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Identification of KDD Problems from Medical Data

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Artificial Intelligence Methods in Intelligent Algorithms (CSOC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 985))

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Abstract

In our paper, we continue with our research and in cooperation with Faculty of Medicine and Research Institute called Academy. In this paper, we would like to present our knowledge about the identification of KDD (Knowledge discovery in database) problems from medical data. Our research is focused on medical data from the field of cardiology. We are drawing attention to the main issues like identifying the diagnosis or multiple diagnoses of the patient, identification of the influence of the medical parameters on the patient’s prognosis and identifying the parameters of a particular diagnosis. We have used various data mining method to achieve the proper results. In our paper, we present the best results that are representing our research.

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References

  1. Ngai, E.W.T., Xiu, L., Chau, D.C.K.: Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst. Appl. 36(2), 2592–2602 (2009)

    Article  Google Scholar 

  2. Frické, M.: The knowledge pyramid: a critique of the DIKW hierarchy. J. Inf. Sci. 35(2), 131–142 (2009)

    Article  MathSciNet  Google Scholar 

  3. Statistics of death. Eurostat, Eurostat explained

    Google Scholar 

  4. Gofman, J.W., Young, W., Tandy, R.: Ischemic heart disease, atherosclerosis, and longevity. Circulation 34(4), 679–697 (1966)

    Article  Google Scholar 

  5. Sudhakar, K., Manimekalai, M.: Study of heart disease prediction using data mining. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(1) (2014)

    Google Scholar 

  6. Breiman, L.: Classification and Regression Trees. Routledge, New York (2017)

    Book  Google Scholar 

  7. Loh, W.-Y.: Classification and regression trees. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 1(1), 14–23 (2011)

    Article  Google Scholar 

  8. Ma, Y., Guo, G. (eds.): Support vector Machines Applications. Springer, New York (2014)

    Google Scholar 

  9. Cai, Y.-D., et al.: Support vector machines for predicting protein structural class. BMC Bioinform. 2(1), 3 (2001)

    Article  Google Scholar 

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Acknowledgments

This publication is the result of implementation of the project: “UNIVERSITY SCIENTIFIC PARK: CAMPUS MTF STU - CAMBO” (ITMS: 26220220179) supported by the Research & Development Operational Program funded by the EFRR.

This publication is the result of implementation of the project VEGA 1/0272/18: “Holistic approach of knowledge discovery from production data in compliance with Industry 4.0 concept” supported by the VEGA.

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Authors and Affiliations

  1. Faculty of Materials Science and Technology in Trnava, Institute of Applied Informatics, Automation and Mechatronics, Slovak University of Technology in Bratislava, Bratislava, Slovakia

    Andrea Nemethova, Martin Nemeth & German Michalconok

  2. Faculty of Medicine, Comenius University in Bratislava, Bratislava, Slovakia

    Allan Bohm

  3. Research Institute of Academy, Bratislava, Slovakia

    Allan Bohm

Authors
  1. Andrea Nemethova
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  2. Martin Nemeth
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  3. German Michalconok
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  4. Allan Bohm
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Corresponding author

Correspondence to Andrea Nemethova .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

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Nemethova, A., Nemeth, M., Michalconok, G., Bohm, A. (2019). Identification of KDD Problems from Medical Data. In: Silhavy, R. (eds) Artificial Intelligence Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 985. Springer, Cham. https://doi.org/10.1007/978-3-030-19810-7_19

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