Department of Neurology, MacKay Memorial Hospital, Taipei, Taiwan.
Department of Medicine, Mackay Medical College, New Taipei City, Taiwan.
J Med Syst. 2020 Apr 23;44(6):107. doi: 10.1007/s10916-020-01578-7.
Mild cognitive impairment (MCI) may be caused by Alzheimer's disease, Parkinson's disease (PD), cerebrovascular accident, nutritional or metabolic disorders, or mental disorders. It is important to determine the cause and treatment of dementia as early as possible because dementia may appear in remission. Decline in MCI cognitive function may affect a patient's walking performance. Therefore, all participants in this study participated in an experiment using a portable gait analysis system to perform walk, time up and go, and jump tests. The collected gait parameters are used in a machine learning classification model based on a support vector machine (SVM) and principal component analysis (PCA). The aim of the study is to predict different types of MCI patients based on gait information. It is shown that the machine learning classification model can predict different types of MCI patients. Specifically, the PCA-SVM model demonstrated better classification performance with 91.67% accuracy and 0.9714 area under the receiver operating characteristic curve (ROC AUC) using the polynomial kernel function in classifying PD-MCI and non-PD-MCI patients.
轻度认知障碍（MCI）可能由阿尔茨海默病、帕金森病（PD）、脑血管意外、营养或代谢紊乱或精神障碍引起。尽早确定痴呆症的病因和治疗方法很重要，因为痴呆症可能会出现缓解。MCI 认知功能下降可能会影响患者的行走表现。因此，本研究的所有参与者都参与了一项使用便携式步态分析系统进行行走、时间起立行走和跳跃测试的实验。所收集的步态参数用于基于支持向量机（SVM）和主成分分析（PCA）的机器学习分类模型。该研究的目的是基于步态信息预测不同类型的 MCI 患者。结果表明，机器学习分类模型可以预测不同类型的 MCI 患者。具体来说，使用多项式核函数，PCA-SVM 模型在对 PD-MCI 和非 PD-MCI 患者进行分类时，准确率为 91.67%，接收器工作特征曲线（ROC AUC）下面积为 0.9714，表现出更好的分类性能。
