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Optimizing predictive performance without sacrificing explainability: Comparing logistic regression and ensemble decision trees for abdominal aortic aneurysm repair outcomes

JVS-Vascular Insights(2025)10.1016/j.jvsvi.2025.100300Source: DataRank Database

Optimizing predictive performance without sacrificing explainability: Comparing logistic regression and ensemble decision trees for abdominal aortic aneurysm repair outcomes is a research paper published in JVS-Vascular Insights (2025). On theSindex it has a DataRank of 0. It has been cited 2 times.

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Enrichment:Funding (6 grants)OA: goldIDs (PubMed)

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Authors (9)

Aakash K. Kottakota,Katherine E. Kerr ORCID,Pete H. Gueldner ORCID,Micah J. Guffey,Indrani Sen

Live enrichment

PubMed:21097893

National Institutes of Health

Grant: HL156246

American Heart Association Inc

Grant: 24PRE1195123

National Science Foundation

Grant: 2,318,665

National Science Foundation

Grant: 1,747,452

National Science Foundation

Grant: 2,422,725

National Science Foundation

Grant: 2,139,321

goldLicense: http://creativecommons.org/licenses/by-nc-nd/4.0/

publisherhttps://www.jvsvi.org/article/S2949-9127(25)00117-5/pdf

publisherhttps://api.elsevier.com/content/article/PII:S2949912725001175?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S2949912725001175?httpAccept=text/plain