Parkinson's Disease Detection using Spiral Drawings

Parkinson's disease is a neurological disorder that primarily affects people over the age of 60, often leading to motor impairment (MI) such as tremors, rigidity, and slowness. The disease's severity has been found to be linked to a decline in handwriting quality, with patients exhibiting reduced speed and pressure while writing. Biomarkers can aid in the diagnosis, monitoring, and prediction of the disease's progression, making it critical to accurately identify them. A convolutional neural network (CNN) is used in this study to analyze spiral drawing patterns from Parkinson's patients and healthy individuals, with the aim of creating a system that can effectively differentiate between the two groups and predict the PD stage. The model was trained on data from 280 patients and achieved an overall accuracy of 94.2%. Identifying biomarkers could provide valuable insights into the disease's causes and lead to better diagnosis and treatment outcome