Predicting The Pattern Of Under-five Mortality In Ethiopia Using Data Mining Technology The Case Of Butajira Rural Health Program.

Introduction:The under-five deaths in Ethiopiarepresent 48% of all mortality. More than halfof the under-five deaths occurred during the first year of life, and 53% of these before 2 monthsof age.Data miningis a collection of techniques for efficient automated discovery of previouslyunknown, valid, novel, useful and understandable patterns in large databases.rnObjective:The main objective of this study is to explore the potential applicability of datamining to predict the determinants, levels and pattern of under-five mortality in Ethiopia,particularly for the Butajira rural health program sites. This can greatly support for policymakers, planners, and healthcare providers working on the control of under-five childrenmortality in Ethiopia.rnMethods andMaterial:The methodology used for this research was a hybrid six-step CiosKnowledge Discovery Process. The required data was collected from Butajira rural healthprogramdatabase covering the period 1987-2008. The researcher used two popular data miningalgorithms (C4.5 J48 Decision Trees and Naïve Bayes Classifier) to develop the predictivemodel using a larger dataset (11,600 cases). The researcher also used a 10-fold cross validationand 90% split test mode fordataminingmethods of the two predictive models for performancecomparison purposes.rnResults:The results indicated that the decision tree (J48algorithm) is the best predictor withpruned parameter of the tree of 90% split test mode; it has 97.49% accuracy on the holdoutdataset (this predictive accuracy is better than any reported in the literature), Naïve BayesClassifier came out to be the second with supervised discretization has 96.67% accuracy.rnConclusion:The results from this study were very capable and confirmed the belief thatapplyingdata mining techniques could indeed support a predictive model building task thatpredicts the pattern of under-five mortality in Ethiopia; particularly for Butajira rural healthprogramsitesarepossible. In the future, more classification studies by using a possible largeamount ofButajira rural health programdemographic and surveillance sitesdataset records withepidemiological information and employing other classification algorithms, tools and techniquescouldyield better results.

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