Comparative Performance of Clinician and Computational Approaches in Forecasting Adverse Outcomes in Intermittent Claudication.

Journal: Annals of vascular surgery
Published Date: May 14, 2025

Abstract

BACKGROUND: Recent evidence has shown that machine learning (ML) techniques can accurately forecast adverse cardiovascular and limb events in patients with intermittent claudication. This is the first study to compare the predictive performance of ML versus traditional logistic regression (LR) and clinicians.

Authors

  • Bharadhwaj Ravindhran
    Academic Vascular Surgical Unit, Hull York Medical School, Hull, UK. Electronic address: b.ravindhran@nhs.net.
  • Arthur Lim
    Academic Vascular Surgical Unit, Hull York Medical School, Hull, UK.
  • Sean Pymer
    Academic Vascular Surgical Unit, Hull York Medical School, Hull, UK.
  • Jonathon Prosser
    Academic Vascular Surgical Unit, Hull York Medical School, Hull, UK.
  • Joseph Cutteridge
    Academic Vascular Surgical Unit, Hull York Medical School, Hull, UK.
  • Shahani Nazir
    Academic Vascular Surgical Unit, Hull York Medical School, Hull, UK.
  • Abduraheem Mohamed
    Academic Vascular Surgical Unit, Hull York Medical School, Hull, UK.
  • Murad Hemadneh
    Academic Vascular Surgical Unit, Hull York Medical School, Hull, UK.
  • Ross Lathan
    Academic Vascular Surgical Unit, Hull York Medical School, Hull, UK.
  • Rakesh Kapur
    Academic Vascular Surgical Unit, Hull York Medical School, Hull, UK.
  • Brian Frederick Johnson
    Academic Vascular Surgical Unit, Hull York Medical School, Hull, UK.
  • George Edward Smith
    Academic Vascular Surgical Unit, Hull York Medical School, Hull, UK.
  • Daniel Carradice
    Academic Vascular Surgical Unit, Hull York Medical School, Hull, UK.
  • Ian C Chetter
    Academic Vascular Surgical Unit, Hull York Medical School, Hull, UK.

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