A Study of Machine Learning Models’ Ability to Predict Coronary Artery Disease Progression

Simple item page
dc.contributor.authorAli, Akbar
dc.contributor.authorSingh, Davinder
dc.contributor.authorYousuf, Muhammad Ali
dc.contributor.authorThompson, Ellen
dc.date.accessioned2026-01-06T20:52:04Z
dc.date.issued2025-04
dc.format.extent1 page
dc.genreposters
dc.identifierdoi:10.13016/m2wakb-2ke7
dc.identifier.urihttp://hdl.handle.net/11603/41412
dc.language.isoen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectUMBC Lab for Healthcare Engineering
dc.subjectCoronary Artery Disease (CAD)
dc.subjectopen-source machine learning (ML) model
dc.subjectPredict Coronary Artery Disease Progression
dc.titleA Study of Machine Learning Models’ Ability to Predict Coronary Artery Disease Progression
dc.typeText
dcterms.creatorhttps://orcid.org/0009-0005-7213-7262

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Ali_AStudyofGenerativeAIModelsAbilitytoPredictCoronaryArteryDiseaseProgression.pdf
Size:
271.07 KB
Format:
Adobe Portable Document Format
Download

Collections

UMBC Faculty Collection
UMBC Computer Science and Electrical Engineering Department