A machine learning approach towards assessing consistency and reproducibility an application to graft survival across three kidney transplantation eras

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dc.article.end-page21en
dc.article.start-page1en
dc.citation.doi10.3389/FDGTH.2024.1427845en
dc.contributor.authorOkechinyere Achilonuen
dc.contributor.authorG Obaidoen
dc.contributor.authorB Ogbuokirien
dc.contributor.authorK Arulebaen
dc.contributor.authorEustasius Musengeen
dc.contributor.authorJune Fabianen
dc.date.accessioned2024-11-19T09:06:54Z
dc.date.available2024-11-19T09:06:54Z
dc.facultyFACULTY OF HEALTH SCIENCESen
dc.identifier.citationSCOPUSen
dc.identifier.urihttps://hdl.handle.net/10539/42716
dc.journal.titleA machine learning approach towards assessing consistency and reproducibility an application to graft survival across three kidney transplantation erasen
dc.journal.volume6en
dc.titleA machine learning approach towards assessing consistency and reproducibility an application to graft survival across three kidney transplantation erasen
dc.typeJournal Articleen
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