ArticleBMC public health2024
Bayesian network analysis of factors influencing type 2 diabetes, coronary heart disease, and their comorbidities.
Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Influencing factors of oral frailty in Chinese maintenance hemodialysis patients: a Bayesian network analysis.Renal failure · 2026Article
- Evaluation and Comparison of Machine Learning Methods for Type 2 Diabetes Classification and Associated Factors.Healthcare informatics research · 2026Article
- Causal Bayesian network learning: application to the causal analysis of masticatory function and coronary heart disease in older adults.BMC medical informatics and decision making · 2026Article
- Path and Bayesian network analyses in the complex design of a well-being survey via New Zealand's Integrated Data Infrastructure.Frontiers in psychiatry · 2026Article
- Risk factors and interrelationships of taxane-related peripheral neuropathy: a Bayesian network analysis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Association of C reactive protein triglyceride glucose index with mortality in coronary heart disease and type 2 diabetes from NHANES data.Scientific reports · 2025Article
- DAGSLAM: causal Bayesian network structure learning of mixed type data and its application in identifying disease risk factors.BMC medical research methodology · 2025Article
- The Effect and Mechanism of Regular Exercise on Improving Insulin Impedance: Based on the Perspective of Cellular and Molecular Levels.International journal of molecular sciences · 2025Review
- Construction and Verification of a Frailty Risk Prediction Model for Elderly Patients with Coronary Heart Disease Based on a Machine Learning Algorithm.Reviews in cardiovascular medicine · 2025Article
- Erratum: Characteristics and Comorbidities Influencing Mortality Risk Among Hereditary Angioedema Patients.Journal of health economics and outcomes research · 2025Article
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15 authors.
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Abstract
objectiveBayesian network (BN) models were developed to explore the specific relationships between influencing factors and type 2 diabetes mellitus (T2DM), coronary heart disease (CAD), and their comorbidities. The aim was to predict disease occurrence and diagnose etiology using these models, thereby informing the development of effective prevention and control strategies for T2DM, CAD, and their comorbidities.
methodEmploying a case-control design, the study compared individuals with T2DM, CAD, and their comorbidities (case group) with healthy counterparts (control group). Univariate and multivariate Logistic regression analyses were conducted to identify disease-influencing factors. The BN structure was learned using the Tabu search algorithm, with parameter estimation achieved through maximum likelihood estimation. The predictive performance of the BN model was assessed using the confusion matrix, and Netica software was utilized for visual prediction and diagnosis.
resultThe study involved 3,824 participants, including 1,175 controls, 1,163 T2DM cases, 982 CAD cases, and 504 comorbidity cases. The BN model unveiled factors directly and indirectly impacting T2DM, such as age, region, education level, and family history (FH). Variables like exercise, LDL-C, TC, fruit, and sweet food intake exhibited direct effects, while smoking, alcohol consumption, occupation, heart rate, HDL-C, meat, and staple food intake had indirect effects. Similarly, for CAD, factors with direct and indirect effects included age, smoking, SBP, exercise, meat, and fruit intake, while sleeping time and heart rate showed direct effects. Regarding T2DM and CAD comorbidities, age, FBG, SBP, fruit, and sweet intake demonstrated both direct and indirect effects, whereas exercise and HDL-C exhibited direct effects, and region, education level, DBP, and TC showed indirect effects.
conclusionThe BN model constructed using the Tabu search algorithm showcased robust predictive performance, reliability, and applicability in forecasting disease probabilities for T2DM, CAD, and their comorbidities. These findings offer valuable insights for enhancing prevention and control strategies and exploring the application of BN in predicting and diagnosing chronic diseases.
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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 Socratic graph.