ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2023-23-3-595-607ntv-35Research ArticleИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ И КОГНИТИВНЫЕ ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИARTIFICIAL INTELLIGENCE AND COGNITIVE INFORMATION TECHNOLOGIESИнтеллектуальная поддержка клинических решений при небольших выборках числа пациентовIntelligent clinical decision support for small patient datasetshttps://orcid.org/0000-0002-5483-716XВатьянА. С.VatianA. S.

Ватьян Александра Сергеевна — кандидат технических наук, доцент

sc 57191870868 

Санкт-Петербург, 197101 

Alexandra S. Vatian — PhD, Assistant Professor 

sc 57191870868 

Saint Petersburg, 197101 

alexvatyan@gmail.comhttps://orcid.org/0000-0001-7417-6947ГолубевА. А.GolubevA. A.

Голубев Александр Андреевич — аспирант 

Санкт-Петербург, 197101 

Alexander A. Golubev — PhD Student 

Saint Petersburg, 197101 

9459539@gmail.comhttps://orcid.org/0000-0002-1361-6037ГусароваН. Ф.GusarovaN. F.

Гусарова Наталия Федоровна — кандидат технических наук, доцент 

sc 57162764200 

Санкт-Петербург, 197101 

Natalia F. Gusarova — PhD, Associate Professor 

sc 57162764200 

Saint Petersburg, 197101 

natfed@list.ruhttps://orcid.org/0000-0001-6206-8033ДобренкоН. В.DobrenkoN. V.

Добренко Наталья Викторовна — кандидат технических наук, доцент

sc 56499375200 

Санкт-Петербург, 197101 

Natalia V. Dobrenko — PhD, Associate Professor 

sc 56499375200 

Saint Petersburg, 197101 

graziokisa@yandex.ruhttps://orcid.org/0000-0001-6953-5239ЗубаненкоА. А.ZubanenkoA. A.

Зубаненко Алексей Александрович — медицинский директор 

sc 57215436184 

Санкт-Петербург, 191119 

Aleksei A. Zubanenko — Clinical Director 

sc 57215436184 

Saint Petersburg, 191119 

zubdocmri@gmail.comhttps://orcid.org/0000-0001-6117-1266КустоваЕ. С.KustovaE. S.

Кустова Екатерина Сергеевна — студент 

Санкт-Петербург, 197101 

Ekaterina S. Kustova — Student 

Saint Petersburg, 197101 

Katya.Kustova@gmail.comhttps://orcid.org/0000-0002-9046-2457ТатариноваА. А.TatarinovaA. A.

Татаринова Анна Андреевна — кандидат медицинских наук, старший научный сотрудник, старший научный сотрудник

sc 6603195545 

Санкт-Петербург, 197341 

Anna A. Tatarinova — PhD (Medicine), Senior Researcher 

sc 6603195545 

Saint Petersburg, 197341 

antsvet.18@mail.ruhttps://orcid.org/0000-0003-1886-2867ТомиловИ. В.TomilovI. V.

Томилов Иван Вячеславович — старший лаборант 

sc 57772599000 

Санкт-Петербург, 197101 

Ivan V. Tomilov — Senior Laboratory Assistant 

sc 57772599000 

Saint Petersburg, 197101 

ivan-tomilov3@yandex.ruhttps://orcid.org/0000-0001-7777-6972ШовкоплясГ. Ф.ShovkoplyasG. F.

Шовкопляс Григорий Филиппович — инженер 

sc 57222048908 

Санкт-Петербург, 197101 

Grigorii F. Shovkoplyas — Engineer 

sc 57222048908 

Saint Petersburg, 197101 

grigory.96@gmail.comУниверситет ИТМОРоссияITMO UniversityRussian FederationООО «ИМВИЖН»РоссияImaging Medical Vision (IMV) LLCRussian FederationНациональный медицинский исследовательский центр им. В.А. АлмазоваРоссияAlmazov National Medical Research CenterRussian Federation202311122024233595607Copyright © Ватьян А.С., Голубев А.А., Гусарова Н.Ф., Добренко Н.В., Зубаненко А.А., Кустова Е.С., Татаринова А.А., Томилов И.В., Шовкопляс Г.Ф., 20242024Ватьян А.С., Голубев А.А., Гусарова Н.Ф., Добренко Н.В., Зубаненко А.А., Кустова Е.С., Татаринова А.А., Томилов И.В., Шовкопляс Г.Ф.Vatian A.S., Golubev A.A., Gusarova N.F., Dobrenko N.V., Zubanenko A.A., Kustova E.S., Tatarinova A.A., Tomilov I.V., Shovkoplyas G.F.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ntv.elpub.ru/jour/article/view/35

Введение. Рассмотрены пути обоснования клинического решения врачей в условиях отсутствия клинических протоколов лечения. Метод. Выполнена сравнительная оценка статистических методов ранжирования клинических симптомов по степени значимости для прогнозирования исхода заболевания в условиях небольшой выборки числа пациентов с COVID-19 и сердечно-сосудистыми заболеваниями в анамнезе. Набор данных (141 пациент, 81 фактор) сформирован по материалам электронных медицинских карт пациентов ФГБУ «Национальный медицинский исследовательский центр имени В.А. Алмазова». Выделен поднабор контролируемых факторов риска (51 фактор). Для ранжирования факторов использованы методы дескриптивной статистики (однофакторный дисперсионный анализ, тесты Манна-Уитни и χ²) и методы снижения размерности (одномерная линейная регрессия в сочетании с множественной логистической регрессией, обобщенный дискриминантный анализ, а также различные варианты алгоритмов дерева решений). Для сравнения результатов ранжирования и оценки статистической устойчивости применена корреляция Кендалла, визуализированная в виде тепловой карты и позиционного графика. Основные результаты. Установлено, что использование методов дескриптивной статистики правомерно при ранжировании на небольших размерах выборки пациентов. Показано, что ансамблирование результатов ранжирования может оказаться статистически несостоятельным. Сделан вывод, что позиции одних и тех же признаков, полученных при ранжировании их в составе полного набора и поднабора признаков, не совпадают, поэтому при выборе метода статистической обработки для экспертной оценки следует учитывать содержательную постановку задачи. Показано, что статистическая устойчивость ранжирования в условиях малых выборок зависит от количества учитываемых признаков, и эта зависимость существенно отличается для разных методов ранжирования. Обсуждение. Предложенная методика интеллектуальной поддержки и верификации клинических решений в аспекте выбора наиболее значимых клинических признаков может найти применение для выбора и обоснования тактики ведения пациентов при отсутствии клинических протоколов.

The ways of substantiating the clinical decision of doctors in the absence of clinical treatment protocols are considered. A comparative evaluation of various statistical methods for ranking clinical symptoms in terms of significance for predicting the outcome of the disease in a small sample of patients with COVID-19 and a history of cardiovascular diseases was performed. The data set (141 patients, 81 factors) was formed based on the materials of electronic medical records of patients of the Federal State Budgetary Institution “National Medical Research Center named after V.A. Almazov”. A subset of controllable risk factors (51 factors) was identified. Descriptive statistics methods (one-way ANOVA, Mann-Whitney and χ² tests) and dimensionality reduction methods (univariate linear regression combined with multiple logistic regression, generalized discriminant analysis, and various decision tree algorithms) were used to rank the factors. To compare the ranking results and evaluate the statistical stability, Kendall’s correlation was used, visualized as a heat map and a positional graph. It has been established that the use of descriptive statistics methods is justified when ranking on a small sample size of patients. It is shown that the ensemble of ranking results may be statistically inconsistent. It is concluded that the positions of the same features obtained by ranking them as part of a complete set and a subset of features do not match; therefore, when choosing a statistical processing method for expert evaluation, one should take into account the meaningful formulation of the problem. It is shown that the statistical stability of ranking under conditions of small samples depends on the number of features taken into account, and this dependence is significantly different for different ranking methods. The proposed method of intellectual support and verification of clinical decisions in terms of choosing the most significant clinical signs can be used to select and justify the tactics of managing patients in the absence of clinical protocols.

поддержка клинических решенийклиническая экспертизаранжирование признаковнебольшие группыстатистические методыclinical decision supportclinical expertisefeature rankingsmall cohortsstatistical methodsРабота поддержана грантом Президента Российской Федерации для государственной поддержки молодых российских ученых – кандидатов наук МК-5723.2021.1.6.The work was supported by the grant of the President of the Russian Federation for state support of young Russian scientists — candidates of sciences MK-5723.2021.1.6ReferencesAdu-Amankwaah J., Mprah R., Adekunle A.O., Noah M.L.N., Adzika G.K., Machuki J.O., Sun H. The cardiovascular aspect of COVID-19 // Annals of Medicine. 2021. V. 53. N 1. P. 227–236. https://doi.org/10.1080/07853890.2020.1861644Adu-Amankwaah J., Mprah R., Adekunle A.O., Noah M.L.N., Adzika G.K., Machuki J.O., Sun H. The cardiovascular aspect of COVID-19. Annals of Medicine, 2021, vol. 53, no. 1, pp. 227–236. https://doi.org/10.1080/07853890.2020.1861644Madjid M., Safavi-Naeini P., Solomon S.D., Vardeny O. Potential effects of coronaviruses on the cardiovascular system: a review // JAMA Cardiology. 2020. V. 5. N 7. P. 831–840. https://doi.org/10.1001/jamacardio.2020.1286Madjid M., Safavi-Naeini P., Solomon S.D., Vardeny O. Potential effects of coronaviruses on the cardiovascular system: a review. JAMA Cardiology, 2020, vol. 5, no. 7, pp. 831–840. https://doi.org/10.1001/jamacardio.2020.1286Румянцев П.О., Саенко В.Д., Румянцева У.В. Статистические методы анализа в клинической практике. Часть I. Одномерный статистический анализ // Проблемы эндокринологии. 2009. Т. 55. № 5. С. 48–55. https://doi.org/10.14341/probl200955548-55Rumyantsev P.O., Saenko U.V., Rumyantseva U.V. Statistical methods for the analyses in clinical practice. Part 1. Univariate statistical analysis. Problems of Endocrinology, 2009, vol. 55, no. 5, pp. 48–55. (in Russian). https://doi.org/10.14341/probl200955548-55Remeseiro B., Bolon-Canedo V. A review of feature selection methods in medical applications // Computers in Biology and Medicine. 2019. V. 112. P. 103375. https://doi.org/10.1016/j.compbiomed.2019.103375Remeseiro B., Bolon-Canedo V. A review of feature selection methods in medical applications. Computers in Biology and Medicine, 2019, vol. 112, pp. 103375. https://doi.org/10.1016/j.compbiomed.2019.103375Soares I., Dias J., Rocha H., do Carmo Lopes M., Ferreira B. Feature selection in small databases: a medical-case study // IFMBE Proceedings. 2016. V. 57. P. 814–819. https://doi.org/10.1007/978-3-319-32703-7_158Soares I., Dias J., Rocha H., do Carmo Lopes M., Ferreira B. Feature selection in small databases: a medical-case study. IFMBE Proceedings, 2016, vol. 57, pp. 814–819. https://doi.org/10.1007/978-3-319-32703-7_158Nezhad M.Z., Zhu D., Li X., Yang K., Levy Ph. SAFS: A deep feature selection approach for precision medicine // Proc. of the 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2016. P. 501–506. https://doi.org/10.1109/bibm.2016.7822569Nezhad M.Z., Zhu D., Li X., Yang K., Levy Ph. SAFS: A deep feature selection approach for precision medicine. Proc. of the 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2016, pp. 501–506. https://doi.org/10.1109/bibm.2016.7822569Alelyani S. Stable bagging feature selection on medical data // Journal of Big Data. 2021. V. 8. N 11. P. 11. https://doi.org/10.1186/s40537-020-00385-8Alelyani S. Stable bagging feature selection on medical data. Journal of Big Data, 2021, vol. 8, no. 11, pp. 11. https://doi.org/10.1186/s40537-020-00385-8Wu L., Hu Y., Liu X., Zhang X., Chen W., Yu A.S.L., Kellum J.A., Waitman L.R., Liu M. Feature ranking in predictive models for hospital-acquired acute kidney injury // Scientific Reports. 2018. V. 8. .P 17298. https://doi.org/10.1038/s41598-018-35487-0Wu L., Hu Y., Liu X., Zhang X., Chen W., Yu A.S.L., Kellum J.A., Waitman L.R., Liu M. Feature ranking in predictive models for hospital-acquired acute kidney injury. Scientific Reports, 2018, vol. 8, pp. 17298. https://doi.org/10.1038/s41598-018-35487-0Golugula A., Lee G., Madabhushi A. Evaluating feature selection strategies for high dimensional, small sample size datasets // Proc. of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2011. P. 949–952. https://doi.org/10.1109/iembs.2011.6090214Golugula A., Lee G., Madabhushi A. Evaluating feature selection strategies for high dimensional, small sample size datasets. Proc. of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 949–952. https://doi.org/10.1109/iembs.2011.6090214Gao L., Wu W. Relevance assignation feature selection method based on mutual information for machine learning // Knowledge-Based Systems. 2020. V. 209. P. 106439 https://doi.org/10.1016/j.knosys.2020.106439Gao L., Wu W. Relevance assignation feature selection method based on mutual information for machine learning. Knowledge-Based Systems, 2020, vol. 209, pp. 106439 https://doi.org/10.1016/j.knosys.2020.106439Wang B., Li R., Lu Z., Huang Y. Does comorbidity increase the risk of patients with covid-19: Evidence from meta-analysis // Aging. 2020. V. 12. N 7. P. 6049–6057. https://doi.org/10.18632/aging.103000Wang B., Li R., Lu Z., Huang Y. Does comorbidity increase the risk of patients with covid-19: Evidence from meta-analysis. Aging, 2020, vol. 12, no. 7, pp. 6049–6057. https://doi.org/10.18632/aging.103000Amin M.S., Chiam Y., Varathan K.D. Identification of significant features and data mining techniques in predicting heart disease // Telematics and Informatics. 2019. V. 36. P. 82–93. https://doi.org/10.1016/j.tele.2018.11.007Amin M.S., Chiam Y., Varathan K.D. Identification of significant features and data mining techniques in predicting heart disease. Telematics and Informatics, 2019, vol. 36, pp. 82–93. https://doi.org/10.1016/j.tele.2018.11.007Joloudari J.H., Joloudari E.H., Saadatfar H., Ghasemigol M., Razavi S.M., Mosavi A., Nabipour N., Shamshirband S., Nadai L. Coronary artery disease diagnosis; ranking the significant features using a random trees model // International Journal of Environmental Research and Public Health. 2020. V. 17. N 3. P. 731. https://doi.org/10.3390/ijerph17030731Joloudari J.H., Joloudari E.H., Saadatfar H., Ghasemigol M., Razavi S.M., Mosavi A., Nabipour N., Shamshirband S., Nadai L. Coronary artery disease diagnosis; ranking the significant features using a random trees model. International Journal of Environmental Research and Public Health, 2020, vol. 17, no. 3, pp. 731. https://doi.org/10.3390/ijerph17030731Pasha S.J., Mohamed E.S. Novel feature reduction (NFR) model with machine learning and data mining algorithms for effective disease risk prediction // IEEE Access. 2020. V. 8. P. 184087–184108. https://doi.org/10.1109/ACCESS.2020.3028714Pasha S.J., Mohamed E.S. Novel feature reduction (NFR) model with machine learning and data mining algorithms for effective disease risk prediction. IEEE Access, 2020, vol. 8, pp. 184087–184108. https://doi.org/10.1109/ACCESS.2020.3028714Alam Z., Rahman S., Rahman S. A Random Forest based predictor for medical data classification using feature ranking // Informatics in Medicine Unlocked. 2019. V. 15. P. 100180. https://doi.org/10.1016/j.imu.2019.100180Alam Z., Rahman S., Rahman S. A Random Forest based predictor for medical data classification using feature ranking. Informatics in Medicine Unlocked, 2019, vol. 15, pp. 100180. https://doi.org/10.1016/j.imu.2019.100180Saqlain S.M., Sher M., Shah F.A., Khan I., Ashraf M.U., Awais M., Ghani A. Fisher score and Matthews correlation coefficient-based feature subset selection for heart disease diagnosis using support vector machines // Knowledge and Information Systems. 2019. V. 58. N 1. P. 139–167. https://doi.org/10.1007/s10115-018-1185-ySaqlain S.M., Sher M., Shah F.A., Khan I., Ashraf M.U., Awais M., Ghani A. Fisher score and Matthews correlation coefficient-based feature subset selection for heart disease diagnosis using support vector machines. Knowledge and Information Systems, 2019, vol. 58, no. 1, pp. 139–167. https://doi.org/10.1007/s10115-018-1185-yShah S.S.M., Batool S.S., Khan I., Muhammad Ashraf U., Abbas S.H., Hussain S.A. Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis // Physica A: Statistical Mechanics and its Applications. 2017. V. 482. P. 796– 807. https://doi.org/10.1016/j.physa.2017.04.113Shah S.S.M., Batool S.S., Khan I., Muhammad Ashraf U., Abbas S.H., Hussain S.A. Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis. Physica A: Statistical Mechanics and its Applications, 2017, vol. 482, pp. 796–807. https://doi.org/10.1016/j.physa.2017.04.113Abdollahi J., Nouri-Moghaddam B. Feature selection for medical diagnosis: Evaluation for using a hybrid Stacked-Genetic approach in the diagnosis of heart disease // arXiv. 2021. arXiv:2103.08175. https://doi.org/10.48550/arXiv.2103.08175Abdollahi J., Nouri-Moghaddam B. Feature selection for medical diagnosis: Evaluation for using a hybrid Stacked-Genetic approach in the diagnosis of heart disease. arXiv, 2021, arXiv:2103.08175. https://doi.org/10.48550/arXiv.2103.08175Velusamy D., Ramasamy K. Ensemble of heterogeneous classifiers for diagnosis and prediction of coronary artery disease with reduced feature subset // Computer Methods and Programs in Biomedicine. 2021. V. 198. P. 105770. https://doi.org/10.1016/j.cmpb.2020.105770Velusamy D., Ramasamy K. Ensemble of heterogeneous classifiers for diagnosis and prediction of coronary artery disease with reduced feature subset. Computer Methods and Programs in Biomedicine, 2021, vol. 198, pp. 105770. https://doi.org/10.1016/j.cmpb.2020.105770Ghosh P., Azam S., Jonkman M., Karim A., Shamrat F.M.J., Ignatious E., Shultana S., Beeravolu A.R., De Boer F. Efficient prediction of cardiovascular disease using machine learning algorithms with relief and LASSO feature selection techniques // IEEE Access. 2021. V. 9. P. 19304–19326. https://doi.org/10.1109/ACCESS.2021.3053759Ghosh P., Azam S., Jonkman M., Karim A., Shamrat F.M.J., Ignatious E., Shultana S., Beeravolu A.R., De Boer F. Efficient prediction of cardiovascular disease using machine learning algorithms with relief and LASSO feature selection techniques. IEEE Access, 2021, vol. 9, pp. 19304–19326. https://doi.org/10.1109/ ACCESS.2021.3053759Zhou F., Yu T., Du R., Fan G., Liu Y., Liu Z., Xiang J., Wang Y., Song B., Gu X., Guan L., Wei Y., Li H., Wu X., Xu J., Tu S., Zhang Y., Chen H., Cao B. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study // Lancet. 2020. V. 395. P. 1054–1062. https://doi.org/10.1016/S0140-6736(20)30566-3Zhou F., Yu T., Du R., Fan G., Liu Y., Liu Z., Xiang J., Wang Y., Song B., Gu X., Guan L., Wei Y., Li H., Wu X., Xu J., Tu S., Zhang Y., Chen H., Cao B. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet, 2020, vol. 395, pp. 1054–1062. https://doi.org/10.1016/S0140-6736(20)30566-3Ruan Q., Yang K., Wang W., Jiang L., Song J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China // Intensive Care Medicine. 2020. V. 46. N 5. P. 846–848. https://doi.org/10.1007/s00134-020-05991-xRuan Q., Yang K., Wang W., Jiang L., Song J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Medicine, 2020, vol. 46, no. 5, pp. 846–848. https://doi.org/10.1007/s00134-020-05991-xLi X., Xu S., Yu M., Wang K., Tao Y., Zhou Y., Shi J., Zhou M., Wu B., Yang Z., Zhang C., Yue J., Zhang Z., Renz H., Liu X., Xie J., Xie M., Zhao J. Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan // Journal of Allergy and Clinical Immunology. 2020. V. 146. N 1. P. 110–118. https://doi.org/10.1016/j.jaci.2020.04.006Li X., Xu S., Yu M., Wang K., Tao Y., Zhou Y., Shi J., Zhou M., Wu B., Yang Z., Zhang C., Yue J., Zhang Z., Renz H., Liu X., Xie J., Xie M., Zhao J. Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan. Journal of Allergy and Clinical Immunology, 2020, vol. 146, no. 1, pp. 110–118. https://doi.org/10.1016/j.jaci.2020.04.006Liu X., Xue S., Xu J., Ge H., Mao Q., Xu X., Jiang H. Clinical characteristics and related risk factors of disease severity in 101 COVID-19 patients hospitalized in Wuhan, China // Acta Pharmacologica Sinica. 2022. V. 43. N 1. P. 64–75. https://doi.org/10.1038/s41401-021-00627-2Liu X., Xue S., Xu J., Ge H., Mao Q., Xu X., Jiang H. Clinical characteristics and related risk factors of disease severity in 101 COVID-19 patients hospitalized in Wuhan, China. Acta Pharmacologica Sinica, 2022, vol. 43, no. 1, pp. 64–75. https://doi.org/10.1038/s41401-021-00627-2Alshaikh M.K.,Alotair H.,Alnajjar F., Sharaf H.,Alhafi B.,Alashgar L., Aljuaid M. Cardiovascular risk factors among patients infected with COVID-19 in Saudi Arabia // Vascular Health and Risk Management. 2021. V. 17. P. 161–168. https://doi.org/10.2147/vhrm.s300635Alshaikh M.K., Alotair H., Alnajjar F., Sharaf H., Alhafi B., Alashgar L., Aljuaid M. Cardiovascular risk factors among patients infected with COVID-19 in Saudi Arabia. Vascular Health and Risk Management, 2021, vol. 17, pp. 161–168. https://doi.org/10.2147/vhrm.s300635Phelps M., Christensen D.M., Gerds T., Fosbøl E., Torp-Pedersen Ch., Schou M., Køber L., Kragholm K., Andersson Ch., BieringSørensen T., Christensen H.C., Andersen M.P., Gislason G. Cardiovascular comorbidities as predictors for severe COVID-19 infection or death // European Heart Journal — Quality of Care and Clinical Outcomes. 2021. V. 7. N 2. P. 172–180. https://doi.org/10.1093/ehjqcco/qcaa081Phelps M., Christensen D.M., Gerds T., Fosbøl E., Torp-Pedersen Ch., Schou M., Køber L., Kragholm K., Andersson Ch., BieringSørensen T., Christensen H.C., Andersen M.P., Gislason G. Cardiovascular comorbidities as predictors for severe COVID-19 infection or death. European Heart Journal – Quality of Care and Clinical Outcomes, 2021, vol. 7, no. 2, pp. 172–180. https://doi.org/10.1093/ehjqcco/qcaa081Kovvuri V.R.R., Liu S., Seisenberger M., Fan X., Muller B., Fu H. On understanding the influence of controllable factors with a feature attribution algorithm: a medical case study // Proc. of the 2022 International Conference on Innovations in Intelligent SysTems and Applications (INISTA). 2022. P. 1–8. https://doi.org/10.1109/inista55318.2022.9894147Kovvuri V.R.R., Liu S., Seisenberger M., Fan X., Muller B., Fu H. On understanding the influence of controllable factors with a feature attribution algorithm: a medical case study. Proc. of the 2022 International Conference on Innovations in Intelligent SysTems and Applications (INISTA), 2022, pp. 1–8. https://doi.org/10.1109/inista55318.2022.9894147Lundberg S.M., Lee S.I. A unified approach to interpreting model predictions // NIPS’17: Proc. of the 31st International Conference on Neural Information Processing Systems. 2017. P. 4768–4777.Lundberg S.M., Lee S.I. A unified approach to interpreting model predictions. NIPS’17: Proc. of the 31st International Conference on Neural Information Processing Systems, 2017, pp. 4768–4777.Bhadra T., Mallik S., Hasan N., Zhao Z. Comparison of five supervised feature selection algorithms leading to top features and gene signatures from multi-omics data in cancer // BMC Bioinformatics. 2022. V. 23. N 3S. P. 153. https://doi.org/10.1186/s12859-022-04678-yBhadra T., Mallik S., Hasan N., Zhao Z. Comparison of five supervised feature selection algorithms leading to top features and gene signatures from multi-omics data in cancer. BMC Bioinformatics, 2022, vol. 23, no. 3S, pp. 153. https://doi.org/10.1186/s12859-022-04678-yBarraza N., Moro S., Ferreyra M., de la Peña A. Mutual information and sensitivity analysis for feature selection in customer targeting: A comparative study // Journal of Information Science. 2019. V. 45. N 1. P. 53–67. https://doi.org/10.1177/0165551518770967Barraza N., Moro S., Ferreyra M., de la Peña A. Mutual information and sensitivity analysis for feature selection in customer targeting: A comparative study. Journal of Information Science, 2019, vol. 45, no. 1, pp. 53–67. https://doi.org/10.1177/0165551518770967Bouchlaghem Y., Akhiat Y., Amjad S. Feature selection: A Review and comparative study // E3S Web of Conferences. 2022. V. 351. .P 01046. https://doi.org/10.1051/e3sconf/202235101046Bouchlaghem Y., Akhiat Y., Amjad S. Feature selection: A Review and comparative study. E3S Web of Conferences, 2022, vol. 351, pp. 01046. https://doi.org/10.1051/e3sconf/202235101046Chen R.-C., Dewi Ch., Huang S.-W., Caraka R.E. Selecting critical features for data classification based on machine learning methods // Journal of Big Data. 2020. V. 7. N 1. P. 52. https://doi.org/10.1186/s40537-020-00327-4Chen R.-C., Dewi Ch., Huang S.-W., Caraka R.E. Selecting critical features for data classification based on machine learning methods. Journal of Big Data, 2020, vol. 7, no. 1, pp. 52. https://doi.org/10.1186/s40537-020-00327-4Sun P., Wang D., Mok V.C., Shi L. Comparison of feature selection methods and machine learning classifiers for radiomics analysis in glioma grading // IEEE Access. 2019. V. 7. P. 102010–102020. https://doi.org/10.1109/access.2019.2928975Sun P., Wang D., Mok V.C., Shi L. Comparison of feature selection methods and machine learning classifiers for radiomics analysis in glioma grading. IEEE Access, 2019, vol. 7, pp. 102010–102020. https://doi.org/10.1109/access.2019.2928975Nguyen G., Kim D., Nguyen A. The effectiveness of feature attribution methods and its correlation with automatic evaluation scores // Proc. of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021). 2021.Nguyen G., Kim D., Nguyen A. The effectiveness of feature attribution methods and its correlation with automatic evaluation scores. Proc. of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021.Amrhein V., Korner-Nievergelt F., Roth T. The earth is flat (p > 0.05): significance thresholds and the crisis of unreplicable research // PeerJ. 2017. V. 5. P. e3544. https://doi.org/10.7717/peerj.3544Amrhein V., Korner-Nievergelt F., Roth T. The earth is flat (p > 0.05): significance thresholds and the crisis of unreplicable research. PeerJ, 2017, vol. 5, pp. e3544. https://doi.org/10.7717/peerj.3544Kolukisa B., Hacilar H., Goy G., Kus M., Bakir-Gungor B., Aral A., Gungor V.C. Evaluation of classification algorithms, linear discriminant analysis and a new hybrid feature selection methodology for the diagnosis of coronary artery disease // Proc. of the 2018 IEEE International Conference on Big Data (Big Data). 2018. P. 2232– 2238. https://doi.org/10.1109/BigData.2018.8622609Kolukisa B., Hacilar H., Goy G., Kus M., Bakir-Gungor B., Aral A., Gungor V.C. Evaluation of classification algorithms, linear discriminant analysis and a new hybrid feature selection methodology for the diagnosis of coronary artery disease. Proc. of the 2018 IEEE International Conference on Big Data (Big Data), 2018, pp. 2232– 2238. https://doi.org/10.1109/BigData.2018.8622609Ricciardi C., Valente A.S., Edmunds K., Cantoni V., Green R., Fiorillo A., Picone I., Santini S., Cesarelli M. Linear discriminant analysis and principal component analysis to predict coronary artery disease // Health Informatics Journal. 2020. V. 26. N 3. P. 2181–2192. https://doi.org/10.1177/1460458219899210Ricciardi C., Valente A.S., Edmunds K., Cantoni V., Green R., Fiorillo A., Picone I., Santini S., Cesarelli M. Linear discriminant analysis and principal component analysis to predict coronary artery disease. Health Informatics Journal, 2020, vol. 26, no. 3, pp. 2181– 2192. https://doi.org/10.1177/1460458219899210Breiman L. Random Forests // Machine Learning. 2001. V. 45. P. 5–32. https://doi.org/10.1023/A:1010933404324Breiman L. Random Forests. Machine Learning, 2001, vol. 45, pp. 5–32. https://doi.org/10.1023/A:1010933404324Ke G., Meng Q., Finley T., Wang T., Chen W., Ma W., Ye Q., Liu T.-Y. LightGBM: A highly efficient gradient boosting decision tree // Proc. of the 31st Conference on Neural Information Processing Systems (NIPS 2017). 2017. P. 3149–3157.Ke G., Meng Q., Finley T., Wang T., Chen W., Ma W., Ye Q., Liu T.-Y. LightGBM: A highly efficient gradient boosting decision tree. Proc. of the 31st Conference on Neural Information Processing Systems (NIPS 2017), 2017, pp. 3149–3157.Prokhorenkova L., Gusev G., Vorobev A., Dorogush A.V., Gulin A. CatBoost: unbiased boosting with categorical features // Proc. of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018). 2018.Prokhorenkova L., Gusev G., Vorobev A., Dorogush A.V., Gulin A. CatBoost: unbiased boosting with categorical features. Proc. of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), 2018.

The authors declare that there are no conflicts of interest present.