Azadeh Abkar - Department of Computer Engineering, Faculty of Computer, Karoon Institute of Higher Education, Ahvaz, Iran
Amin Golabpour - School of Allied Medical Sciences, Shahroud University of Medical Sciences, Shahroud, Iran

Introduction: Diagnosis of high-risk maternal pregnancy is one of the most important issues during pregnancy and can be of great help to pregnant mothers. Also, early diagnosis can reduce mortality and morbidity in mothers.Material and Methods: In this study, the data of ۱۰۱۴ pregnant mothers were used, which includes ۲۷۲ people with high-risk pregnancies, ۷۴۲ people with medium-risk and low-risk pregnancies. Also, the data include six independent variables. A combination of Bayesian belief network algorithms and particle optimization was used to predict pregnancy risk.Results: For validation, the data model was divided into two sets of training and testing based on the method of ۳۰-۷۰. Then the proposed model was designed by training data. Then the model for training and testing data was evaluated in terms of accuracy parameters ۹۹.۱۸ and ۹۸.۳۲% accuracy were obtained, respectively. It has also performed between ۰.۵ and ۸% better than similar work in the past.Conclusion: In this study, a new model for designing Bayesian belief network was presented and it was found that this model can be useful for predicting maternal pregnancy risk.

High-Risk Pregnancy, Bayesian Belief Network, Particle Optimization, Data Mining