Prediction Model for Breastfeeding practice among Ethiopian Children using Decision tree and Rule induction Algorithms : Data mining

Abstract Background: Ethiopia have adopted infant feeding guidelines based on World Health Organization's standards to reduce the burden of infant and child mortality due to poor breastfeeding practice. But, breastfeeding practice is still one of challenges affecting infants and child health causing significant amount of deaths(23%-28%) yearly in Ethiopia. Breastfeeding practice is associated with different individual and community specific socio-cultural factors in different countries. Ethiopia is a populated country of communities with a very diverse cultural and societal values administered in nine different regions. Therefore, it is very important to assess breastfeeding practice among the various communities to identify the factors at individual and community level in order to come up with preventive intervention protocols that matches to each particular region. Hence, the study intended to assess patterns of breastfeeding practice among the communities within each specific region and develop predictive model of breastfeeding practice using data mining algorithms in Ethiopia. Different experiments were conducted in four scenarios with two test option (10 cross validation and percentage splits) and different parameter values using J48 and PART algorithms to select best predictive model for developing breastfeeding decision support system using java application programming interface. Results : About 54.8% (6390) and 3.8% (445) of the mothers have ever and never breastfed their children within the previous five years of the survey respectively while 40.8% (4757) mothers were breastfeeding until the survey date. Both J48 and PART algorithms were able to predict breastfeeding practice with an accuracy of 96.86% and 96.77% respectively. 2316 (96.94%) and 1071 (96.04%) mothers were correctly classified as Normal and poor respectively using PART algorithm with 70-30 percentage-split test option. Only 66 (3.06%) and 43 (3.96%) mothers were misclassified as false positive and false negative respectively. Conclusions : Almost, half of the mothers with 1-4 births within the five years before the survey have had normal breastfeeding practice. Both J48 and PART algorithms have best fitted to predict breastfeeding practice and can be used to deploy a decision support model of breastfeeding practice as a supporting tool for health practitioners.