Exploring stroke risk factors in different genders using Bayesian networks: a cross-sectional study involving a population of 134,382

The exploration of stroke risk factors provides crucial information for healthcare planning and priority setting. This study aims to utilize Bayesian network modeling to explore stroke risk factors in different genders. We collected data from 10 cities and 13 counties in Shanxi Province, China, through questionnaire surveys, physical examinations, and laboratory tests. Logistic regression and Bayesian modeling were employed to analyze the risk factors for stroke in different genders. Preliminary analysis of stroke risk factors was conducted using chi-square tests and logistic regression models. Variables that showed statistical significance were included in the construction of the Bayesian model. Bayesian structure learning was achieved using the Max-Min Hill-Climbing algorithm, and parameter learning utilized maximum likelihood estimation. The study identified both common and gender-specific risk factors for stroke. Common risk factors for both males and females included region, marital status, education level, age, family history of stroke, secondhand smoke exposure, snoring, abnormal blood lipids, hypertension, diabetes, and coronary heart disease. Gender-specific factors were smoking and respiratory pause for males, and alcohol consumption for females. The Bayesian Network (BN) model further revealed structural relationships among these factors, showing that abnormal blood lipids, hypertension, and age were direct risk factors for stroke in males, with snoring, education level, and respiratory pause as indirect factors. For females, direct risk factors included age, hypertension, and secondhand smoke exposure, while snoring was an indirect factor. Stroke risk factors vary by gender, highlighting the importance of gender-specific prevention and intervention strategies.