<abstract> <p>Stroke is a major chronic non-communicable disease with high incidence, high mortality, and high recurrence. To comprehensively digest its risk factors and take some relevant measures to lower its prevalence is of great significance. This study aimed to employ Bayesian Network (BN) model with Max-Min Hill-Climbing (MMHC) algorithm to explore the risk factors for stroke. From April 2019 to November 2019, Shanxi Provincial People's Hospital conducted opportunistic screening for stroke in ten rural areas in Shanxi Province. First, we employed propensity score matching (PSM) for class balancing for stroke. Afterwards, we used Chi-square testing and Logistic regression model to conduct a preliminary analysis of risk factors for stroke. Statistically significant variables were incorporated into BN model construction. BN structure learning was achieved using MMHC algorithm, and its parameter learning was achieved with Maximum Likelihood Estimation. After PSM, 748 non-stroke cases and 748 stroke cases were included in this study. BN was built with 10 nodes and 12 directed edges. The results suggested that age, fasting plasma glucose, systolic blood pressure, and family history of stroke constitute direct risk factors for stroke, whereas sex, educational levels, high density lipoprotein cholesterol, diastolic blood pressure, and urinary albumin-to-creatinine ratio represent indirect risk factors for stroke. BN model with MMHC algorithm not only allows for a complicated network relationship between risk factors and stroke, but also could achieve stroke risk prediction through Bayesian reasoning, outshining traditional Logistic regression model. This study suggests that BN model boasts great prospects in risk factor detection for stroke.</p> </abstract>

ObjectivesMultimorbidity (MMD) is a medical condition that is linked with high prevalence and closely related to many adverse health outcomes and expensive medical costs. The present study aimed to construct Bayesian networks (BNs) with Max-Min Hill-Climbing algorithm (MMHC) algorithm to explore the network relationship between MMD and its related factors. We also aimed to compare the performance of BNs with traditional multivariate logistic regression model.MethodsThe data was downloaded from the Online Open Database of CHARLS 2018, a population-based longitudinal survey. In this study, we included 10 variables from data on demographic background, health status and functioning, and lifestyle. Missing value imputation was first performed using Random Forest. Afterward, the variables were included into logistic regression model construction and BNs model construction. The structural learning of BNs was achieved using MMHC algorithm and the parameter learning was conducted using maximum likelihood estimation.ResultsAmong 19,752 individuals (9,313 men and 10,439 women) aged 64.73 ± 10.32 years, there are 9,129 ones without MMD (46.2%) and 10,623 ones with MMD (53.8%). Logistic regression model suggests that physical activity, sex, age, sleep duration, nap, smoking, and alcohol consumption are associated with MMD (P &lt; 0.05). BNs, by establishing a complicated network relationship, reveals that age, sleep duration, and physical activity have a direct connection with MMD. It also shows that education levels are indirectly connected to MMD through sleep duration and residence is indirectly linked to MMD through sleep duration.ConclusionBNs could graphically reveal the complex network relationship between MMD and its related factors, outperforming traditional logistic regression model. Besides, BNs allows for risk reasoning for MMD through Bayesian reasoning, which is more consistent with clinical practice and thus holds some application prospects.

Hyperhomocysteinemia (HHcy) is a condition closely associated with cardiovascular and cerebrovascular diseases. Detecting its risk factors and taking some relevant interventions still represent the top priority to lower its prevalence. Yet, in discussing risk factors, Logistic regression model is usually adopted but accompanied by some defects. In this study, a Tabu Search-based BNs was first constructed for HHcy and its risk factors, and the conditional probability between nodes was calculated using Maximum Likelihood Estimation. Besides, we tried to compare its performance with Hill Climbing-based BNs and Logistic regression model in risk factor detection and discuss its prospect in clinical practice. Our study found that Age, sex, α1-microgloblobumin to creatinine ratio, fasting plasma glucose, diet and systolic blood pressure represent direct risk factors for HHcy, and smoking, glycosylated hemoglobin and BMI constitute indirect risk factors for HHcy. Besides, the performance of Tabu Search-based BNs is better than Hill Climbing-based BNs. Accordingly, BNs with Tabu Search algorithm could be a supplement for Logistic regression, allowing for exploring the complex network relationship and the overall linkage between HHcy and its risk factors. Besides, Bayesian reasoning allows for risk prediction of HHcy, which is more reasonable in clinical practice and thus should be promoted.