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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">moitvivt</journal-id>
      <journal-title-group>
        <journal-title xml:lang="ru">Моделирование, оптимизация и информационные технологии</journal-title>
        <trans-title-group xml:lang="en">
          <trans-title>Modeling, Optimization and Information Technology</trans-title>
        </trans-title-group>
      </journal-title-group>
      <issn pub-type="epub">2310-6018</issn>
      <publisher>
        <publisher-name>Издательство</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.26102/2310-6018/2020.30.3.025</article-id>
      <article-id pub-id-type="custom" custom-type="elpub">806</article-id>
      <title-group>
        <article-title xml:lang="ru">Сравнение эффективности различных методов отбора признаков для решения задачи бинарной классификации предсказания наступления беременности при проведении экстракорпорального оплодотворения</article-title>
        <trans-title-group xml:lang="en">
          <trans-title>Comparison of the efficiency of different selecting features methods for solving the binary classification problem of predicting in vitro fertilization pregnancy</trans-title>
        </trans-title-group>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes">
          <contrib-id contrib-id-type="orcid">0000-0002-4318-5223</contrib-id>
          <name-alternatives>
            <name name-style="eastern" xml:lang="ru">
              <surname>Синотова</surname>
              <given-names>Светлана Леонидовна</given-names>
            </name>
            <name name-style="western" xml:lang="en">
              <surname>Sinotova</surname>
              <given-names>Svetlana L.</given-names>
            </name>
          </name-alternatives>
          <email>sveta.volkova92@mail.ru</email>
          <xref ref-type="aff">aff-1</xref>
        </contrib>
        <contrib contrib-type="author" corresp="yes">
          <contrib-id contrib-id-type="orcid">0000-0002-2084-3916</contrib-id>
          <name-alternatives>
            <name name-style="eastern" xml:lang="ru">
              <surname>Лимановская</surname>
              <given-names>Оксана Викторовна</given-names>
            </name>
            <name name-style="western" xml:lang="en">
              <surname>Limanovskaya</surname>
              <given-names>Oksana V.</given-names>
            </name>
          </name-alternatives>
          <email>o.v.limanovskaia@urfu.ru</email>
          <xref ref-type="aff">aff-2</xref>
        </contrib>
        <contrib contrib-type="author" corresp="yes">
          <contrib-id contrib-id-type="orcid">0000-0002-3119-478X</contrib-id>
          <name-alternatives>
            <name name-style="eastern" xml:lang="ru">
              <surname>Плаксина</surname>
              <given-names>Анна Николаевна</given-names>
            </name>
            <name name-style="western" xml:lang="en">
              <surname>Plaksina</surname>
              <given-names>Anna N.</given-names>
            </name>
          </name-alternatives>
          <email>burberry20@yandex.ru</email>
          <xref ref-type="aff">aff-3</xref>
        </contrib>
        <contrib contrib-type="author" corresp="yes">
          <contrib-id contrib-id-type="orcid">0000-0003-1127-2792</contrib-id>
          <name-alternatives>
            <name name-style="eastern" xml:lang="ru">
              <surname>Макутина</surname>
              <given-names>Валерия Андреевна</given-names>
            </name>
            <name name-style="western" xml:lang="en">
              <surname>Makutina</surname>
              <given-names>Valerija A.</given-names>
            </name>
          </name-alternatives>
          <email>makutina_v@rambler.ru</email>
          <xref ref-type="aff">aff-4</xref>
        </contrib>
      </contrib-group>
      <aff-alternatives id="aff-1">
        <aff xml:lang="ru">Институт фундаментального образования ФГАОУ ВО «УрФУ имени первого Президента России Б.Н. Ельцина»</aff>
        <aff xml:lang="en">Institute Of Fundamental Education, Fsaei He «Urfu Named After The First President Of Russia B.N.Yeltsin»</aff>
      </aff-alternatives>
      <aff-alternatives id="aff-2">
        <aff xml:lang="ru">Институт фундаментального образования ФГАОУ ВО «УрФУ имени первого Президента России Б.Н. Ельцина»</aff>
        <aff xml:lang="en">Institute Of Fundamental Education, Fsaei He «Urfu Named After The First President Of Russia B.N.Yeltsin»</aff>
      </aff-alternatives>
      <aff-alternatives id="aff-3">
        <aff xml:lang="ru">ФГБОУ ВО «Уральский государственный медицинский университет Минздрава РФ»</aff>
        <aff xml:lang="en">FSBEI HE «USMU of the Ministry of Health of the Russian Federation»</aff>
      </aff-alternatives>
      <aff-alternatives id="aff-4">
        <aff xml:lang="ru">АО «Центр семейной медицины»</aff>
        <aff xml:lang="en">The Family Medicine Centre</aff>
      </aff-alternatives>
      <pub-date pub-type="epub">
        <day>01</day>
        <month>01</month>
        <year>2026</year>
      </pub-date>
      <volume>1</volume>
      <issue>1</issue>
      <elocation-id>10.26102/2310-6018/2020.30.3.025</elocation-id>
      <permissions>
        <copyright-statement>Copyright © Авторы, 2026</copyright-statement>
        <copyright-year>2026</copyright-year>
        <license license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/">
          <license-p>This work is licensed under a Creative Commons Attribution 4.0 International License</license-p>
        </license>
      </permissions>
      <self-uri xlink:href="https://moitvivt.ru/ru/journal/article?id=806"/>
      <abstract xml:lang="ru">
        <p>Определение круга факторов, влияющих на объект исследования, является важнейшей&#13;
задачей медицинских исследований. Ее решение осложняется большим числом разнообразных&#13;
данных, включающих в себя обширную анамнестическую информацию и данные клинических&#13;
исследований часто сочетающимся с ограниченным количеством наблюдаемых пациентов.&#13;
Данная работа посвящена сравнению результатов, полученных различными методами отбора&#13;
признаков для поиска набора предикторов, на основе которого создана модель с лучшим&#13;
качеством прогноза, для решения задачи бинарной классификации предсказания наступления&#13;
беременности при проведении экстракорпорального оплодотворения (ЭКО). В качестве&#13;
признаков использовались данные анамнеза женщин, представленные в бинарном виде. Выборка&#13;
состояла из 68 признаков и 689 объектов. Признаки были исследованы на наличие взаимной&#13;
корреляции, после чего применены методы и алгоритмы для поиска отбора значимых факторов:&#13;
непараметрические критерии, интервальная оценка долей, Z-критерий для разности двух долей,&#13;
взаимная информация, алгоритмы RFECV, ADD-DELL, Relief, алгоритмы, основанные на&#13;
важности перестановок (Boruta, Permutation Importance, PIMP), алгоритмы отбора признаков при&#13;
помощи модели (lasso, random forest). Для сравнения качества отобранных наборов признаков&#13;
построены различные классификаторы, посчитана их метрика AUC и сложность модели. Все&#13;
модели имеют высокое качество предсказания (AUC выше 95%). Лучшие из них построены на&#13;
признаках, отобранных с помощью непараметрических критериев, отбора при помощи модели&#13;
(lasso-регрессия), алгоритмов Boruta, Permutation Importance, RFECV, ReliefF. Оптимальным&#13;
набором предикторов был выбран набор, состоящий из 30 бинарных признаков, полученный&#13;
алгоритмом Boruta, из-за меньшей сложности модели при сравнительно высоком качестве (AUC&#13;
модели 0,983). К значимым признакам отнесены: данные о наличии беременностей в анамнезе в&#13;
целом, о внематочных и замерших беременностях, самостоятельных и срочных родах, абортах&#13;
на ранних сроках в частности; гипертония, ишемия, инсульт, тромбозы, язвы, ожирение,&#13;
сахарный диабет у ближайших родственников; проведение гормонального лечения в настоящее&#13;
время, не связанного с процедурой ЭКО; аллергия; вредные профессиональные факторы;&#13;
наличие нормальной продолжительности и стабильности менструального цикла без приема&#13;
медицинских препаратов; гистероскопия, лапароскопия и лапаротомия в анамнезе; проведение&#13;
резекций любого органа в мочеполовой системе; первая ли попытка ЭКО, наличие любых&#13;
хирургических вмешательств, заболеваний мочеполовой системы; возраст и ИМТ пациентки;&#13;
отсутствие хронических заболеваний; наличие диффузной фиброзно-кистозной мастопатии,&#13;
гипотиреоза.</p>
      </abstract>
      <trans-abstract xml:lang="en">
        <p>Determination of the range of factors affecting the object of research is the most important&#13;
task of medical research. Its solution is complicated by a large amount of diverse data, including&#13;
extensive anamnestic information and data from clinical studies, often combined with a limited number&#13;
of observed patients. This work is devoted to the comparison of the results obtained by various feature&#13;
selection methods for the search for a set of predictors, on the basis of which a model with the best&#13;
forecast quality was created, for solving the problem of binary classification of predicting the onset of&#13;
pregnancy during in vitro fertilization (IVF). The data from the anamnesis of women, presented in binary&#13;
form, were used as features. The sample consisted of 68 features and 689 objects. The signs were&#13;
examined for the presence of cross-correlation, after which methods and algorithms were applied to&#13;
search for a selection of significant factors: nonparametric criteria, interval estimate of the shares, Zcriterion for the difference of two shares, mutual information, RFECV, ADD-DELL, Relief algorithms,&#13;
algorithms based on the permutation importance (Boruta, Permutation Importance, PIMP), feature&#13;
selection algorithms using model feature importance (lasso, random forest). To compare the quality of&#13;
the selected sets of features, various classifiers were built, their metric AUC and the complexity of the&#13;
model were calculated. All models have high prediction quality (AUC above 95%). The best of them&#13;
are based on features selected using nonparametric criteria, model selection (lasso regression), Boruta,&#13;
Permutation Importance, RFECV and ReliefF algorithms. The optimal set of predictors is a set of 30&#13;
binary features obtained by the Boruta algorithm, due to the lower complexity of the model with a&#13;
relatively high quality (AUC of the model 0.983). Significant signs includes: data about pregnancies in&#13;
the anamnesis in general, ectopic and regressive pregnancies, independent and term childbirth, abortions&#13;
up to 12 weeks; hypertension, ischemia, stroke, thrombosis, ulcers, obesity, diabetes mellitus in the&#13;
immediate family; currently undergoing hormonal treatment not associated with the IVF procedure;&#13;
allergies; harmful professional factors; normal duration and stability of the menstrual cycle without&#13;
taking medication; hysteroscopy, laparoscopy and laparotomy; resection of any organ in the&#13;
genitourinary system; is it the first IVF, the presence of any surgical interventions, diseases of the&#13;
genitourinary system; the age and BMI of the patient; absence of chronic diseases; the presence of&#13;
diffuse fibrocystic mastopathy, hypothyroidism.</p>
      </trans-abstract>
      <kwd-group xml:lang="ru">
        <kwd>отбор признаков</kwd>
        <kwd>задача бинарной классификации</kwd>
        <kwd>анализ малых данных</kwd>
        <kwd>машинное обучение</kwd>
        <kwd>вспомогательные репродуктивные технологии</kwd>
      </kwd-group>
      <kwd-group xml:lang="en">
        <kwd>feature selection</kwd>
        <kwd>binary classification problem</kwd>
        <kwd>small data analysis</kwd>
        <kwd>machine learning</kwd>
        <kwd>assisted reproductive technologies</kwd>
      </kwd-group>
      <funding-group>
        <funding-statement xml:lang="ru">Исследование выполнено без спонсорской поддержки.</funding-statement>
        <funding-statement xml:lang="en">The study was performed without external funding.</funding-statement>
      </funding-group>
    </article-meta>
  </front>
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    <fn-group>
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</article>