A Proposed Model for Health Management and Expert Diagnosis System for the Prediction of Common Diseases for Ethiopia (East Africa)

International Journal of Medical Science
© 2018 by SSRG - IJMS Journal
Volume 5 Issue 2
Year of Publication : 2018
Authors : Mohammed Yesuf Getu and Husain Shahnawaz
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How to Cite?

Mohammed Yesuf Getu and Husain Shahnawaz, "A Proposed Model for Health Management and Expert Diagnosis System for the Prediction of Common Diseases for Ethiopia (East Africa)," SSRG International Journal of Medical Science, vol. 5,  no. 2, pp. 1-9, 2018. Crossref, https://doi.org/10.14445/23939117/IJMS-V5I2P101

Abstract:

Many rural & pastoral communities in Ethiopia have extremely limited access to medical advice. People are traveling long distances to avail medical facilities, and there is a shortage of medical experts in most of the health institutions. This results in slow service, and ends up waiting long hours without receiving any attention for the patient. Expert on particular diseases is also not available at the health centers, in that case, patients are misdiagnosed and abused medically. Hence medical expert systems can play a significant role in such scenarios where medical experts are not readily available. This work presents the patients registration system and design of a knowledge-based expert system that aims to provide the prediction of the diseases to the patients and medical advisors to expedite their non-surgical diagnostic system.

Keywords:

Expert Diagnosis system, Prediction system, Common diseases, Support Vector Machine, Symptoms and signs of diseases.

References:

[1] World bank report on Rural Population of Ethiopia. ” https://data.worldbank.org/indicator/SP.RUR.TOTL.ZS” 
[2] Rural population of Ethiopia. https://www.indexmundi.com/facts/ethiopia/rural-population 
[3] Nana Yaw Asabere (2012). mMES: A Mobile Medical Expert System for Health Institutions in Ghana, International Journal of Science and Technology, volume 2 No6. 2012. www.ejournalofsciences.org. 
[4] Adehor A.B. and Burrell P.R. (2008), “An Intelligent Decision Support System for the Prompt Diagnosis of Malaria and Typhoid fever in the Malaria Belt of Africa”, Artificial Intelligence in Theory and Practice II, Pg288-295. www.dl.ifi.org/index.php/AICT/article/. 
[5] Adehor A.B and Burrell P.R. (2008),“ The integrated Management of Healthare Strategies and Differential Diagnosis by Expert System Technology: a single- dimensional approach”, World Academy of Sciences, Engineering and Technology,pp533-538,2008, http://www.waset.org/journa. 
[6] Adetunmbi A.O, Oguntimilehin A. and Falaki S.O (2012), “Web-Based Medical Assistant System for Malaria Diagnosis and Therapy”, GESJ: Computer Science and Telecommunications No1(33), Pg 42-53. 
[7] WHO (2007), “Taking Stock: Health Workers Shortages and the Response to AIDS”. 
[8] Rashid Ansumana et al (2013), “Presumptive Self-diagnosis of Malaria and other febrile illness in Sierra Leone”, Pan African Medical Journal. Availabe online at:http://www.panafrican-med-journal.com 
[9] Oguntimilehin A and Ademola E.O, A Framework for Mobile Health Management for Diseases in Nigeria with Benefits and Challenges, International Journal of Computing, Communications and Networking, Vol 3 No 1 April- June, 2014 Pg 19-24, http://www.warse.org 
[10] O.Oluwagbemi and B. Oladunni (2010), “Diagnosis and Recommender System for some neglected tropical diseases”, International Journal of Natural and Applied Sciences, 6(2), pg 181-188. www.tapasintitute.org/ journals/ijonas. 
[11] O.W Samuel, M. Omisore and B.A. Ojokoh (2013), “A Web Based Decision Support System driven by Fuzzy Logic for the diagnosis of typhoid fever”, Expert Systems with Applications 40(2013), pg 4164-417, www.elsevier.com/locate/eswa, Retrieved 21/05/2015. 
[12] J.P. Tchapet Njafa, S.G. Nana Engo and P.Woafo (2013), “Quantum Associative Memory for the diagnosis of some tropical diseases”, Cornell University,www.arxiv.org. 
[13] Hossain, Mohammad Shahadat, et al. "A belief rule-based expert system to diagnose influenza." Strategic Technology (IFOST), 2014 9th International Forum on. IEEE, 2014. 
[14] Abdullah, Azian Azamimi, Zulkarnay Zakaria, and Nur Farahiyah Mohamad. "Design and development of fuzzy expert system for diagnosis of hypertension." 2011 Second International Conference on Intelligent Systems, Modelling and Simulation. IEEE, 2011. 
[15] Hole, Komal R., and Vijay S. Gulhane. "Rule-Based Expert System for the Diagnosis of Memory Loss Diseases‖." International Journal of Innovative Science, Engineering & Technology 1.3 (2014). 
[16] Neshat, M., et al. "Fuzzy expert system design for diagnosis of liver disorders." Knowledge Acquisition and Modeling, 2008. KAM'08. International Symposium on. IEEE, 2008. 
[17] Ajam, Noura. "Heart Diseases Diagnoses using Artificial Neural Network." Network and Complex Systems 5.4 (2015): 7-10. 
[18] Patel, Maitri, Atul Patel, and Paresh Virparia. "Rule Based Expert System for Viral Infection Diagnosis." International Journal of Advanced Research in Computer Science and Software Engineering 3.5 (2013): 591-595. 
[19] Bursuk, E., M. Ozkan, and B. Ilerigelen. "A medical expert system in cardiological diseases." [Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint. Vol. 2. IEEE, 1999. 
[20] Ali, S., P. Chia, and K. Ong. "Graphical knowledge-based protocols for chest pain management." Computers in Cardiology, 1999. IEEE, 1999. 
[21] Hossain, Md S., K. Andersson, and S. Naznin. "A Belief Rule Based Expert System to Diagnose Measles under Uncertainty." 
[22] Prasad, Badir, et al. "A knowledge-based system for tutoring bronchial asthma diagnosis." Computer-Based Medical Systems, 1989. Proceedings., Second Annual IEEE Symposium on. IEEE, 1989.
[23]. Gebremariam, Solomon. A Self-Learning Knowledge Based System for Diagnosis and Treatment of Diabetes. Diss. AAU, 2013. 
[24] Ibrahim, Fatimah, et al. "Expert system for early diagnosis of eye diseases infecting the Malaysian population." TENCON 2001. Proceedings of IEEE Region 10 International Conference on Electrical and Electronic Technology. Vol. 1. IEEE, 2001. 
[25]. Fatima, Bekaddour, and Chikh Mohammed Amine. "A neuro-fuzzy inference model for breast cancer recognition." International Journal of Computer Science & Information Technology 4.5 (2012): 163. 
[25] Medical Expert diagnosis system “http://valivach.com/projects_en_expert_system.html” 
[26] Andrew H. Sung, Srinivas Mukkamala, ”Feature Selection for Intrusion Detection using Neural Networks and Support Vector Machines”, Transport Research Record published by TRB Journal 2003,Issue number 1822, ISSN: 0361-1981, pp 33-39. 
[27] Srinivas Mukkamala, Andrew H. Sung, Ajith Abraham, Intrusion detection using an ensemble of intelligent paradigms, Journal of Network and Computer Applications, Volume 28, Issue 2, April 2005, Pages 167-182, ISSN 1084-8045, 10.1016/j.jnca.2004.01.003. 
[28] L.V. Ganyun, Cheng Haozhong, Zhai Haibao, Dong Lixin, Fault diagnosis of power transformer based on multi-layer SVM classifier, Electric Power Systems Research, Volume 74, Issue 1, April 2005, Pages 1-7, ISSN 0378-7796, 10.1016/j.epsr.2004.07.008. 
[29] Emre Çomak, Ahmet Arslan, İbrahim Türkoğlu, A decision support system based on support vector machines for diagnosis of the heart valve diseases, Computers in Biology and Medicine, Volume 37, Issue 1, January 2007, Pages 21-27, ISSN 0010-4825, 10.1016/j.compbiomed.2005.11.002. 
[30] Shang-Ming Zhou; Gan, J.Q.; , "Constructing L2-SVM-Based Fuzzy Classifiers in High-Dimensional Space With Automatic Model Selection and Fuzzy Rule Ranking," Fuzzy Systems, IEEE Transactions on , vol.15, no.3, pp.398-409, June 2007, doi: 10.1109/TFUZZ.2006.882464 
[31] Jung-Hsien Chiang; Pei-Yi Hao; , "Support vector learning mechanism for fuzzy rule-based modeling: a new approach," Fuzzy Systems, IEEE Transactions on , vol.12, no.1, pp. 1- 12, Feb. 2004, doi: 10.1109/TFUZZ.2003.817839 
[32] Vapnik, V.N., “The Nature of Statistical Learning Theory”, 1st ed., Springer-Verlag, New York, 1995, series: Information Science and Statistics, ISBN: 978-0-387-98780-4