A machine learning method for incomplete and imbalanced medical data

0484058 - ÚTIA 2018 RIV CZ eng C - Konferenční příspěvek (zahraniční konf.)
Salman, I. - Vomlel, Jiří
A machine learning method for incomplete and imbalanced medical data.
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, V.; Inuiguchi, M.; Štěpnička, M.), s. 188-195. ISBN 978-80-7464-932-5.
[Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /20./. Pardubice (CZ), 17.09.2017-20.09.2017]
Grant CEP: GA ČR(CZ) GA16-12010S
Institucionální podpora: RVO:67985556
Klíčová slova: Machine Learning * Data Analysis * Bayesian networks * Imbalanced Data * Acute Myocardial Infarction
Obor OECD: Automation and control systems
http://library.utia.cas.cz/separaty/2017/MTR/vomlel-0484058.pdf

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.
Trvalý link: http://hdl.handle.net/11104/0279537