ArticleScientific reports2026
Predicting the effects of temperature variability on nutritional status of children under five in Sub-Saharan Africa using machine learning.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Rising temperatures due to climate change pose significant risks to the nutritional status of under-five children, particularly in Sub-Saharan Africa (SSA). This study investigates the influence of temperature increases on nutritional status (wasting, stunting, and underweight) in SSA. Based on Demographic and Health Survey (DHS) data for under-five children and global meteorological reanalysis data, we employed multiple supervised machine learning methods to predict the impact of temperature variability on nutritional status indicators, including stunting, underweight, and wasting, while controlling for socioeconomic variables such as household income and maternal education. Different metrics were used to evaluate the forecasting performance. In addition, multivariable logistic regression was employed to test for the causal-effect relationship. A total of 345,837 participants from 22 SSA countries were analyzed using data from 2005 to 2023. Among the algorithms tested, XG Boost achieved the highest accuracy for underweight prediction (Accuracy = 0.7832), Random Forest for stunting (Accuracy = 0.7023), and logistic regression for wasting (Accuracy = 0.6634). For different countries, accuracies ranging from 0.65 to 0.90, with highest in Uganda (decision tree, Accuracy = 0.9042 for stunting) and lowest in Burundi (XG Boost, Accuracy = 0.6426 for wasting). Causal-effect analysis revealed that each 1 °C rise in average temperature increased the odds of stunting by approximately 1% (OR 1.01, 95% CI: 1.00–1.10), underweight by about 3% (OR 1.03, 95% CI: 1.01–1.06), and wasting by around 10% (OR 1.10, 95% CI: 1.08–1.12). Although the incremental increases per degree appear modest, such temperature-related risks may translate into substantial population-level impacts in climate-vulnerable settings. Higher household income and maternal education were associated with improved nutritional outcomes and attenuated the adverse effects of rising temperatures, indicating a protective socioeconomic effect. Supervised machine learning models can effectively leverage complex datasets to predict the impact of temperature variability on nutritional status, reinforcing the importance of integrated policies and climate-smart agricultural practices for safeguarding the health of under-five children in SSA.
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