Fuzzy Controllers in Evaluation of Survival Length in Cancer Patients

Document type: Bookchapters
Peer reviewed: Yes
Author(s): Elisabeth Rakus-Andersson, Hang Zettervall, Henrik Forssell
Title: Fuzzy Controllers in Evaluation of Survival Length in Cancer Patients
Book: Recent Advances in Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets and Related Topics. Volume II: Applications
Year: 2011
Volume: II
Pagination: 203-222
Editor: K. Atanassov, W. Homenda, O. Hryniewicz, J. Kacprzyk, M. Krawczak, Z. Nahorski, E. Szmidt, S. Zadrozny
ISBN: 13 9788389475367
Publisher: System Research Institute, Polish Academy of Sciences
City: Warsaw, Poland
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
Authors e-mail: Elisabeth.Andersson@bth.se, Hang.Zettervall@bth.se, Henrik.Forssell@ltblekinge.se
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 control actions. Apart from crisp versions of control we often adopt their fuzzy variants developed by Mamdani and Assilian or Sugeno. Fuzzy control algorithms are furnished with softer mechanisms, when comparing them to classical control.
The algorithms are particularly adaptable to support medical systems, often handling 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 paper as the utilization of fuzzy control actions for the purpose of making the survival prognoses.
Subject: Mathematics\General
Medical Sciences
Keywords: Mamdani controller, Sugeno controller, Control estimation of survival length