摘要：During period of women-pregnancy a rise some type of diabetes-disease as a result of high-blood-glucose level called the Gestational Diabetes Mellitus(GDM). As this type of diabetes harms the mother and her baby at the same time, and the chance of developing it is one in every 25 pregnancies. So, the risk of rising type2 diabetes afterward. The goal of this work is to design a model which can prognosticate the opportunity of diabetes during pregnancy with best accuracy. The blood-sugar concentration is the main characteristic that used to diagnose the infection, beside that the characteristic of smoky pregnant women in addition consider the weight and genetic factor of the pregnant woman. IID3–Decision tree as classifier used and evaluated by comparing the proposed with three other exist methods. Decision Tree, SVM and Naïve-Bayes are used in performance comparison on the same pregnant women dataset. To assessment the the functioning of proposed-model use Precision-measure, Accuracy, F-Measure, and Recall. Results obtained show the accuracy of proposed model is close level accuracy 88.36%. The results and findings are gotten by applied it on 986 GDM pregnant women during 2 years depend on some questionnaire about the conditions. As the results of this work is 56% of smoking pregnant women have overweight with high blood pressure(uncontrolled cases), while the rate of smoking pregnant women with moderate weight is 31% medium blood pressure (controlled cases), normal blood pressure is gotten with 9% of those non-smoking pregnant overweight-women with genetic factor , and 4% of those smoking pregnant women have normal-weight with normal blood pressure.
