ArticleThe Journal of the Egyptian Public Health Association2026
Comparative performance of machine learning models in predicting childhood diarrhea: implications for public health surveillance.
Article in The Journal of the Egyptian Public Health Association, 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
backgroundDiarrhea remains one of the leading causes of under-5 mortality in Nigeria. Traditional logistic regression has been used to assess risk factors; however, machine learning (ML) models complement the prediction capability by untying tangled interactions. This study presents an analysis of childhood diarrhea predictors in Nigeria using both approaches to inform strategic interventions.
methodsThe 2018 NDHS (Nigeria Demographic and Health Survey) data were assessed, and 33,924 under-5 children’s data were analyzed. Predictor variables include socioeconomic, environmental, behavioral, and household factors. Adjusted odds ratios (aORs) were estimated using logistic regression, while ML models (CatBoost, LightGBM, XGBoost) were used to evaluate non-linear relationships. Performance of the models was compared based on feature importance rankings.
resultsNotably, there was a striking regional variation, with the highest prevalence of diarrhea recorded in the North-East (aOR = 2.04, p < 0.001). Region and child’s age were the most important predictors in both regression and ML models, with ML importance of 100 and 76.1, respectively. Higher socioeconomic status had a strongly protective association, as evidenced by a clear wealth gradient (Very rich: aOR = 0.57, p < 0.001). Similarly, safe disposal of children’s feces (aOR = 0.92, p = 0.031) was associated with reduced odds of diarrhea, as was higher maternal education (aOR = 0.76, p = 0.010). Among the ML models, CatBoost provided the best balance of performance (AUC = 0.706, Balanced Accuracy = 0.660, sensitivity = 71.3%), suggesting its utility in ruling out the condition, although all models, including LR (AUC = 0.704), showed a good discriminative ability.
conclusionThis study emphasizes the complementarity of logistic regression and ML to identifying diarrhea predictors. The principal findings indicate a strategic combination of geographically focused WASH (Water, Sanitation, and Hygiene) interventions, age-stratified prevention, and poverty alleviation strategies. These findings can inform Nigeria’s interventions to reduce diarrhea in children and achieve SDG 3.2.
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