An inference mechanism using Bayes-based classifiers in pregnancy care

Significant advances on smart decision support systems (DSSs) development have influenced important results on pregnancy care. Nevertheless, even considering the efforts to reduce the number of women deaths due to problems related to pregnancy, this decrease presented less impact than other areas of human development. Hypertensive disorders in pregnancy, particularly pre-eclampsia and eclampsia, account for significant proportion of perinatal morbidity and maternal mortality. In this context, this paper proposes an inference model that uses data mining (DM) techniques capable for operating in a data set to extract patterns and assist in knowledge discovery. Identifying hypertensive crises that complicate pregnancy, it can impact in a meaningful reduction the incidence of sequelae and death of pregnant women. Comparison between two Bayesian classifiers is performed in this work to better classify the hypertensive disorders severity. Results showed that Naïve Bayes classifier had an excellent performance, presenting better precision and F-measure, compared to the other experimented classifiers. Even finding a good performance to predict hypertensive disorders, other Bayesian methods need to be evaluated, as well as other DM techniques such as those based on artificial intelligence (AI) and tree-based methods.