A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms
American Journal of Artificial Intelligence
Volume 3, Issue 1, June 2019, Pages: 9-16
Received: Jun. 16, 2019; Accepted: Jul. 13, 2019; Published: Aug. 13, 2019
Views 140      Downloads 28
Authors
Samuel Ekene Nnebe, Department of Computer Science, Ambrose Alli University, Ekpoma, Nigeria
Nora Augusta Ozemoya Okoh, Department of Computer Science, Ambrose Alli University, Ekpoma, Nigeria
Adetokunbo Mac Gregor John-Otumu, Department of Computer Science, Ambrose Alli University, Ekpoma, Nigeria
Emmanuel Osaze Oshoiribhor, Department of Computer Science, Ambrose Alli University, Ekpoma, Nigeria
Article Tools
Follow on us
Abstract
Lassa fever is an acute viral haemorrhagic fever that is awfully infectious through infected rodents in the mastomysnatalensis species that are complex reservoirs capable of excreting the virus through their urine, saliva, excreta and other body fluids to man. The virus is a single stranded RNA virus belonging to the arenaviridae family. It presents no definite signs or symptoms and clinical analysis is often problematic especially at the early onset of the disease. Accurate diagnosis requires highly specialized laboratories, which are expensive and not readily available to the entire populace. Early diagnosis and treatment of Lassa fever is very vital for survival. In this study, we identified that fuzzy logic and rule-based techniques are the only artificial intelligence supported approach that has been used to develop an expert system for diagnosing the dreaded Lassa fever as an alternative to laboratory methodology. It is noted that rule-based is not an efficient technique in the designing expert systems based on its shortcomings such as opaque relations between rules, ineffective search strategy, and its inability to learn; while the fuzzy based technique does not also support the ability to learn but good in areas such as knowledge representation, uncertainty tolerance, imprecision tolerance, and explanation ability. Based on these information gathered, the authors decided to design a hybridized intelligent framework driven by the integration of Neural Network (NN), Fuzzy logic (FL) and Case Based Reasoning (CBR) based on their individual strengths put together in order to proffer a quick and reliable diagnosis for Lassa fever infection using observed clinical symptoms that could aid medical practitioners in decision making.
Keywords
Intelligence, Hybrid Model, Neuro-fuzzy CBR, Expert System, Lassa Fever
To cite this article
Samuel Ekene Nnebe, Nora Augusta Ozemoya Okoh, Adetokunbo Mac Gregor John-Otumu, Emmanuel Osaze Oshoiribhor, A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms, American Journal of Artificial Intelligence. Vol. 3, No. 1, 2019, pp. 9-16. doi: 10.11648/j.ajai.20190301.12
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
K. Sanjay and P. Rajkishore (2015), Importance of Expert System Shell in Development of Expert System, International Journal of Innovative Research and Development, 4 (3): 128-133.
[2]
S. A. Fatumo, E. Adetiba, and J. O. Onaolapo (2013). Implementation of XpertMalTyph: An Expert System for Medical Diagnosis of the Complications of Malaria and Typhoid, IOSR Journal of Computer Engineering, 8 (5): 34-40.
[3]
J. C. Obi and A. A. Imianvan (2011). Interactive Neuro-Fuzzy Expert System for Diagnosis of Leukemia, Global Journal of Computer Science and Technology, 11 (12): 42-50.
[4]
S. Tunmibi, O. Adeniji, A. Aregbesola, and D. Ayodeji (2013). A Rule Based Expert System for Diagnosis of Fever, International Journal of Advanced Research, 1 (7): 343-348.
[5]
M. Patel, A. Patel, and P. Virparia (2013), Rule Based Expert System for Viral Infection Diagnosis, International Journal of Advanced Research in Computer Science and Software Engineering, 3 (5).
[6]
M. S. Hossain, M. S. Khalid, S. Akter, and S. Dey (2014). A belief rule-based expert system to diagnose influenza, In proceedings of Strategic Technology 9th International Forum 2014 IEEE.
[7]
R. H. Komal and S. G. Vijay (2014), Rule-Based Expert System for the Diagnosis of Memory Loss Diseases, International Journal of Innovative Science, Engineering & Technology, 1 (3).
[8]
K. C. Eze, T. A. T. Salami, I. C. Eze, A. E. Pogoson, N. Omordia and M. O. Ugochukwu (2010), High Lassa fever activity in Northern part of Edo state, Nigeria: Re-analysis of confirmatory test results, Afr Journal of Health Science, 17: 52-56.
[9]
M. D. Bowen, P. E. Rollin, T. G. Ksiazek, H. L. Hustad, D. G. Bausch, and A. H. Demby (2000), Genetic diversity among Lassa virus strains. Journal of Virol, 74 (15): 6992-7004.
[10]
O. Ogbu, E. Ajuluchukwu, and C. J. Uneke (2007), Lassa fever in West Africa sub-region: An Overview, Journal of Vect Borne Dis. 44 (1): 1-11.
[11]
Centre for Disease Control and Prevention. Lassa fever fact sheet. 2014. Available: http://www.cdc. gov/ncidod/dvrd/spb/mnpages/dispages/factsheets/lassa_fever_fact_sheet. pdf. Accessed January 2016. Google Scholar
[12]
World Health Organisation. World Health Organisation fact sheet on Lassa fever. 2015; 10 (11). Google Scholar.
[13]
A. I. Nasir and M. F. Sani (2015), Outbreak, pathogen containment and laboratory investigation of Lassa fever in Nigeria: How prepared are we? International Journal of Tropical Disease and Health, 10 (1): 1-10.
[14]
M. A. Hambali, A. A. Akinyemi, and J. D. Luka (2017). Expert System For Lassa Fever Diagnosis Using Rule Based Approach, Annals Computer Science Series, 15 (2): 68-74.
[15]
R. O. Osaseri, E. A. Onibere, and A. R. Usiobiafo (2014). Fuzzy Expert Model for Diagnosis of Lassa fever, Journal of the Nigerian Association of Mathematical Physics, 27: 533-540.
[16]
A. A. Imianvan and J. C. Obi (2012). Cognitive Neuro-Fuzzy System for Hypotension Control, Computer Engineering and Intelligent Systems, 3 (6): 21-31.
[17]
J. C. Obi and A. A. Imianvan (2011). Interactive Neuro-Fuzzy Expert System for Diagnosis of Leukemia, Global Journal of Computer Science and Technology, 11 (12): 42-50.
[18]
T. Manikandan, N. Bharathi, M. Sathish, and V. Asokan (2017), Hybrid Neuro-Fuzzy System for Prediction of Lung Disease Based on the Observed Symptom Values, Journal of Chemical and Pharmaceutical Sciences, 8: 69-76.
[19]
M. Nagarajasri and M. Padmavathamma (2013), Threshold Neuro Fuzzy Expert System for Diagnosis of Breast Cancer, International Journal of Computer Applications, 66 (8): 6-10.
[20]
J. M. Gumpy, I. Goni, and M. (2018), Neuro-Fuzzy Approach for Diagnosing and Control of Tuberculosis. The International Journal of Computational Science, Information Technology and Control Engineering, 5 (1): 1-10.
[21]
Goni, I., Ngene, C. U., Manga, I., and Auwal, N., and Sunday, J. C. (2018), Intelligent System for Diagnosing Tuberculosis Using Adaptive Neuro-Fuzzy, Asian Journal of Research in Computer Science, 2 (1): 1-9.
[22]
Oladele, T. M., Okonji, C. D., Adekanmi, A., and Abiola, F. F. (2018). Neuro-Fuzzy Expert System for Diagnosis of Thyroid Diseases, Annale Computer Science Series, 16 (2): 45-54.
[23]
E. P. Ephzibah and V. Sundarapandian (2012). A Neuro Fuzzy Expert System for Heart Disease Diagnosis, Computer Science & Engineering: An International Journal, 2 (1): 17-23.
[24]
M. E. Shaabani, T. Banirostam, and A. Hedayati (2016), Implementation of Neuro Fuzzy System for Diagnosis of Multiple Sclerosis, International Journal of Computer Science and Network, 5 (1): 157-164.
[25]
A. O. Egwali, and J. C. Obi (2015), An Adaptive Neuro-Fuzzy Inference System for Diagnosis of EHF, The Pacific Journal of Science and Technology, 16 (1): 251-261.
[26]
A. Omotosho, A. E. Oluwatobi, and O. R. Oluwaseun, (2018). A Neuro-Fuzzy System for the Classification of Cells as Cancerous or Non-Cancerous, International Journal of Medical Research and Health Sciences, 7 (5): 155-166.
[27]
J. J. Tom and N. P. Anebo (2018), A Neuro-Fuzzy Based Model for Diagnosis of Monkeypox Diseases, International Journal of Computer Science Trends and Technology, 6 (2): 143-153.
[28]
S. Maskara, A. Kushwaha, and S. Bhardwaj (2018), Adaptive Neuro Fuzzy Expert System for Disease Diagnosis, International Journal of Innovations in Engineering and Technology, 10 (2): 121-123.
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186