Prediction of Two Year Survival Following Elective Repair of Abdominal Aortic Aneurysms at A Single Centre Using A Random Forest Classification Algorithm.

Journal: European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery

PMID: 39638233

Abstract

OBJECTIVE: The decision to electively repair an abdominal aortic aneurysm (AAA) involves balancing the risk of rupture, peri-procedural death, and life expectancy. Random forest classifiers (RFCs) are powerful machine learning algorithms. The aim of this study was to construct and validate a random forest machine learning tool to predict two year survival following elective AAA repair.

Authors

Daniel C Thompson

Department of Vascular Surgery, James Cook University Hospital, Middlesbrough, UK.
Rhiannon Hackett

Department of Perioperative Medicine, James Cook University Hospital, Middlesbrough, UK.
Peng F Wong

Department of Vascular Surgery, James Cook University Hospital, Middlesbrough, UK.
Gerard Danjoux

Department of Perioperative Medicine, James Cook University Hospital, Middlesbrough, UK.
Reza Mofidi

Department of Vascular Surgery, James Cook University Hospital, Middlesbrough, UK. Electronic address: reza.mofidi@nhs.net.

Keywords

Aged Aged, 80 and over Algorithms Aortic Aneurysm, Abdominal Elective Surgical Procedures Endovascular Procedures Female Humans Machine Learning Male Middle Aged Predictive Value of Tests Random Forest Retrospective Studies Risk Assessment Risk Factors Time Factors Treatment Outcome

External Resources

View on PubMed Access via DOI PubMed (39638233)

Prediction of Two Year Survival Following Elective Repair of Abdominal Aortic Aneurysms at A Single Centre Using A Random Forest Classification Algorithm.

Abstract

Authors

Keywords

External Resources

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