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