Models of Parkinson's Disease Patient Gait

Files

JBHI_PD_2019.pdf (1.02 MB)

Date

2020-11

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

Keywords

Health Information Management, Electrical and Electronic Engineering, Computer Science Applications, Biotechnology, Parkinson's Disease

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.

URI

http://hdl.handle.net/10464/16887

Collections

Computer Science