Exploring factors related to heart attack complicated with hypertension using a Bayesian network model: a study based on the China Health and Retirement Longitudinal Study - Zhang, Haifen; Zhang, Xiaotong; Yao, Xiaodong; Wang, Qiang (Frontiers in Public Health, 2023)
Objectives: While Bayesian networks (BNs) represents a good approach to discussing factors related to many diseases, little attention has been poured into heart attack combined with hypertension (HAH) using BNs. This study aimed to explore the complex network relationships between HAH and its related factors, and to achieve the Bayesian reasoning for HAH, thereby, offering a scientific reference for the prevention and treatment of HAH.The data was downloaded from the Online Open Database of CHARLS 2018, a population-based longitudinal survey. In this study, we included 16 variables from data on demographic background, health status and functioning, and lifestyle. First, Elastic Net was first used to make a feature selection for highly-related variables for HAH, which were then included into BN model construction. The structural learning of BNs was achieved using Tabu algorithm and the parameter learning was conducted using maximum likelihood estimation.Results: Among 19,752 individuals (9313 men and 10439 women) aged 64.73±10.32 years, Among 19,752 individuals (9313 men and 10439 women), there are 8370 ones without HAH (42.4%) and 11382 ones with HAH (57.6%).