BibTeX
CSL-JSON
MLA
Harvard
A Hybrid Data Mining Method For The Medical Classification Of Chest Pain
release_rwmdijohzjgn7pjipz3yedx6hu
by
Sung Ho Ha, Seong Hyeon Joo
Published
by Zenodo.
2010
Abstract
Data mining techniques have been used in medical
research for many years and have been known to be effective. In order
to solve such problems as long-waiting time, congestion, and delayed
patient care, faced by emergency departments, this study concentrates
on building a hybrid methodology, combining data mining techniques
such as association rules and classification trees. The methodology is
applied to real-world emergency data collected from a hospital and is
evaluated by comparing with other techniques. The methodology is
expected to help physicians to make a faster and more accurate
classification of chest pain diseases.
In text/plain
format
Archived Files and Locations
application/pdf 136.6 kB
file_hodr5cbe3jagffk6wwsiurhwci
|
zenodo.org (repository) web.archive.org (webarchive) |
Read Archived PDF
Preserved and Accessible
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
access all versions, variants, and formats of this works (eg, pre-prints)
Cite This
Lookup Links
oaDOI/unpaywall (OA fulltext)
Datacite Metadata (via API)
Worldcat
wikidata.org
CORE.ac.uk
Semantic Scholar
Google Scholar
Datacite Metadata (via API)
Worldcat
wikidata.org
CORE.ac.uk
Semantic Scholar
Google Scholar