ArticleFrontiers in public health2022
Prediction of Atrial Fibrillation in Hospitalized Elderly Patients With Coronary Heart Disease and Type 2 Diabetes Mellitus Using Machine Learning: A Multicenter Retrospective Study.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 19 citations in OpenAlex.
- Association between atherogenic index of plasma trajectory and new-onset coronary heart disease in Chinese elderly people: a prospective cohort study.Journal of geriatric cardiology : JGC · 2025Article
- Predicting depression risk with machine learning models: identifying familial, personal, and dietary determinants.BMC psychiatry · 2025Article
- Exploration and analysis of risk factors for coronary artery disease with type 2 diabetes based on SHAP explainable machine learning algorithm.Scientific reports · 2025Article
- AI-based Assessment of Risk Factors for Coronary Heart Disease in Patients With Diabetes Mellitus and Construction of a Prediction Model for a Treatment Regimen.Reviews in cardiovascular medicine · 2025Article
- Predicting Mortality in Atrial Fibrillation Patients Treated with Direct Oral Anticoagulants: A Machine Learning Study Based on the MIMIC-IV Database.Journal of clinical medicine · 2025Article
- Relationship between elevated serum direct bilirubin and atrial fibrillation risk among patients with coronary artery disease.Frontiers in medicine · 2025Article
- Application of machine learning to predict the occurrence of venous thromboembolism in patients hospitalized for coronary artery disease: a single-center retrospective study.Frontiers in cardiovascular medicine · 2025Article
- Machine learning-based model to predict composite thromboembolic events among Chinese elderly patients with atrial fibrillation.BMC cardiovascular disorders · 2024Article
- Dapagliflozin and sacubitril on myocardial microperfusion in patients with post-acute myocardial infarction heart failure and type 2 diabetes.World journal of clinical cases · 2024Article
- The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment.Reviews in cardiovascular medicine · 2024Review
- Development of a Nomogram That Predicts the Risk of Atrial Fibrillation in Patients with Coronary Heart Disease.Risk management and healthcare policy · 2024Article
- Integrated bagging-RF learning model for diabetes diagnosis in middle-aged and elderly population.PeerJ. Computer science · 2024Article
- Predicting diabetic kidney disease for type 2 diabetes mellitus by machine learning in the real world: a multicenter retrospective study.Frontiers in endocrinology · 2023Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The objective of this study was to use machine learning algorithms to construct predictive models for atrial fibrillation (AF) in elderly patients with coronary heart disease (CHD) and type 2 diabetes mellitus (T2DM). Methods: The diagnosis and treatment data of elderly patients with CHD and T2DM, who were treated in four tertiary hospitals in Chongqing, China from 2015 to 2021, were collected. Five machine learning algorithms: logistic regression, logistic regression+least absolute shrinkage and selection operator, classified regression tree (CART), random forest (RF) and extreme gradient lifting (XGBoost) were used to construct the prediction models. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were used as the comparison measures between different models. Results: A total of 3,858 elderly patients with CHD and T2DM were included. In the internal validation cohort, XGBoost had the highest AUC (0.743) and sensitivity (0.833), and RF had the highest specificity (0.753) and accuracy (0.735). In the external verification, RF had the highest AUC (0.726) and sensitivity (0.686), and CART had the highest specificity (0.925) and accuracy (0.841). Total bilirubin, triglycerides and uric acid were the three most important predictors of AF. Conclusion: The risk prediction models of AF in elderly patients with CHD and T2DM based on machine learning algorithms had high diagnostic value. The prediction models constructed by RF and XGBoost were more effective. The results of this study can provide reference for the clinical prevention and treatment of AF.
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