A machine learning method for incomplete and imbalanced medical data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00484058" target="_blank" >RIV/67985556:_____/17:00484058 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21340/17:00313379
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
A machine learning method for incomplete and imbalanced medical data
Original language description
Our research reported in this paper is twofold. In the first part of the paper we usenstandard statistical methods to analyze medical records of patients suffering myocardialninfarction 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 betweennthe two countries. In the second part of the paper we present an idea how to deal withnincomplete and imbalanced data for tree-augmented naive Bayesian (TAN). All resultsnpresented in this paper are based on a real data about 603 patients from a hospital innthe Czech Republic and about 184 patients from two hospitals in Syria.
Czech name
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Czech description
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Project
<a href="/en/project/GA16-12010S" target="_blank" >GA16-12010S: Conditional independence structures: combinatorial and optimization methods</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
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
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
188-195
Publisher name
University of Ostrava
Place of publication
Ostrava
Event location
Pardubice
Event date
Sep 17, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000418391500021

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