose of this puzzle. Early detection and effective treatment of Parkinson’s disease can significantly improve the quality of life for those affected. This highlights the significance of early disease detection. The objective of this initiative is to facilitate the identification of Parkinson’s disease through the use of hand-drawn graphics. Three unique Machine Learning algorithms are used in this article. The adaptability of the model has been demonstrated through the usage of a variety of designs, including but not limited to spirals, waves, cubes, and triangles. Experiment results showed that datasets 89.7% correct, and proposed XGBOOST algorithm is 98.75% accurate.