Fuzzy Logic in a Decision Support System in the Domain of Coronary Heart Disease Risk Assessment
Alfons Schuster, Kenneth Adamson, David Bell · Studies in fuzziness and soft computing · 2002
Every day humans are confronted in numerous occasions with tasks that include the management and the processing of information of various degrees of complexity. Regardless of what the actual information consists of, its degree of complexity, or simplicity, can be associated with the number of recognised parts and the extent of their interrelationship (Klir and Folger 1988). The capability to manage such information considerably depends on the actual understanding of the person(s) involved. The more experienced the person the better the understanding and the information management. Further, although different persons may approach the same problem differently a solution is very often based on a combination of different strategies. This paper has a focus on two strategies: First, a very common way of managing complex information for domain experts, or humans in general, is to reduce the complexity of the information by allowing a certain degree of uncertainty without loosing the actual content of the original information. In a very natural, but also radical way, complexity reduction occurs when humans summarise information onto vague linguistic expressions. For example, a clinician may say to a person: “Your blood pressure is ok , your heart rate is just fine , and your cholesterol values are normal”. Note that despite the availability of precise values for blood pressure, heart rate and cholesterol the clinician uses the vague linguistic terms ok, just fine and normal to describe the person’s state of health. These terms however are expressive and satisfactory for further decision-making (Ross TJ 1995). Fuzzy logic is a technique that, in many situations, may provide a solution for the modelling of such situations (Zadeh 1996). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.