Discovering Mexican Birth Rate Patterns Using Machine Learning Techniques

Maria Somodevilla; David Limon Cantu; Ivo Pineda; ConcepciÃ³n PÃ©rez de Celis; Darnes VilariÃ±o

doi:10.24297/ijct.v15i1.1708

Authors

Maria Somodevilla Autonomous University Benemerita of Puebla, Mexico
David Limon Cantu PueblaÂ´s Autonomus Benemerit University
Ivo Pineda Autonomous University Benemerita of Puebla, Mexico
ConcepciÃ³n PÃ©rez de Celis Autonomous University Benemerita of Puebla, Mexico
Darnes VilariÃ±o Autonomous University Benemerita of Puebla, Mexico

DOI:

https://doi.org/10.24297/ijct.v15i1.1708

Keywords:

Data Mining, Birth Rate, Mexico, Clustering, Classification

Abstract

In this research, we attempt to discover patterns that describe and predict the birth rate in Mexico by using data mining techniques based on relevant demographic and economic information about Mexico. More than twelve million births data obtained from the General Directorate of Health Information in the period 2008-2013 were analyzed. The acquired knowledge allows us to say that in Mexico the birth rate is affected by the social welfare, education and marginality at county level. Due to the diversity of the population and the large number of socioeconomic factors involved in Mexican society, it is difficult to find general impact factors for this issue. The results of this research are not intended to be definitive but its aim is to provide indicators that may influence decisions about birth control in Mexico.