Abstract
The aim of this article is to assess or to complete the medical hypothesis on the further prepared clinical data with the use of data mining methods. In our research, we focus on cardiological datasets of patients who underwent coronary angiography and who were indicated for the ischemic heart disease. These patients are divided into four stages of clinical diagnosis. The clinical hypothesis is pointing on the clinical parameters, which have significant impact on the probability of the occurrence of the myocardial infraction. For the data analysis, we use STATISTICA 13 software.
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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 1/0673/15: “Knowledge discovery for hierarchical control of technological and production processes” supported by the VEGA.
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Peterkova, A., Michalconok, G. (2017). Assessing of the Importance of Medical Parameters on the Risk of the Myocardial Infraction Using Statistical Analysis and Neural Networks. In: Silhavy, R., Silhavy, P., Prokopova, Z., Senkerik, R., Kominkova Oplatkova, Z. (eds) Software Engineering Trends and Techniques in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 575. Springer, Cham. https://doi.org/10.1007/978-3-319-57141-6_25
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DOI: https://doi.org/10.1007/978-3-319-57141-6_25
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