Models of Parkinson's Disease Patient Gait

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@Article{Hughes:2019:JBHI,

• author = "James Hughes and Sheridan Houghten and Joseph Alexander Brown",
• journal = "IEEE Journal of Biomedical and Health Informatics",
• title = "Models of Parkinson's Disease Patient Gait",
• year = "2019",
• 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.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/JBHI.2019.2961808",
• ISSN = "2168-2208",
• notes = "Also known as \cite{8939380}",

}

Genetic Programming entries for James Alexander Hughes Sheridan Houghten Joseph Alexander Brown

Citations