Automatic Disability Categorisation based on ADLs among Older Adults in a Nationally Representative Population using Data Mining Methods.
Annu Int Conf IEEE Eng Med Biol Soc
; 2019: 2466-2469, 2019 Jul.
Article
em En
| MEDLINE
| ID: mdl-31946397
ABSTRACT
The world's ageing population is rapidly increasing but people's healthspan is not being sustained. Activities of daily living and Montreal Cognitive Assessment scores from the first wave of a large nationally representative longitudinal study in ageing (TILDA) were analysed using multiple correspondence analysis, k-means clustering, network analysis and association rules mining, to find latent patterns in the data and categorise disability among older adults. It was observed that 6.2% of the population had a greater degree of frailty, specifically cognitive impairment. Additionally, the overall population showed difficulty in performing physically demanding activities. Thus, self-reported ADLs have a diagnostic importance as they indicate the level of cognitive and physical functional decline in the older population.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Atividades Cotidianas
/
Pessoas com Deficiência
/
Mineração de Dados
Tipo de estudo:
Observational_studies
/
Risk_factors_studies
Limite:
Aged
/
Humans
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article