Bayesian network analysis of factors influencing type 2 diabetes, coronary heart disease, and their comorbidities

Abstract

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.

Method: 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.

Result: 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.

Conclusion: 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.

Keywords: BN; Comorbidity; Coronary heart disease; Influencing factors; Type 2 diabetes.

Fig. 1

BN diagram of influencing factors of T2DM

Fig. 2

BN diagram of influencing factors of CAD

Fig. 3

BN diagram of influencing factors of comorbidity

Fig. 4

ROC curves of each disease model. Note: (A) ROC curve of T2DM prediction, (B) ROC curve of CAD prediction, (C) ROC curve of comorbidity prediction

Fig. 5

Prediction inference of T2DM influencing factors by BN model I

Fig. 6

Prediction inference of T2DM influencing factors by BN model II

Fig. 7

Prediction inference of CAD influencing factors by BN model I

Fig. 8

Prediction inference of CAD influencing factors by BN model II

Fig. 9

Prediction inference of comorbidity influencing factors by BN model I

Fig. 10

Prediction inference of comorbidity influencing factors by BN model II

Fig. 11

Diagnostic inference of BN model for T2DM influencing factors

Fig. 12

Diagnostic inference of BN model for CAD influencing factors

Fig. 13

Diagnostic inference of BN model for comorbidity influencing factors