ArticleScientific reports2020
AI-based prediction for the risk of coronary heart disease among patients with type 2 diabetes mellitus.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Healthcare professionals' perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels.BMC health services research · 2025Pooled it
- Pooled it
- Cohort Profile: The North East London Diabetes Study (NELDS).International journal of epidemiology · 2026Article
- Development and validation of an interpretable machine learning model for predicting the risk of coronary heart disease risk in diabetes mellitus patients: a dual-center retrospective study.BMC medical informatics and decision making · 2026Article
- Artificial Intelligence in Diabetes Care: Applications, Challenges, and Opportunities Ahead.Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists · 2025Review
- 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
- T2D-LVDD: neural network-based predictive models for left ventricular diastolic dysfunction in type 2 diabetes.Diabetology & metabolic syndrome · 2025Article
- Surrogate markers of insulin resistance and coronary artery disease in type 2 diabetes: U-shaped TyG association and insights from machine learning integration.Lipids in health and disease · 2025Article
- A Real-Time Computer-Aided Diagnosis System for Coronary Heart Disease Prediction Using Clinical Information.Reviews in cardiovascular medicine · 2025Article
- Construction of a prediction model for coronary heart disease in type 2 diabetes mellitus: a cross-sectional study.Scientific reports · 2025Article
- AI-driven tools for the prediction of obesity-related vascular diseases: stakeholder perspectives and challenges.Frontiers in public health · 2025Article
- Molecular Aspects in the Development of Type 2 Diabetes and Possible Preventive and Complementary Therapies.International journal of molecular sciences · 2024Review
- Machine learning in precision diabetes care and cardiovascular risk prediction.Cardiovascular diabetology · 2023Review
- Utility of an Artificial Intelligence Enabled Electrocardiogram for Risk Assessment in Liver Transplant Candidates.Digestive diseases and sciences · 2023Article
- MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women.Scientific reports · 2023Article
- Establishment of a diagnostic model of coronary heart disease in elderly patients with diabetes mellitus based on machine learning algorithms.Journal of geriatric cardiology : JGC · 2022Article
- A study on the risk prediction of heart disease in diabetes patients based on machine learning.Science progressArticle
- Clinical significance of N-terminal natriuretic peptide combined with inflammatory factors, oxidative stress factors and blood lipid detection in elderly patients with Type-2 diabetes complicated with coronary heart disease.Pakistan journal of medical sciencesArticle
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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
Type 2 diabetes mellitus (T2DM) is one common chronic disease caused by insulin secretion disorder that often leads to severe outcomes and even death due to complications, among which coronary heart disease (CHD) represents the most common and severe one. Given a huge number of T2DM patients, it is thus increasingly important to identify the ones with high risks of CHD complication but the quantitative method is still not available. Here, we first curated a dataset of 1,273 T2DM patients including 304 and 969 ones with or without CHD, respectively. We then trained an artificial intelligence (AI) model using randomly selected 4/5 of the dataset and use the rest data to validate the performance of the model. The result showed that the model achieved an AUC of 0.77 (fivefold cross-validation) on the training dataset and 0.80 on the testing dataset. To further confirm the performance of the presented model, we recruited 1,253 new T2DM patients as totally independent testing dataset including 200 and 1,053 ones with or without CHD. And the model achieved an AUC of 0.71. In addition, we implemented a model to quantitatively evaluate the risk contribution of each feature, which is thus able to present personalized guidance for specific individuals. Finally, an online web server for the model was built. This study presented an AI model to determine the risk of T2DM patients to develop to CHD, which has potential value in providing early warning personalized guidance of CHD risk for both T2DM patients and clinicians.
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