Towards application of FML in suspicion of non-common diseases

G. Acampora, T. Kiseliova, K. Pagava, A. Vitiello

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)
1 Downloads (Pure)


In this paper we present the preliminary results of application of Fuzzy Markup Language (FML) to suspect a non-common disease. Under non-common diseases we understand rare diseases. From the broad point of view this problem belongs to the computer-assisted decision support in medical diagnostics and can be supported by fuzzy logic controllers. We can use conventional methods to diagnose a rare disease if it can be exhibited by outstanding symptoms. For example, there are several search machines and data banks that allow to find a rare disease clearly exhibited by a patient's symptoms/signs. But it is very difficult to diagnose a rare disease if it masks as a common disease. Diagnostic of rare diseases is connected with lack, uncertainty and imprecision of knowledge, medical mistake and even medical failure. Additionally, very often a common disease is also established with some degree of belief, thus, the expressions such as "it is possible that a patient has a particular disease" rather often present in the daily medical practice. It is clear that if we would know the common diseases, then deviations from them can be considered as a sign of non-common diseases. In this paper we investigate such deviations with the help of FML. We show how FML mechanism can be adjusted to suspect a rare disease, and discuss the appropriateness of the available operators.
Original languageEnglish
Title of host publicationProceedings of the 2011 IEEE International Conference on Fuzzy Systems (FUZZ), 27-30 June 2011, Taipee, Taiwan
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Print)978-1-4244-7316-8
Publication statusPublished - 2011


Dive into the research topics of 'Towards application of FML in suspicion of non-common diseases'. Together they form a unique fingerprint.

Cite this