Handwritten pattern recognition for early Parkinson's disease diagnosis

Abstract

Parkinson's disease is a neurodegenerative disorder that affects around 10 million people in the world and is slightly more prevalent in males. It is characterized by the loss of neurons in a region of the brain known as substantia nigra. The neurons of this region are responsible for synthesizing the neurotransmitter dopamine, and a decrease in the production of this substance may cause motor symptoms, a characteristic of the disease. To obtain a definitive diagnosis, the patient's medical history is analyzed and the subject submitted to a series of clinical exams. One of these exams that can take place in the clinical environment comprises asking the patient to create a series of specific drawings. Our work is based on asking the patients to draw using a software developed for this specific purpose. The drawings will then be passed through a series of image methods to reduce noises and extract the characteristics of 11 metrics of each drawing; finally, these 11 metrics will be stored. Machine learning techniques such as Optimum-Path Forest, Support Vector Machine remove, and Naive Bayes use the dataset to search and learn of the characteristics for the process of classifying individuals distributed into two classes: sick and healthy.