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Introduction <p>Neonatal mortality is still a global public health concern, especially in lower- and middle-income countries like Ethiopia. Machine learning (ML) has shown excellent performance in healthcare prediction tasks due to its capability of efficiently handling interactions within mixed-type tabular data. Therefore, this study aimed to develop an interpretable model that can predict neonatal death based on the Ethiopian Demographic Health Survey (EDHS) dataset.</p> Methods <p>This study utilized the imbalanced EDHS dataset collected from all surveys conducted in 2000, 2005, 2011, 2016, and 2019. We evaluated some basic and tree-based ensemble ML algorithms validated with five-fold cross-validation and 80/20 data splitting. Unbalanced, weighted, and SMOTENC class balancing techniques are utilized across both evaluation strategies. The models were compared based on sensitivity and SHAP interpretability with consideration of F1-score and AUC-PR trade-offs.</p> Results <p>The weighted LightGBM model achieved the highest recall performance (recall = 87.2%) with a slight reduction of F1 = 85.3% and PR-AUC = 92.6% by preserving SHAP interpretability. The SHAP analyses indicate that breastfeeding initiation, number of living children, and ANC visits are the top three key predictors. The dependence plot shows positive SHAP values for delayed breastfeeding initiation, no living children, no antenatal care, lower household member size, grand multiparity, male sex, small birth size, twin births, and short preceding birth intervals.</p> Conclusion <p>The weighted LightGBM model not only had better sensitivity with competitive AUC-PR results but also showed reliable interpretability using SHAP analyses. The model predicted strong neonatal mortality odds associated with delayed breastfeeding initiation, absence of living children, and lack of antenatal care visits. The model also predicted that neonatal mortality would be more likely for low household member size, grand multiparity, male sex, small birth size, twin births, and short preceding birth intervals. Finally, adequate ANC visits and early initiation of breastfeeding were protective and affected survival outcomes for neonates, emphasizing the importance of maternal and newborn healthcare programs.</p>

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Interpretable prediction of neonatal mortality and its key predictors using machine learning and SHAP analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月21日

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  • Kerebih Getinet Bitew,
  • Yaregal Assabie,
  • Tesfa Tegegne

摘要

Introduction

Neonatal mortality is still a global public health concern, especially in lower- and middle-income countries like Ethiopia. Machine learning (ML) has shown excellent performance in healthcare prediction tasks due to its capability of efficiently handling interactions within mixed-type tabular data. Therefore, this study aimed to develop an interpretable model that can predict neonatal death based on the Ethiopian Demographic Health Survey (EDHS) dataset.

Methods

This study utilized the imbalanced EDHS dataset collected from all surveys conducted in 2000, 2005, 2011, 2016, and 2019. We evaluated some basic and tree-based ensemble ML algorithms validated with five-fold cross-validation and 80/20 data splitting. Unbalanced, weighted, and SMOTENC class balancing techniques are utilized across both evaluation strategies. The models were compared based on sensitivity and SHAP interpretability with consideration of F1-score and AUC-PR trade-offs.

Results

The weighted LightGBM model achieved the highest recall performance (recall = 87.2%) with a slight reduction of F1 = 85.3% and PR-AUC = 92.6% by preserving SHAP interpretability. The SHAP analyses indicate that breastfeeding initiation, number of living children, and ANC visits are the top three key predictors. The dependence plot shows positive SHAP values for delayed breastfeeding initiation, no living children, no antenatal care, lower household member size, grand multiparity, male sex, small birth size, twin births, and short preceding birth intervals.

Conclusion

The weighted LightGBM model not only had better sensitivity with competitive AUC-PR results but also showed reliable interpretability using SHAP analyses. The model predicted strong neonatal mortality odds associated with delayed breastfeeding initiation, absence of living children, and lack of antenatal care visits. The model also predicted that neonatal mortality would be more likely for low household member size, grand multiparity, male sex, small birth size, twin births, and short preceding birth intervals. Finally, adequate ANC visits and early initiation of breastfeeding were protective and affected survival outcomes for neonates, emphasizing the importance of maternal and newborn healthcare programs.