A Machine Learning Approach for Severe Maternal Morbidity Prediction at Rafael Calvo Clinic in Cartagena-Colombia

Abstract

There is a huge problem in public health around the world called severe maternal morbidity (SMM). It occurs during pregnancy, delivery, or puerperium. This condition establishes risk for babies and women lives since it’s earlier detection isn’t easy [8]. In order to respond to such a situation, the current study suggests the use of logistic regression, and supports vector machine to construct a predicting model of risk level of maternal morbidity during pregnancy. Patients for the current study was the pregnant women who received prenatal care at Rafael Calvo Clinic in Cartagena, Colombia and final attention in the same clinic. This study presents the results of two machine learning algorithms, logistic regression and support vector machine. We validated the datasets from the first, second and third quarter of pregnancy with both techniques. The study shows that logistic regression achieves the best results with the prenatal control dataset from the first and second quarter and the support vector machine algorithm achieves the best prediction results with the data set from the third quarter. We generated two datasets using the information of medical records on pregnancy patients at Maternidad Rafael Calvo Clinic. The first dataset contains the six initial months of pregnancy data and the second dataset contains the last quarter of pregnancy data. We trained the first model with logistic regression and the datasets corresponding to the first semester of pregnancy. We obtained a classification of 97% sensibility, 51.8% positive predictive value and F1 score of 67.7%. The support vector machine model was implemented with the datasets obtained from the third quarter of pregnancy. We obtained a classifier with 100% of sensibility, 27.0% of precision.