Models of Parkinson's Disease Patient Gait
Models of Parkinson's Disease Patient Gait
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DOI：
10.1109/jbhi.2019.2961808
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发表时间：
2020-11-01
影响因子：
7.7
通讯作者：
Brown, Joseph Alexander
中科院分区：
文献类型：
--
作者：
Hughes, James Alexander;Houghten, Sheridan;Brown, Joseph Alexander
帕金森氏病是一种诊断症状包括步态改变的疾病。这种病很难诊断。需要一种监测患者步态的客观方法，以确保诊断和治疗的有效性。我们研究了极端梯度提升（XGBoost）和人工神经网络（ANN）模型的适用性相比，符号回归（SR）使用遗传编程，被证明是成功的步态在以前的工作。XGBoost和ANN模型的性能优于SR，但SR模型更人性化。
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.