Detection of Parkinson's Disease using Machine Learning Algorithms and Handwriting Analysis

Abstract

Parkinson's Disease is a progressive neurodegenerative disorder of movement that affects your ability to control movement. This disease can prove fatal if not detected at an earlier stage. Motor and non-motor symptoms are raised by the loss of dopamine-producing neurons. Currently, there is no test available to detect disease at early stages where the symptoms may be poorly characterised. Handwriting analysis is one of the traditional aspects of studying human personality and also can be used to identify the symptoms of this disease. Identifying such accurate biomarkers provides roots for better clinical diagnosis. In this paper, we proposed a system that makes use of two types of handwriting analysis, spiral and wave drawings of healthy as well as Parkinson's patients as an input to the system. For feature extraction, we are using a histogram of the oriented gradient. The developed system uses a machine learning algorithm and a random forest classifier for the detection of Parkinson's disease among patients. Our model achieved an accuracy of 86.67 % in the case of spiral drawing and 83.30% with wave drawing.