Data Science Application for Creation of Maternal Morbidity and Mortality Predictive Software

Document Type

Proceeding Paper

Publication Date



In Mexico, the estimated Maternal Mortality Ratio is 34.6 deaths per 100,000 estimated births. Consequently, healthcare facilities and services have given precedence to prenatal care, childbirth services, and postpartum care.

In Mexico, the Ministry of Health maintains an open database concerning maternal deaths, encompassing 58 variables. Among these variables is the CIE (International Statistical Classification of Diseases and Related Health Problems), which covers a total of 248 diseases linked to maternal deaths. Presently, there is no available software for categorizing women undergoing pregnancy check-ups.

This project is rooted in the methodology advanced by International Business Machines (IBM) for the implementation of data science. By creating predictive software, the classification of patients into the two most common causes of death became attainable: eclampsia during labor and secondary or late postpartum hemorrhage.

The software's utilized model was constructed through the Naïve Bayes supervised learning algorithm, yielding an accuracy of 0.7236. The overall precision stood at 0.75, with an overall recall of 0.74, and an overall F1-score of 0.71. For the eclampsia during labor class, precision reached 0.71, recall was 0.94, and the F1- score attained 0.81. As for secondary or late postpartum hemorrhage, precision scored 0.81, recall measured 0.43, and the F1-score was 0.56. This predictive model is executed each time a physician inputs patient data into the system.