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Advances in healthcare and improvements in lifestyle have contributed to a rising population of an aging society. Within the social care profession, this causes concern as resources are continually spread thin resulting in increased stress for individual workers and increased financial implications for established healthcare providers. One possible solution to alleviate stress and free up resources is to employ the use of computational intelligence within a home environment to determine important risk factors which should allow health- and social-care professionals to put preventative measures in place to protect elderly people from harm thereby reducing the financial implications of hospital care. A major limitation of computational risk models can that they can be quite complex and cumbersome due to the richness of data used to derive the model, not all of which is particularly useful for determining associated risk. In this paper, a transparent risk modelling method is presented which as well as computing an overall risk level, also reduces model complexity by determining which data are relevant. The transparent nature of the model allows users to understand the model structure used to compute the risk level which is important if health care professional are expected to use such algorithms in the future.
Original languageEnglish
Number of pages7
Publication statusAccepted/In press - 1 Sept 2018
EventSYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE: IEEE Symposium on Computational Intelligence for Financial Engineering and Economics - Benguluru, India
Duration: 18 Nov 201820 Nov 2018


Abbreviated titleCiFER


  • Risk-analysis
  • Computational modelling


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