Original title: A machine learning method for incomplete and imbalanced medical data
Authors: Salman, I. ; Vomlel, Jiří
Document type: Papers
Conference/Event: Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /20./, Pardubice (CZ), 20170917
Year: 2017
Language: eng
Abstract: Our research reported in this paper is twofold. In the first part of the paper we use\nstandard statistical methods to analyze medical records of patients suffering myocardial\ninfarction from the third world Syria and a developed country - the Czech Republic.\nOne of our goals is to find whether there are statistically significant differences between\nthe two countries. In the second part of the paper we present an idea how to deal with\nincomplete and imbalanced data for tree-augmented naive Bayesian (TAN). All results\npresented in this paper are based on a real data about 603 patients from a hospital in\nthe Czech Republic and about 184 patients from two hospitals in Syria.
Keywords: Acute Myocardial Infarction; Bayesian networks; Data Analysis; Imbalanced Data; Machine Learning
Project no.: GA16-12010S (CEP)
Funding provider: GA ČR
Host item entry: Proceedings of the 20th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty, CZECH-JAPAN SEMINAR 2017, ISBN 978-80-7464-932-5

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2017/MTR/vomlel-0484058.pdf
Original record: http://hdl.handle.net/11104/0279537

Permalink: http://www.nusl.cz/ntk/nusl-371101


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Research > Institutes ASCR > Institute of Information Theory and Automation
Conference materials > Papers