Abstract
Hypertensive disorders are the leading cause of deaths during pregnancy. Risk pregnancy accompaniment is essential to reduce these complications. Decision support systems (DSS) are important tools to patients' accompaniment. These systems provide relevant information to health experts about clinical condition of the patient anywhere and anytime. In this paper, a model that uses the Naive Bayesian classifier is introduced and its performance is evaluated in comparison with the Data Mining (DM) classifier named J48 Decision Tree. This study includes the modeling, performance evaluation, and comparison between models that could be used to assess pregnancy complications. Evaluation analysis of the results is performed through the use of Confusion Matrix indicators. The founded results show that J48 decision tree classifier performs better for almost all the used indicators, confirming its promising accuracy for identifying hypertensive disorders on pregnancy.