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A Boolean consistent fuzzy inference system for diagnosing diseases and its application for determining peritonitis likelihood. (English) Zbl 1343.92219

Summary: Fuzzy inference systems (FIS) enable automated assessment and reasoning in a logically consistent manner akin to the way in which humans reason. However, since no conventional fuzzy set theory is in the Boolean frame, it is proposed that Boolean consistent fuzzy logic should be used in the evaluation of rules. The main distinction of this approach is that it requires the execution of a set of structural transformations before the actual values can be introduced, which can, in certain cases, lead to different results. While a Boolean consistent FIS could be used for establishing the diagnostic criteria for any given disease, in this paper it is applied for determining the likelihood of peritonitis, as the leading complication of peritoneal dialysis (PD). Given that patients could be located far away from healthcare institutions (as peritoneal dialysis is a form of home dialysis) the proposed Boolean consistent FIS would enable patients to easily estimate the likelihood of them having peritonitis (where a high likelihood would suggest that prompt treatment is indicated), when medical experts are not close at hand.

MSC:

92C50 Medical applications (general)
90C70 Fuzzy and other nonstochastic uncertainty mathematical programming
68T37 Reasoning under uncertainty in the context of artificial intelligence
90C09 Boolean programming
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