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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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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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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DOI: https://doi.org/10.1007/978-3-030-19810-7_19
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