Performance Assessment of Decision Tree-Based Predictive Classifiers for Risk Pregnancy Care

The e-Health core concept includes Web usage in an integrated way with tools and services for healthcare. This definition improves access, efficiency, and clinical care quality process that are necessary for a service delivery improvement. Decision support systems (DSSs) belong to a plethora of e-Health concept dimensions. For these systems construction, it is important to find a reliable intelligent mechanism capable to identify diseases that can worsen the patient's clinical condition. Thus, this paper proposes the use of tree-based data mining (DM) techniques for the hypertensive disorders prediction in the risk gestation. It presents the modeling, performance evaluation, and comparison between the tree based classifiers ID3 and NBTree. The 5-fold cross-validation method realizes the performance comparison. Results show that the NBTree classifier obtained better performance, presenting F-measure 0.609, ROC area 0.753, and Kappa statistic 0.4658. This classifier can be a key to a smart system development capable to predict risk events in pregnancy. Therefore, DSSs are a leading solution for the reduction of both mother and fetal mortality.