| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0484058 |
| utime |
20240103215257.4 |
| mtime |
20180104235959.9 |
| WOS |
000418391500021 |
| title
(primary) (eng) |
A machine learning method for incomplete and imbalanced medical data |
| specification |
| page_count |
8 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0484057 |
| ISBN |
978-80-7464-932-5 |
| title
|
Proceedings of the 20th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty, CZECH-JAPAN SEMINAR 2017 |
| page_num |
188-195 |
| publisher |
| place |
Ostrava |
| name |
University of Ostrava |
| year |
2017 |
|
| editor |
|
| editor |
| name1 |
Inuiguchi |
| name2 |
Masahiro |
|
| editor |
| name1 |
Štěpnička |
| name2 |
Martin |
|
|
| keyword |
Machine Learning |
| keyword |
Data Analysis |
| keyword |
Bayesian networks |
| keyword |
Imbalanced Data |
| keyword |
Acute Myocardial Infarction |
| author
(primary) |
| ARLID |
cav_un_auth*0355858 |
| share |
70 |
| name1 |
Salman |
| name2 |
I. |
| country |
SY |
|
| author
|
| ARLID |
cav_un_auth*0101228 |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department |
MTR |
| full_dept |
Department of Decision Making Theory |
| share |
30 |
| name1 |
Vomlel |
| name2 |
Jiří |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0332303 |
| project_id |
GA16-12010S |
| agency |
GA ČR |
| country |
CZ |
|
| abstract
(eng) |
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. |
| action |
| ARLID |
cav_un_auth*0355859 |
| name |
Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /20./ |
| dates |
20170917 |
| mrcbC20-s |
20170920 |
| place |
Pardubice |
| country |
CZ |
|
| RIV |
JD |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20205 |
| reportyear |
2018 |
| num_of_auth |
2 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0279537 |
| confidential |
S |
| mrcbC86 |
3+4 Proceedings Paper Computer Science Artificial Intelligence|Mathematics Applied |
| mrcbC86 |
3+4 Proceedings Paper Computer Science Artificial Intelligence|Mathematics Applied |
| mrcbC86 |
3+4 Proceedings Paper Computer Science Artificial Intelligence|Mathematics Applied |
| arlyear |
2017 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000418391500021 WOS |
| mrcbU63 |
cav_un_epca*0484057 Proceedings of the 20th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty, CZECH-JAPAN SEMINAR 2017 University of Ostrava 2017 Ostrava 188 195 978-80-7464-932-5 |
| mrcbU67 |
340 Novák Vilém |
| mrcbU67 |
340 Inuiguchi Masahiro |
| mrcbU67 |
340 Štěpnička Martin |
|