Predicting Nutritional Status of Under 5 Children Using Data Mining
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This research presents 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 P...
This research presents 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.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. From this study, it is confirmed that applyingdata mining techniques could indeed support a predictive model building task that predicts nutritional status of under-five children in Ethiopia. Therefore, researchers, students and others can use it.
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