ABSTRACT

Parkinson’s disease (PD) is a neurodegenerative movement disease where the symptoms gradually develop start with a slight tremor in one hand and a feeling of stiffness in the body and it became worse over time. It affects over 6 million people worldwide. At present there is no conclusive result for this disease by non-specialist clinicians, particularly in the early stage of the disease where identification of the symptoms are very difficult in its earlier stages. The proposed predictive analytics framework is a combination of K-means clustering and Decision Tree which is used to gain insights from patients. By using machine learning techniques, the problem can be solved with minimal error rate. In this model, a huge amount of data is collected from the normal person and also previously affected person by Parkinson's disease, these data is trained using machine learning algorithms. From the whole data 60% is used for training and 40% is used for testing. There are 24 columns in the data set each column will indicate the symptom values of a patient except the status column. The status column has 0's and 1’s. Those values will decide the person is affected with Parkinson’s disease. 1's indicate person is affected, 0's indicate normal conditions. Also, OpenCV (Open Source Computer Vision Library) a library of programming functions mainly aimed at real-time computer vision was built to provide an infrastructure for computer vision applications and to accelerate the use of machine perception in the real time. Thus, our output will showcase the early detection of the disease and can be able to increase the lifespan of the diseased patient with proper treatments and medications leads to peaceful life.