Skip to main content
SpringerLink
Log in

Application of Artificial Neural Networks in the Problems of the Patient’s Condition Diagnosis in Medical Monitoring Systems

  • Conference paper
  • First Online:
Integrated Computer Technologies in Mechanical Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1113))

Abstract

The problem of accurate medical diagnosis is always urgent for any person. Existing methods for solving the problem of classification of the state of a complex system are considered. The paper proposes a method of classification of patients’ status in medical monitoring systems using artificial neural networks. The artificial neural networks training method uses bee colonies to simulate less training error. The research purpose is to determine the patient’s belonging to a particular class according to the variables of his condition, which are recorded. Examples of using the method to determine the status of patients with urological diseases and liver disease are given. The classification accuracy was more than 80%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bakumenko, N.: Application of the c-means fuzzy clustering method for the patient’s state recognition problems in the medicine monitoring systems. In: Bakumenko, N., Strilets, V., Ugriumov, M. (eds.) CEUR Workshop Proceedings of 3rd International Conference on Computational Linguistics and Intelligent Systems, COLINS 2019, vol. 2362, pp. 218–227 (2019)

    Google Scholar 

  2. Zhang, H.: The Optimality of Naive Bayes. American Association for Artificial Intelligence (2004)

    Google Scholar 

  3. Metsis, V., Androutsopoulos, I., Paliouras, G.: Spam filtering with Naive Bayes—which Naive Bayes? In: Third Conference on Email and Anti-Spam, 27–28 July 2006, Mountain View, California USA (2006)

    Google Scholar 

  4. Rennie, J., Shih, L., Teevan, J., Karger, D.: Tackling the poor assumptions of Naive Bayes classifiers. In: Proceedings of the Twentieth International Conference on Machine Learning, Washington D.C. (2003)

    Google Scholar 

  5. Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory IT-13(1), 21–27 (1967)

    Google Scholar 

  6. Garcia, S., Derrac, J., Cano, J., Herrera, F.: Prototype selection for nearest neighbor classification: taxonomy and empirical study. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 417–435 (2012)

    Article  Google Scholar 

  7. Ho, T.K.: Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, pp. 278–282, 14–16 August 1995

    Google Scholar 

  8. Denisko, D., Hoffman, M.M.: Classification and interaction in random forests. Proc. Natl. Acad. Sci. U.S.A. 115(8), 1690–1692 (2018)

    Article  Google Scholar 

  9. Riddick, G., Song, H., Ahn, S., Walling, J., Borges-Rivera, D., Zhang, W., Fine, H.A.: Predicting in vitro drug sensitivity using Random Forests. Bioinformatics (Oxford, England) 27(2), 220–224 (2011)

    Google Scholar 

  10. Touw, W.G., Bayjanov, J.R., Overmars, L., Backus, L., Boekhorst, J., Wels, M., van Hijum, S.A.: Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle? Brief Bioinform. 14(3), 315–326 (2013)

    Google Scholar 

  11. Basu, S., Kumbier, K., Brown, J.B., Yu, B.: Iterative random forests to discover predictive and stable high-order interactions. Proc. Natl. Acad. Sci. U.S.A. 115(8), 1943–1948 (2018)

    Google Scholar 

  12. Breiman, L.: Random forest, machine learning. In: Proceedings of the Thirteenth International Conference, vol. 45, pp. 5–32 (2001)

    Google Scholar 

  13. Strano, M., Colosimo, B.M.: Logistic regression analysis for experimental determination of forming limit diagrams. Int. J. Mach. Tools Manuf 46(6), 673–682 (2006)

    Article  Google Scholar 

  14. Cramer, J.S.: The origins of logistic regression. Tinbergen Inst. 119, 167–178 (2002)

    Google Scholar 

  15. Kaminski, B., Jakubczyk, M., Szufel, P.: A framework for sensitivity analysis of decision trees. Central Eur. J. Oper. Res. 26(1), 135–159 (2017)

    Article  MathSciNet  Google Scholar 

  16. Karimi, K., Hamilton, H.J.: Generation and interpretation of temporal decision rules. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 3 (2011)

    Google Scholar 

  17. Gao, W., Zhou, Z.-H.: On the doubt about margin explanation of boosting. Artif. Intell. J. 203, 1–18 (2013)

    Google Scholar 

  18. Freund, Y., Schapire, R.E.: A short introduction to boosting. J. Jpn. Soc. Artif. Intell. 14(5), 771–780 (1999)

    Google Scholar 

  19. Kegl, B.: The return of AdaBoost. MH: multi-class Hamming trees. In International Conference on Learning Representations, 2014

    Google Scholar 

  20. Cиcтeмнoe coвepшeнcтвoвaниe элeмeнтoв cлoжныx тexничecкиx cиcтeм нa ocнoвe кoнцeпции oбpaтныx зaдaч [Teкcт]: мoнoгpaфия/ B. E. Cтpeлeц, A. A. Tpoнчyк, E. M. Угpюмoвa и дp.; пoд oбщ. peд. M. Л. Угpюмoвa. – X. : Haц. aэpoкocм. yн-т им. H. E. Жyкoвcкoгo « Xapьк. aвиaц. ин-т » , 148 c (2013)

    Google Scholar 

  21. Угpюмoвa E.M. Oбyчaeмыe иcкyccтвeнныe нeйpoнныe ceти в пocтpoeнии фopмaльныx мaтeмaтичecкиx мoдeлeй cиcтeм пpи aпpиopнoй нeoпpeдeлeннocти дaнныx/E.M. Угpюмoвa// Bicник Xapкiвcькoгo нaцioнaльнoгo yнiвepcитeтy: зб. нayк. пp. Cep. Maтeмaтичнe мoдeлювaння. Iнфopмaцiйнi тexнoлoгiї. Aвтoмaтизoвaнi cиcтeми yпpaвлiння. – 2010. – Bипycк 13 (№890). – C. 237–253 (2010)

    Google Scholar 

  22. Powers, D.M.W.: Evaluation: from precision, recall and f-measure to ROC, informedness, markedness & correlation. J. Mach. Learn. Technol. 2(1), 37–63 (2011)

    Google Scholar 

  23. Flach, P., Hernandez-Orallo, J., Ferri, C.: A coherent interpretation of AUC as a measure of aggregated classification performance. In: Appearing in Proceedings of the 28th International Conference on Machine Learning, Bellevue, WA, USA (2011)

    Google Scholar 

  24. Bi, J., Bennett, K.P.: Regression error characteristic curves. In: Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC (2003)

    Google Scholar 

  25. Stehman, S.V.: Selecting and interpreting measures of thematic classification accuracy. Remote Sens. Environ. 62(1), 77–89 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. V. N. Karazin Kharkiv National University, Kharkiv, Ukraine

    Viktoriia Strilets, Nina Bakumenko, Mykhaylo Ugryumov & Volodymyr Donets

  2. National Aerospace University “Kharkiv Aviation Institute”, Kharkiv, Ukraine

    Serhii Chernysh

Authors
  1. Viktoriia Strilets
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nina Bakumenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Serhii Chernysh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mykhaylo Ugryumov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Volodymyr Donets
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Viktoriia Strilets .

Editor information

Editors and Affiliations

  1. National Aerospace University "Kharkiv Aviation Institute", Kharkov, Ukraine

    Mykola Nechyporuk

  2. National Aerospace University "Kharkiv Aviation Institute", Kharkov, Ukraine

    Vladimir Pavlikov

  3. National Aerospace University "Kharkiv Aviation Institute", Kharkov, Ukraine

    Dmitriy Kritskiy

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Strilets, V., Bakumenko, N., Chernysh, S., Ugryumov, M., Donets, V. (2020). Application of Artificial Neural Networks in the Problems of the Patient’s Condition Diagnosis in Medical Monitoring Systems. In: Nechyporuk, M., Pavlikov, V., Kritskiy, D. (eds) Integrated Computer Technologies in Mechanical Engineering. Advances in Intelligent Systems and Computing, vol 1113. Springer, Cham. https://doi.org/10.1007/978-3-030-37618-5_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-37618-5_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37617-8

  • Online ISBN: 978-3-030-37618-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation