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Selected Algorithms of Computational Intelligence in Gastric Cancer Decision Making
Blekinge Institute of Technology, School of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0002-9920-7946
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2012 (English)In: New Advances in the Basic and Clinical Gastroenterology / [ed] Brzozowski, Thomas, Rijeka, Croatia: InTech , 2012, 529-546 p.Chapter in book (Refereed)
Abstract [en]

Due to the latest research the subject of Computational Intelligence has been divided into five main regions, namely, neural networks, evolutionary algorithms, swarm intelligence, immunological systems and fuzzy systems. Our attention has been attracted by the possibilities of medical applications provided by immunological computation algorithms. Immunological computation systems are based on immune reactions of the living organisms in order to defend the bodies from pathological substances. Especially, the mechanisms of the T-cell reactions to detect strangers have been converted into artificial numerical algorithms. Immunological systems have been developed in scientific books and reports appearing during the two last decades. The basic negative selection algorithm NS was invented by Stefanie Forrest to give rise to some technical applications. We can note such applications of NS as computer virus detection, reduction of noise effect, communication of autonomous agents or identification of time varying systems. Even a trial of connection between a computer and biological systems has been proved by means of immunological computation. Hybrids made between different fields can provide researchers with richer results; therefore associations between immunological systems and neural networks have been developed as well. In the current chapter we propose another hybrid between the NS algorithm and chosen solutions coming from fuzzy systems. This hybrid constitutes the own model of adapting the NS algorithm to the operation decisions “operate” contra “do not operate” in gastric cancer surgery. The choice between two possibilities to treat patients is identified with the partition of a decision region in self and non-self, which is similar to the action of the NS algorithm. The partition is accomplished on the basis of patient data strings/vectors that contain codes of states concerning some essential biological markers. To be able to identify the strings that characterize the “operate” decision we add the own method of computing the patients’ characteristics as real values. The evaluation of the patients’ characteristics is supported by inserting importance weights assigned to powerful biological indices taking place in the operation decision process. To compute the weights of importance the Saaty algorithm is adopted.

Place, publisher, year, edition, pages
Rijeka, Croatia: InTech , 2012. 529-546 p.
Keyword [en]
immunolgical systems, negative selection algorithm, fuzzy systems, operation decision making
National Category
Mathematics Computer Science Medical and Health Sciences
URN: urn:nbn:se:bth-7301DOI: ID: 978-953-51-0521-3OAI: diva2:834900

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Available from: 2012-09-18 Created: 2012-04-18 Last updated: 2016-09-20Bibliographically approved

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Rakus-Andersson, Elisabeth
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