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Predicting Under Nutrition Status of Under-Five Children Using Data Mining Techniques: The Case of 2011 Ethiopian Demographic and Health Survey

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dc.contributor.advisor Yifiru, Martha (PhD)
dc.contributor.author Markos, Zenebe
dc.date.accessioned 2018-12-05T08:47:13Z
dc.date.available 2018-12-05T08:47:13Z
dc.date.issued 2013-06
dc.identifier.uri http://localhost:80/xmlui/handle/123456789/14868
dc.description.abstract Background: under nutrition is one of the leading causes of morbidity and mortality in children under the age of five in most developing countries including Ethiopia. Objective: The general objective of this study was to design a model that predicts the nutritional status of under-five children using data mining techniques. Methodology: This study followed hybrid methodology of Knowledge Discovery Process to achieve the goal of building predictive model using data mining techniques and used secondary data from 2011 Ethiopia Demographic and Health Survey dataset. Hybrid process model was selected since it combines best features of Cross-Industry Standard Process for Data Mining and Knowledge Discovery in Database methodology to identify and describe several explicit feedback loops which are helpful in attaining the research objectives. WEKA 3.6.8 data mining tools and techniques such as J48 decision tree, Naïve Bayes and PART rule induction classifiers were utilized as means to address the research problem. Result: In this particular study, the predictive model developed using PART pruned rule induction found to be best performing having 92.6% of accurate results and 97.8% WROC area. Promising result has been achieved from the rules regarding nutritional status prediction. Conclusion: The results from this study were encouraging and confirmed that applying data mining techniques could indeed support a predictive model building task that predicts nutritional status of under-five children in Ethiopia. In the future, integrating large demographic and health survey dataset and clinical dataset, employing other classification algorithms, tools and techniques could yield better results. Keywords: Predictive modeling, Nutritional status, children, Data mining, EDHS dataset en_US
dc.language.iso en en_US
dc.publisher Addis Ababa University en_US
dc.subject Predictive modeling en_US
dc.subject Nutritional status en_US
dc.subject children en_US
dc.subject Data mining en_US
dc.subject EDHS dataset en_US
dc.title Predicting Under Nutrition Status of Under-Five Children Using Data Mining Techniques: The Case of 2011 Ethiopian Demographic and Health Survey en_US
dc.type Thesis en_US


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