Comparison of Continuous and Discrete Bayesian Network Models for Uric Acid and Related Metabolic Indicators
10.11783/j.issn.1002-3674.2024.02.001
- VernacularTitle:血尿酸与慢性代谢性疾病的连续型和离散型贝叶斯网络效果比较
- Author:
Yu CUI
1
;
Weimei SONG
;
Hao REN
Author Information
1. 山西医科大学卫生统计教研室(030001)
- Keywords:
Continuous Bayesian network;
IPCB algorithm;
Blood uric acid;
Metabolic indicators;
Relevant factor
- From:
Chinese Journal of Health Statistics
2024;41(2):162-166,174
- CountryChina
- Language:Chinese
-
Abstract:
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.
