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Predicting the Pattern of Under-Five Mortality in Ethiopia Using Data Mining Technology: The Case of Butajira Rural Health Program

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dc.contributor.advisor Jemaneh, Getachew (PhD)
dc.contributor.advisor Tefera, Worku (PhD)
dc.contributor.author Tekabe, Be’emnetu
dc.date.accessioned 2018-11-26T07:21:04Z
dc.date.available 2018-11-26T07:21:04Z
dc.date.issued 2012-06
dc.identifier.uri http://etd.aau.edu.et/handle/123456789/14501
dc.description.abstract Introduction: The under-five deaths in Ethiopia represent 48% of all mortality. More than half of the under-five deaths occurred during the first year of life, and 53% of these before 2 months of age. Data mining is a collection of techniques for efficient automated discovery of previously unknown, valid, novel, useful and understandable patterns in large databases. Objective: The main objective of this study is to explore the potential applicability of data mining 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 policy makers, planners, and healthcare providers working on the control of under-five children mortality in Ethiopia. Methods and Material: The methodology used for this research was a hybrid six-step Cios Knowledge Discovery Process. The required data was collected from Butajira rural health program database covering the period 1987-2008. The researcher used two popular data mining algorithms (C4.5 J48 Decision Trees and Naïve Bayes Classifier) to develop the predictive model using a larger dataset (11,600 cases). The researcher also used a 10-fold cross validation and 90% split test mode for data mining methods of the two predictive models for performance comparison purposes. Results: The results indicated that the decision tree (J48 algorithm) is the best predictor with pruned parameter of the tree of 90% split test mode; it has 97.49% accuracy on the holdout dataset (this predictive accuracy is better than any reported in the literature), Naïve Bayes Classifier came out to be the second with supervised discretization has 96.67% accuracy. Conclusion: The results from this study were very capable and confirmed the belief that applying data mining techniques could indeed support a predictive model building task that predicts the pattern of under-five mortality in Ethiopia; particularly for Butajira rural health program sites are possible. In the future, more classification studies by using a possible large amount of Butajira rural health program demographic and surveillance sites dataset records with epidemiological information and employing other classification algorithms, tools and techniques could yield better results. en_US
dc.language.iso en en_US
dc.publisher Addis Ababa University en_US
dc.subject Under-Five Mortality in Ethiopia Using Data Mining Technology en_US
dc.title Predicting the Pattern of Under-Five Mortality in Ethiopia Using Data Mining Technology: The Case of Butajira Rural Health Program en_US
dc.type Thesis en_US

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