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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
Authors: Zenebe Markos, Feleke Doyore, Martha Yifiru, and Jemal Haidar
Source: Journal of Health & Medical Informatics , 5: 152; doi:10.4172/2157-7420.1000152
Topic(s): Child health
Children under five
Country: Africa
Published: FEB 2014
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. The main objective of this study was to design a model that predicts the nutritional status of under-five children using data mining techniques. Methods: 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 (EDHS) 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