Fatemeh Ghaffari Sardasht - PhD of Reproductive Health, Shahid Hasheminejad Hospital, Medical Sciences University Of Mashhad, Mashhad,Iran
Golnoush Shahraki - Master of Biomedical Engineering, Clinical Research Development Unit ,Shahid Hasheminejad Hospital, Medical Sciences University Of Mashhad, Mashhad,Iran
Behrang Rezvani Kakhki - Associate Professor of Emergency medicine, Department of Emergency Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
Behroz Zargarzada - PhD Student of Biomedical Engineering, Department of Biomedical Engineering, Faculty of Engineering, Islamic Azad University Of Mashhad, Mashhad, Iran

Currently, all women with gestational diabetes, even those with no signs of fetal macrosomia, are closely monitored. This type of surveillance involves time and financial costs for both women and health services. If it is possible to use artificial intelligence network to prove which unborn babies of pregnant women are overweight, aggressive monitoring and treatment for those women whose unborn babies are not overweight will be reduced and risks will be avoided. And possible side effects are reduced and the time, resources and anxiety of mothers and health care personnel can be controlled. Therefore, this research was carried out by using perceptron neural network and decision tree and extracting a series of characteristics from pregnant mothers to predict the weight of the baby at birth and the type of delivery. The results indicate that the neural network used has an acceptable efficiency to estimate the newborn birth weight and type of delivery

Newborn birth weight, Type of delivery, Perceptron neural network, Decision tree