Prediction of Freezing of Gait in Parkinson’s Disease Using a Random Forest Model Based on an Orthogonal Experimental Design: A Pilot Study - Chen, Zhonelue; Li, Gen; Gao, Chao; Tan, Yuyan; Liu, Jun; Zhao, Jin; Ling, Yun; Yu, Xiaoliu; Ren, Kang; Chen, Shengdi (Frontiers in Human Neuroscience, 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.