Detection of Parkinson’s disease using Machine Learning Approach

Abstract

Parkinson's disease has recently become one of the most common chronic global diseases among the elderly. The disease is identified by the motor related symptoms caused due to the lack of production of dopamine from the brain cells .But there are other non-motor related symptoms which occur much earlier which can be identified and predict the various stages of disease. The earlier detection of the disease will help us to halt the progression of the disease; thereby the livelihood of the patients remains easy. Traditional methods fail to detect the earlier symptoms of disease due to its complexity in nature, implementation and accuracy. The short comings of the traditional methods can be overcome by using the more efficient automated Machine learning models. Machine learning plays a key role in the Healthcare area because of its accuracy, less computation time and its adaptability. In this research paper, we propose a machine learning based algorithm for early diagnosis, Machine learning techniques XGBClassifier.