Data Science Application for Creation of Maternal Morbidity and Mortality Predictive Software
Rúsbel Domínguez-Domínguez, Germán H. Alférez, Verenice González-Mejia, Norbet Donías
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Rúsbel Domínguez-Domínguez
Faculty of Engineering and Technology, Department of Informática, Montemorelos University, Montemorelos, Nuevo León, Mexico
Germán H. Alférez
School of Computing, Center for Innovation and Research in Computing, Southern Adventist University, Collegedale, Tennessee, United States
Verenice González-Mejia
Medical School, Department of Research Support in Health Sciences, Montemorelos University, Montemorelos, Nuevo León, Mexico
Norbet Donías
Department of Pediatrics, Texas Tech University Health Sciences Center El Paso, El Paso, Texas, United States
Cite this paper as:Domínguez-Domínguez, R., Alférez, G. H., González-Mejia, V., Donías, N. (2023). Data Science Application for Creation of Maternal Morbidity and Mortality Predictive Software.
Journal of Systemics, Cybernetics and Informatics, 21(1), 1-8. https://doi.org/10.54808/JSCI.21.01.1
Online ISSN (Journal): 1690-4524
Abstract
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.
Currently, there is no software that classifies women undergoing pregnancy check-ups (according to their socio-clinical risk of mortality), using variables selected with data science.
This project is rooted in the methodology advanced by International Business Machines (IBM) for the implementation of data science.
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.
