Lin Y, Chen JS, Zhong N, Zhang A, Pan H. A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus.
BMC Med Res Methodol 2023;
23:249. [PMID:
37880592 PMCID:
PMC10601254 DOI:
10.1186/s12874-023-02070-9]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 10/13/2023] [Indexed: 10/27/2023] Open
Abstract
OBJECTIVE
To predict the influencing factors of neonatal pneumonia in pregnant women with diabetes mellitus using a Bayesian network model. By examining the intricate network connections between the numerous variables given by Bayesian networks (BN), this study aims to compare the prediction effect of the Bayesian network model and to analyze the influencing factors directly associated to neonatal pneumonia.
METHOD
Through the structure learning algorithms of BN, Naive Bayesian (NB), Tree Augmented Naive Bayes (TAN), and k-Dependence Bayesian Classifier (KDB), complex networks connecting variables were presented and their predictive abilities were tested. The BN model and three machine learning models computed using the R bnlean package were also compared in the data set.
RESULTS
In constraint-based algorithms, three algorithms had different presentation DAGs. KDB had a better prediction effect than NB and TAN, and it achieved higher AUC compared with TAN. Among three machine learning modes, Support Vector Machine showed a accuracy rate of 91.04% and 67.88% of precision, which was lower than TAN (92.70%; 72.10%).
CONCLUSION
KDB was applicable, and it can detect the dependencies between variables, identify more potential associations and track changes between variables and outcome.
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