Abstract:
Breastfeeding is one of the critical issues in Ethiopia because researches show that 24.0% - 27.0% of infant
death in Ethiopia is due to poor breastfeeding practices. UNICEF has been reported that a good promotion of
breastfeeding practices is a most important strategic plan to reduce child mortality in developed and developing
countries. Hence, it is important to identifying the determinate factors of poor breastfeeding practice, especially
poor countries like Ethiopia. Poor Breastfeeding is a reasonable well-defined problem caused by many factors
that are related to motherhood, environment, community and child. Therefore, it is very important to predict the
determinate factors of poor breastfeeding practice in various communities in the country in order to come up
with feasible intervention strategies to minimize the problem. This research intends to provide a survey of
current techniques of knowledge discovery in large databases using data mining techniques which will be useful
for medical practitioner to improve the breast feeding practices. The assessment was carried out with cross
validation and percentage split of different data mining algorithms such as decision tree, Naive Bayes , Artificial
Neural Network and Bagging.
