Soft computing in medicine

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Yardimci20091029,
  author =       "Ahmet Yardimci",
  title =        "Soft computing in medicine",
  journal =      "Applied Soft Computing",
  volume =       "9",
  number =       "3",
  pages =        "1029--1043",
  year =         "2009",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2009.02.003",
  URL =          "http://www.sciencedirect.com/science/article/B6W86-4VRP213-2/2/141a98fa1c9600c49c0883f71eb8a711",
  keywords =     "genetic algorithms, genetic programming, Soft
                 computing, Fuzzy-neural systems, Medicine",
  abstract =     "Soft computing (SC) is not a new term; we have gotten
                 used to reading and hearing about it daily. Nowadays,
                 the term is used often in computer science and
                 information technology. It is possible to define SC in
                 different ways. Nonetheless, SC is a consortium of
                 methodologies which works synergistically and provides,
                 in one form or another, flexible information processing
                 capability for handling real life ambiguous situations.
                 Its aim is to exploit the tolerance for imprecision,
                 uncertainty, approximate reasoning and partial truth in
                 order to achieve tractability, robustness and low-cost
                 solutions. SC includes fuzzy logic (FL), neural
                 networks (NNs), and genetic algorithm (GA)
                 methodologies. SC combines these methodologies as FL
                 and NN (FL-NN), NN and GA (NN-GA) and FL and GA
                 (FL-GA). Recent years have witnessed the phenomenal
                 growth of bio-informatics and medical informatics by
                 using computational techniques for interpretation and
                 analysis of biological and medical data. Among the
                 large number of computational techniques used, SC,
                 which incorporates neural networks, evolutionary
                 computation, and fuzzy systems, provides unmatched
                 utility because of its demonstrated strength in
                 handling imprecise information and providing novel
                 solutions to hard problems. The aim of this paper is to
                 introduce briefly the various SC methodologies and to
                 present various applications in medicine between the
                 years 2000 and 2008. The scope is to demonstrate the
                 possibilities of applying SC to medicine-related
                 problems. The recent published knowledge about use of
                 SC in medicine is researched in MEDLINE. This study
                 detects which methodology or methodologies of SC are
                 used frequently together to solve the special problems
                 of medicine. According to MEDLINE database searches,
                 the rates of preference of SC methodologies in medicine
                 were found as 68percent of FL-NN, 27percent of NN-GA
                 and 5percent of FL-GA. So far, FL-NN methodology was
                 significantly used in medicine. The rates of using
                 FL-NN in clinical science, diagnostic science and basic
                 science were found as percent83, percent71 and
                 percent48, respectively. On the other hand NN-GA and
                 FL-GA methodologies were mostly preferred by basic
                 science of medicine.

                 Another message emerging from this survey is that the
                 number of papers which used NN-GA methodology has
                 continuously risen until today. Also search results put
                 the case clearly that FL-GA methodology has not applied
                 well enough to medicine yet. Undeniable interest in
                 studying SC methodologies in genetics, physiology,
                 radiology, cardiology, and neurology disciplines proves
                 that studying SC is very fruitful in these disciplines
                 and it is expected that future researches in medicine
                 will use SC more than it is used today to solve more
                 complex problems.",
  notes =        "Only brief mention of GP",
}

Genetic Programming entries for Ahmet Yardimci

Citations