Handwritten pattern recognition for early Parkinson's disease diagnosis

• Autor(a) principal: Bernardo, Lucas S.
• Data de Publicação: 2019
• Outros Autores: Quezada, Angeles, Munoz, Roberto, Maia, Fernanda Martins, Pereira, Clayton R. [UNESP], Wu, Wanqing, de Albuquerque, Victor Hugo C.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• DOI: 10.1016/j.patrec.2019.04.003
• Texto Completo: http://dx.doi.org/10.1016/j.patrec.2019.04.003; http://hdl.handle.net/11449/187552
• topic: image processing; machine learning; Parkinson's disease

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.