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 using fee-for-service Medicare beneficiaries in 2015 who were newly diagnosed with peripheral artery disease and diabetes mellitus. Exposure variables include whether patients received preventive measures in the 6 months following their index date: HbA1c test, foot exam, or vascular imaging study. Outcomes include any reintervention, lower extremity amputation, and death. We fit both logistic regression models as well as random forest models.

Results

There were 88,898 fee-for-servi

SUBMITTER: Austin AM 

PROVIDER: S-EPMC9685056 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature