Predictions of the Status of Undernutrition for Children below Five Using Ensemble Metho

Abstract

Undernutrition is one of the main causes of morbidity and mortality in children under five in most developing countries, including Ethiopia. It increases the risk of infectious diseases, impairs cognitive and physical development, reduces school performance and productivity, and perpetuates intergenerational cycles of poverty and malnutrition. The primary goal of this thesis is to build an ensemble model that predicts the undernutrition status of children under five using data from the 2019 EMDHS. The experiments covered 15082 instances and 20 attributes. Ensemble methods combine several models to deliver better results. Typically, results from an ensemble approach are more accurate than those from a single model. The selected method consists of preprocessing, feature selection, k-fold cross-validation, model building, an ensemble classifier, and final prediction steps. In this work, different machine learning classification models such as the Decision Tree, Support Vector Machine, K-Nearest Neighbors, and Naive Bayes classifiers have been used as base model algorithms with an accuracy rate of 0.92%, 0.94%, 0.92%, and 0.75% respectively. The final result was combined by the stacking ensemble method with logistic regression. The most accurate predictive model, with a 96 % accuracy rate was created using the stacking ensemble method. HAZ, WAZ, WHZ, age in 5 years groups, region, source of drinking water, education level, type of toilet facility, wealth index, total children born, number of antenatal visits, vaccination, breastfeeding duration, ever had nutritious food and plain water has given are the major features that contribute to undernutrition in children under-five. The findings of this study provided encouraging evidence that using the ensemble method could support the development of a predictive model that predicts the nutritional status of children under five in Ethiopia. Future research could produce better results by combining large datasets from clinical and hospital datasets. Future research may also include children over the age of five and children with obesity as a malnutrition status