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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">cyberspace</journal-id><journal-title-group><journal-title xml:lang="ru">Философские проблемы информационных технологий и киберпространства</journal-title><trans-title-group xml:lang="en"><trans-title>Philosophical Problems of IT &amp; Cyberspace (PhilIT&amp;C)</trans-title></trans-title-group></journal-title-group><issn pub-type="epub">2305-3763</issn><publisher><publisher-name>Пятигорский государственный университет</publisher-name></publisher></journal-meta><article-meta><article-id custom-type="elpub" pub-id-type="custom">cyberspace-136</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ПРИКЛАДНЫЕ СФЕРЫ ИНФОРМАЦИОННЫХ ТЕХНОЛОГИЙ</subject></subj-group></article-categories><title-group><article-title>Нейроэкспертные системы в построении информационной модели степени изменения сосудистой стенки у больных с каротидным атеросклерозом</article-title><trans-title-group xml:lang="en"><trans-title>NEURAL NETWORK SYSTEM IN THE BUILDING INFORMATIONMODELS OF DEGREE OF CHANGES OF VASCULAR WALLIN PATIENTS WITH CAROTID ATHEROSCLEROSIS</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Розыходжаева</surname><given-names>Г. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Rozikhodjaeva</surname><given-names>G. A.</given-names></name></name-alternatives><email xlink:type="simple">gulnoradm(d)jnbox.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Икрамова</surname><given-names>З. Т.</given-names></name><name name-style="western" xml:lang="en"><surname>Ikramova</surname><given-names>Z. T.</given-names></name></name-alternatives><email xlink:type="simple">gulnoradm(d)jnbox.ru</email><xref ref-type="aff" rid="aff-2"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Розыходжаева</surname><given-names>Д. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Rozikhodzjaeva</surname><given-names>D. A.</given-names></name></name-alternatives><email xlink:type="simple">gulnoradm(d)jnbox.ru</email><xref ref-type="aff" rid="aff-3"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Центральная клиническая больница № 1 Медико-санитарного объединения</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Central Clinical Hospital № 1 of Medico-Sanitary Association</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>Ташкентский институт усовершенствования врачей</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Institute of doctor's improvement</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-3"><aff xml:lang="ru"><institution>Ташкентский Университет информационных технологий</institution><country>Россия</country></aff><aff xml:lang="en"><institution>University of Information technologies</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2012</year></pub-date><pub-date pub-type="epub"><day>08</day><month>07</month><year>2021</year></pub-date><volume>0</volume><issue>2</issue><fpage>73</fpage><lpage>80</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Розыходжаева Г.А., Икрамова З.Т., Розыходжаева Д.А., 2021</copyright-statement><copyright-year>2021</copyright-year><copyright-holder xml:lang="ru">Розыходжаева Г.А., Икрамова З.Т., Розыходжаева Д.А.</copyright-holder><copyright-holder xml:lang="en">Rozikhodjaeva G.A., Ikramova Z.T., Rozikhodzjaeva D.A.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://cyberspace.pgu.ru/jour/article/view/136">https://cyberspace.pgu.ru/jour/article/view/136</self-uri><abstract><p>В статье приведен вариант классификации степени утолщения комплекса «интима-медиа» общей сонной артерии (ТКИМ ОСА). При этом основным инструментом для построения информационной модели служили алгоритмы и методы синтеза искусственных нейронных сетей (ИНС). Анализ основан на результатах обследования 242 больных в возрасте 40-90 лет. Математическая классификация использовалась для оптимизации оценки каротидного атеросклероза. ИНС состояла из нейронов входного, скрытого и выходного слоев. Вначале специалистами ультразвуковой диагностики с помощью метода дуплексного ангиосканирования достигнута правильная классификация объективных данных. Использованная модель ИНС успешно классифицировала в 84,5% случаев и была особенно эффективной при разбивке на 4 класса. Система классификации служит для быстрого установления медицинского диагноза</p></abstract><trans-abstract xml:lang="en"><p>In this article include the system of classification of degree of thickening complex "intima-media" of the common carotid artery (CCA IMT). The main tool for building information models were algorithms and methods for synthesis of artificial neural networks (ANN). The analysis is based on the results of the survey 242 subjaects aged 40-90 years. The mathematical classification for optimizing assessment of carotid atherosclerosis was used. ANN was made up of neurons in the input, hidden and output layers. First, the correct classification of these data was obtained by ultrasound specialists with duplex scanning. ANN model does successfully classified in 84.5%. We found that ANN is effective, when designated four classes. The result of this classification system is rapid establishment of medical diagnosis</p></trans-abstract><kwd-group xml:lang="ru"><kwd>искусственные нейронные сети (ИНС)</kwd><kwd>атеросклероз</kwd><kwd>толщина комплекса «интима-медиа» общей сонной артерии</kwd></kwd-group><kwd-group xml:lang="en"><kwd>artificial neural networks (ANN)</kwd><kwd>atherosclerosis</kwd><kwd>the thickness of the complex "intima-media" of the common carotid artery</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Игнатьев Н.А. Извлечение явных знаний из разнотипных данных с помощью нейронных сетей // Вычислительные технологии. — Новосибирск, 2003.- Т.8, №2.- С.69-73.</mixed-citation><mixed-citation xml:lang="en">Игнатьев Н.А. 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