Problems in pregnancy, modeling fetal mortality through the Naïve Bayes classifier


  • Israel Campero Jurado Polytechnic University of Pachuca
  • Daniel Robles Camarillo
  • Eric Simancas Acevedo


pregnancy, fetal mortality, naïve bayes, artificial intelligence


Fetal mortality represent a problem in society, deaths are related to different causes. However, there are also a large number of unknown causes. Studies around the world, broadly speaking, have found that a total of 275,914 strong fetuses occurred in the United States between 2004 and 2014 compared to 7,571 maternal deaths. The Gaussian Naïve Bayes classifier is presented to assist in the reduction fetal mortality with 4 variables obtained through INEGI which are: gender, gestational age, maternal age, fetuses (a single pregnancy or a multiple pregnancy). The sample has a total of 2000 data where there is a balance of classes with 50% of cases of fetal death against 50% of births without complications in the year 2017. As a result the number of mislabeled points out of a total 400 points were 17. With a percentage of 96%, 96% and 96% for meaures precision, recall and f1-score respectively.




How to Cite

Jurado, I. C., Camarillo, D. R., & Acevedo, E. S. (2020). Problems in pregnancy, modeling fetal mortality through the Naïve Bayes classifier. International Journal of Combinatorial Optimization Problems and Informatics, 11(3), 121–129. Retrieved from