Cardiac patients’ surgery outcome and associated factors in Ethiopia: application of Machine learning

Simple item page
dc.contributor.authorMelaku Tadege
dc.date.accessioned2024-04-18T11:11:29Z
dc.date.available2024-04-18T11:11:29Z
dc.date.issued2024
dc.descriptionIn this retrospective cohort, a total of 1,520 cardiac patients who were on follow up from February 2012- January 2023 in two cardiac centers (ElOuzeir Cardiac Center and cardiac center Ethiopia) were included. Saturated oxygen, age, ejection fraction, duration of cardiac center stays after surgery, waiting time to surgery, haemoglobin value, and creatinine value were assessed. Machine learning algorism Their charts were reviewed and machine learning algorithms were applied for data analysis. For machine learning algorithms comparison, lift and AUC was applied.
dc.identifier.urihttps://rdmc.aphi.gov.et/handle/123456789/87
dc.language.isoen
dc.titleCardiac patients’ surgery outcome and associated factors in Ethiopia: application of Machine learning
dc.typeDataset
dspace.entity.type
local.access.levelAccessible upon reasonable request
local.contributor.emailmelakutadege@yahoo.com
local.contributor.organizationBahir Dar University
local.contributor.phone+251918552766
local.contributor.unitSchool of statistical and computational science
local.coverage.ageYes
local.coverage.geographicNational
local.coverage.sexYes
local.datacollection.ended2023-01-01
local.datacollection.started2022-02-01
local.datatypeSurvey
local.disseminatedbyBahir Dar University
local.formatCSV
local.has.geospatialYes
local.has.microdataYes
local.idAPHI-RDMC-042
local.keywordsMachine learning, Cardiac disease, Ethiopia, Cardiac surgery
local.objectiveThe main objective of the current study was to assess the prevalence of death due to cardiac disease and its risk factors of among heart patients in Ethiopia.
local.subject.areaHealth and Health related

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Cardiac.csv
Size:
180.47 KB
Format:
Unknown data format
Download

Collections

Emergency medicine