Abstract
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.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Nazarenko, G.I., Guliyev, Ya.I., Ermakov, D.E.: Medical Information Systems Theory and Practice. Fizmatlit, Moscow (2005)
Abu-Nasser, B.: Medical Expert Systems Survey. Int. J. Eng. Inf. Syst. 1(7), 218–224 (2017)
Aндpeйчикoв, A.B., Aндpeйчикoвa, O.H.: Интeллeктyaльныe инфopмaциoнныe cиcтeмы. Финaнcы и cтaтиcтикa, Moscow (2004)
Pyмянцeв, П.O., Caeнкo, B.A.: Cтaтиcтичecкиe мeтoды aнaлизa в клиничecкoй пpaктикe. ГУ PMHЦ PAMH, Oбнинcк (2009)
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)
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)
Aigelsreiter, A., Neumann, J., Pichler, M.: Hepatocellular carcinomas with intracellular hyaline bodies have a poor prognosis. Liver Int. 37(4), 600–610 (2017)
Чyбyкoвa, И.A.: Data Mining. M ИHTУИT БИHOM Лaбopaтopия знaний, 384 (2008)
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)
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)
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)
Shah-Hosseini, H.: Binary tree time adaptive self-organizing map. Neurocomputing 74(11), 1823–1839 (2011)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Haimoudi, E.K., Abdoun, O., Ezziyyani, M. (2019). Towards an Intelligent Data Analysis System for Decision Making in Medical Diagnostics. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 914. Springer, Cham. https://doi.org/10.1007/978-3-030-11884-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-11884-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11883-9
Online ISBN: 978-3-030-11884-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)