Comparative Study of Various Machine Learning Techniques for Parkinson Disease Detection Based on Handwriting

The most prevalent neurodegenerative condition that substantially impairs elderly people's motor capabilities is Parkinson's disease (PD). Even today, in less developed regions of the world, the diagnosis and monitoring of PD remain an expensive and difficult process. This is comparative study of methods for predicting Parkinson's disease using hand-drawn spiral pictures using computer vision and machine learning approaches. In this paper, five machine learning algorithms are used, which are Decision Tree, K-Nearest Neighbors, Support Vector Classifier, Logistic Regression, and Random Forest. Along with this, HOG feature descriptor is used for the extraction of the features from the spiral images. In this work, the KNN machine learning algorithm performed with the best accuracy of 90%. Similarly, the Random Forest is the second best algorithm with accuracy 83.3%.