Research Article
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Diagnosing COVID-19, Prioritizing Treatment, and Planning Vaccination Priority via Fuzzy Parameterized Fuzzy Soft Matrices

Year 2022, Issue: 39, 54 - 83, 30.06.2022

Zeynep Parla Parmaksız Burak Arslan Samet Memiş Serdar Enginoğlu

https://doi.org/10.53570/jnt.1128289
Cited By: 3

Abstract

In the fight against the COVID-19 pandemic, it is vital to rapidly diagnose possible contagions, treat patients, plan follow-up procedures with correct and effective use of resources and ensure the formation of herd immunity. The use of machine learning and statistical methods provides great convenience in dealing with too many data produced during research. Since access to the PCR test used for the diagnosis of COVID-19 may be limited, the test is relatively too slow to yield results, the cost is high, and its reliability is controversial; thus, making a symptomatic classification before the PCR is timesaving and far less costly. In this study, by modifying a state-of-the-art classification method, namely Comparison Matrix-Based Fuzzy Parameterized Fuzzy Soft Classifier (FPFS-CMC), an effective method is developed for a rapid diagnosis of COVID-19. The paper then presents the accuracy, sensitivity, specificity, and F1-score values that represent the diagnostic performances of the modified method. The results show that the modified method can be adopted as a competent and accurate diagnosis procedure. Afterwards, a tirage study is performed by calculating the patients’ risk scores to manage inpatient overcrowding in healthcare institutions. In the subsequent section, a vaccine priority algorithm is proposed to be used in the case of a possible crisis until the supply shortage of a newly developed vaccine is over if a possible variant of COVID-19 that is highly contagious is insensitive to the vaccine. The accuracy of the algorithm is tested with real-life data. Finally, the need for further research is discussed.

Keywords

Medical diagnosing prioritizing treatment planning vaccination priority fpfs-matrices soft decision-making

Supporting Institution

TUBİTAK

Project Number

1689B012131957

Thanks

The authors thank Beşiktaş Arts and Sciences Centre and The Scientific and Technological Research Council of Turkey (TUBİTAK) for their valuable support. This study was presented at Regeneron International Science and Engineering Fair (ISEF) 2022 by Zeynep Parla Parmaksız.

References

  • C. Cortes, V. Vapnik, Support-vector Networks, Machine Learning 20 (3) (1995) 273–297.
  • J. M. Keller, M. R. Gray, J. A. Givens, A Fuzzy K-nearest Neighbor Algorithm, IEEE Transactions on Systems, Man, and Cybernetics 15 (1985) 580–585.
  • Y. Freund, R. E. Schapire, A Decision-theoretic Generalization of On-line Learning and an Application to Boosting, Journal of Computer and System Sciences 55 (1) (1997) 119–139.
  • L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone, Classification and Regression Trees. 3rd Edn., CRC Press, Wadsworth, 1998.
  • B. Handaga, H. Onn, T. Herawan, FSSC: An Algorithm for Classifying Numerical Data using Fuzzy Soft Set Theory, International Journal of Fuzzy System Applications 2 (4) (2012) 29–46.
  • S. A. Lashari, R. Ibrahim, N. Senan, Medical Data Classification using Similarity Measure of Fuzzy Soft Set-based Distance Measure, Journal of Telecommunication, Electronic and Computer Engineering 9 (2–9) (2017) 95–99.
  • I. T. R. Yanto, R. R. Seadudin, S. A. Lashari, Haviluddin A Numerical Classification Technique Based on Fuzzy Soft Set using Hamming Distance, in: R. Ghazali, M. M. Deris, N. M. Nawi, J. H. Abawajy (Eds.), Third International Conference on Soft Computing and Data Mining, Johor, Malaysia, 2018, pp. 252–260.
  • S. Memiş, S. Enginoğlu, U. Erkan, A Classification Method in Machine Learning Based on Soft Decision-Making via Fuzzy Parameterized Fuzzy Soft Matrices, Soft Computing 26 (2022) 1165–1180.
  • S. Memiş, 2021. FPFS-CMC. GitHub Repository. Retrieved from https://github.com/sametmemis/FPFS-CMC.git
  • D. Dua, C. Graff, 2019. UCI Machine Learning Repository [Database].
  • A. T. Erol, Predictive Indicators and Risk Factors in COVID-19 Mortality in Intensive Care: Retrospective Observatory Study, Master’s Thesis in Medicine, University of Health Sciences (2020) İstanbul Turkiye.
  • F. Çoktaş, Assessment of Risk Factors Related to COVID-19 Disease in Healthcare Workers, Master’s Thesis in Medicine, University of Health Sciences (2020) İstanbul Turkiye.
  • M. S. Gold, D. Sehayek, S. Gabrielli, X. Zhang, C. McCusker, M. Ben-Shoshan, COVID-19 and Comorbidities: A Systematic Review and Meta-Analysis, Postgraduate Medicine 132(8) (2020) 749–755.
  • M. Parohan, S. Yaghoubi, A. Seraji, M. H. Javanbakht, P. Sarraf, M. Djalali, Risk Factors for Mortality in Patients with Coronavirus Disease 2019 (COVID19) Infection: A Systematic Review and Meta-Analysis of Observational Studies, The Aging Male 23 (5) (2020) 1416–1424.
  • Turkish Ministry of Health, (2020). COVID-19 Weekly Status Report 12/10/2020 – 18/10/2020 Ankara. Retrieved from https://covid19.saglik.gov.tr/Eklenti/39169/0/covid-19-weekly-situation-report---42-weekpdf.pdf?_tag1=B1A9C0854AC47DADC8FF332CC2CB9A336F580D4B
  • Turkish Ministry of Health, (2021). Vaccination Group Ranking. Retrieved from https://covid19asi.saglik.gov.tr/EN-80295/list-of-covid-19-vaccination-groups.html
  • Z. Zheng, F. Peng, B. Xua, J. Zhao, H. Liu, J. Peng, Q. Li, C. Jiang, Y. Zhou, S. Liu, C. Ye, P. Zhang, Y. Xing, H. Guo, W. Tang, Risk Factors of Critical & Mortal COVID-19 Cases: A Systematic Literature Review and Meta-Analysis, Journal of Infection 81 (2020) 16–25.
  • F. Zhou, T. Yu, R. Du, G. Fan, Y. Liu, Z. Liu, J. Xiang, Y. Wang, B. Song, X. Gu, L. Guan, Y. Wei, H. Li, X. Wu, J. Xu, S. Tu, Y. Zhang, H. Chen, B. Cao, Clinical Course and Risk Factors for Mortality of Adult Inpatients with COVID-19 in Wuhan, China: A Retrospective Cohort Study, The Lancet 395 (2020) 1054–1062.
  • H. Harikrishnan, (2020, April). Symptoms and COVID Presence (May 2020 data), Version 1. Retrieved June 21, 2021, from https://www.kaggle.com/datasets/hemanthhari/symptoms-and-covid-presence.
  • J. Thyadi, (2021, July). Covid-19 Symptoms, Version 1. Retrieved June 21, 2022, from https://www.kaggle.com/datasets/jayasreethyadi/covid19-symptoms.
  • S. Shahane, (2021, Mar). Brazilian Covid Symptomatic Patients Data, Version 1. Retrieved June 21, 2022, from https://www.kaggle.com/datasets/saurabhshahane/brazilian-covid-symptomatic-patients-data.
  • M. Mehryar, A. Rostamizadeh, A. Talwalkar, Foundations of Machine Learning. 2nd Edn., The MIT Press, London, 2018.
  • S. Enginoğlu, N. Çağman, Fuzzy Parameterized Fuzzy Soft Matrices and Their Application in Decision-making, TWMS Journal of Applied and Engineering Mathematics 10 (4) (2020) 1105–1115.
  • N. Çağman, F. Çıtak, S. Enginoğlu, Fuzzy Parameterized Fuzzy Soft Set Theory and Its Applications, Turkish Journal of Fuzzy Systems 1 (1) (2010) 21–35.
  • S. Memiş, S. Enginoğlu, U. Erkan, Numerical Data Classification via Distance-based Similarity Measures of Fuzzy Parameterized Fuzzy Soft Matrices, IEEE Access 9 (2021) 88583–88601.
  • S. Enginoğlu, S. Memiş, F. Karaaslan, A New Approach to Group Decision-making Method Based on TOPSIS under Fuzzy Soft Environment, Journal of New Results in Science 8 (2) (2019) 42–52.
  • S. Memiş, 2021. EMK19. GitHub Repository. Retrieved from https://github.com/sametmemis/EMK19.git
  • S. Enginoğlu, S. Memiş, Comment on Fuzzy Soft Sets [The Journal of Fuzzy Mathematics 9(3), 2001, 589–602], International Journal of Latest Engineering Research and Applications 3 (9) (2018) 1–9.
  • T. Aydın, S. Enginoğlu, A Configuration of Five of The Soft Decision-making Methods via Fuzzy Parameterized Fuzzy Soft Matrices and Their Application to A Performance-based Value Assignment Problem, in: M. Kılıç, K. Özkan, M. Karaboyacı, K. Taşdelen, H. Kandemir, A. Beram (Eds.), Second International Conferences on Science and Technology: Natural Science and Technology (ICONST-NST) Prizren, Kosovo, 2019, pp. 56–67.
  • T. Aydın, S. Enginoğlu, Configurations of SDM Methods Proposed between 1999 and 2012: A Follow-up Study, in: K. Yıldırım (Ed.) Fourth International Conference on Mathematics: “An İstanbul Meeting for World Mathematicians”, İstanbul, Turkiye, 2020, pp. 183–202.
  • S. Enginoğlu, S. Memiş, A Configuration of Some Soft Decision-making Algorithms via fpfs-matrices, Cumhuriyet Science Journal 39 (4) (2018) 871–881.
  • S. Enginoğlu, T. Öngel, Configurations of Several Soft Decision-making Methods to Operate in Fuzzy Parameterized Fuzzy Soft Matrices Space, Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 21 (1) (2020) 58–71.
  • S. Enginoğlu, T. Aydın, S. Memiş, B. Arslan, Operability-oriented Configurations of The Soft Decision-making Methods Proposed between 2013 and 2016 and Their Comparisons, Journal of New Theory 2021 (34) (2021) 82–114.
  • S. Enginoğlu, T. Aydın, S. Memiş, B. Arslan, SDM Methods’ Configurations (2017-2019) and Their Application to A Performance-based Value Assignment Problem: A Follow up Study, Annals of Optimization Theory and Practice 4 (1) (2021) 41–85.
  • S. Memiş, B. Arslan, T. Aydın, S. Enginoğlu, Ç. Camcı, A Classification Method Based on Hamming Pseudo-similarity of Intuitionistic Fuzzy Parameterized Intuitionistic Fuzzy Soft Matrices, Journal of New Results in Science 10 (2) (2021) 59–76.
  • M. Stone, Cross-validatory Choice and Assessment of Statistical Predictions, Journal of the Royal Statistical Society Series B (Methodological) 36 (1974) 111–147.
  • D. Üstün, A. Toktaş, A. Akdağlı, Deep Neural Network–based Soft Computing the Resonant Frequency of E–shaped Patch Antennas, International Journal of Electronics and Communications 102 (2019) 54–61.
  • T. Fawcett, An Introduction to ROC Analysis, Pattern Recognition Letters 27 (2006) 861–874.
  • U. Erkan, A Precise and Stable Machine Learning Algorithm: Eigenvalue Classification (EigenClass), Neural Computing and Applications 33 (10) (2021) 5381–5392.
  • F. Saygılı, Hypertension Diagnosis and Treatment Guide, Turkish Society of Endocrinology and Metabolism, 2019.
  • Z. Rakipoğlu, (2019, April 4). The Death Rate from Heart Diseases in Turkiye is 42 Percent, Anadolu Agency (2020, July 17). Retrieved from https://www.aa.com.tr/tr/saglik/turkiyede-kalp-hastaliklarinda-olum-orani-yuzde-42/1442301.
  • TKD, (2020). Cardiovascular Risk Calculator, Turkish Society of Cardiology Web Site. Retrieved on August 18, 2020, from https://tkd.org.tr/cardibil/kalp-damar-sagligi/cardivaskuler-risk-hesaplama.
  • H. Dülek, Z. Tuzcular Vural, I. Gönenç, Risk Factors in Cardiovascular Diseases, The Journal of Turkish Family Physician 9 (2) (2018) 53–58.
  • M. Özdoğan, (2020, October 6). For Some Common Cancers Lifetime Statistics, Retrieved on June 21, 2021, from https://www.drozdogan.com/kanser-yasam-suresi-istatistikleri/
  • National Cancer Institute (NCI) (2020) Surveillance, Epidemiology, and End Results (SEER) Program. (2020, October 6). Retrieved from https://seer.cancer.gov/statfacts/html/all.html
  • F. S. Taş, K. Cengiz, E. Erdem, A. Karataş, C. Kaya, Causes of Mortality in Acute and Chronic Renal Failure, Fırat Medical Journal 16 (3) (2011) 120–124.
  • E. Ahlqvist, P. Storm, A. Käräjämäki, M. Martinell, M. Dorkhan, A. Carlsson, P. Vikman, R. B. Prasad, D. M. Aly, P. Almgren, Y. Wessman, N. Shaat, P. Spégel, H. Mulder, E. Lindholm, O. Melander, O. Hansson, U. Malmqvist, A. Lernmark, K. Lahti, T. Forsén, T. Tuomi, A. H. Rosengren, L. Groop, Novel Subgroups of Adult-onset Diabetes and Their Association with Outcomes: A Data-driven Cluster Analysis of Six Variables, Lancet Diabetes Endocrinol 6 (2018) 361–369.
  • Turkish Diabetes Foundation, Diabetes Diagnosis and Treatment Guide. Armoni Nüans Baskı Sanatları A. Ş., (2017).
  • T. Chen, D. Wu, H. Chen, W. Yan, D. Yang, G. Chen, K. Ma, D. Xu, H. Yu, H. Wang, T. Wang, W. Guo, J. Chen, C. Ding, X. Zhang, J. Huang, M. Han, S. Li, X. Luo, J. Zhao, Q. Ning, Clinical Characteristics of 113 Deceased Patients with Coronavirus Disease 2019: Retrospective Study, British Medical Journal 368 (2020) m1091.
  • S. Enginoğlu, S. Memiş, A Review on Some Soft Decision-making Methods. Proceedings of The International Conference on Mathematical Studies and Applications 2018 Karamanoğlu Mehmetbey University, Karaman, Turkiye, 4-6 October 2018.
  • S. Memiş, 2021. YE12. GitHub repository. Retrieved from https://github.com/sametmemis/YE12.git

Year 2022, Issue: 39, 54 - 83, 30.06.2022

Zeynep Parla Parmaksız Burak Arslan Samet Memiş Serdar Enginoğlu

https://doi.org/10.53570/jnt.1128289
Cited By: 3

Abstract

Project Number

1689B012131957

References

  • C. Cortes, V. Vapnik, Support-vector Networks, Machine Learning 20 (3) (1995) 273–297.
  • J. M. Keller, M. R. Gray, J. A. Givens, A Fuzzy K-nearest Neighbor Algorithm, IEEE Transactions on Systems, Man, and Cybernetics 15 (1985) 580–585.
  • Y. Freund, R. E. Schapire, A Decision-theoretic Generalization of On-line Learning and an Application to Boosting, Journal of Computer and System Sciences 55 (1) (1997) 119–139.
  • L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone, Classification and Regression Trees. 3rd Edn., CRC Press, Wadsworth, 1998.
  • B. Handaga, H. Onn, T. Herawan, FSSC: An Algorithm for Classifying Numerical Data using Fuzzy Soft Set Theory, International Journal of Fuzzy System Applications 2 (4) (2012) 29–46.
  • S. A. Lashari, R. Ibrahim, N. Senan, Medical Data Classification using Similarity Measure of Fuzzy Soft Set-based Distance Measure, Journal of Telecommunication, Electronic and Computer Engineering 9 (2–9) (2017) 95–99.
  • I. T. R. Yanto, R. R. Seadudin, S. A. Lashari, Haviluddin A Numerical Classification Technique Based on Fuzzy Soft Set using Hamming Distance, in: R. Ghazali, M. M. Deris, N. M. Nawi, J. H. Abawajy (Eds.), Third International Conference on Soft Computing and Data Mining, Johor, Malaysia, 2018, pp. 252–260.
  • S. Memiş, S. Enginoğlu, U. Erkan, A Classification Method in Machine Learning Based on Soft Decision-Making via Fuzzy Parameterized Fuzzy Soft Matrices, Soft Computing 26 (2022) 1165–1180.
  • S. Memiş, 2021. FPFS-CMC. GitHub Repository. Retrieved from https://github.com/sametmemis/FPFS-CMC.git
  • D. Dua, C. Graff, 2019. UCI Machine Learning Repository [Database].
  • A. T. Erol, Predictive Indicators and Risk Factors in COVID-19 Mortality in Intensive Care: Retrospective Observatory Study, Master’s Thesis in Medicine, University of Health Sciences (2020) İstanbul Turkiye.
  • F. Çoktaş, Assessment of Risk Factors Related to COVID-19 Disease in Healthcare Workers, Master’s Thesis in Medicine, University of Health Sciences (2020) İstanbul Turkiye.
  • M. S. Gold, D. Sehayek, S. Gabrielli, X. Zhang, C. McCusker, M. Ben-Shoshan, COVID-19 and Comorbidities: A Systematic Review and Meta-Analysis, Postgraduate Medicine 132(8) (2020) 749–755.
  • M. Parohan, S. Yaghoubi, A. Seraji, M. H. Javanbakht, P. Sarraf, M. Djalali, Risk Factors for Mortality in Patients with Coronavirus Disease 2019 (COVID19) Infection: A Systematic Review and Meta-Analysis of Observational Studies, The Aging Male 23 (5) (2020) 1416–1424.
  • Turkish Ministry of Health, (2020). COVID-19 Weekly Status Report 12/10/2020 – 18/10/2020 Ankara. Retrieved from https://covid19.saglik.gov.tr/Eklenti/39169/0/covid-19-weekly-situation-report---42-weekpdf.pdf?_tag1=B1A9C0854AC47DADC8FF332CC2CB9A336F580D4B
  • Turkish Ministry of Health, (2021). Vaccination Group Ranking. Retrieved from https://covid19asi.saglik.gov.tr/EN-80295/list-of-covid-19-vaccination-groups.html
  • Z. Zheng, F. Peng, B. Xua, J. Zhao, H. Liu, J. Peng, Q. Li, C. Jiang, Y. Zhou, S. Liu, C. Ye, P. Zhang, Y. Xing, H. Guo, W. Tang, Risk Factors of Critical & Mortal COVID-19 Cases: A Systematic Literature Review and Meta-Analysis, Journal of Infection 81 (2020) 16–25.
  • F. Zhou, T. Yu, R. Du, G. Fan, Y. Liu, Z. Liu, J. Xiang, Y. Wang, B. Song, X. Gu, L. Guan, Y. Wei, H. Li, X. Wu, J. Xu, S. Tu, Y. Zhang, H. Chen, B. Cao, Clinical Course and Risk Factors for Mortality of Adult Inpatients with COVID-19 in Wuhan, China: A Retrospective Cohort Study, The Lancet 395 (2020) 1054–1062.
  • H. Harikrishnan, (2020, April). Symptoms and COVID Presence (May 2020 data), Version 1. Retrieved June 21, 2021, from https://www.kaggle.com/datasets/hemanthhari/symptoms-and-covid-presence.
  • J. Thyadi, (2021, July). Covid-19 Symptoms, Version 1. Retrieved June 21, 2022, from https://www.kaggle.com/datasets/jayasreethyadi/covid19-symptoms.
  • S. Shahane, (2021, Mar). Brazilian Covid Symptomatic Patients Data, Version 1. Retrieved June 21, 2022, from https://www.kaggle.com/datasets/saurabhshahane/brazilian-covid-symptomatic-patients-data.
  • M. Mehryar, A. Rostamizadeh, A. Talwalkar, Foundations of Machine Learning. 2nd Edn., The MIT Press, London, 2018.
  • S. Enginoğlu, N. Çağman, Fuzzy Parameterized Fuzzy Soft Matrices and Their Application in Decision-making, TWMS Journal of Applied and Engineering Mathematics 10 (4) (2020) 1105–1115.
  • N. Çağman, F. Çıtak, S. Enginoğlu, Fuzzy Parameterized Fuzzy Soft Set Theory and Its Applications, Turkish Journal of Fuzzy Systems 1 (1) (2010) 21–35.
  • S. Memiş, S. Enginoğlu, U. Erkan, Numerical Data Classification via Distance-based Similarity Measures of Fuzzy Parameterized Fuzzy Soft Matrices, IEEE Access 9 (2021) 88583–88601.
  • S. Enginoğlu, S. Memiş, F. Karaaslan, A New Approach to Group Decision-making Method Based on TOPSIS under Fuzzy Soft Environment, Journal of New Results in Science 8 (2) (2019) 42–52.
  • S. Memiş, 2021. EMK19. GitHub Repository. Retrieved from https://github.com/sametmemis/EMK19.git
  • S. Enginoğlu, S. Memiş, Comment on Fuzzy Soft Sets [The Journal of Fuzzy Mathematics 9(3), 2001, 589–602], International Journal of Latest Engineering Research and Applications 3 (9) (2018) 1–9.
  • T. Aydın, S. Enginoğlu, A Configuration of Five of The Soft Decision-making Methods via Fuzzy Parameterized Fuzzy Soft Matrices and Their Application to A Performance-based Value Assignment Problem, in: M. Kılıç, K. Özkan, M. Karaboyacı, K. Taşdelen, H. Kandemir, A. Beram (Eds.), Second International Conferences on Science and Technology: Natural Science and Technology (ICONST-NST) Prizren, Kosovo, 2019, pp. 56–67.
  • T. Aydın, S. Enginoğlu, Configurations of SDM Methods Proposed between 1999 and 2012: A Follow-up Study, in: K. Yıldırım (Ed.) Fourth International Conference on Mathematics: “An İstanbul Meeting for World Mathematicians”, İstanbul, Turkiye, 2020, pp. 183–202.
  • S. Enginoğlu, S. Memiş, A Configuration of Some Soft Decision-making Algorithms via fpfs-matrices, Cumhuriyet Science Journal 39 (4) (2018) 871–881.
  • S. Enginoğlu, T. Öngel, Configurations of Several Soft Decision-making Methods to Operate in Fuzzy Parameterized Fuzzy Soft Matrices Space, Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 21 (1) (2020) 58–71.
  • S. Enginoğlu, T. Aydın, S. Memiş, B. Arslan, Operability-oriented Configurations of The Soft Decision-making Methods Proposed between 2013 and 2016 and Their Comparisons, Journal of New Theory 2021 (34) (2021) 82–114.
  • S. Enginoğlu, T. Aydın, S. Memiş, B. Arslan, SDM Methods’ Configurations (2017-2019) and Their Application to A Performance-based Value Assignment Problem: A Follow up Study, Annals of Optimization Theory and Practice 4 (1) (2021) 41–85.
  • S. Memiş, B. Arslan, T. Aydın, S. Enginoğlu, Ç. Camcı, A Classification Method Based on Hamming Pseudo-similarity of Intuitionistic Fuzzy Parameterized Intuitionistic Fuzzy Soft Matrices, Journal of New Results in Science 10 (2) (2021) 59–76.
  • M. Stone, Cross-validatory Choice and Assessment of Statistical Predictions, Journal of the Royal Statistical Society Series B (Methodological) 36 (1974) 111–147.
  • D. Üstün, A. Toktaş, A. Akdağlı, Deep Neural Network–based Soft Computing the Resonant Frequency of E–shaped Patch Antennas, International Journal of Electronics and Communications 102 (2019) 54–61.
  • T. Fawcett, An Introduction to ROC Analysis, Pattern Recognition Letters 27 (2006) 861–874.
  • U. Erkan, A Precise and Stable Machine Learning Algorithm: Eigenvalue Classification (EigenClass), Neural Computing and Applications 33 (10) (2021) 5381–5392.
  • F. Saygılı, Hypertension Diagnosis and Treatment Guide, Turkish Society of Endocrinology and Metabolism, 2019.
  • Z. Rakipoğlu, (2019, April 4). The Death Rate from Heart Diseases in Turkiye is 42 Percent, Anadolu Agency (2020, July 17). Retrieved from https://www.aa.com.tr/tr/saglik/turkiyede-kalp-hastaliklarinda-olum-orani-yuzde-42/1442301.
  • TKD, (2020). Cardiovascular Risk Calculator, Turkish Society of Cardiology Web Site. Retrieved on August 18, 2020, from https://tkd.org.tr/cardibil/kalp-damar-sagligi/cardivaskuler-risk-hesaplama.
  • H. Dülek, Z. Tuzcular Vural, I. Gönenç, Risk Factors in Cardiovascular Diseases, The Journal of Turkish Family Physician 9 (2) (2018) 53–58.
  • M. Özdoğan, (2020, October 6). For Some Common Cancers Lifetime Statistics, Retrieved on June 21, 2021, from https://www.drozdogan.com/kanser-yasam-suresi-istatistikleri/
  • National Cancer Institute (NCI) (2020) Surveillance, Epidemiology, and End Results (SEER) Program. (2020, October 6). Retrieved from https://seer.cancer.gov/statfacts/html/all.html
  • F. S. Taş, K. Cengiz, E. Erdem, A. Karataş, C. Kaya, Causes of Mortality in Acute and Chronic Renal Failure, Fırat Medical Journal 16 (3) (2011) 120–124.
  • E. Ahlqvist, P. Storm, A. Käräjämäki, M. Martinell, M. Dorkhan, A. Carlsson, P. Vikman, R. B. Prasad, D. M. Aly, P. Almgren, Y. Wessman, N. Shaat, P. Spégel, H. Mulder, E. Lindholm, O. Melander, O. Hansson, U. Malmqvist, A. Lernmark, K. Lahti, T. Forsén, T. Tuomi, A. H. Rosengren, L. Groop, Novel Subgroups of Adult-onset Diabetes and Their Association with Outcomes: A Data-driven Cluster Analysis of Six Variables, Lancet Diabetes Endocrinol 6 (2018) 361–369.
  • Turkish Diabetes Foundation, Diabetes Diagnosis and Treatment Guide. Armoni Nüans Baskı Sanatları A. Ş., (2017).
  • T. Chen, D. Wu, H. Chen, W. Yan, D. Yang, G. Chen, K. Ma, D. Xu, H. Yu, H. Wang, T. Wang, W. Guo, J. Chen, C. Ding, X. Zhang, J. Huang, M. Han, S. Li, X. Luo, J. Zhao, Q. Ning, Clinical Characteristics of 113 Deceased Patients with Coronavirus Disease 2019: Retrospective Study, British Medical Journal 368 (2020) m1091.
  • S. Enginoğlu, S. Memiş, A Review on Some Soft Decision-making Methods. Proceedings of The International Conference on Mathematical Studies and Applications 2018 Karamanoğlu Mehmetbey University, Karaman, Turkiye, 4-6 October 2018.
  • S. Memiş, 2021. YE12. GitHub repository. Retrieved from https://github.com/sametmemis/YE12.git
There are 51 citations in total.

Details

Primary Language English
Subjects Applied Mathematics
Journal Section Research Article
Authors

Zeynep Parla Parmaksız 0000-0002-9106-5111

Burak Arslan 0000-0002-1724-8841

Samet Memiş 0000-0002-0958-5872

Serdar Enginoğlu 0000-0002-7188-9893

Project Number 1689B012131957
Publication Date June 30, 2022
Submission Date June 9, 2022
Published in Issue Year 2022 Issue: 39

Cite

APA Parmaksız, Z. P., Arslan, B., Memiş, S., Enginoğlu, S. (2022). Diagnosing COVID-19, Prioritizing Treatment, and Planning Vaccination Priority via Fuzzy Parameterized Fuzzy Soft Matrices. Journal of New Theory(39), 54-83. https://doi.org/10.53570/jnt.1128289
AMA Parmaksız ZP, Arslan B, Memiş S, Enginoğlu S. Diagnosing COVID-19, Prioritizing Treatment, and Planning Vaccination Priority via Fuzzy Parameterized Fuzzy Soft Matrices. JNT. June 2022;(39):54-83. doi:10.53570/jnt.1128289
Chicago Parmaksız, Zeynep Parla, Burak Arslan, Samet Memiş, and Serdar Enginoğlu. “Diagnosing COVID-19, Prioritizing Treatment, and Planning Vaccination Priority via Fuzzy Parameterized Fuzzy Soft Matrices”. Journal of New Theory, no. 39 (June 2022): 54-83. https://doi.org/10.53570/jnt.1128289.
EndNote Parmaksız ZP, Arslan B, Memiş S, Enginoğlu S (June 1, 2022) Diagnosing COVID-19, Prioritizing Treatment, and Planning Vaccination Priority via Fuzzy Parameterized Fuzzy Soft Matrices. Journal of New Theory 39 54–83.
IEEE Z. P. Parmaksız, B. Arslan, S. Memiş, and S. Enginoğlu, “Diagnosing COVID-19, Prioritizing Treatment, and Planning Vaccination Priority via Fuzzy Parameterized Fuzzy Soft Matrices”, JNT, no. 39, pp. 54–83, June 2022, doi: 10.53570/jnt.1128289.
ISNAD Parmaksız, Zeynep Parla et al. “Diagnosing COVID-19, Prioritizing Treatment, and Planning Vaccination Priority via Fuzzy Parameterized Fuzzy Soft Matrices”. Journal of New Theory 39 (June 2022), 54-83. https://doi.org/10.53570/jnt.1128289.
JAMA Parmaksız ZP, Arslan B, Memiş S, Enginoğlu S. Diagnosing COVID-19, Prioritizing Treatment, and Planning Vaccination Priority via Fuzzy Parameterized Fuzzy Soft Matrices. JNT. 2022;:54–83.
MLA Parmaksız, Zeynep Parla et al. “Diagnosing COVID-19, Prioritizing Treatment, and Planning Vaccination Priority via Fuzzy Parameterized Fuzzy Soft Matrices”. Journal of New Theory, no. 39, 2022, pp. 54-83, doi:10.53570/jnt.1128289.
Vancouver Parmaksız ZP, Arslan B, Memiş S, Enginoğlu S. Diagnosing COVID-19, Prioritizing Treatment, and Planning Vaccination Priority via Fuzzy Parameterized Fuzzy Soft Matrices. JNT. 2022(39):54-83.

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