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

Zenebe Markos

doi:10.4172/2157-7420.1000152

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, Naive 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.

Highlights

IntroductionNutrition is at the heart of most global health problems – especially in the area of child survival where child under nutrition is an underlying cause of more than one-third (3.5 million) prevalence of all child deaths under the age of five in developing countries
Good nutrition is an essential component of good health
Models developed using J48 decision tree, Naïve Bayes classifier and PART rule induction algorithms have good accuracy and ROC results during test experiments. Based on these the experiments were designed for four purposes; to investigate the effect of tree pruning methods when building a decision tree model, to observe how attribute selection affects the classification accuracy, to compare J48 decision tree, Naïve Bayes and PART rule induction classifier and to extract significant rules

Summary

Introduction

Nutrition is at the heart of most global health problems – especially in the area of child survival where child under nutrition is an underlying cause of more than one-third (3.5 million) prevalence of all child deaths under the age of five in developing countries. Of the 112 million underweight children and 178 million children who suffer from stunting, 160 million (90%) live in just 36 developing countries, constituting almost half (46%) of the cases [1]. At least half of these deaths are caused by malnutrition. 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

Objectives

Methods

Results

Conclusion