Research Article
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Year 2021, Volume: 2 Issue: 3, 92 - 102, 30.12.2021
https://doi.org/10.51753/flsrt.1010253

Abstract

References

  • Akcam, M. O., & Takada, K. (2002). Fuzzy modelling for selecting headgear types. The European Journal of Orthodontics, 24(1), 99-106.
  • Bates, J., & Young, M. (2003). Applying fuzzy logic to medical decision making in the intensive care unit. American Journal of Respiratory and Critical Care Medicine, 167(7), 948–952.
  • Benecchi, L. (2006). Neuro-fuzzy systems for prostate cancer diagnosis. Urology, 68(2), 357–361.
  • Blackmore, C. C., Mecklenburg, R. S., & Kaplan, G. S. (2011). Effectiveness of clinical decision support in controlling inappropriate imaging. Journal of the American College of Radiology, 8(1), 19-25.
  • CDC, (2020). United States of America Centers for Disease Control and Prevention. Retrieved from Centers for Disease Control and Prevention:https://web.archive.org/web/20200302201644/https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-guidance-management-patients.html , Last accessed on December 4, 2020.
  • Cismondi, F., Celi, L. A., Fialho, A. S., Vieira, S. M., Reti, S. R., Sousa, J. M., & Finkelstein, S. N. (2013). Reducing unnecessary lab testing in the ICU with Artificial Intelligence. Internal Journal of Medical Informatics, 82(5), 345-358.
  • Ewald, F., & Mohammad, A. (2015). Optimal Placement and Sizing of Shunt Capacitor Banks in the Presence of Harmonics. In E. F. Mohammad A.S. Masoum, Power Quality in Power Systems and Electrical Machines (Second Edition) (pp. 887-959). Elsevier Inc.
  • Genc, B. N. (2020). Critical management of Covid-19 pandemic in Turkey. Frontiers in Life Sciences and Related Technologies, 1(2), 69-73.
  • Grant, P., & Naesh, O. (2005). Fuzzy logic and decision-making in anaesthetics. Journal of Royal Society of Medicine, 98(1), 7-9.
  • Guan, W., & et al. (2020). Clinical characteristics of Coronavirus. The New England Journal of Medicine, 382(18), 1708-1720.
  • Hickson, L., & Khemka, I. (2014). Chapter Six - The Psychology of Decision Making, In Hodapp R.M. (eds) International Review of Research in Developmental Disabilities. Volume 47, (pp. 185-229), Academic Press.
  • Hossein, A., Gholamzadeh, M., & Shahmoradi, L. (2018). Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review. Computer Methods and Programs in Biomedicine, 161, 145-172.
  • Hunter, L. E. (2009). The Processes of Life: An Introduction to Molecular Biology. MIT Press.
  • Kayacan, E., & Mojtaba , A. (2016). Fundamentals of Type-1 Fuzzy Logic Theory. In M. A. Erdal KAYACAN, Fuzzy Neural Networks for Real Time Control Applications (pp. 13-24). Oxford: Elsevier.
  • Kohli, R., & Piontek, F. (2008). DSS in Healthcare: Advances and Opportunities. In C. W. Frada Burstein, International Handbook on Information Systems (pp. 483-493). Springer.
  • Liu, K., Chen, Z., Wu, J., Tan, Y., Wang, L., Yan, Y., . . . Long, J. (2019). "Big Medical Data Decision-Making Intelligent System Exploiting Fuzzy Inference Logic for Prostate Cancer in Developing Countries. IEEE Access, 7, 2348-2363.
  • Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1-13.
  • Miller, R., Pople Jr, H., & Myers, J. (1982). INTERNIST-1: An experimental computer - based diagnostic consultant for general internal medicine. New England Journal of Medicine, 307(8), 468-76.
  • Musen, M., Shahar, Y., & Shortliffe, E. (2006). Clinical Decision-Support Systems. In: Shortliffe E.H., Cimino J.J. (eds) Biomedical Informatics. Health Informatics. (pp. 698-736). New York: Springer.
  • Nascimento, L., & Ortega, N. (2002). Fuzzy linguistic model for evaluating the risk of neonatal death. Rev Saude Publica, 36(6), 686–692.
  • NHS, (2021). United Kingdom National Health Service, Symptoms of Coranavirus. Retrieved from NHS: https://www.nhs.uk/conditions/coronavirus-covid-19/symptoms/, Last accessed on December 4, 2020.
  • Novak, V., Dvorák, A., & Perfilieva, I. (2016). Insight into Fuzzy Modeling. John Wiley & Sons.
  • Pedrycz, W. (1994). Why triangular membership functions?. Fuzzy Sets and Systems, 64(1), 21-30.
  • Pereira, J., Tonelli, P., Barros, L., & Ortega, N. (2004). Clinical signs of pneumonia in children: association with and prediction of diagnosis by fuzzy sets theory. Brazilian Journal of Medical and Biological Research, 37(5), 701–709.
  • Ross. (2004). Fuzzy Logic with Engineering Applications. 2nd ed. West Sussex: John Wiley & Sons Ltd.
  • Shahbazova, S. N., Sugeno, M., & Kacprzyk, J. (Eds.). (2020). Recent Developments in Fuzzy Logic and Fuzzy Sets: Dedicated to Lotfi A. Zadeh (Vol. 391). Springer Nature.
  • Sen, Z. (2009). Bulanık Mantık İlkeleri ve Modelleme (Mühendislik ve Sosyal Bilimler), İstanbul: Su Vakfı Yayınları.
  • Sivanandam, S., Sumathi, S., & Deepa, S. (2007). Introduction to Fuzzy Logic using MATLAB. Berlin: Springer.
  • Stanley, R., Moss, R., Van Stoecker, W., & Aggarwal, C. (2003). A fuzzy based histogram analysis technique for skin lesion descrimination in dermatology clinical images. Computerized Medical Imaging and Graphics, 27(5), 387–396.
  • RTMH. (2021). Republic of Turkey Ministry of Health, COVID-19 Information Bulletin. Retrieved from https://covid19.saglik.gov.tr, Last accessed on October 7, 2021.
  • Uras, M. E. (2021). In silico comparative analysis of SARS-CoV-2 nucleocapsid (N) protein using bioinformatics tools. Frontiers in Life Sciences and Related Technologies, 2(1), 1-9.
  • WHO. (2021). World Health Organization. Coronavirus disease 2019 (COVID-19) Situation Report – 89; Diagnostic testing for SARS-CoV-2. Retrieved from https://www.who.int/health-topics/coronavirus, Last accessed on October 7, 2021.
  • Zadeh, L. (1965). Fuzzy set, Information and Control, 8(3), 338-353.
  • Zadeh, L. (1973). Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, man, and Cybernetics, 3(1), 28-44.
  • Zadeh, L. (1975). The concept of linguistic variable and its application to approximate reasoning. Information Sciences, 8(3), 199-249.
  • Zadeh, L.A. (1996). Fuzzy logic= computing with words, IEEE Trans. On Fuzzy Systems, 2, 103-111.
  • Zhao, J., & Bose, B. (2002). Evaluation of membership functions for fuzzy logic controlled induction motor drive. 28th Annual Conference of the Industrial Electronics Society (pp. 229-234). Sevilla: IEEE.

Estimation of infection risk using symptoms of COVID-19: an approach based on fuzzy expert system

Year 2021, Volume: 2 Issue: 3, 92 - 102, 30.12.2021
https://doi.org/10.51753/flsrt.1010253

Abstract

According to the published reports and studies, the symptoms of the disease caused by the COVID-19 virus have not yet been fully determined. It is a major stress on clinicians to make a correct and consistent decision about whether to apply the test or not, as many factors with extreme uncertainty need to be evaluated at once. In this study, it is aimed to provide assistance to the clinicians by processing the data using fuzzy logic based decision support system at the time of the decision-making process. In the designed fuzzy logic based decision support system, a fuzzy rule-base was created with linguistic information by interpreting the symptoms that are naturally uncertain by experts. With the help of the obtained fuzzy rule base, the input data of symptoms will be processed and the risk of a person being infected will be obtained as an output. As the results of the estimation module constructed with the existing parameters are examined, it is observed to be compatible with the data published before. In this context, a data set with 50 different patients were designed randomly to evaluate the system. For the analysis of the nonlinear mapping obtained with the Mamdani type fuzzy inference system, random test data is used and infection risk at rates varying between 12.5-83% was determined. The fuzzy logic based decision support system for COVID-19 can be accepted as applicable, flexible, and trustworthy for clinicians. It can be said that this system is not only suitable for COVID-19 but also applicable for future epidemics.

References

  • Akcam, M. O., & Takada, K. (2002). Fuzzy modelling for selecting headgear types. The European Journal of Orthodontics, 24(1), 99-106.
  • Bates, J., & Young, M. (2003). Applying fuzzy logic to medical decision making in the intensive care unit. American Journal of Respiratory and Critical Care Medicine, 167(7), 948–952.
  • Benecchi, L. (2006). Neuro-fuzzy systems for prostate cancer diagnosis. Urology, 68(2), 357–361.
  • Blackmore, C. C., Mecklenburg, R. S., & Kaplan, G. S. (2011). Effectiveness of clinical decision support in controlling inappropriate imaging. Journal of the American College of Radiology, 8(1), 19-25.
  • CDC, (2020). United States of America Centers for Disease Control and Prevention. Retrieved from Centers for Disease Control and Prevention:https://web.archive.org/web/20200302201644/https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-guidance-management-patients.html , Last accessed on December 4, 2020.
  • Cismondi, F., Celi, L. A., Fialho, A. S., Vieira, S. M., Reti, S. R., Sousa, J. M., & Finkelstein, S. N. (2013). Reducing unnecessary lab testing in the ICU with Artificial Intelligence. Internal Journal of Medical Informatics, 82(5), 345-358.
  • Ewald, F., & Mohammad, A. (2015). Optimal Placement and Sizing of Shunt Capacitor Banks in the Presence of Harmonics. In E. F. Mohammad A.S. Masoum, Power Quality in Power Systems and Electrical Machines (Second Edition) (pp. 887-959). Elsevier Inc.
  • Genc, B. N. (2020). Critical management of Covid-19 pandemic in Turkey. Frontiers in Life Sciences and Related Technologies, 1(2), 69-73.
  • Grant, P., & Naesh, O. (2005). Fuzzy logic and decision-making in anaesthetics. Journal of Royal Society of Medicine, 98(1), 7-9.
  • Guan, W., & et al. (2020). Clinical characteristics of Coronavirus. The New England Journal of Medicine, 382(18), 1708-1720.
  • Hickson, L., & Khemka, I. (2014). Chapter Six - The Psychology of Decision Making, In Hodapp R.M. (eds) International Review of Research in Developmental Disabilities. Volume 47, (pp. 185-229), Academic Press.
  • Hossein, A., Gholamzadeh, M., & Shahmoradi, L. (2018). Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review. Computer Methods and Programs in Biomedicine, 161, 145-172.
  • Hunter, L. E. (2009). The Processes of Life: An Introduction to Molecular Biology. MIT Press.
  • Kayacan, E., & Mojtaba , A. (2016). Fundamentals of Type-1 Fuzzy Logic Theory. In M. A. Erdal KAYACAN, Fuzzy Neural Networks for Real Time Control Applications (pp. 13-24). Oxford: Elsevier.
  • Kohli, R., & Piontek, F. (2008). DSS in Healthcare: Advances and Opportunities. In C. W. Frada Burstein, International Handbook on Information Systems (pp. 483-493). Springer.
  • Liu, K., Chen, Z., Wu, J., Tan, Y., Wang, L., Yan, Y., . . . Long, J. (2019). "Big Medical Data Decision-Making Intelligent System Exploiting Fuzzy Inference Logic for Prostate Cancer in Developing Countries. IEEE Access, 7, 2348-2363.
  • Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1-13.
  • Miller, R., Pople Jr, H., & Myers, J. (1982). INTERNIST-1: An experimental computer - based diagnostic consultant for general internal medicine. New England Journal of Medicine, 307(8), 468-76.
  • Musen, M., Shahar, Y., & Shortliffe, E. (2006). Clinical Decision-Support Systems. In: Shortliffe E.H., Cimino J.J. (eds) Biomedical Informatics. Health Informatics. (pp. 698-736). New York: Springer.
  • Nascimento, L., & Ortega, N. (2002). Fuzzy linguistic model for evaluating the risk of neonatal death. Rev Saude Publica, 36(6), 686–692.
  • NHS, (2021). United Kingdom National Health Service, Symptoms of Coranavirus. Retrieved from NHS: https://www.nhs.uk/conditions/coronavirus-covid-19/symptoms/, Last accessed on December 4, 2020.
  • Novak, V., Dvorák, A., & Perfilieva, I. (2016). Insight into Fuzzy Modeling. John Wiley & Sons.
  • Pedrycz, W. (1994). Why triangular membership functions?. Fuzzy Sets and Systems, 64(1), 21-30.
  • Pereira, J., Tonelli, P., Barros, L., & Ortega, N. (2004). Clinical signs of pneumonia in children: association with and prediction of diagnosis by fuzzy sets theory. Brazilian Journal of Medical and Biological Research, 37(5), 701–709.
  • Ross. (2004). Fuzzy Logic with Engineering Applications. 2nd ed. West Sussex: John Wiley & Sons Ltd.
  • Shahbazova, S. N., Sugeno, M., & Kacprzyk, J. (Eds.). (2020). Recent Developments in Fuzzy Logic and Fuzzy Sets: Dedicated to Lotfi A. Zadeh (Vol. 391). Springer Nature.
  • Sen, Z. (2009). Bulanık Mantık İlkeleri ve Modelleme (Mühendislik ve Sosyal Bilimler), İstanbul: Su Vakfı Yayınları.
  • Sivanandam, S., Sumathi, S., & Deepa, S. (2007). Introduction to Fuzzy Logic using MATLAB. Berlin: Springer.
  • Stanley, R., Moss, R., Van Stoecker, W., & Aggarwal, C. (2003). A fuzzy based histogram analysis technique for skin lesion descrimination in dermatology clinical images. Computerized Medical Imaging and Graphics, 27(5), 387–396.
  • RTMH. (2021). Republic of Turkey Ministry of Health, COVID-19 Information Bulletin. Retrieved from https://covid19.saglik.gov.tr, Last accessed on October 7, 2021.
  • Uras, M. E. (2021). In silico comparative analysis of SARS-CoV-2 nucleocapsid (N) protein using bioinformatics tools. Frontiers in Life Sciences and Related Technologies, 2(1), 1-9.
  • WHO. (2021). World Health Organization. Coronavirus disease 2019 (COVID-19) Situation Report – 89; Diagnostic testing for SARS-CoV-2. Retrieved from https://www.who.int/health-topics/coronavirus, Last accessed on October 7, 2021.
  • Zadeh, L. (1965). Fuzzy set, Information and Control, 8(3), 338-353.
  • Zadeh, L. (1973). Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, man, and Cybernetics, 3(1), 28-44.
  • Zadeh, L. (1975). The concept of linguistic variable and its application to approximate reasoning. Information Sciences, 8(3), 199-249.
  • Zadeh, L.A. (1996). Fuzzy logic= computing with words, IEEE Trans. On Fuzzy Systems, 2, 103-111.
  • Zhao, J., & Bose, B. (2002). Evaluation of membership functions for fuzzy logic controlled induction motor drive. 28th Annual Conference of the Industrial Electronics Society (pp. 229-234). Sevilla: IEEE.
There are 37 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Serhat Özbey 0000-0003-4131-7090

Ahmet Koluman 0000-0001-5308-8884

Sezai Tokat 0000-0003-0193-8220

Publication Date December 30, 2021
Submission Date October 22, 2021
Published in Issue Year 2021 Volume: 2 Issue: 3

Cite

APA Özbey, S., Koluman, A., & Tokat, S. (2021). Estimation of infection risk using symptoms of COVID-19: an approach based on fuzzy expert system. Frontiers in Life Sciences and Related Technologies, 2(3), 92-102. https://doi.org/10.51753/flsrt.1010253

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