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Predicting severe stunting and its determinants among under-five in Eastern African Countries: A machine learning

Halid Worku Jemil¹, Sonia Worku Semayneh², Altaseb Beyene Kassaw³

¹Department of Health Informatics, College of Medicine and Health Science, Wollo University, Dessie, Ethiopia.

|January 2, 2026

Insights

Severe stunting in East African children is predicted by machine learning, identifying key factors like poor household conditions and lack of exclusive breastfeeding. Interventions should focus on maternal support and education to reduce stunting risks.

Area of Science:

Public Health
Pediatrics
Machine Learning Applications

Background:

Severe stunting is a critical public health issue in low- and middle-income countries, particularly in Eastern Africa, impacting millions of children under five and contributing to mortality.
Limited research has explored severe stunting using machine learning (ML) in Eastern Africa, highlighting a gap in understanding its determinants.

Purpose of the Study:

To predict severe stunting among children under five in Eastern Africa using ML algorithms.
To identify the major determinants contributing to severe stunting.
To enhance model interpretability using Shapley Additive explanations (SHAP) and Association Rule Mining (ARM).

Main Methods:

A cross-sectional study utilizing Demographic and Health Survey (DHS) data from 2012-2022 in Eastern Africa.
Analysis of data from 76,019 children, sourced from 136,074 children, using Python and R.
Model performance evaluated using accuracy and Area Under the Curve (AUC), with SHAP and ARM for determinant interpretation.

Main Results:

The Random Forest model demonstrated the highest performance with 87% accuracy and an AUC of 0.83.
Key predictors for increased severe stunting risk included not practicing exclusive breastfeeding, being from Burundi, child underweight status, poor household conditions, male gender, short maternal height, maternal underweight, small birth size, home delivery, primary maternal education, unimproved toilet facilities, and distance to health facilities.

Conclusions:

The Random Forest model effectively predicts severe stunting in Eastern African populations.
Integrated interventions are crucial, focusing on socioeconomic support for mothers, enhanced maternal education, promotion of exclusive breastfeeding and facility deliveries, improved sanitation and hygiene, and accessible healthcare services.

Read full analysis

Abstract

Introduction:

Severe stunting is one of the primary public health challenges in LMIC including Eastern African Countries, which affects millions of children. In addition, it was a major contributor for mortality and related complication of children aged under five. However, there is limited study conducted severe form of stunting by employing Machine learning (ML) in Eastern African Countries. Therefore, our study was demonstrated to predict and identify its major determinants using ML algorithms, furthermore, to improve model explainablity. Our study used Shapley Additive explanations (SHAP) and ARM to identify the determinants of severe stunting among under-five.

Methods:

cross-sectional study was conducted using DHS data from 2012-2022 in East Africa. 136,074 children were the source populations, and 76,019 children were the study population. Data were analyzed using Python version 3.7 and R version 4.3.3 for data preprocessing, modeling, and statistical analysis. Model performance was evaluated using accuracy and AUC. Furthermore, the SHAP analysis and ARM was used to further explain and interpret the determinants of severe stunting among children under five.

Results:

The Random Forest performed the best in this analysis, with an accuracy of 87% and an AUC score of 0.83. The analysis indicated that women's who do not practicing exclusive breastfeeding (SHAP value = +0.41), being from Burundi (SHAP value = +0.04), children being underweight (SHAP value = +0.25), lived in poor household (SHAP value = +0.40), child gender being male(SHAP value = +0.23), mothers height being short (SHAP value = +0.03), mothers being underweight (SHAP value = +0.18), child size at birth being small (SHAP value = +0.21), women's being delivered in home(SHAP value = +0.07), mothers education being primary (SHAP value = +0.20), unimproved toilet (SHAP value = +0.06), distance to health facility being a big problem (SHAP value = +0.02), were associated with increase the risk of severe stunting among under five.

Conclusion:

The Random Forest was the best-performing model for predicting severe stunting in Eastern African countries. To decrease the effects of severe stunting, integrated interventions should provide support for mothers with lower socioeconomic conditions, strengthen maternal education, empower women to practice exclusive breastfeeding, encourage facility deliveries, increase access for households to sanitary facilities, provide education on personal and environmental hygiene, provide mothers with information on the importance of complementary feeding for children as well as for the mothers, and provide near health facilities for mothers and essential care services.

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