Identifying predictors of independence in toileting activities using machine learning.
Introduction: Toileting independence is a key goal in stroke rehabilitation, yet no consensus exists regarding the factors influencing its achievement. This study identifies predictors of toileting independence in stroke patients using supervised machine learning with a random forest algorithm based...
| Publicado en: | Advances in Rehabilitation Vol. 39; no. 1; pp. 73 - 81 |
|---|---|
| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Paradigm Publishing Services
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=185266055&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185266055 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17344948 5F0P jtl: Advances in Rehabilitation issn: 17344948 maglogo: N pubinfo: dt: 2025 vid: 39 iid: 1 pid: 35924 pub: Paradigm Publishing Services artinfo: ui: 185266055 185266055 185266055 10.5114/areh.2025.148014 185266055 ppf: 73 ppct: 8 formats: tig: atl: Identifying predictors of independence in toileting activities using machine learning. aug: au: Kenta Kunoh Daisuke Kimura Shintaro Ishikawa Hiromu Sakuragi Kazumasa Yamada affil: Department of Rehabilitation, Yamada Hospital, Japan sug: subj: Toileting Activities of Daily Living Evaluation Machine Learning Utilization Random Forest Utilization Stroke Rehabilitation Human Japan Female Male Middle Age Aged Aged, 80 and Over Stroke Patients Electronic Health Records Age Factors Cognition Scales Clinical Assessment Tools Grip Strength Posture Hematologic Tests Prediction Models Data Analysis Software Descriptive Statistics Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Female Male ab: Introduction: Toileting independence is a key goal in stroke rehabilitation, yet no consensus exists regarding the factors influencing its achievement. This study identifies predictors of toileting independence in stroke patients using supervised machine learning with a random forest algorithm based on a multidimensional dataset. Material and methods: The analysis used medical records from 30 stroke patients. The dataset included basic attributes (1 item), physical and cognitive functions (7 items), and laboratory tests (15 items). Toileting independence was classified into two categories, independent or dependent, as determined using machine learning. Results: The random forest model achieved 75% accuracy in predicting toileting independence. Five factors were identified as significant predictors: the Hasegawa Dementia Scale-Revised (HDS-R), 6-minute walk test (6MWT), Berg Balance Scale (BBS), albumin levels, and age. These results indicate that cognitive function, lower limb performance, balance ability, nutritional status, and age play critical roles in achieving toileting independence. Conclusions: This study highlights the multidimensional nature of toileting independence, emphasizing cognitive, physical, and nutritional factors. The findings can guide rehabilitation strategies tailored to individual needs. Furthermore, the application of machine learning demonstrates its potential to uncover complex patterns, offering a robust framework for improving rehabilitation outcomes in stroke patients pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|