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Using Predictive Classifiers to Prevent Infant Mortality in the Brazilian Northeast

Ramos, R. R. ; Silva, C. ; Moreira, M. ; Rodrigues, J. R. ; Oliveira, A. O. ; Monteiro, O.

Using Predictive Classifiers to Prevent Infant Mortality in the Brazilian Northeast, Proc IEEE ComSoc - International Conference on e-Health Networking, Applications and Services Healthcom, Dalian, China, Vol. , pp. - , October, 2017.

Digital Object Identifier: 10.1109/HealthCom.2017.8210811

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Despite the fact that infant mortality rates have been decreased in recent years, this issue stills being considered alarming to Brazilian health system indicators. In this context, the GISSA framework, an intelligent governance framework for Brazilian health system, emerges as a smart system for the Federal Government program, called Stork Network. Its main objective is to improve the healthcare for pregnant women as well as their newborns. This application aims to generate alerts focusing on the health status verification of newborns and pregnant woman to support decision-makers in preventive actions that may mitigate severe problems. Therefore, this paper presents the LAIS, an Intelligent health analysis system that uses data mining (DM) to generate newborns death risk alerts through probability-based methods. Results show that the Naïve Bayes classifier presents better performance than the other DM approaches to the used pregnancy data set analysis of this work. This approach performed an accuracy of 0.982 and a Receiver Operating Characteristic (ROC) Area of 0.921. Both indicators suggest the proposed model may contribute to the reduction of maternal and fetal deaths.