EARLY DETECTION OF PARKINSON'S DISEASE USING MACHINE LEARNING

COMPUTER SCIENCE AND ENGINEERING

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. Voice data sets obtained from the UCI Machine learning repository if given as the input for voice data analysis. Also our proposed system provides accurate results by integrating spiral drawing inputs of normal and Parkinson’s affected patients. From these drawings Random forest classification algorithm is used which converts these drawings into pixels for classification and the extracted values are been matched with the trained database to extract various features and results are produced with maximum accuracy. 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.

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