<?xml version="1.0" encoding="utf-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "https://jats.nlm.nih.gov/publishing/1.3/JATS-journalpublishing1-3.dtd">
<article article-type="research-article" dtd-version="1.3" xml:lang="en">
  <front xmlns:xlink="http://www.w3.org/1999/xlink">
    <journal-meta>
      <journal-title-group>
        <journal-title>Computing, Telecommunication and Control</journal-title>
        <trans-title-group xml:lang="ru">
          <trans-title>Информатика, телекоммуникации и управление</trans-title>
        </trans-title-group>
      </journal-title-group>
      <issn pub-type="epub">2687-0517</issn>
    </journal-meta>
    <article-meta xmlns:xlink="http://www.w3.org/1999/xlink">
      <article-id pub-id-type="publisher-id">4</article-id>
      <article-id pub-id-type="doi">10.18721/JCSTCS.19304</article-id>
      <title-group>
        <article-title>Hybrid intelligent system for stratifying the risk of coronary heart disease</article-title>
        <trans-title-group xml:lang="ru">
          <trans-title>Гибридная интеллектуальная система стратификации риска ишемической болезни сердца</trans-title>
        </trans-title-group>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0001-8553-640X</contrib-id>
          <name>
            <surname>Kudayeva</surname>
            <given-names>Fatimat</given-names>
          </name>
          <email>kfatimat@yandex.ru</email>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0009-4666-3356</contrib-id>
          <name>
            <surname>Bitsuev</surname>
            <given-names>Timur</given-names>
          </name>
          <email>timur_kbsu@mail.ru</email>
        </contrib>
      </contrib-group>
      <pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-09-30">
        <day>30</day>
        <month>09</month>
        <year>2026</year>
      </pub-date>
      <volume>19</volume>
      <issue>3</issue>
      <fpage>46</fpage>
      <lpage>56</lpage>
      <self-uri xmlns:xlink="http://www.w3.org/1999/xlink" content-type="pdf" xlink:href="https://infocom.spbstu.ru/userfiles/files/articles/2026/3/46-56.pdf"/>
      <abstract xml:lang="en">
        <p>The article proposes a hybrid intelligent system for stratifying the risk of coronary heart disease (CHD). It includes ensemble forecasting, natural language processing, and explainable AI technologies. It is demonstrated that the integration of structured clinical features and unstructured text data improves the accuracy of CHD risk assessment compared to the traditional SCORE2 scales. On test datasets, high values of AUC-ROC and F1-score were achieved, while the interpretability of the predictions is ensured by constructing waterfall plots that reflect the contribution of key risk factors. The developed software package will be useful in cardiology for supporting clinical decision-making.</p>
      </abstract>
      <kwd-group xml:lang="en">
        <kwd>hybrid intelligent system</kwd>
        <kwd>ensemble machine learning</kwd>
        <kwd>cardiovascular outcome predic-tion</kwd>
        <kwd>natural language processing</kwd>
        <kwd>artificial intelligence</kwd>
        <kwd>SHAP analysis</kwd>
        <kwd>clinical decision support</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <back>
    <ref-list>
      <title>References</title>
      <ref id="ref1">
        <mixed-citation publication-type="journal">Zhang P.-I., Hsu C.-C., Kao Y. et al. Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 2020, Vol. 28, Article no. 93. DOI: 10.1186/s13049-020-00786-x</mixed-citation>
      </ref>
      <ref id="ref2">
        <mixed-citation publication-type="journal">Olsson de Capretz P., Björkelund A., Björk J., Ohlsson M., Mokhtari A., Nyström A., Ekelund U. Machine learning for early prediction of acute myocardial infarction or death in acute chest pain patients using electrocardiogram and blood tests at presentation. BMC Medical Informatics and Decision Making, 2023, Vol. 23, Article no. 25. DOI: 10.1186/s12911-023-02119-1</mixed-citation>
      </ref>
      <ref id="ref3">
        <mixed-citation publication-type="journal">Liu N., Koh Z.X., Goh J., Lin Z., Haaland B., Ting B.P., Ong M.E.H. Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection. BMC Medical Informatics and Decision Making, 2014, Vol. 14, Article no. 75. DOI: 10.1186/1472-6947-14-75</mixed-citation>
      </ref>
      <ref id="ref4">
        <mixed-citation publication-type="journal">Tolpygina S.N., Martsevich S.Yu. Study of a trend in the frequency of using main drug classes indicated for the treatment of patients with chronic coronary heart disease in 2004 to 2014: data from the CHD prog-nosis registry. The Clinician, 2016, Vol. 10, No. 1, Pp. 29–35. DOI: 10.17650/1818-8338-2016-10-1-29-35</mixed-citation>
      </ref>
      <ref id="ref5">
        <mixed-citation publication-type="journal">Tolpygina S.N., Martsevich S.Yu. Cardiovascular risk stratification in stable coronary artery disease based on prognostic scores and models. Cardiovascular Therapy and Prevention, 2020, Vol. 19, No. 3, Pp. 240–247. DOI: 10.15829/1728-8800-2020-2528</mixed-citation>
      </ref>
      <ref id="ref6">
        <mixed-citation publication-type="journal">Tolpygina S.N., Martsevich S.Yu., Deev A.D. Enhanced risk stratification scale for predicting death and non-fatal cardiovascular events in patients of the PROGNOZ IBS registry. Cardiovascular Therapy and Prevention, 2021, Vol. 20, No. 7, Pp. 143–152. DOI: 10.15829/1728-8800-2021-3060</mixed-citation>
      </ref>
      <ref id="ref7">
        <mixed-citation publication-type="journal">Tolpygina S.N., Martsevich S.Yu., Gofman E.A., Deev A.D. Prognostic value of intstrumental diagnostics in chronic coronary heart disease. Data from “PROGNOS CHD”. Cardiovascular Therapy and Preven-tion, 2014, Vol. 13, No. 4, Pp. 29–35. DOI: 10.15829/1728-8800-2014-4-29-35</mixed-citation>
      </ref>
      <ref id="ref8">
        <mixed-citation publication-type="journal">Golukhova E.Z., Keren M.A., Zavalikhina T.V., Bulaeva N.I., Akatov D.S., Sigaev I.Yu., Yakhyaeva K.B., Kolesnikov D.A. Potential of machine learning methods in operational risk stratification in patients with coronary artery disease scheduled for coronary bypass surgery. Russian Journal of Cardiology, 2023, ol. 28, No. 2, Article no. 5211. DOI: 10.15829/1560-4071-2023-5211</mixed-citation>
      </ref>
      <ref id="ref9">
        <mixed-citation publication-type="journal">Motwani M., Dey D., Berman D.S., et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. European Heart Journal, 2017, Vol. 38, No. 7, Pp. 500–507. DOI: 10.1093/eurheartj/ehw188</mixed-citation>
      </ref>
      <ref id="ref10">
        <mixed-citation publication-type="journal">Kakadiaris I.A., Vrigkas M., Yen A.A., Kuznetsova T., Budoff M., Naghavi M. Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA. Journal of the American Heart Association, 2018, Vol. 7, No. 22, Article no. e009476. DOI: 10.1161/JAHA.118.009476</mixed-citation>
      </ref>
      <ref id="ref11">
        <mixed-citation publication-type="journal">Commandeur F., Slomka P.J., Goeller M., et al. Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study. Cardiovascular Research, 2020, Vol. 116, No. 14, P. 2216–2225. DOI: 10.1093/cvr/cvz321</mixed-citation>
      </ref>
      <ref id="ref12">
        <mixed-citation publication-type="journal">Piepoli M.F., Hoes A.W., Agewall S., et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts). Developed with the special contribution of the European Association for Cardiovascular Prevention &amp; Rehabilitation (EACPR). European Heart Journal, 2016, Vol. 37, No. 29, P. 2315–2381. DOI: 10.1093/eurheartj/ehw106</mixed-citation>
      </ref>
      <ref id="ref13">
        <mixed-citation publication-type="journal">Conroy R.M., Pyörälä K., Fitzgerald A.P., et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. European Heart Journal, 2003, Vol. 24, No. 11, P. 987–1003. DOI: 10.1016/s0195-668x(03)00114-3</mixed-citation>
      </ref>
      <ref id="ref14">
        <mixed-citation publication-type="journal">SCORE2 working group and ESC Cardiovascular risk collaboration. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. European Heart Journal, 2021, Vol. 42, No. 25, Pp. 2439–2454. DOI: 10.1093/eurheartj/ehab309</mixed-citation>
      </ref>
      <ref id="ref15">
        <mixed-citation publication-type="journal">Visseren F.L.J., Mach F., Smulders Y.M., et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice. European Heart Journal, 2021, Vol. 42, No. 34, Pp. 3227–3337. DOI: 10.1093/eurheartj/ehab484</mixed-citation>
      </ref>
      <ref id="ref16">
        <mixed-citation publication-type="journal">VanHouten J.P., Starmer J.M., Lorenzi N.M., Maron D.J., Lasko T.A. Machine learning for risk pre-diction of acute coronary syndrome. AMIA Annual Symposium Proceedings Archive, 2014, 2014, Pp. 1940–1949.</mixed-citation>
      </ref>
      <ref id="ref17">
        <mixed-citation publication-type="journal">Topol E.J. High performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 2019, Vol. 25, P. 44–56. DOI: 10.1038/s41591-018-0300-7</mixed-citation>
      </ref>
      <ref id="ref18">
        <mixed-citation publication-type="journal">Krittanawong C., Zhang H., Wang Z., Aydar M., Kitai T. Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology (JACC), 2017, Vol. 69, No. 21, Pp. 2657–2664. DOI: 10.1016/j.jacc.2017.03.571</mixed-citation>
      </ref>
      <ref id="ref19">
        <mixed-citation publication-type="journal">Johnson K.W., Torres Soto J., Glicksberg B.S., Shameer K., Miotto R., Ali M., Ashley E., Dudley J.T. Artificial intelligence in cardiology. Journal of the American College of Cardiology (JACC), 2018, Vol. 71, No. 23, Pp. 2668–2679. DOI: 10.1016/j.jacc.2018.03.521</mixed-citation>
      </ref>
      <ref id="ref20">
        <mixed-citation publication-type="journal">Lundberg S.M., Lee S.-I. A unified approach to interpreting model predictions. arXiv:1705.07874, 2017. DOI: 10.48550/arXiv.1705.07874</mixed-citation>
      </ref>
      <ref id="ref21">
        <mixed-citation publication-type="journal">Al’Aref S.J., Anchouche K., Singh G., et al. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. European Heart Journal, 2019, Vol. 40, No. 24, Pp. 1975–1986. DOI: 10.1093/eurheartj/ehy404</mixed-citation>
      </ref>
      <ref id="ref22">
        <mixed-citation publication-type="journal">Dey D., Slomka P.J., Leeson P., Comaniciu D., Shrestha S., Sengupta P.P., Marwick T.H. Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. Journal of the American College of Cardiology (JACC), 2019, Vol. 73, No. 11, Pp. 1317–1335. DOI: 10.1016/j.jacc.2018.12.054</mixed-citation>
      </ref>
      <ref id="ref23">
        <mixed-citation publication-type="journal">Janosi A., Steinbrunn W., Pfisterer M., Detrano R. Heart Disease. UCI Machine Learning Repository, 1989. DOI: 10.24432/C52P4X</mixed-citation>
      </ref>
      <ref id="ref24">
        <mixed-citation publication-type="journal">Lundberg S.M., Erion G., Chen H., DeGrave A., Prutkin J.M., Nair B., Katz R., Himmelfarb J., Bansal N., Lee S.-I. From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2020, Vol. 2, Pp. 56–67. DOI: 10.1038/s42256-019-0138-9</mixed-citation>
      </ref>
    </ref-list>
  </back>
</article>
