ArticleBMC medical research methodology2022
Using a cohort study of diabetes and peripheral artery disease to compare logistic regression and machine learning via random forest modeling.
Article in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- Diabetic Foot Ulcer Classification Models Using Artificial Intelligence and Machine Learning Techniques: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Pooled it
- Artificial Intelligence in Peripheral Artery Disease: A Science Advisory From the American Heart Association.Circulation. Population health and outcomes · 2026Review
- Establishment and validation of an electrocardiogram vector-based machine learning model for the conversion of prone position electrocardiograms into standard electrocardiograms.European heart journal. Digital health · 2026Article
- Application of multi-scale feature extraction and explainable machine learning in chest x-ray position evaluation within an integrated learning framework.European radiology · 2026Article
- Features Associated with Therapy Switch Among PPD CorEvitas Psoriasis Registry Patients.Dermatology and therapy · 2026Article
- Analysis of medical costs and two-model prediction for patients with severe mental disorders in Gansu Province, China.Frontiers in public health · 2026Article
- Clinical predictive fusion network for accurate disease prediction in patient cohorts.Scientific reports · 2025Article
- The contemporary natural history of minor amputation among diabetic patients with peripheral arterial disease.Journal of vascular surgery · 2025Article
- Prediction of preterm birth using machine learning: a comprehensive analysis based on large-scale preschool children survey data in Shenzhen of China.BMC pregnancy and childbirth · 2024Article
- Association between tissue loss type and amputation risk among Medicare patients with concomitant diabetes and peripheral arterial disease.Journal of vascular surgery · 2024Article
- Long-Term Mortality Predictors Using a Machine-Learning Approach in Patients With Chronic Limb-Threatening Ischemia After Peripheral Vascular Intervention.Journal of the American Heart Association · 2024Article
- Machine Learning in Vascular Medicine: Optimizing Clinical Strategies for Peripheral Artery Disease.Current cardiovascular risk reports · 2024Review
- A Non-Invasive Hemoglobin Detection Device Based on Multispectral Photoplethysmography.Biosensors · 2023Article
- Machine learning reveals sex-specific associations between cardiovascular risk factors and incident atherosclerotic cardiovascular disease.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis 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.
methodsCohort 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.
resultsThere were 88,898 fee-for-service Medicare beneficiaries diagnosed with peripheral artery disease and diabetes mellitus in our cohort. The rate of preventative treatments in the first six months following diagnosis were 52% (n = 45,971) with foot exams, 43% (n = 38,393) had vascular imaging, and 50% (n = 44,181) had an HbA1c test. The directionality of the influence for all covariates considered matched those results found with the random forest and logistic regression models. The most predictive covariate in each approach differs as determined by the t-statistics from logistic regression and variable importance (VI) in the random forest model. For amputation we see age 85 + (t = 53.17) urban-residing (VI = 83.42), and for death (t = 65.84, VI = 88.76) and reintervention (t = 34.40, VI = 81.22) both models indicate age is most predictive.
conclusionsThe use of random forest models to analyze data and provide predictions for patients holds great potential in identifying modifiable patient-level and health-system factors and cohorts for increased surveillance and intervention to improve outcomes for patients. Random forests are incredibly high performing models with difficult interpretation most ideally suited for times when accurate prediction is most desirable and can be used in tandem with more common approaches to provide a more thorough analysis of observational data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.