Early Detection of Parkinson’s Disease using Machine Learning and Convolutional Neural Networks from Drawing Movements


Sarah Fan1 and Yu Sun2, 1USA, 2California State Polytechnic University, USA


Parkinson’s disease (PD) is a progressive neurodegenerative disorder that causes uncontrollable movements and difficulty with balance and coordination. It is highly important for early detection of Parkinson’s disease in order for patients to receive proper treatment. This paper aims to aid in the early detection of Parkinson’s disease by using a convolutional neural network for PD detection from drawing movements. This CNN consists of 2 convolutional layers, 2 max-pooling layers, 2 dropout layers, 2 dense layers, and a flattened layer. Additionally, our approach explores multiple types of drawings, specifically spiral, meander, and wave datasets hand-drawn by patients and healthy controls to find the most effective one in the discrimination process. The models can be continuously trained in which the test data can be inputted to differentiate between healthy controls and PD patients. By analyzing the training and validation accuracy and loss, we were able to find the most appropriate model and dataset combination, which was the spiral drawing with an accuracy of 85%. With a proper model and a larger dataset for increased accuracy, this approach has the potential to be implemented in a clinical setting.


Machine Learning, Deep Learning, Parkinson Disease.

Full Text  Volume 12, Number 15