Comparison of Continuous and Discrete Bayesian Network Models for Uric Acid and Related Metabolic Indicators
Objective The continuous and discrete Bayesian network models of serum uric acid and related metabolic indexes were established to explore the influencing factors of serum uric acid and compare the characteristics and advantages of the results of the two networks.Methods A total of 4646 patients with serum uric acid and metabolic diseases from chronic disease monitoring in Shanxi Province in 2015 were selected.IPCB algorithm was used to establish a continuous Bayesian network of serum uric acid.Meanwhile,the above indicators were discretized,and MMHC was used to establish a discrete Bayesian network of high uric acid.Results The discrete Bayesian network found 14 edges,in which triglyceride and diastolic blood pressure abnormalities were directly related to the occurrence of high uric acid,leading to the occurrence of high uric acid.Age was an indirect factor;Age,TG,LDL,HDL,SP and DP are directly related to uric acid level.With the increase of age,TG and LDL and the decrease of HDL,uric acid level increases,while the increase of uric acid level leads to the increase of SP and DP.TC is indirectly related to uric acid.Conclusion The two network models adapt to different data types,but the continuous Bayesian network has more direct correlation factors and better overall explanatory degree.
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