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Updated: Dec 23, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Gait-Based Machine Learning for Classifying Patients with Different Types of Mild Cognitive Impairment
Pei-Hao Chen1,2, Chieh-Wen Lien3, Wen-Chun Wu1
1Department of Neurology, MacKay Memorial Hospital, Taipei, Taiwan.
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.
Insights
This study uses gait analysis and machine learning to identify different types of mild cognitive impairment (MCI). The PCA-SVM model accurately distinguishes Parkinson
Area of Science:
- Neurology
- Biomedical Engineering
- Machine Learning in Healthcare
Background:
- Mild cognitive impairment (MCI) has diverse etiologies including Alzheimer's disease and Parkinson's disease (PD).
- Early diagnosis and treatment of dementia are crucial due to potential remission.
- Cognitive decline in MCI can manifest as impaired walking performance.
Purpose of the Study:
- To predict different subtypes of MCI patients using gait information.
- To investigate the utility of machine learning models for MCI classification based on gait parameters.
- To differentiate between Parkinson's disease with MCI (PD-MCI) and non-PD-MCI.
Main Methods:
- Participants underwent gait analysis using a portable system, performing walk, Time Up and Go, and jump tests.
- Gait parameters were processed using machine learning classification models, specifically Support Vector Machine (SVM) with Principal Component Analysis (PCA).
- A polynomial kernel function was employed within the PCA-SVM model.
Main Results:
- The machine learning classification model successfully predicted different types of MCI patients.
- The PCA-SVM model achieved 91.67% accuracy in classifying PD-MCI versus non-PD-MCI.
- The PCA-SVM model demonstrated a ROC AUC of 0.9714 for PD-MCI classification.
Conclusions:
- Gait analysis combined with machine learning offers a promising approach for classifying MCI subtypes.
- The PCA-SVM model shows high efficacy in distinguishing PD-MCI from other forms of MCI.
- Objective gait metrics can serve as valuable biomarkers for early detection and subtyping of cognitive impairment.
