Most standard cardiovascular disease (CVD) risk assessment models are based on traditional quantitative approaches. Such models oversimplify complex interactions emanating from the imprecise nature of CVD risk factors. As such, approaches that can handle uncertainty due to imprecision need to be explored. This study proposes a cardiovascular risk classification model based on the geometry of fuzzy sets, which allows for a multidimensional display of qualitative properties associated with risk attributes—that are defined in a fuzzy sense. Within this structure, a risk concept (which defines the degree of risk severity) is simply a continuum of points of the hypercube. Consequently, an individual’s risk status would naturally be represented by an ordered fuzzy within the continuum. This representation forms an excellent comparative framework through measures of similarity where an individual’s relative position in the continuum can be given as degrees of compatibility with the underlying risk concepts.
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This work was supported by the Pan African University, Institute of Basic Sciences, Technology and Innovation under the Commission of the African Union.
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Communicated by V. Loia.
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Barini, G.O., Ngoo, L.M. & Mwangi, R.W. Application of a fuzzy unit hypercube in cardiovascular risk classification. Soft Comput 23, 12521–12527 (2019). https://doi.org/10.1007/s00500-019-03802-0
- Fuzzy hypercube
- Similarity measure
- Risk continuum