Navegando por Autor "Correia, Jonathas Rodrigues"

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    Quantificação do sinal do tremor parkinsoniano em exames manuscritos em espiral utilizando Machine Learning
    (Instituto Federal de Educação Ciência e Tecnologia de Alagoas, 2023-10-27) Correia, Igor Matheus Barros; Medeiros, Leonardo Melo de; http://lattes.cnpq.br/1080593968001453; Costa, Breno Jacinto Duarte da; http://lattes.cnpq.br/7418697922506495; Costa, Alex Emanuel Barros; http://lattes.cnpq.br/2231272728491909
    Parkinson’s disease (PD) is a progressive neurological disease that develops gradually and causes motor symptoms, such as tremors, slow movement (bradykinesia) and postural imbalance. PD affects millions of people worldwide, especially the elderly population. Data from the World Health Organization (WHO) shows that approximately 1% of the world’s population over the age of 65 has the disease. Diagnosing PD is difficult and misdiagnosis is common, especially in the early stages. Micrography is a technique commonly used in the diagnosis of Parkinson’s disease and essentially consists of performing handwritten examinations. In this context, Machine Learning (ML) emerges as a powerful tool to apply data classification techniques that can identify patterns and assist in the detection of PD. This study uses two Machine Learning models, namely Support Vector Machine (SVM) and Random Forest Classifier (RFC), with the aim of classifying handwritten exams and contributing to improving the accuracy and effectiveness in diagnosing PD. The results show that both models achieved promising performance, with high accuracy rates, reaching 86% for the RFC and 82% for the SVM. These results suggest that the application of Machine Learning to classify handwritten exams can be a valuable tool in diagnosing PD.