Supporting the Characterization of Preeclampsia Patients Through Descriptive and Clustering Analysis

Abstract

One of the most common causes of maternal death during pregnancy is preeclampsia. A deeper understanding of the patient’s features can aid in the hospital’s clinical care distribution. However, at the IESS Los Ceibos Hospital, these types of studies have not been carried out for preeclampsia. Therefore, in this work, we describe the application of descriptive and clustering analysis to characterize preeclamptic patients. Preeclamptic patients treated at the IESS Los Ceibos Hospital in Guayaquil comprised the dataset used in this study. Descriptive and clustering analysis allowed us to find that severe preeclampsia (O141) is the most common diagnosis when preeclamptic patients arrive at the hospitalization unit, representing 79.5% of the cases. Moreover, women whose maternal age falls between 26 and 35 years have the highest prevalence of preeclampsia, representing 55.4% of the cases. Finally, adult patients in their late 30s or older are often diagnosed with severe preeclampsia (O141) and often require many hours of hospital care during the first two visits. These findings will help to generate care and prevention policies, such as the use of a low dose of aspirin, in these age groups to avoid the complications that preeclampsia can cause.