cardiotomskСибирский журнал клинической и экспериментальной медициныSiberian Journal of Clinical and Experimental Medicine2713-29272713-265XTSU publishing10.29001/2073-8552-2020-35-4-22-31cardiotomsk-1070Research ArticleОБЗОРЫ И ЛЕКЦИИREVIEWS AND LECTURESНечеткие классификаторы в диагностике сердечно-сосудистых заболеваний. ОбзорFuzzy classifiers in cardiovascular disease diagnostics: Reviewhttps://orcid.org/0000-0002-9355-7638ХодашинскийИ. А.HodashinskyI. A.

Ходашинский Илья Александрович, д-р техн. наук, профессор, профессор кафедры комплексной информационной безопасности электронно-вычислительных систем.

634050, Томск, пр. Ленина, 40

Ilya A. Hodashinsky, Dr. Sci. (Tech.), Professor, Department of Integrated Cybersecurity of Electronic Computer Systems

40, Lenin ave., Tomsk, 634050

hodashn@rambler.ruТомский государственный университет систем управления и радиоэлектроникиРоссияTomsk State University of Control Systems and RadioelectronicsRussian Federation2020241220203542231Copyright © Ходашинский И.А., 20202020Ходашинский И.А.Hodashinsky I.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.sibjcem.ru/jour/article/view/1070

Сложность биологических объектов делает разработку компьютеризированных медицинских систем непростым алгоритмическим решением из-за естественной неопределенности, присущей указанным объектам. Человеческое мышление основано на неточных, приблизительных данных, анализ которых позволяет формировать четкие решения. На практике может не существовать точной математической модели биологических объектов, или такая модель может быть слишком сложной для реализации. В этом случае нечеткая логика является подходящим инструментом решения указанной проблемы. Проблема медицинской диагностики может рассматриваться как проблема классификации. В статье представлен литературный обзор применения нечетких классификаторов в области диагностики сердечно-сосудистых заболеваний. Основным достоинством нечетких классификаторов по сравнению с другими методами искусственного интеллекта является возможность интерпретации полученного результата классификации. Обзор направлен на расширение знаний различных исследователей, работающих в области медицинской диагностики.

The complexity of biological objects makes the development of computerized medical systems a difficult algorithmic decision due to the natural uncertainty inherent in these objects. Human thinking is based on vague and approximate data that can be analyzed to form clear decisions. An exact mathematical model of biological objects may not exist in practice, or such a model may be too complex to implement. In this case, fuzzy logic is a suitable tool for solving the specified problem. The problem of medical diagnosis can be viewed as a classification problem. The article presents a literature review of the use of fuzzy classifiers in diagnostics of cardiovascular diseases. The main advantage of fuzzy classifiers in comparison with other artificial intelligence methods is the ability to interpret the resulting classification result. The review aims to expand the knowledge of various researchers working in the field of medical diagnostics.

нечеткий классификаторсердечно-сосудистые заболеваниямедицинская диагностикаfuzzy classifiercardiovascular diseasesmedical diagnosticsработа поддержана Министерством науки и высшего образования Российской Федерации (проект № FEWM-2020-0042).this work was supported by the Ministry of Science and Higher Education of the Russian Federation (FEWM-2020-0042).ReferencesFernandes M., Vieira S.M., Leite F., Palos C., Finkelstein S., Sousa J.M.C. Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review. Artif. Intell. Med. 2020;102:101762. DOI: 10.1016/j.artmed.2019.101762.Fernandes M., Vieira S.M., Leite F., Palos C., Finkelstein S., Sousa J.M.C. Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review. Artif. Intell. Med. 2020;102:101762. DOI: 10.1016/j.artmed.2019.101762.Mustaqeem A., Anwar S.M., Majid M. A modular cluster based collaborative recommender system for cardiac patients. Artif. Intell. Med. 2020;102:101761. DOI: 10.1016/j.artmed.2019.101761.Mustaqeem A., Anwar S.M., Majid M. A modular cluster based collaborative recommender system for cardiac patients. Artif. Intell. Med. 2020;102:101761. DOI: 10.1016/j.artmed.2019.101761.Souza-Pereira L., Pombo N., Ouhbi S., Felizardo V., Garcia N. Clinical decision support systems for chronic diseases: A systematic literature review. Comput. Methods Program Biomed. 2020;195:105565. DOI: 10.1016/j.cmpb.2020.105565.Souza-Pereira L., Pombo N., Ouhbi S., Felizardo V., Garcia N. Clinical decision support systems for chronic diseases: A systematic literature review. Comput. Methods Program Biomed. 2020;195:105565. DOI: 10.1016/j.cmpb.2020.105565.Olakotan O.O., Yusof M.M. Evaluating the alert appropriateness of clinical decision support systems in supporting clinical workfl ow. Journal Biomedical Informatics. 2020;106:103453. DOI: 10.1016/j.jbi.2020.103453.Olakotan O.O., Yusof M.M. Evaluating the alert appropriateness of clinical decision support systems in supporting clinical workfl ow. Journal Biomedical Informatics. 2020;106:103453. DOI: 10.1016/j.jbi.2020.103453.MsRae M.P., Bozkurt B., Ballantyne C.M., Sanchez X., Christodoulides N., Simmons G. et al. Cardiac ScoreCard: A diagnostic multivariate index assay system for predicting a spectrum of cardiovascular disease. Expert Systems with Applications: An International Journal. 2016;54:136–147. DOI: 10.1016/j.eswa.2016.01.029.MsRae M.P., Bozkurt B., Ballantyne C.M., Sanchez X., Christodoulides N., Simmons G. et al. Cardiac ScoreCard: A diagnostic multivariate index assay system for predicting a spectrum of cardiovascular disease. Expert Systems with Applications: An International Journal. 2016;54:136–147. DOI: 10.1016/j.eswa.2016.01.029.Thukral S., Rana V. Versatility of fuzzy logic in chronic diseases: A review. Medical Hypotheses. 2019;122:150–156. DOI: 10.1016/j.mehy.2018.11.017.Thukral S., Rana V. Versatility of fuzzy logic in chronic diseases: A review. Medical Hypotheses. 2019;122:150–156. DOI: 10.1016/j.mehy.2018.11.017.Gadaras I., Mikhailov L. An interpretable fuzzy rule-based classification methodology for medical diagnosis. Artif. Intell. Med. 2009;47(1):25–41. DOI: 10.1016/j.artmed.2009.05.003.Gadaras I., Mikhailov L. An interpretable fuzzy rule-based classification methodology for medical diagnosis. Artif. Intell. Med. 2009;47(1):25–41. DOI: 10.1016/j.artmed.2009.05.003.Mokeddem S.A. A fuzzy classification model for myocardial infarction risk assessment. Applied Intelligens. 2018;48:1233–1250. DOI: 10.1007/s10489-017-1102-1.Mokeddem S.A. A fuzzy classifi cation model for myocardial infarction risk assessment. Applied Intelligens. 2018;48:1233–1250. DOI: 10.1007/s10489-017-1102-1.Nauck D., Kruse R. Obtaining interpretable fuzzy classification rules from medical data. Artif. Intell. Med. 1999;16(2):149–169. DOI: 10.1016/s0933-3657(98)00070-0.Nauck D., Kruse R. Obtaining interpretable fuzzy classification rules from medical data. Artif. Intell. Med. 1999;16(2):149–169. DOI: 10.1016/s0933-3657(98)00070-0.Kalantari A., Kamsin A., Shamshirband S., Gani A., Alinejad-Rokny H., Chronopoulos A.T. Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions. Neurocomputing. 2018;276:2–22. DOI: 10.1016/j.neucom.2017.01.126.Kalantari A., Kamsin A., Shamshirband S., Gani A., Alinejad-Rokny H., Chronopoulos A.T. Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions. Neurocomputing. 2018;276:2–22. DOI: 10.1016/j.neucom.2017.01.126.Jemal H., Kechaou Z., Ayed M.B. Enhanced decision support systems in intensive care unit based on intuitionistic fuzzy sets. Advances in Fuzzy Systems. 2017;(5b):1–8. DOI: 10.1155/2017/7371634.Jemal H., Kechaou Z., Ayed M.B. Enhanced decision support systems in intensive care unit based on intuitionistic fuzzy sets. Advances in Fuzzy Systems. 2017;(5b):1–8. DOI: 10.1155/2017/7371634.Pota M., Esposito M., Pietro G. Designing rule-based fuzzy systems for classification in medicine. Knowl-Based Systems. 2017;124(C):105–132. DOI: 10.1016/j.knosys.2017.03.006.Pota M., Esposito M., Pietro G. Designing rule-based fuzzy systems for classification in medicine. Knowl-Based Systems. 2017;124(C):105–132. DOI: 10.1016/j.knosys.2017.03.006.Minutolo A., Esposito M., Pietro G. A fuzzy framework for encoding uncertainty in clinical decision-making. Knowl-Based Systems. 2016;98:95–116. DOI: 10.1016/j.knosys.2016.01.020.Minutolo A., Esposito M., Pietro G. A fuzzy framework for encoding uncertainty in clinical decision-making. Knowl-Based Systems. 2016;98:95–116. DOI: 10.1016/j.knosys.2016.01.020.Ahmadi H., Gholamzadeh M., Shahmoradi L., Nilashi M., Rashvand P. Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review. Comput. Methods Program Biomed. 2018;161:145–172. DOI: 10.1016/j.cmpb.2018.04.013.Ahmadi H., Gholamzadeh M., Shahmoradi L., Nilashi M., Rashvand P. Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review. Comput. Methods Program Biomed. 2018;161:145–172. DOI: 10.1016/j.cmpb.2018.04.013.Kour H., Manhas J., Sharma V. Usage and implementation of neuro-fuzzy systems for classification and prediction in the diagnosis of different types of medical disorders: a decade review. Artif. Intell. Rev. 2020;53:4651–4706. DOI: 10.1007/s10462-020-09804-x.Kour H., Manhas J., Sharma V. Usage and implementation of neuro-fuzzy systems for classifi cation and prediction in the diagnosis of diff erent types of medical disorders: a decade review. Artif. Intell. Rev. 2020;53:4651–4706. DOI: 10.1007/s10462-020-09804-x.Sajadi N.A., Borzouei S., Mahjub H., Farhadian M. Diagnosis of hypothyroidism using a fuzzy rule-based expert system. Cliical Epidemiology and Global Health. 2019;7(4):519–524. DOI: 10.1016/j.cegh.2018.11.007.Sajadi N.A., Borzouei S., Mahjub H., Farhadian M. Diagnosis of hypothyroidism using a fuzzy rule-based expert system. Cliical Epidemiology and Global Health. 2019;7(4):519–524. DOI: 10.1016/j.cegh.2018.11.007.Arji G., Ahmadi H., Nilashi M., Rashid T.A., Ahmed O.H., Aljojo N. et al. Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification. Biocybernetics and Biomedical Engineering. 2019;39(4):937–955. DOI: 10.1016/j.bbe.2019.09.004.Arji G., Ahmadi H., Nilashi M., Rashid T.A., Ahmed O.H., Aljojo N. et al. Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classifi cation. Biocybernetics and Biomedical Engineering. 2019;39(4):937–955. DOI: 10.1016/j.bbe.2019.09.004.Кобринский Б.А. Нечеткость в клинической медицине и необходимость ее отражения в экспертных системах. Врач и информационные технологии. 2016;5:6–14.Kobrinskii B.A. Fuzzy in clinical medicine and the need to refl ect in expert systems. Information Technologies and Phisician. 2016;5:6–14 (In Russ.).Amato F., Lopez A., Pena-Mendez E.M., Vanhara P., Hampl A., Havel J. Artificial neural networks in medical diagnosis. J. Appl. Biomed. 2013;11(2):47–58. DOI: 10.2478/v10136-012-0031-x.Amato F., Lopez A., Pena-Mendez E.M., Vanhara P., Hampl A., Havel J. Artifi cial neural networks in medical diagnosis. J. Appl. Biomed. 2013;11(2):47–58. DOI: 10.2478/v10136-012-0031-x.Jiang J., Wang H., Xie J., Guo X., Guan Y., Yu Q. Medical knowledge embedding based on recursive neural network for multi-disease diagnosis. Artif. Intell. Med. 2020;103:101772. DOI: 10.1016/j.artmed.2019.101772.Jiang J., Wang H., Xie J., Guo X., Guan Y., Yu Q. Medical knowledge embedding based on recursive neural network for multi-disease diagnosis. Artif. Intell. Med. 2020;103:101772. DOI: 10.1016/j.artmed.2019.101772.Alizadehsani R. Machine learning-based coronary artery disease diagnosis: A comprehensive review. Computers in Biology and Medicine. 2019;111:103346. DOI: 10.1016/j.compbiomed.2019.103346.Alizadehsani R. Machine learning-based coronary artery disease diagnosis: A comprehensive review. Computers in Biology and Medicine. 2019;111:103346. DOI: 10.1016/j.compbiomed.2019.103346.Acharya U.R., Fujita H., Sudarshan V.K., Oh S.L., Adam M., Tan J.H. et al. Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal. Knowl.-Based Syst. 2017;132(15):156–166. DOI: 10.1016/j.knosys.2017.06.026.Acharya U.R., Fujita H., Sudarshan V.K., Oh S.L., Adam M., Tan J.H. et al. Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal. Knowl.-Based Syst. 2017;132(15):156–166. DOI: 10.1016/j.knosys.2017.06.026.Yang H.-H., Wu C.-L. Rough sets to help medical diagnosis – Evidence from a Taiwan’s clinic. Expert System with Applications. 2009;36(5):9293–9298. DOI: 10.1016/j.eswa.2008.12.003.Yang H.-H., Wu C.-L. Rough sets to help medical diagnosis – Evidence from a Taiwan’s clinic. Expert System with Applications. 2009;36(5):9293–9298. DOI: 10.1016/j.eswa.2008.12.003.Zhang Z., Shi Y., Gao G. A rough set-based multiple criteria linear programming approach for the medical diagnosis and prognosis. Expert System with Applications. 2009;36(5):8932–8937. DOI: 10.1016/j.eswa.2008.11.007.Zhang Z., Shi Y., Gao G. A rough set-based multiple criteria linear programming approach for the medical diagnosis and prognosis. Expert System with Applications. 2009;36(5):8932–8937. DOI: 10.1016/j.eswa.2008.11.007.Wang M., Chen H. Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Applied Soft Computing. 2020;88:105946. DOI: 10.1016/j.asoc.2019.105946.Wang M., Chen H. Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Applied Soft Computing. 2020;88:105946. DOI: 10.1016/j.asoc.2019.105946.Chen H.L., Yang B., Wang G., Wang S.-J., Liu J., Liu D.-Y. Support vector machine based diagnostic system for breast Cancer using swarm intelligence. J. Med. Syst. 2012;36(4):2505–2519. DOI: 10.1007/s10916-011-9723-0.Chen H.L., Yang B., Wang G., Wang S.-J., Liu J., Liu D.-Y. Support vector machine based diagnostic system for breast Cancer using swarm intelligence. J. Med. Syst. 2012;36(4):2505–2519. DOI: 10.1007/s10916-011-9723-0.Tan K.C., Yu Q., Heng C.M., Lee T.H. Evolutionary computing for knowledge discovery in medical diagnosis. Artif. Intell. Med. 2003;27(2):129–154. DOI: 10.1016/S0933-3657(03)00002-2.Tan K.C., Yu Q., Heng C.M., Lee T.H. Evolutionary computing for knowledge discovery in medical diagnosis. Artif. Intell. Med. 2003;27(2):129–154. DOI: 10.1016/S0933-3657(03)00002-2.Park Y.-J., Chun S.-H., Kim B.-C. Cost-sensitive case-based reasoning using a genetic algorithm: Application to medical diagnosis. Artif. Intell. Med. 2011;51(2):133–145. DOI: 10.1016/j.artmed.2010.12.001.Park Y.-J., Chun S.-H., Kim B.-C. Cost-sensitive case-based reasoning using a genetic algorithm: Application to medical diagnosis. Artif. Intell. Med. 2011;51(2):133–145. DOI: 10.1016/j.artmed.2010.12.001.Exarchos T.P., Tsipouras M.G., Exarchos C.P., Papaloukas C., Fotiadis D.I., Michalis L.K. A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree. Artif. Intell. Med. 2007;40(3):187–200. DOI: 10.1016/j.artmed.2007.04.001.Exarchos T.P., Tsipouras M.G., Exarchos C.P., Papaloukas C., Fotiadis D.I., Michalis L.K. A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree. Artif. Intell. Med. 2007;40(3):187–200. DOI: 10.1016/j.artmed.2007.04.001.Mastoi Q., Wah T.Y., Raj R.G., Iqbal U. Automated diagnosis of coronary artery disease: A review and workflow. Cardiol. Res. Pract. 2018;2016282. DOI: 10.1155/2018/2016282.Mastoi Q., Wah T.Y., Raj R.G., Iqbal U. Automated diagnosis of coronary artery disease: A review and workfl ow. Cardiol. Res. Pract. 2018;2016282. DOI: 10.1155/2018/2016282.Kora P., Meenakshi K., Swaraja K., Rajani A., Islam K.M. Detection of cardiac arrhythmia using fuzzy logic. Inform. Med. Unlocked. 2019;17:100257. DOI: 10.1016/j.imu.2019.100257.Kora P., Meenakshi K., Swaraja K., Rajani A., Islam K.M. Detection of cardiac arrhythmia using fuzzy logic. Inform. Med. Unlocked. 2019;17:100257. DOI: 10.1016/j.imu.2019.100257.Lahsasna A., Ainon R.N., Zainuddin R., Bulgiba A. Design of a fuzzybased decision support system for coronary heart disease diagnosis. J. Med. Syst. 2012;36(5):3293–3306. DOI: 10.1007/s10916-012-9821-7.Lahsasna A., Ainon R.N., Zainuddin R., Bulgiba A. Design of a fuzzybased decision support system for coronary heart disease diagnosis. J. Med. Syst. 2012;36(5):3293–3306. DOI: 10.1007/s10916-012-9821-7.Melin P., Miramontes I., Prado-Arechiga G. A hybrid model based on modular neural networks and fuzzy systems for classification of blood pressure and hypertension risk diagnosis. Expert System with Applications. 2018;107:146–164. DOI: 10.1016/j.eswa.2018.04.023.Melin P., Miramontes I., Prado-Arechiga G. A hybrid model based on modular neural networks and fuzzy systems for classification of blood pressure and hypertension risk diagnosis. Expert System with Applications. 2018;107:146–164. DOI: 10.1016/j.eswa.2018.04.023.Anooj P.K. Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules. Journal of King Saud University – Computer and Information Sciences. 2012;24(1):27–40. DOI: 10.1016/j.jksuci.2011.09.002.Anooj P.K. Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules. Journal of King Saud University – Computer and Information Sciences. 2012;24(1):27–40. DOI: 10.1016/j.jksuci.2011.09.002.Горбунов И.В., Ходашинский И.А. Методы построения трехкритериальных Парето-оптимальных нечетких классификаторов. Искусственный интеллект и принятие решений. 2015;(2):75–87.Gorbunov I.V., Hodashinsky I.A. Methods of buildings Pareto-optimal fuzzy classifi ers. Artifi cial Intelligence and Decision Making. 2015;2:75–87 (In Russ.).Yankovskaya A.E., Gorbunov I.V., Hodashinsky I.A. Tradeoff search methods between interpretability and accuracy of the identification fuzzy systems based on rules. Pattern Recognition and Image Analysis. 2017;27(2):243–265. DOI: 10.1134/S1054661817020134.Yankovskaya A.E., Gorbunov I.V., Hodashinsky I.A. Tradeoff search methods between interpretability and accuracy of the identifi cation fuzzy systems based on rules. Pattern Recognition and Image Analysis. 2017;27(2):243–265. DOI: 10.1134/S1054661817020134.Zadeh L. Fuzzy sets. Information and Control. 1965;8(3):338–353. DOI: 10.1016/S0019-9958(65)90241-X.Zadeh L. Fuzzy sets. Information and Control. 1965;8(3):338–353. DOI: 10.1016/S0019-9958(65)90241-X.Zadeh L. Knowledge representation in fuzzy logic. IEEE Transaction and Knowledge and Data Engeneering. 1989;1(1):89–100. DOI: 10.1109/69.43406.Zadeh L. Knowledge representation in fuzzy logic. IEEE Transaction and Knowledge and Data Engeneering. 1989;1(1):89–100. DOI: 10.1109/69.43406.Мех М.А., Ходашинский И.А. Сравнительный анализ применения методов дифференциальной эволюции для оптимизации параметров нечетких классификаторов. Известия Российской академии наук. Теория и системы управления. 2017;4:65–75.Mekh M.A., Hodashinsky I.A. Comparative analysis of the application of diff erential evolution methods to optimize the parameters of fuzzy classifiers. Journal of Computer and Systems Sciences International. 2017;56(4):616–626.Ходашинский И.А., Горбунов И.В. Оптимизация параметров нечетких систем на основе модифицированного алгоритма пчелиной колонии. Мехатроника, автоматизация, управление. 2012;(10): 15–20.Hodashinsky I.A., Gorbunov I.V. Optimization of fuzzy system parameters using the modified bee colonyies algorithm. Mehatronika, Avtomatizacija, Upravlenie. 2012;(10):15–20 (In Russ.).Ходашинский И.А., Сарин К.С. Отбор классифицирующих признаков: сравнительный анализ бинарных метаэвристик и популяционного алгоритма с адаптивной памятью. Программирование. 2019;(5):3–9. DOI: 10.1134/S0132347419050030.Hodashinsky I.A., Sarin K.S. Feature selection: Comparative analysis of binary metaheuristics and population based algorithm with adaptive memory. Programming and Computer Software. 2019;45(5):221–227. DOI: 10.1134/S0361768819050037.Ходашинский И.А. Идентификация нечетких систем на базе алгоритма имитации отжига и методов, основанных на производных. Информационные технологии. 2012;(3):14–20.Hodashinsky I.A. Simulated annealing and methods based on derivatives for fuzzy systems identification. Information Technologies. 2012;(3):14–20 (In Russ.).Marateb H.R., Goudarzi S. A noninvasive method for coronary artery diseases diagnosis using a clinically-interpretable fuzzy rule-based system. J. Res. Med. Sci. 2015;20(3):214–223.Marateb H.R., Goudarzi S. A noninvasive method for coronary artery diseases diagnosis using a clinically-interpretable fuzzy rule-based system. J. Res. Med. Sci. 2015;20(3):214–223.Pal D., Mandana K.M., Pal S., Sarkar D., Chakraborty C. Fuzzy expert system approach for coronary artery disease screening using clinical parameters. Knowl.-Based Syst. 2012;36:162–174. DOI: 10.1016/j.knosys.2012.06.013.Pal D., Mandana K.M., Pal S., Sarkar D., Chakraborty C. Fuzzy expert system approach for coronary artery disease screening using clinical parameters. Knowl.-Based Syst. 2012;36:162–174. DOI: 10.1016/j.knosys.2012.06.013.Mohammadpour R.A., Abedi S.M., Bagheri S., Ghaemian A. Fuzzy rule-based classification system for assessing coronary artery disease. Comput. Math. Methods Med. 2015;2015:564867. DOI: 10.1155/2015/564867.Mohammadpour R.A., Abedi S.M., Bagheri S., Ghaemian A. Fuzzy rule-based classification system for assessing coronary artery disease. Comput. Math. Methods Med. 2015;2015:564867. DOI: 10.1155/2015/564867.Marateb H.R., Mansourian M., Faghihimani E., Amini M., Farina D. A hybrid intelligent system for diagnosing microalbuminuria in type 2 diabetes patients without having to measure urinary albumin. Comput. Biol. Med. 2014;45:34–42. DOI: 10.1016/j.compbiomed.2013.11.006.Marateb H.R., Mansourian M., Faghihimani E., Amini M., Farina D. A hybrid intelligent system for diagnosing microalbuminuria in type 2 diabetes patients without having to measure urinary albumin. Comput. Biol. Med. 2014;45:34–42. DOI: 10.1016/j.compbiomed.2013.11.006.European ST-T database directory. URL: https://physionet.org/content/edb/1.0.0/.European ST-T database directory. URL: https://physionet.org/content/edb/1.0.0/.MIT-BIH arrhythmia database. URL: https://physionet.org/content/mitdb/1.0.0/.MIT-BIH arrhythmia database. URL: https://physionet.org/content/mitdb/1.0.0/.Wang A., An N., Chen G., Li L., Alterovitz G. Predicting hypertension without measurement: A non-invasive, questionnaire-based approach. Expert System with Applications. 2015;42(21):7601–7609. DOI: 10.1016/j.eswa.2015.06.012.Wang A., An N., Chen G., Li L., Alterovitz G. Predicting hypertension without measurement: A non-invasive, questionnaire-based approach. Expert System with Applications. 2015;42(21):7601–7609. DOI: 10.1016/j.eswa.2015.06.012.Das S., Ghosh P.K., Kar S. Hypertension diagnosis: A comparative study using fuzzy expert system and neuro fuzzy system. In: IEEE International Conference on Fuzzy Systems. Hyderabad: IEEE; 2013. DOI: 10.1109/FUZZ-IEEE.2013.6622434.Das S., Ghosh P.K., Kar S. Hypertension diagnosis: A comparative study using fuzzy expert system and neuro fuzzy system. In: IEEE International Conference on Fuzzy Systems. Hyderabad: IEEE; 2013. DOI: 10.1109/FUZZ-IEEE.2013.6622434.Su T.-J., Wang S.-M., Vu H.-Q., Jou J.-J., Sun C.-K. Mean arterial pressure control system using model predicative control and particle swarm optimization. Microsystem Technologies. 2018;24:147–153. DOI: 10.1007/s00542-016-3212-9.Su T.-J., Wang S.-M., Vu H.-Q., Jou J.-J., Sun C.-K. Mean arterial pressure control system using model predicative control and particle swarm optimization. Microsystem Technologies. 2018;24:147–153. DOI: 10.1007/s00542-016-3212-9.Sharma R., Deepak K.K., Gaur P., Joshi D. An optimal interval type2 fuzzy logic control based closed-loop drug administration to regulate the mean arterial blood pressure. Computer Methods and Programs Biomedicine. 2020;185:105167. DOI: 10.1016/j.cmpb.2019.105167.Sharma R., Deepak K.K., Gaur P., Joshi D. An optimal interval type-2 fuzzy logic control based closed-loop drug administration to regulate the mean arterial blood pressure. Computer Methods and Programs Biomedicine. 2020;185:105167. DOI: 10.1016/j.cmpb.2019.105167.Ramirez E., Melin P., Prado-Arechiga G. Hybrid model based on neural networks, type-1 and type-2 fuzzy systems for 2-lead cardiac arrhythmia classifi cation. Expert System with Applications. 2019;126:295–307. DOI: 10.1016/j.eswa.2019.02.035.Ramirez E., Melin P., Prado-Arechiga G. Hybrid model based on neural networks, type-1 and type-2 fuzzy systems for 2-lead cardiac arrhythmia classification. Expert System with Applications. 2019;126:295–307. DOI: 10.1016/j.eswa.2019.02.035.Wolpert D., Macready W. No free lunch theorems for optimization. IEEE Transaction Evolutionary Computation. 1997;1(1):67–82. DOI: 10.1109/4235.585893.Wolpert D., Macready W. No free lunch theorems for optimization. IEEE Transaction Evolutionary Computation. 1997;1(1):67–82. DOI: 10.1109/4235.585893.

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