A Fuzzy Logic Expert System for Diagnosing EHF and Evaluating its Risk Level

Annie Oghenerukevbe Egwali, Francis Aien-Akho Imouokhome

Abstract


Abstract – The complex nature of Ebola hemorrhagic fever (EHF), particularly at the early stages of its outbreak, makes it difficult to diagnose because it manifests symptoms that are similar to those commonly associated with other diseases. Diagnostic decisions of physicians are therefore subjective, calling for an intelligent system for the diagnosis of EHF. This study proposes a fuzzy logic system that takes account of human limitations of medical professionals in the diagnosis of EHF and evaluation of its Risk Level in patients. To achieve the aim of the study, a fuzzy logic- controlled system was designed and implemented using MATLAB fuzzy logic tool box. Input variables to the fuzzy logic controller are (i) patient’s symptoms (PS), (ii) laboratory test results (TR), (iii) patient’s itinerary history (PH), and (iv) number of days since initial infection (ND). The proposed system, which is able to diagnose EHF and evaluate its risk level in patients, shows varying output values as the input parameters vary; with TR having a dominant effect on the results. Fuzzy Logic is proved here to be a powerful tool to design computer-based health diagnostics systems to deal with vagueness or imprecision in disease diagnosis. The fuzzy logic system designed in this study addresses the limitations that are usually associated with the subjective decisions of physicians in the orthodox procedures of diagnosing EHF and evaluating its risk levels in patients.


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References

S. Shakeel, “Ebola Virus Disease: an Emerging Threat to Pakistan”, Journal of Medical Diagnostic Methods, vol. 6, no.1, pp. 1-2, January 2017.

D. Passi, S. Sharma, S. R. Dutta, P. Dudeja, and V. Sharma, “Ebola Virus Disease (The Killer Virus): Another Threat to Humans and Bioterrorism: Brief Review and Recent Updates”, Journal of Clinical and Diagnostic Research. Vol. 9. No.6, pp. LE01-LE08, June 2015. DOI: 10.7860/JCDR/2015/13062.6100

J. R. Spengler, E. D. Ervin, J. S. Towner, P. E. Rollin, and S. T. Nichol, “Perspectives on West Africa Ebola Virus Disease Outbreak, 2013–2016”, Emerging Infectious Diseases, Vol. 22, No. 6, pp. 956-963, June 2016. www.cdc.gov/eid. DOI: http://dx.doi.org/10.32032/eid2206.160021

B. Ganiwada, A. Tanuja, V. S. N. Murthy, and P. K. Kola, “Ebola - A review of threatening epidemic”, Int. J. of Allied Med. Sci. and Clin. Research, Vol-5(2) pp. 517-530, April – June, 2017.

Acute (2015). “Ebola Virus Disease”. Acute Communicable Disease Control Manual (B-73). Available at: http://www.publichealth.lacounty.gov/acd/procs/b73/DiseaseChapters/B73Ebola.pdf.

InternationalSOS. Fighting Ebola in West Africa. 2014. https://www.internationalsos.com/hotline/fighting-ebola-in-west-africa.

World Health Organisation, 2017, Ebola Situation Report - 30 March 2016, http://www.who.int/about/copyright/en/

WHO Factsheet (2017). Available online athttp://www.who.int/mediacentre/factsheets/fs103/en/ Last visited on September 19, 2017. “Ebola virus disease Fact sheet No. 103”. World Health Organization. September 2014

S. Kumar, R. Kumari, R. Pandey, And V. A Sharma, “Ebola Virus Disease: Biology, Diagnosis”, Treatment and Prevention of Epidemics, Proc Indian Natn Sci Acad 83 No. 1, March 2017 pp. 103-149

Centers for Disease Control and Prevention. Ebola Virus Disease, 2014. Available at: http://www.cdc.gov/vhf/ebola/healthcare-us/preparing/clinicians.html

A. A. Ansari, “Clinical features and pathobiology of Ebolavirus infection”, J of utoimmunity.vol. 55, pp.1-9.

Y. J. Peter, Ebola Virus Disease, IOSR Journal of Dental and Medical Sciences (IOSR-JDMS), vol. 16, no. 8 Ver. IX , PP 71-74, August 2017. DOI: 10.9790/0853-1608097174 www.iosrjournals.org

A. MacNeil, and P. E. Rollin, “Ebola and Marburg haemorrhagic fevers”, PLoS Negl Trop Dis, vol. 6, no. 6, pp. 1-7, 2012. Available from: Academic Search Complete, EBSCOhost.

WHO. Ebola Virus Disease 2014. Available at: http://www.searo.who.int/entity/emerging_diseases/ebola/keyfacts_ebola_virus_disease.pdf.

Echwalu E. Disease Briefing: Ebola Hemorrhagic Fever, 2014. Tomas Reuters. An Abbreviated Entry 2014. Available at: http://www.whiov.cas.cn/xwdt_105286/kydt/201408/W020140822749968603837.pdf.

WHO. Ebola hemorrhagic fever fact sheet 103 2012. Available at: http://www.who.int/mediacentre/factsheets/fs103/en/.

A, Wolz, “Face to Face with Ebola — An Emergency Care Center in Sierra Leone”, N Engl, J Med; vol. 371, pp. 1081-1083, September 2014. DOI: 10.1056/NEJMp1410179

T. R. Frieden, I. Damon, B. P. Bell, T. Kenyon, and S. Nichol, “Ebola 2014 — New Challenges, New Global Response and Responsibility”, N Engl J Med 2014; vol. 371, pp.1177-1180, September 2014 DOI: 10.1056/NEJMp1409903, http://www.nejm.org/doi/10.1056/NEJMp1409903

G. A. Matua, D. M. Van der Wal, and Locsin RC, “Ebola Hemorrhagic Fever Outbreaks: Strategies for Effective Epidemic Management, Containment and Control”, Braz. J Infect Dis., Vol. 19, No. 3, April 2015, pp. 308-313.

E. M. Leroy, J. P. Gonzalez, and S. Baize, “Ebola and Marburg haemorrhagic fever viruses: major scientific advances, but a relatively minor public health threat for Africa” Clin Microbiol Infect. vol. 17, pp. 964–976, 2011. http://dx.doi. org/10.1111/j.1469-0691.2011.03535.x, PMid:21722250.

P. Sharma, D. B. V. Singh, M. K. Bandil, and N. Mishra, “Decision Support System for Malaria and Dengue Disease Diagnosis (DSSMD)” International Journal of Information and Computation Technology, vol. 3, no. 7, pp. 633-640, 2013.

N. Sahai, D. Shrivastava, and P. Srivastava P. “Diagnosis of the Jaundice Using Fuzzy Expert System” International journal of Biomedical Engineering and Science, vol. 1, no. 3, pp. 15-19, October 2014.

E. S. Singh, “A Fuzzy Rule Based Expert System to Diagnostic the Mental Illness (MIDExS)”. International Journal of Innovative Research in Computer and Communication Engineering, vol. 3, no. 9, pp. 8759-8764, September 2015.

P. B. Tintu, and R. Paulin, “Detect Breast Cancer using Fuzzy C means Techniques in Wisconsin Prognostic Breast Cancer (WPBC) Data Sets”. International Journal of Computer Applications Technology and Research, vol. 2, no. 5, pp. 614 – 617, 2013.

V. Jain, and S. Raheja. “Improving the Prediction Rate of Diabetes Using Fuzzy Expert System”, International Journal of Information Technology and Computer Science, vol. 10, pp. 84-91, September 2015.

O. J. Ayangbekun, and I. A. Jimoh “Fuzzy Logic Application to Brain Diseases Diagnosis” Journal of Emerging Trends in Computing and Information Sciences, vol. 6, no. 3, pp. 144-148, March 2015.

N. Ziasabounchi and I. Askerzade, “ANFIS Based Classification Model for Heart Disease Prediction”. International Journal of Electrical & Computer Sciences IJECS-IJENS, Vol. 14, No. 2, pp. 146402-7373, April 2014.

R. Safdari, M. Kadivar, M. Langarizadeh, A. F. Nejad, and F. Kermai, “Developing a Fuzzy Expert System to Predict the Risk of Neonatal Death”, Acta Inform Med., vol. 24, no. 1, pp. 34-37, February 2016.

K. Rahul, S. Anupam, and T. Ritu, “Fuzzy Neuro Systems for Machine Learning for Large Data Sets”. Proceedings of the IEEE International Advance Computing Conference 6-7, Patiala, India 2009; 541-545.

M. Baheti, “Study of need and Framework of Expert Systems for Medical Diagnosis”. IOSR J Comput Eng. (IOSR-JCE) e-ISSN: 2278-0661, p-ISSN: 2278-8727, 2016; 45-48.

E. Al-Daoud, “Identifying DNA splice sites using patterns statistical properties and fuzzy neural networks”, EXCLI J., vol. 8, pp. 195-202, 2009.

M. T. Adewunmi, and Y. A. Adekunle, “Clinical Decision Support System for Diagnosis of Pneumonia in Children?, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 8, pp. 1-4, 2013.


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