Towards an Intelligent Data Analysis System for Decision Making in Medical Diagnostics

  • El Khatir HaimoudiEmail author
  • Otman Abdoun
  • Mostafa Ezziyyani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 914)


Artificial neural networks (ANN) are currently massively used in different fields, especially for very complex problems. In this work we propose an approach to use these systems, and in particular the paradigm of the self-organizing map (SOM) in the medical field. The idea is to use this paradigm to develop an intelligent system able of learning to analyze, classify, and visualize multi-parameter objects in a reduced two-dimensional space in the form of object maps. This approach allows for the visual analysis and interpretation of data to reveal the most informative indicators for decision making. The application in the medical field aims to help make a very good diagnosis to make the most relevant decisions in order to provide appropriate treatment depending on the patient’s state.


Data analysis Artificial neural networks Self-organizing map Learning Classification Visually interpreting Medical Information Systems Medical diagnostics Decision making 


  1. 1.
    Nazarenko, G.I., Guliyev, Ya.I., Ermakov, D.E.: Medical Information Systems Theory and Practice. Fizmatlit, Moscow (2005)Google Scholar
  2. 2.
    Abu-Nasser, B.: Medical Expert Systems Survey. Int. J. Eng. Inf. Syst. 1(7), 218–224 (2017)Google Scholar
  3. 3.
    Aндpeйчикoв, A.B., Aндpeйчикoвa, O.H.: Интeллeктyaльныe инфopмaциoнныe cиcтeмы. Финaнcы и cтaтиcтикa, Moscow (2004)Google Scholar
  4. 4.
    Pyмянцeв, П.O., Caeнкo, B.A.: Cтaтиcтичecкиe мeтoды aнaлизa в клиничecкoй пpaктикe. ГУ PMHЦ PAMH, Oбнинcк (2009)Google Scholar
  5. 5.
    Liang, W., Shen, G., Zhang, Y.: Development and validation of a nomogram for predicting the survival of patients with non-metastatic nasopharyngeal carcinoma after curative treatment. Chin. J. Cancer 1, 98–106 (2016)CrossRefGoogle Scholar
  6. 6.
    Бoкepия, O.Л., Бaзapcaдaeвa, T.C., Швapц, B.A., Axoбeкoв, A.A.: Эффeктивнocть cтaтинoтepaпии в пpoфилaктикe фибpилляциипpeд cepдий y пaциeнтoв пocлe aopтoкopoнapнoгoшyнтиpoвaния Aннaлыapитмoлoгии. AHHAЛЫ APИTMOЛOГИИ 11(3), 161–169 (2014)Google Scholar
  7. 7.
    Aigelsreiter, A., Neumann, J., Pichler, M.: Hepatocellular carcinomas with intracellular hyaline bodies have a poor prognosis. Liver Int. 37(4), 600–610 (2017)CrossRefGoogle Scholar
  8. 8.
    Чyбyкoвa, И.A.: Data Mining. M ИHTУИT БИHOM Лaбopaтopия знaний, 384 (2008)Google Scholar
  9. 9.
    Aggarwal, V., Ahlawat, A.K., Pandey, B.N.: A weight initialization approach for training self organizing maps for clustering applications. In: Proceedings of 3rd International Conference on Advance Computing Conference (IACC) (2013)Google Scholar
  10. 10.
    Balabin, M.R., Lomakina, I.E.: Neural network approach to quantum-chemistry data: accurate prediction of density functional theory energies. J. Chem. Phys. 131(7) (2009)CrossRefGoogle Scholar
  11. 11.
    Abaei, G., Selamat, A., Fujita, H.: An empirical study based on semi-supervised hybrid self-organizing map for software defect forecast. Knowl.-Based Syst. 74, 28–39 (2015)CrossRefGoogle Scholar
  12. 12.
    Shah-Hosseini, H.: Binary tree time adaptive self-organizing map. Neurocomputing 74(11), 1823–1839 (2011)CrossRefGoogle Scholar
  13. 13.
    El Khatir, H., Fakhouri, H., Cherrat, L., Ezziyyani, M.: Towards a new approach to improve the classification accuracy of the kohonen’s self-organizing map during learning process. Int. J. Adv. Comput. Sci. Appl. 7(3), 224–229 (2016)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • El Khatir Haimoudi
    • 1
    Email author
  • Otman Abdoun
    • 1
  • Mostafa Ezziyyani
    • 2
  1. 1.Polydisciplinary FacultyUniversity UAELaracheMorocco
  2. 2.Faculty of Science and TechnicsUniversity UAETangierMorocco

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