Department of Epidemiology and Medical Statistics, School of Public Health, Guangdong Medical University, Dongguan, 523808, Guangdong, China.
Department of Infection Control, Ankang Hospital of Traditional Chinese Medicine, Ankang, 725000, Shaanxi, China.
BMC Public Health. 2024 May 8;24(1):1267. doi: 10.1186/s12889-024-18737-x.
Bayesian 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.
Employing 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.
The 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.
The 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.
贝叶斯网络（BN）模型被开发用于探索影响因素与 2 型糖尿病（T2DM）、冠心病（CAD）及其合并症之间的具体关系。目的是使用这些模型预测疾病发生并诊断病因，从而为 T2DM、CAD 及其合并症的有效预防和控制策略的制定提供信息。
采用病例对照设计，将 T2DM、CAD 及其合并症患者（病例组）与健康对照者（对照组）进行比较。采用单因素和多因素 Logistic 回归分析来识别疾病影响因素。使用 Tabu 搜索算法学习 BN 结构，通过最大似然估计进行参数估计。使用混淆矩阵评估 BN 模型的预测性能，并使用 Netica 软件进行可视化预测和诊断。
该研究共纳入 3824 名参与者，包括 1175 名对照者、1163 例 T2DM 患者、982 例 CAD 患者和 504 例合并症患者。BN 模型揭示了直接和间接影响 T2DM 的因素，如年龄、地区、教育水平和家族史（FH）。变量如运动、LDL-C、TC、水果和甜食摄入具有直接影响，而吸烟、饮酒、职业、心率、HDL-C、肉类和主食摄入具有间接影响。同样，对于 CAD，具有直接和间接影响的因素包括年龄、吸烟、SBP、运动、肉类和水果摄入，而睡眠时间和心率则具有直接影响。对于 T2DM 和 CAD 合并症，年龄、FBG、SBP、水果和甜食摄入均具有直接和间接影响，而运动和 HDL-C 具有直接影响，地区、教育水平、DBP 和 TC 具有间接影响。
使用 Tabu 搜索算法构建的 BN 模型展示了强大的预测性能、可靠性和在预测 T2DM、CAD 及其合并症疾病概率方面的适用性。这些发现为增强预防和控制策略以及探索 BN 在预测和诊断慢性病方面的应用提供了有价值的见解。
