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A hybrid ensemble approach to accelerate the classification accuracy for predicting malnutrition among under-five

Md Nafiul Alam Khan1, Rossita Mohamad Yunus1

  • 1Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia.

Nutrition (Burbank, Los Angeles County, Calif.)
|January 15, 2023
PubMed

Insights

A new hybrid ensemble model significantly improves malnutrition prediction in sub-Saharan African children. This machine learning approach enhances accuracy, crucial for child survival and development in the region.

Area of Science:

  • Pediatrics
  • Public Health
  • Machine Learning

Background:

  • Malnutrition significantly contributes to under-five mortality in sub-Saharan Africa.
  • Nutritional intake is critical for child growth and development.
  • Over one-third of child deaths in the region are linked to malnutrition.

Purpose of the Study:

  • To develop a majority voting-based hybrid ensemble (MVBHE) learning model.
  • To enhance the prediction accuracy of malnutrition data for under-five children.
  • To address the critical issue of child malnutrition in sub-Saharan Africa.

Main Methods:

  • Utilized secondary data from Demographic and Health Surveys in sub-Saharan Africa.
  • Employed bagging, boosting, and voting algorithms including random forest, decision tree, eXtreme Gradient Boosting, and k-nearest neighbors.
  • Developed a majority voting-based hybrid ensemble (MVBHE) model.

Main Results:

  • The MVBHE model achieved 96% prediction accuracy for malnutrition.
  • This significantly outperformed individual models: random forest (81%), decision tree (60%), eXtreme Gradient Boosting (79%), and k-nearest neighbors (74%).
  • The MVBHE model demonstrated superior performance across accuracy, precision, recall, and F1 score.

Conclusions:

  • The MVBHE model is highly effective for predicting childhood malnutrition in sub-Saharan Africa.
  • The study recommends the MVBHE model for its enhanced predictive power.
  • Improving malnutrition prediction is vital for targeted interventions and reducing child mortality.
Abstract

Keywords:
Ensemble learningMachine learningMajority votingNutritionUnder-five childsub-Saharan Africa

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