A Novel Model for Diagnosing High-Risk Pregnancies Using Bayesian Belief Network Algorithm and Particle Optimization

Introduction: Diagnosis of high-risk maternal pregnancy is one the most important issues during and can be great help to pregnant mothers. Also, early diagnosis reduce mortality morbidity in mothers.Material Methods: In this study, data 1014 mothers were used, which includes 272 people with pregnancies, 742 medium-risk low-risk pregnancies. include six independent variables. A combination Bayesian belief network algorithms particle optimization was used predict risk.Results: For validation, model divided into two sets training testing based on method 30-70. Then proposed designed by data. for evaluated terms accuracy parameters 99.18 98.32% obtained, respectively. It has also performed between 0.5 8% better than similar work past.Conclusion: a new designing presented it found that useful predicting risk.