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 Jul:2019:2466-2469. doi: 10.1109/EMBC.2019.8856780.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Activities of Daily Living*
  • Aged
  • Aging
  • Data Mining*
  • Disabled Persons* / classification
  • Humans
  • Longitudinal Studies