Abstract
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.
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This study was funded by the National Taipei University of Technology and MacKay Memorial Hospital (NTUT-MMH-107-06, MMH-TT-10706).
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Pei-Hao Chen, Chieh-Wen Lien, Wen-Chun Wu, Lu-Shan Lee, Jin-Siang Shaw declare that they have no conflict of interest.
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study was reviewed and approved by the MacKay Memorial Hospital Institutional Review Board (number 18MMHIS005e and 18MMHIS152).
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Informed consent was obtained from all individual participants included in the study.
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Chen, PH., Lien, CW., Wu, WC. et al. Gait-Based Machine Learning for Classifying Patients with Different Types of Mild Cognitive Impairment. J Med Syst 44, 107 (2020). https://doi.org/10.1007/s10916-020-01578-7
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DOI: https://doi.org/10.1007/s10916-020-01578-7