ACME: A Classification Model for Explaining the Risk of Preeclampsia Based on Bayesian Network Classifiers and a Non-Redundant Feature Selection Approach

Abstract

While preeclampsia is the leading cause of maternal death in Guayas province (Ecuador), its causes have not yet been studied in depth. The objective of this research is to build a Bayesian network classifier to diagnose cases of preeclampsia while facilitating the understanding of the causes that generate this disease. Data for the years 2017 through 2023 were gathered retrospectively from medical histories of patients treated at “IESS Los Ceibos” hospital in Guayaquil, Ecuador. Naïve Bayes (NB), The Chow–Liu Tree-Augmented Naïve Bayes (TANcl), and Semi Naïve Bayes (FSSJ) algorithms have been considered for building explainable classification models. A proposed Non-Redundant Feature Selection approach (NoReFS) is proposed to perform the feature selection task. The model trained with the TANcl and NoReFS was the best of them, with an accuracy close to 90%. According to the best model, patients whose age is above 35 years, have a severe vaginal infection, live in a rural area, use tobacco, have a family history of diabetes, and have had a personal history of hypertension are those with a high risk of developing preeclampsia.