Predicting hypertensive disorders in high-risk pregnancy using the random forest approach

• Main Authors: Moreira, Mario W.L., Rodrigues, Joel J.P.C., Oliveira, Antonio M.B., Saleem, Kashif, Venancio Neto, Augusto J.
• Format: Conference Proceeding
• Language: English
• Subjects: Data mining; Decision support systems (DSS); Decision trees; e-Health; Hypertension; Mathematical model; Pregnancy; Radio frequency; Training; Vegetation
• ISSN: 1938-1883
• DOI: 10.1109/ICC.2017.7996964

The incidence of hypertension associated with pregnancy contributes significantly to increase maternal and fetal deaths during pregnancy and childbirth. Due to its high incidence rate and several complications, the study of this disorder has been subject of numerous investigations in an attempt to determine its prevention and improve the treatment conduction. In this context, this paper uses a data mining (DM) technique, named random forest (RF), applied to health care to early identification of these disorders. It also presents the modeling, performance assessment, and comparison with other DM methods to evaluate the performance of the proposed model. Results showed that the RF classifier had a regular performance, presenting the best values for true positive Rate (TP Rate) and recall in the prediction of preeclampsia superimposed on chronic hypertension compared to the other experimented classifiers. Even finding a good performance to predict hypertensive disorders, other tree-based methods need to be evaluated, as well as other DM techniques. Discovering reliable information of pregnant women suffering from the hypertensive disease is an important path to reduce the high rate of deaths, mainly, in developing countries where 99% of these deaths occur.