目的:建立贝叶斯网络(BN)模型,探讨影响因素与2型糖尿病(T2DM)之间的具体关系,冠心病(CAD),和他们的合并症。目的是使用这些模型预测疾病发生和诊断病因,从而为制定有效的T2DM预防和控制策略提供信息,CAD,和他们的合并症。
方法:采用案例控制设计,该研究比较了患有T2DM的个体,CAD,和他们的合并症(病例组)与健康同行(对照组)。进行单因素和多因素Logistic回归分析以确定疾病的影响因素。BN结构是使用禁忌搜索算法学习的,通过最大似然估计实现参数估计。使用混淆矩阵评估了BN模型的预测性能,并利用Netica软件进行视觉预测和诊断。
结果:该研究涉及3,824名参与者,包括1,175个控件,1,163例T2DM,982例CAD病例,和504例合并症病例。BN模型揭示了直接和间接影响T2DM的因素,比如年龄,区域,教育水平,家族史(FH)。像运动这样的变量,LDL-C,TC,水果,和甜食摄入表现出直接的影响,吸烟时,酒精消费,职业,心率,HDL-C,肉,和主食摄入有间接影响。同样,对于CAD,具有直接和间接影响的因素包括年龄,吸烟,SBP,锻炼,肉,和水果摄入量,而睡眠时间和心率则有直接影响。关于T2DM和CAD合并症,年龄,FBG,SBP,水果,甜食摄入表现出直接和间接的影响,而运动和HDL-C表现出直接效应,和区域,教育水平,DBP,TC表现出间接影响。
结论:使用禁忌搜索算法构建的BN模型展示了强大的预测性能,可靠性,以及在预测T2DM疾病概率中的适用性,CAD,和他们的合并症。这些发现为加强预防和控制策略以及探索BN在预测和诊断慢性疾病中的应用提供了有价值的见解。
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.