Exploring Related Factors of Chronic Obstructive Pulmonary Disease Based on Elastic Net and Bayesian Network

Abstract Objective This study aimed to construct Bayesian networks to analyze the network relationship between COPD and its related factors, and to explore the influencing intensity on COPD through network reasoning. Method Firstly Elastic Net and MMHC hybrid algorithm were adopted to screen the variables of the data of COPD in Shanxi Province from 2014 to 2015 and construct Bayesian networks respectively, and the parameters were estimated by maximum likelihood estimation. Results After feature selection by Elastic Net, 10 variables closely related to COPD finally entered the model. The COPD Bayesian networks constructed by MMHC algorithm showed that smoking status, household air pollution, family history, cough, air hunger or dyspnea were directly related to COPD, in which smoking status, household air pollution and family history were the parent nodes of COPD, and cough, air hunger or dyspnea represented the child nodes of COPD. In other words, smoking status, household air pollution, family history were related to the occurrence of COPD, and COPD would affect cough, air hunger or dyspnea. Gender was indirectly linked to COPD through smoking status. Conclusion Using Elastic Net to knock out some weakly-associated influencing factors of COPD in the variable screening stage, Bayesian networks could reveal the complex network relationship between COPD and its relevant factors well, making it more convenient to carry out targeted prevention and control of COPD. As such, Bayesian networks enjoyed a good prospect of application in analyzing disease-related factors.