Early Detection of Parkinson’s Disease Using Spiral Test

A long-term degenerative condition called Parkinson’s disease (PD) affects a person’s neural system and motor memory. There is no reliable and efficient treatment for PD. However, the spiral test and speech smearing can be used to detect Parkinson’s disease in the early stage. This proposed work aims to detect Parkinson’s disease using the analysis of a spiral image-based dataset using two approaches. First, by using machine learning algorithms like Random Forest, XG-Boost, K-Nearest Neighbor, and Support Vector Machine, the accuracies obtained are 86.67%, 73.33%, 80.00%, and 76.67%, respectively. The second approach based on Convolutional Neural Network achieved the accuracy of 85%. The proposed method helps in automating the PD detection process with good accuracy.