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Diagmal: A Malaria Coactive Neuro-Fuzzy Expert System

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12254))

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Abstract

In the process of clarifying whether a patient or patients is suffering from a disease or not, diagnosis plays a significant role. The procedure is quite slow and cumbersome, and some patients may not be able to pursue the final test results and diagnosis. The method in this paper comprises many fact-finding and data-mining methods. Artificial Intelligence techniques such as Neural Networks and Fuzzy Logic were fussed together in emerging the Coactive Neuro-Fuzzy Expert System diagnostic tool. The authors conducted oral interviews with the medical practitioners whose knowledge were captured into the knowledge based of the Fuzzy Expert System. Neuro-Fuzzy expert system diagnostic software was implemented with Microsoft Visual C# (C Sharp) programming language and Microsoft SQL Server 2012 to manage the database. Questionnaires were administered to the patients and filled by the medical practitioners on behalf of the patients to capture the prevailing symptoms. The study demonstrated the practical application of neuro-fuzzy method in diagnosis of malaria. The hybrid learning rule has greatly enhanced the proposed system performance when compared with existing systems where only the back-propagation learning rule were used for implementation. It was concluded that the diagnostic expert system developed is as accurate as that of the medical experts in decision making. DIAGMAL is hereby recommended to medical practitioners as a diagnostic tool for malaria.

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References

  1. Adebiyi, M., et al.: Computational investigation of consistency and performance of the biochemical network of the malaria parasite, plasmodium falciparum. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11623, pp. 231–241. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24308-1_19

    Chapter  Google Scholar 

  2. Carter, R., Mendes, K.N.: Evolutionary and historical aspects of the burden of malaria. Clin. Microbiol. Rev. 15, 564–594 (2002)

    Article  Google Scholar 

  3. White, N.J.: Antimalarial drug resistance. J. Clin. Invest. 113, 1084–1092 (2004)

    Article  Google Scholar 

  4. Kettelhut, M.M., Chiodini, P.L., Edwards, H., Moody, A.: External quality assessment schemes raise standards: evidence from UKNEQAS parasitology subschemes. J. Clin. Pathol. 56, 927–932 (2003)

    Article  Google Scholar 

  5. Coleman, R.E., et al.: Comparison of field and expert laboratory microscopy for active surveillance for asymptomatic Plasmodium falaparum and Plasmodium vivax in western Thailand. Am. J. Trop. Med. Hyg. 67, 144–154 (2002)

    Google Scholar 

  6. Bates, I., Bekoe, V., Asamoa-Adu, A.: Improving the accuracy of malaria-related laboratory tests in Ghana. Malar. J. 3 (2004). Article number: 38. https://doi.org/10.1186/1475-2875-3-38

  7. Mitiku, K., Mengistu, G., Gelaw, B.: The reliability of blood film examination for malaria at the peripheral health unit. Ethiop. J. Health Dev. 17, 197–204 (2003)

    Google Scholar 

  8. Adebayo, O., Asani, E.O., Ogundokun, R.O., Ananti, E.C., Adegun, A.: A neuro-fuzzy based system for the classification of cells as cancerous of non-cancerous. Int. J. Med. Res. Health Sci. 7(5), 155–166 (2018)

    Google Scholar 

  9. Djam, X.Y., Kimbi, Y.H.: Fuzzy expert system for the management of hypertension. Pac. J. Sci. Technol. 12(1), 390–402 (2011)

    Google Scholar 

  10. Donfack, A.F., Abdullahi, M., Ezugwu, A.E., Alkali, S.A.: Online system for diagnosis and treatment of malaria (2009)

    Google Scholar 

  11. Lala, O.G., Emuoyibofarhe, O.J., Fajuyigbe, O., Onaolapo, S.O.: Diamaltycin for the diagnosis of malaria and typhoid fever: a decision support system for medical application. In: Proceedings of the First International Conference on Mobile Computing, Wireless Communication, E-Health, M-Health & Telemedicine (MWEMTem 2008), Held at Ladoke Akintola University of Technology (LAUTECH), Ogbomosho (2008)

    Google Scholar 

  12. Olabiyisi, S.O., Omidiora, E.O., Olaniyan, M.O., Dorikoma, O.: A decision support system model for diagnosing tropical diseases using fuzzy logic. Afr. J. Comput. ICT 4(2), 1–6 (2011)

    Google Scholar 

  13. Adekoya, A.F., Akinwale, A.T., Oke, O.E.: A medical expert system for managing tropical diseases. In: Proceedings of the Third Conference on Science and National Development, pp. 74–86 (2008)

    Google Scholar 

  14. Obot, O.U., Uzoka, F.M.E.: Fuzzy rule-based framework for the management of tropical diseases. Int. J. Eng. Inform. 1(1), 7–17 (2008)

    Google Scholar 

  15. Imhanlahimi, R.E., John-Otumu, A.M.: Application of expert system for diagnosing medical conditions: a methodological review. Eur. J. Comput. Sci. Inf. Technol. 7(2), 12–25 (2019)

    Google Scholar 

  16. Osubor and Chiemeke: An adaptive neuro fuzzy inference system for the diagnosis of malaria. NISEB J. 14(4), 212–222 (2015)

    Google Scholar 

  17. Agboizebeta, I.A., Chukwuyeni, O.J.: Application of neuro-fuzzy expert system for the probe and prognosis of thyroid disoder. Int. J. Fuzzy Log. Syst. (IJFLS) 2(2), 1–11 (2012)

    Article  Google Scholar 

  18. Ayo, F.E., et al.: A fuzzy based method for diagnosis of acne skin disease severity. i-Manager’s J. Pattern Recogn. 5(2), 10 (2018)

    Article  Google Scholar 

  19. Awotunde, J.B., Matiluko, O.E., Fatai, O.W.: Medical diagnosis system using fuzzy logic. Afr. J. Comput. ICT 7(2), 99–106 (2014)

    Google Scholar 

  20. Jimoh, R.G., Afolayan, A.A., Awotunde, J.B., Matiluko, E.O.: Fuzzy logic based expert system in the diagnosis of Ebola virus. Ilorin J. Comput. Sci. Inf. Technol. 2(1), 73–94 (2017)

    Google Scholar 

  21. Thompson, T., Sowunmi, O., Misra, S., Fernandez-Sanz, L., Crawford, B., Soto, R.: An expert system for the diagnosis of sexually transmitted diseases–ESSTD. J. Intell. Fuzzy Syst. 33(4), 2007–2017 (2017)

    Article  Google Scholar 

  22. Azeez, N.A., et al.: A fuzzy expert system for diagnosing and analyzing human diseases. In: Abraham, A., Gandhi, N., Pant, M. (eds.) IBICA 2018. AISC, vol. 939, pp. 474–484. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16681-6_47

    Chapter  Google Scholar 

  23. Lawanya Shri, M., Ganga Devi, E., Balusamy, B., Kadry, S., Misra, S., Odusami, M.: A fuzzy based hybrid firefly optimization technique for load balancing in cloud datacenters. In: Abraham, A., Gandhi, N., Pant, M. (eds.) IBICA 2018. AISC, vol. 939, pp. 463–473. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16681-6_46

    Chapter  Google Scholar 

  24. Alhassan, J.K., Misra, S., Umar, A., Maskeliūnas, R., Damaševičius, R., Adewumi, A.: A fuzzy classifier-based penetration testing for web applications. In: Rocha, Á., Guarda, T. (eds.) ICITS 2018. AISC, vol. 721, pp. 95–104. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73450-7_10

    Chapter  Google Scholar 

  25. Ayo, F.E., Awotunde, J.B., Ogundokun, R.O., Folorunso, S.O., Adekunle, A.O.: A decision support system for multi-target disease diagnosis: a bioinformatics approach. Heliyon 6(3), e03657 (2020)

    Article  Google Scholar 

  26. Jimoh, R.G., Awotunde, J.B., Babatunde, A.O., Ameen, A.O, James, T.R., Fatai, O.W.: Simulation of medical diagnosis system for malaria using fuzzy logic. Int. J. Inf. Process. Commun. (IJIPC) 2(1) (2014)

    Google Scholar 

  27. Oladele, T.O., Ogundokun, R.O., Adebiyi, M.O.: Datasets on malaria disease [data set]. Zenodo (2019). http://doi.org/10.5281/zenodo.3592442

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Authors and Affiliations

  1. Department of Computer Science, University of Ilorin, Ilorin, Kwara State, Nigeria

    Tinuke Omolewa Oladele & Joseph Bamidele Awotunde

  2. Department of Computer Science, Landmark University Omu Aran, Omu Aran, Kwara State, Nigeria

    Roseline Oluwaseun Ogundokun, Marion Olubunmi Adebiyi & Jide Kehinde Adeniyi

Authors
  1. Tinuke Omolewa Oladele
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  2. Roseline Oluwaseun Ogundokun
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  3. Joseph Bamidele Awotunde
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  4. Marion Olubunmi Adebiyi
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  5. Jide Kehinde Adeniyi
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Corresponding author

Correspondence to Roseline Oluwaseun Ogundokun .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Oladele, T.O., Ogundokun, R.O., Awotunde, J.B., Adebiyi, M.O., Adeniyi, J.K. (2020). Diagmal: A Malaria Coactive Neuro-Fuzzy Expert System. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12254. Springer, Cham. https://doi.org/10.1007/978-3-030-58817-5_32

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  • DOI: https://doi.org/10.1007/978-3-030-58817-5_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58816-8

  • Online ISBN: 978-3-030-58817-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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