Models of Parkinson's Disease Patient Gait
Date
Authors
Hughes, James Alexander
Houghten, Sheridan
Brown, Joseph Alexander
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Parkinson's Disease is a disorder with diagnostic symptoms that include a change to a walking gait. The disease is problematic to diagnose. An objective method of monitoring the gait of a patient is required to ensure the effectiveness of diagnosis and treatments. We examine the suitability of Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) Models compared to Symbolic Regression (SR) using genetic programming that was demonstrated to be successful in previous works on gait. The XGBoost and ANN models are found to out-perform SR, but the SR model is more human explainable.
Description
Citation
J. A. Hughes, S. Houghten and J. A. Brown, "Models of Parkinson's Disease Patient Gait," in IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 11, pp. 3103-3110, Nov. 2020, doi: 10.1109/JBHI.2019.2961808.