基于正交试验设计的随机森林模型预测帕金森病冻结步态：一项初步研究
Prediction of Freezing of Gait in Parkinson's Disease Using a Random Forest Model Based on an Orthogonal Experimental Design: A Pilot Study.
作者信息
Chen Zhonelue, Li Gen, Gao Chao, Tan Yuyan, Liu Jun, Zhao Jin, Ling Yun, Yu Xiaoliu, Ren Kang, Chen Shengdi
机构信息
Gyenno Science Co., Ltd., Shenzhen, China.
Department of Neurology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
出版信息
Front Hum Neurosci. 2021 Mar 22;15:636414. doi: 10.3389/fnhum.2021.636414. eCollection 2021.
PURPOSE
The purpose of this study was to introduce an orthogonal experimental design (OED) to improve the efficiency of building and optimizing models for freezing of gait (FOG) prediction.
METHODS
A random forest (RF) model was developed to predict FOG by using acceleration signals and angular velocity signals to recognize possible precursor signs of FOG (preFOG). An OED was introduced to optimize the feature extraction parameters.
RESULTS
The main effects and interaction among the feature extraction hyperparameters were analyzed. The false-positive rate, hit rate, and mean prediction time (MPT) were 27%, 68%, and 2.99 s, respectively.
CONCLUSION
The OED was an effective method for analyzing the main effects and interactions among the feature extraction parameters. It was also beneficial for optimizing the feature extraction parameters of the FOG prediction model.
目的
本研究的目的是引入正交试验设计（OED），以提高构建和优化步态冻结（FOG）预测模型的效率。
方法
开发了一种随机森林（RF）模型，通过使用加速度信号和角速度信号来识别FOG的可能前驱体征（preFOG），从而预测FOG。引入OED来优化特征提取参数。
结果
分析了特征提取超参数之间的主效应和交互作用。误报率、命中率和平均预测时间（MPT）分别为27%、68%和2.99秒。
结论
OED是分析特征提取参数之间主效应和交互作用的有效方法。它也有利于优化FOG预测模型的特征提取参数。
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