A hybrid ensemble approach to accelerate the classification accuracy for predicting malnutrition among under-five children in sub-Saharan African countries

Background: The proper intake of nutrients is essential to the growth and maturation of youngsters. In sub- Saharan Africa, 1 in 7 children dies before age 5 y, and more than a third of these deaths are attributed to malnutrition. The main purpose of this study was to develop a majority voting-based hybrid ensemble (MVBHE) learning model to accelerate the prediction accuracy of malnutrition data of under-five children in sub-Saharan Africa.Methods: This study used available under-five nutritional secondary data from the Demographic and Health Surveys performed in sub-Saharan African countries. The research used bagging, boosting, and voting algo- rithms, such as random forest, decision tree, eXtreme Gradient Boosting, and k-nearest neighbors machine learning methods, to generate the MVBHE model.Results: We evaluated the model performances in contrast to each other using different measures, including accuracy, precision, recall, and the F1 score. The results of the experiment showed that the MVBHE model (96%) was better at predicting malnutrition than the random forest (81%), decision tree (60%), eXtreme Gra- dient Boosting (79%), and k-nearest neighbors (74%).Conclusions: The random forest algorithm demonstrated the highest prediction accuracy (81%) compared with the decision tree, eXtreme Gradient Boosting, and k-nearest neighbors algorithms. The accuracy was then enhanced to 96% using the MVBHE model. The MVBHE model is recommended by the present study as the best way to predict malnutrition in under-five children.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

The study aimed to develop a majority voting-based hybrid ensemble learning model to improve the prediction accuracy of malnutrition data in under-five children in sub-Saharan Africa. The study used under-five nutritional secondary data from the Demographic and Health Surveys in sub-Saharan African countries, and employed bagging, boosting, and voting algorithms along with random forest, decision tree, eXtreme Gradient Boosting, and k-nearest neighbors machine learning methods to create the MVBHE model. The results showed that the MVBHE model (96%) outperformed the random forest (81%), decision tree (60%), eXtreme Gradient Boosting (79%), and k-nearest neighbors (74%) models in predicting malnutrition.