Gait-Based Machine Learning for Classifying Patients with Different Types of Mild Cognitive Impairment.
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 remi...
| Publicado en: | Journal of Medical Systems Vol. 44; no. 6; pp. 1 - 7 |
|---|---|
| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2020
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143571483&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143571483 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jun2020 vid: 44 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143571483 143571483 143571483 10.1007/s10916-020-01578-7 143571483 ppf: 1 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Gait-Based Machine Learning for Classifying Patients with Different Types of Mild Cognitive Impairment. aug: au: Chen, Pei-Hao Lien, Chieh-Wen Wu, Wen-Chun Lee, Lu-Shan Shaw, Jin-Siang affil: Department of Neurology, MacKay Memorial Hospital, Taipei, Taiwan sug: subj: Cognition Disorders Classification Machine Learning Gait Analysis Cognition Disorders Prognosis Human Support Vector Machine Sensitivity and Specificity Factor Analysis ROC Curve Wearable Sensors Minimum Data Set Male Female Descriptive Statistics Aged Walking Jumping Clinical Assessment Tools Funding Source Aged: 65+ years Male Female ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|