Using a cohort study of diabetes and peripheral artery disease to compare logistic regression and machine learning via random forest modeling

Using a cohort study of diabetes and peripheral artery disease to compare logistic regression and machine learning via random forest modeling

Abstract Background This study illustrates the use of logistic regression and machine learning methods, specifically random forest models, in health services research by analyzing outcomes for a cohort of patients with concomitant peripheral artery disease and diabetes mellitus. Methods Cohort study...

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Bibliographic Details
Main Authors: Andrea M. Austin, Niveditta Ramkumar, Barbara Gladders, Jonathan A. Barnes, Mark A. Eid, Kayla O. Moore, Mark W. Feinberg, Mark A. Creager, Marc Bonaca, Philip P. Goodney
Format: Article
Language:English
Published: BMC 2022-11-01
Series:BMC Medical Research Methodology
Subjects:
Random forest
machine learning
critical limb ischemia
diabetes
amputation
reintervention
Online Access:https://doi.org/10.1186/s12874-022-01774-8

Internet

https://doi.org/10.1186/s12874-022-01774-8

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