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A machine learning method for incomplete and imbalanced medical data
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SYSNO ASEP 0484058 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title A machine learning method for incomplete and imbalanced medical data Author(s) Salman, I. (SY)
Vomlel, Jiří (UTIA-B) RID, ORCIDNumber of authors 2 Source Title Proceedings of the 20th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty, CZECH-JAPAN SEMINAR 2017. - Ostrava : University of Ostrava, 2017 / Novák Vilém ; Inuiguchi Masahiro ; Štěpnička Martin - ISBN 978-80-7464-932-5 Pages s. 188-195 Number of pages 8 s. Publication form Print - P Action Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /20./ Event date 17.09.2017 - 20.09.2017 VEvent location Pardubice Country CZ - Czech Republic Event type WRD Language eng - English Country CZ - Czech Republic Keywords Machine Learning ; Data Analysis ; Bayesian networks ; Imbalanced Data ; Acute Myocardial Infarction Subject RIV JD - Computer Applications, Robotics OECD category Automation and control systems R&D Projects GA16-12010S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 UT WOS 000418391500021 Annotation Our research reported in this paper is twofold. In the first part of the paper we use
standard statistical methods to analyze medical records of patients suffering myocardial
infarction from the third world Syria and a developed country - the Czech Republic.
One of our goals is to find whether there are statistically significant differences between
the two countries. In the second part of the paper we present an idea how to deal with
incomplete and imbalanced data for tree-augmented naive Bayesian (TAN). All results
presented in this paper are based on a real data about 603 patients from a hospital in
the Czech Republic and about 184 patients from two hospitals in Syria.Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2018
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