2024-05-152024-05-152024-05-152023-10-27https://repositorio.ifal.edu.br/handle/123456789/525Parkinson’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.Acesso AbertoSistemas de InformaçãoDoença de Parkinson – MicrografiaMachine Learning – Diagnóstico da Doença de ParkinsonExames em espiraisMachine Learning - Parkinson’s diseaseParkinson’s disease - MicrographySpiral ExamsTrabalho de Conclusão de CursoCNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO::SISTEMAS DE INFORMACAO