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Fuzzified Clustering and Point Set Continuous Approximation in Prognosticating Gastric Cancer Surgery
Blekinge Institute of Technology, School of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0002-9920-7946
Blekinge Institute of Technology, School of Engineering, Department of Mathematics and Natural Sciences.
2014 (English)Conference paper, Published paper (Refereed) Published
Abstract [en]

We discuss two computational techniques in the current paper. In the first part, we aim at employing FCM (fuzzy c-means) clustering to compute membership degrees of two clusters providing decisions to perform surgery or not for a testing set of 25 gastric cancer patients. The second part handles mathematical modelling of a common function approximating the information obtained from the c-means procedure. After constructing the equation of the function, we can make the decision about the surgery in the form of the surgery degree for an arbitrary gastric cancer patient. A centre, dealing with mathematical techniques concerning surgery prognoses, can quickly decide about surgery for the patient who lives in a remote place. A transmission of information among the centre and some hospitals, interested in adopting the centre services, can facilitate surgery decision-making. This trial can be treated as a contribution in the telemedicine domain.

Place, publisher, year, edition, pages
Barcelona, Spain: IARIA , 2014.
Keyword [en]
c-means clustering, surgery degrees, clinical characteristic value, weights of importance, truncated π-functions.
National Category
Mathematics Medical and Health Sciences
Identifiers
URN: urn:nbn:se:bth-6705Local ID: oai:bth.se:forskinfo38A08D8B5CAF3A84C1257CC2004717B2ISBN: 978-1-61208-327-8 (print)OAI: oai:DiVA.org:bth-6705DiVA: diva2:834237
Conference
eTELEMED 2014 - The Sixth International Conference on eHealth, Telemedicine, and Social Medicine
Available from: 2014-04-23 Created: 2014-04-22 Last updated: 2016-09-20Bibliographically approved

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Rakus-Andersson, ElisabethZettervall, Hang
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