Biyoterörist Harp Maddelerinin Yayılımının Tahminine Yönelik Bulanık Mantık Tabanlı Karar Destek Sistemlerinin Tasarlanması

Öz

Biyoterörist harp maddelerinin, ülkeler arasında imzalanan antlaşmalara göre üretimi, geliştirilmesi ve depolanması kısıtlanmıştır. Fakat biyoterörist harp maddelerinin sahip olduğu avantajlardan dolayı terörist gruplar tarafından ülkelere karşı kullanılma ihtimali yüksektir. Bu risk karşısında ülkelerin belirli önlemler ve planlamalarının olması gerekmektedir. Bu planlamalar arasında biyoterörist harp maddelerinin ve bu maddelerin sebep oldukları hastalıkların erken teşhisi bulunmaktadır. Bu çalışmada biyolojik harp maddelerinin teşhis ve yayılımının tahmini için bulanık mantık tabanlı karar destek sistemi tasarlanmıştır. Tasarlanan sistemde hastalıklara özgü semptomlar seçilmiş ve sistemin giriş değişkenleri olarak kullanılmıştır. Semptomlara göre enfekte olma riski % cinsinden elde edilmiştir. Çalışmada Mamdani ve Sugeno bulanık çıkarım sistemleri kullanılmıştır. Farklı üyelik fonksiyonları ve durulaştırma yöntemleri kullanılarak sonuçlar alınmaya çalışılmıştır. Rastgele oluşturulmuş 500 hasta verisi, farklı modellere göre işlendiğinde %0 ila %100 arasında değişen enfeksiyon riski tahmini çıktıları elde edilmiştir.

Sonuç olarak, tasarlanan bulanık karar destek sistemi biyoterörizm alanında kullanıldığında başarılı çıktıların alındığı ve bulanık mantık tabanlı karar destek sistemlerinin biyoterörizm ve sağlık alanında kullanılabileceği kanısına varılmıştır.

Anahtar Kelimeler

• Bulanık Mantık
• Biyoterörizm
• Salgın
• Uzman sistemler
• Kitle imha silahları
• Klinik karar destek sistemleri

Fuzzy Logic Based Decision Support Systems Designed for Estimating Spread of Bioterrorist War Agents

Öz

According to agreements signed between countries, the production, development, and storage of bioterrorist warfare materials are restricted. However, due to the advantages of bioterrorist warfare agents, they are likely to be used by terrorist groups against countries. In the face of this risk, countries need to have certain precautions and plans. These plans include bioterrorist warfare agents and early detection of the diseases they cause. In this study, a fuzzy logic-based decision support system was designed for the diagnosis and prediction of the spread of biological warfare agents. In the designed system, disease-specific symptoms were selected and used as input variables in the system. The risk of being infected by symptoms was obtained to be %. Mamdani and Sugeno fuzzy inference systems were used in the study. Different membership functions and defuzzification methods have been used to obtain results. When the data of 500 randomly generated patients was processed according to different models, infection risk estimation outputs ranging from 0% to 100% were obtained.

As a result, it was concluded that successful outputs were obtained when the designed fuzzy decision support system was used in the field of bioterrorism and that fuzzy logic-based decision support systems can be used in the fields of bioterrorism and health.

Anahtar Kelimeler

• Fuzzy logic
• Bioterrorism
• Epidemics
• Expert systems
• Weapons of mass destruction
• Clinical decision support systems

Kaynakça

1. Abiyev, H. R., & Abizade, S. (2016). Diagnosing Parkinson’s Diseases Using Fuzzy Neural System. Comput Math Methods Med., 1-9.
2. Akçam, M. O., & Takada, K. (2002). Fuzzy modelling for selecting headgear types. Eur J Orthod., 99-106.
3. Allahverdi, N. (2014). Design of Fuzzy Expert Systems and Its Applications in Some Medical Areas. International Journal of Applied Mathematics, Electronics and Computers, 1-8.
4. Australian Government Department of Health. (2021, May 11). Review of Biological Agents of Security Concern. Retrieved from https://www1.health.gov.au/internet/main/publishing.nsf/Content/B6A946FB22DDD445CA257EF50014BE89/$File/FINAL-REPORT-Review-Biological-Agents-Security-Concern.pdf
5. Barras, V., & Greub, G. (2014). History of biological warfare and bioterrorism. Clin Microbiol Infect, 497-502.
6. Bates, J. T., & Young, M. P. (2003). Applying fuzzy logic to medical decision making in the intensive care unit. Am J Respir Crit Care Med, 948-952.
7. Belinda, O. E., & Emadomi, M. I. (2015). Fuzzy Logic Based Approach to Early Diagnosis of Ebola Hemorrhagic Fever. Proceedings of the World Congress on Engineering and Computer Science 2015 Vol II. San Francisco.
8. Benecchi, L. (2006). Neuro-fuzzy systems for prostate cancer diagnosis. Urology, 357-361.

9. CDC. (2012). Brucellosis. Retrieved from https://www.cdc.gov/brucellosis/symptoms/index.html
10. CDC. (2016). Smallpox. Retrieved from https://www.cdc.gov/smallpox/symptoms/index.html
11. CDC. (2018, May 6). Bioterrorism Agents/Diseases. Retrieved from CDC: https://emergency.cdc.gov/agent/agentlist-category.asp
12. CDC. (2020). Symptoms of Anthrax. Retrieved from https://www.cdc.gov/anthrax/symptoms/index.html
13. CDC. (2021). Ebola (Ebola Virus Disease). Retrieved from https://www.cdc.gov/vhf/ebola/symptoms/index.html
14. CDC, NCEZID, DVBD. (2018). Plague. Retrieved from https://www.cdc.gov/plague/symptoms/index.html
15. 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. Int J Med Inform, 345-358.
16. Doganay, M., & Aygen, B. (2003). Human brucellosis: an overview. Int J Infect Dis, 173-182. Erdin, B. N. (2019). Biyoterörizmin Epidemiyolojisi, Biyoterörizmde Savunma ve Korunma, Biyoterörizm ve Mikrobiyoloji Laboratuvarı. Klinik Mikrobiyoloji Uzmanlık Derneği (KLİMUD) e-Bülten, 25-32.
17. Esakandari, H., Nabi-Afjadi, M., Fakkari-Afjadi, J., Farahmandian, N., Miresmaeili, S., & Bahreini, E. (2020). A comprehensive review of COVID-19 characteristics. Biol Proced Online, 19-28.
18. FDA. (2019). Escherichia coli (E. coli). Retrieved from https://www.fda.gov/food/foodborne-pathogens/escherichia-coli-e-coli
19. Feldmann, H., & Geisbert, T. (2011). Ebola haemorrhagic fever. Lancet, 849-862.
20. Gayathri, B. M., & Sumathi, C. P. (2015). Mamdani fuzzy inference system for breast cancer risk detection. 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), (pp. 1-6). Madurai.
21. Grant, P., & Naesh, O. (2005). Fuzzy logic and decision-making in anaesthetics. J R Soc Med., 73.
22. Guan, W., Ni, Z., Hu, Y., Liang, W., Ou, C., & He, J. (2020). Clinical Characteristics of Coronavirus. N Engl J Med, 1708-1720.
23. Gündoğan, C. (2019). Uygun Radyolojik Tetkik İstemi İçin Hastane Bilgi Sistemine Entegre Otomatik Karar Destek Sistemi Tasarımı. İzmir, Türkiye: Dokuz Eylül Üniversitesi Sağlık Bilimleri Enstitüsü, Medikal İnformatik Anabilim Dalı.
24. Henderson, D. (1999). Smallpox: clinical and epidemiologic features. Emerg Infect Dis, 537-539.
25. Hülseweh, B. (2013). CBRN Protection Managing the Threat of Chemical, Biological, Radioactive and Nuclear Weapons. In A. H. Richardt, Characteristics of Biological Warfare Agents – Diversity of Biology (pp. 103-123). Weinheim: Wiley-VCH.
26. Internation Standardization Organizastion (ISO). (2018). ISO 31000:2018 Risk Değerlendirme. Cenevre, İsviçre.
27. Jımoh, R., Afolayan, A., Awotunde, J., & Matıluko, O. (2017). Fuzzy Logıc Based Expert System In The Dıagnosıs Of Ebola Virus. Ilorın Journal Of Computer Scıence And Informatıon Technology, 73-94.
28. Kamal, S., Rashid, A., Bakar, M., & Ahad, M. (2011). Anthrax: an update. Asian Pac J Trop Biomed, 496-501.
29. Kaya, H. (2018). Akciğer Hastalıkları Teşhisinde Sınıflandırma ve Bulanık Mantık Yöntemlerinin Uygulanması. Ankara, Türkiye: Ankara Üniversitesi Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Anabilim Dalı.
30. Kocabaş, H. (2020). Kimyasal, Biyolojik, Radyolojik ve Nükleer Savaş Ajanlarına Yönelik Dekontaminasyon Yöntemleri ve Sistemleri. Ankara: Milli Savunma Üniversitesi Alparslan Savunma Bilimleri Enstitüsü.
31. Krashenyi, I., Popov, A., Ramirez, J., & Gorriz, J. M. (2015). Application of fuzzy logic for Alzheimer's disease diagnosis. 2015 Signal Processing Symposium (SPSympo), (pp. 1-4). Debe.
32. Mayo Clinic. (2020). E. Coli. Retrieved from https://www.mayoclinic.org/diseases-conditions/e-coli/symptoms-causes/syc-20372058
33. Mayo Clinic. (2020). Smallpox. Retrieved from https://www.mayoclinic.org/diseases-conditions/smallpox/symptoms-causes/syc-20353027
34. Mead, P., & Griffin, P. (1998). Escherichia coli O157:H7. Lancet, 1207-1212.
35. Meer, K., Mkrtchyan, L., & Nagy, A. (2013). CBRN Detection Framework Using Fuzzy Logic. Proceedings of the 10th International ISCRAM Conference. Baden.
36. Melin, P., Miramontes, I., & Prado-Arechiga, G. (2018). A hybrid model based on modular neural networks and fuzzy systems for classification of blood pressure and hypertension risk diagnosis. Expert Systems With Applications, 146-164.
37. Meselson, M., Guillemin, J., Hugh-Jones, M., Langmuir, A., Popova, I., Shelokov, A., & Yampolskaya, O. (1994). The Sverdlovsk Anthrax Outbreak of 1979. Science, 1202-1208.
38. Moore, Z., Seward, J., & Lane, J. (2006). Smallpox. Lancet, 425-435.
39. Nascimento, L., & Ortega, N. S. (2002). Fuzzy linguistic model for evaluating the risk of neonatal death. Rev Saude Publica, 686-692.
40. NHS. (2020). Main symptoms of coronavirus (COVID-19). Retrieved from https://www.nhs.uk/conditions/coronavirus-covid-19/symptoms/main-symptoms/
41. NSY. (2011). Anthrax (malignant edema, woolsorters' disease). Retrieved from https://www.health.ny.gov/diseases/communicable/anthrax/fact_sheet.html
42. Pathak, A. K., & Arul, V. J. (2020). A Predictive Model for Heart Disease Diagnosis Using Fuzzy Logic and Decision Tree. In A. K. Pathak, & V. J. Arul, Smart Computing Paradigms: New Progresses and Challenges. Advances in Intelligent Systems and Computing (pp. 131-140). Singapore: Springer.
43. Pereira, J. R., Tonelli, P. A., Barros, L. C., & Ortega, N. S. (2004). Clinical signs of pneumonia in children: association with and prediction of diagnosis by fuzzy sets theory. Braz J Med Biol Res., 701-709.
44. Prentice, M., & Rahalison, L. (2007). Plague. Lancet, 1196-1207.
45. Rollins, S., Rollins, S., & Ryan, E. (2003). Yersinia pestis and the Plague. Am J Clin Pathol, 78-85.
46. Ryan, J. R. (2016). Biosecurity and Bioterrorism: Containing and Preventing Biological Threats Second Edition. Oxford, Cambridge: Butterworth-Heinemann.
47. Safari, S., Baratloo, A., Rouhipour, A., Ghelichkhani, P., & Yousefifard, M. (2015). Ebola Hemorrhagic Fever as a Public Health Emergency of International Concern; a Review Article. Emerg (Tehran), 3-7.
48. Seleem, M., Boyle, S., & Sriranganathan, N. (2010). Brucellosis: A re-emerging zoonosis. Vet Microbiol, 392-398.
49. Sousa, Z. (2014). Key features of Ebola hemorrhagic fever: a review. Asya Pac J Trop Biomed, 841-844.
50. Stanley, R. J., Moss, R. H., Van, S. W., & Aggarwal, C. (2003). Fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images. Comput Med Imaging Graph., 387-96.
51. Su, C., & Brandt, L. (1995). Escherichia coli O157:H7 infection in humans. Ann Intern Med, 698-714.
52. Sweeney, D., Hicks, C., Cui, X., Li, Y., & Eichacker, P. (2011). Anthrax Infection. Am J Respir Crit Care Med, 1333-1341.
53. T.C. Sağlık Bakanlığı. (2020). Covid-19 (Sars-Cov-2 Enfeksiyonu) Genel Bilgiler, Epidemiyoloji ve Tanı. Retrieved from https://covid19.saglik.gov.tr/TR-66337/genel-bilgiler-epidemiyoloji-ve-tani.html
54. T.C. Sağlık Bakanlığı Halk Sağlığı Genel Müdürlüğü. (2019). Bruselloz. Retrieved from https://hsgm.saglik.gov.tr/tr/zoonotikvektorel-bruselloz/detay
55. T.C. Sağlık Bakanlığı Halk Sağlığı Genel Müdürlüğü. (2019). Şarbon. Retrieved from https://hsgm.saglik.gov.tr/tr/zoonotikvektorel-sarbon/detay
56. T.C. Sağlık Bakanlığı Türkiye Hudut ve Sahiller Sağlık Genel Müdürlüğü. (2019). Veba (Plague). Retrieved from https://www.seyahatsagligi.gov.tr/site/HastalikDetay/Veba
57. Tian, D., & Z., T. (2014). Comparison and analysis of biological agent category lists based on biosafety and biodefense. PloS one, 1-6.
58. WHO. (2017). Plague. Retrieved from https://www.who.int/news-room/fact-sheets/detail/plague
59. WHO. (2018). E. Coli. Retrieved from https://www.who.int/news-room/fact-sheets/detail/e-coli
60. WHO. (2019). Smallpox. Retrieved from https://www.who.int/health-topics/smallpox#tab=tab_2
61. WHO. (2020). Coronavirus. Retrieved from https://www.who.int/health-topics/coronavirus#tab=tab_3
62. WHO. (2021). Ebola virus disease. Retrieved from https://www.who.int/news-room/fact-sheets/detail/ebola-virus-disease