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Background <p>Diarrhea 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.</p> Methods <p>The 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.</p> Results <p>Notably, there was a striking regional variation, with the highest prevalence of diarrhea recorded in the North-East (aOR = 2.04, <i>p</i> &lt; 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, <i>p</i> &lt; 0.001). Similarly, safe disposal of children’s feces (aOR = 0.92, <i>p</i> = 0.031) was associated with reduced odds of diarrhea, as was higher maternal education (aOR = 0.76, <i>p</i> = 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.</p> Conclusion <p>This 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.</p>

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Comparative performance of machine learning models in predicting childhood diarrhea: implications for public health surveillance

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月10日

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  • Joseph O. Ashaolu,
  • Taiwo S. Akanji,
  • Victoria I. Ayansola,
  • Agbolade J. Sunday,
  • Omoyajowo A. Esther,
  • Sylvain Y.M. Some

摘要

Background

Diarrhea 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.

Methods

The 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.

Results

Notably, 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.

Conclusion

This 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.