The Mamdani Expert-system with Parametric Families of Fuzzy Constraints in Evaluation of Cancer Patient Survival Length

Document type: Bookchapters
Peer reviewed: Yes
Author(s): Elisabeth Rakus-Andersson
Title: The Mamdani Expert-system with Parametric Families of Fuzzy Constraints in Evaluation of Cancer Patient Survival Length
Book: Emerging Paradigms in Machine Learning
Year: 2013
Volume: 13
Pagination: 359-378
Editor: Ramanna S., Jain L.C, Howlett R. J.
ISBN: 978-3-642-28698-8
Publisher: Springer
City: Berlin Heidelberg New York
URI/DOI: 10.1007/978-3-642-28699-5
Organization: Blekinge Institute of Technology
Department: School of Engineering - Dept. of Mathematics & Natural Sciences (Sektionen för ingenjörsvetenskap - Avd.för matematik och naturvetenskap)
School of Engineering S-371 79 Karlskrona
+46 455 38 50 00
http://www.bth.se/ing/
Language: English
Abstract: Strict analytic formulas are the tools usually derived for determining the formal relationships between a sample of independent variables and a variable which they affect. If we cannot formalize the function tying the independent and dependent variables then we will utilize some expert-system control actions. We often adopt their fuzzy variants developed by Mamdani, Sugeno and Takagi. Fuzzy expert-system algorithms are furnished with softer mechanisms, when comparing them to crisp versions. An efficient action of these softer mechanisms depends on the proper fuzzification of variables. At the stage of fuzzifying the variable levels we will prove some parametric expressions, which rearrange one function to several forms needed by the expert-system algorithm. The general parametric equation of membership functions allows creating arbitrary lists without any intuitive assumptions.
The fuzzy expert-system algorithms are particularly adaptable to support medical tasks to solve. These tasks often cope with uncertain premises and conclusions. From the medical point of view it would be desirable to prognosticate the survival length for patients suffering from gastric cancer. We thus formulate the objective of the current chapter as the utilization of the Mamdani fuzzy control actions as a methodology adapted for the purpose of making the survival prognoses.
Subject: Computer Science\Artificial Intelligence
Mathematics\General
Medical Sciences
Keywords: Parametric s-functions, Mamdani expert-system, Estimation of survival length
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