A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing (ELSA).
| Publicado en: | Frontiers in Aging pp. 1 - 13 |
|---|---|
| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Frontiers Media S.A.
2025
|
| 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=184969932&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184969932 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 26736217 NC2A jtl: Frontiers in Aging issn: 26736217 maglogo: N pubinfo: dt: 2025 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 184969932 184969932 184969932 10.3389/fragi.2025.1501168 184969932 ppf: 1 ppct: 12 formats: tig: atl: A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing (ELSA). aug: au: Hughes, Charmayne Mary Lee Zhang, Yan Pourhossein, Ali Jurasova, Terezia affil: Age-Appropriate Human-Machine Systems, Institute of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany sug: subj: Frailty Syndrome Classification Frailty Syndrome Diagnosis Machine Learning Algorithms Evaluation Aging Diagnosis, Computer Assisted Validity Human Comparative Studies Prospective Studies Validation Studies Logistic Regression Random Forest Sensitivity and Specificity England Computer Simulation Boosting Machine Learning Algorithms Multilayer Perceptrons Male Female Adult Middle Age T-Tests Adult: 19-44 years Middle Aged: 45-64 years Male Female pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
|---|