ArticleJournal of medical systems2020
Gait-Based Machine Learning for Classifying Patients with Different Types of Mild Cognitive Impairment.
Article in Journal of medical systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- Innovations in dementia screening: a systematic review and meta-analysis of virtual reality assessments.Frontiers in psychology · 2025Pooled it
- Advances in gait research related to Alzheimer's disease.Frontiers in neurology · 2025Pooled it
- Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review.PloS one · 2024Pooled it
- Analyzing Mobility Indicators Using Machine Learning to Detect Mild Cognitive Impairment: A Systematic Scoping Review.Applied clinical informatics · 2025Article
- Quantifying Mild Cognitive Impairments in Older Adults Using Multi-modal Wearable Sensor Data in a Kitchen Environment.medRxiv : the preprint server for health sciences · 2025Article
- Machine Learning Model for Mild Cognitive Impairment Stage Based on Gait and MRI Images.Brain sciences · 2024Article
- Machine vision-based gait scan method for identifying cognitive impairment in older adults.Frontiers in aging neuroscience · 2024Article
- Artificial intelligence for dementia prevention.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Method for Classifying Schizophrenia Patients Based on Machine Learning.Journal of clinical medicine · 2023Article
- Identification of a Gait Pattern for Detecting Mild Cognitive Impairment in Parkinson's Disease.Sensors (Basel, Switzerland) · 2023Article
- Mild Cognitive Impairment: Data-Driven Prediction, Risk Factors, and Workup.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Prediction of Cognitive Degeneration in Parkinson's Disease Patients Using a Machine Learning Method.Brain sciences · 2022Article
- Machine Learning Approach to Support the Detection of Parkinson's Disease in IMU-Based Gait Analysis.Sensors (Basel, Switzerland) · 2022Article
- Machine learning classification of multiple sclerosis patients based on raw data from an instrumented walkway.Biomedical engineering online · 2022Article
- Mild Cognitive Impairment Subtypes Are Associated With Peculiar Gait Patterns in Parkinson's Disease.Frontiers in aging neuroscience · 2022Article
- Machine learning can predict mild cognitive impairment in Parkinson's disease.Frontiers in neurology · 2022Article
- Stable Sparse Classifiers predict cognitive impairment from gait patterns.Frontiers in psychology · 2022Article
- Dimensionality Reduction of Human Gait for Prosthetic Control.Frontiers in bioengineering and biotechnology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.