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Background <p>Malaria remains a major public health challenge, particularly in sub-Saharan Africa, where women of reproductive age are especially vulnerable during pregnancy and childbirth. To identify key predictors and improve predictive accuracy, machine learning algorithms such as Random Forest were applied, along with SHAP analysis, to a large multi-country DHS dataset, with class imbalance addressed using Tomek Links and Random Over-Sampling.</p> Methods <p>This study employed a weighted dataset of 153,015 participants from the Demographic and Health Survey (DHS) conducted across ten sub-Saharan African countries. Data preprocessing and analysis were carried out using STATA version 17 and Python 3.10. Feature scaling was applied to standardize numerical variables, ensuring uniform weighting across predictors and improving model stability. An 80:20 data split ratio was applied, and class imbalance was addressed using Tomek Links combined with Random Over-Sampling. Eight models were selected and trained using both balanced and unbalanced datasets. The model performance was evaluated using metrics such as ROC-AUC, accuracy, recall, F1 score, and precision.</p> Results <p>The Random Forest algorithm performed best in this study, with an accuracy of 83%, an F1 score of 82%, recall of 80%, precision of 84%, and an AUC of 88%. Fifty-five percent of participants used mosquito nets. The SHAP analysis showed that Age above 34, being employed, frequent social media use, higher education, institutional deliveries, and female-headed households increased bed net use, while fewer ANC visits and being divorced decreased its use.</p> Conclusion <p>Age above 34, being employed, frequent social media use, higher education, institutional deliveries, and female-headed households increased bed net use, while fewer ANC visits and being divorced decreased its use. Strengthening social media use for health information, promoting women's education, encouraging institutional delivery, motivate for antenatal care services, and providing support to socially and economically vulnerable women are essential strategies to enhance mosquito net utilization.</p>

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Leveraging machine learning to predict mosquito bed net utilization among women of reproductive age in sub-Saharan Africa

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月6日

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  • Nebebe Demis Baykemagn,
  • Tesfahun Zemene Tafere,
  • Getachew Teshale,
  • Andualem Yalew Aschalew,
  • Melak Jejaw,
  • Kaleb Assegid Demissie,
  • Azmeraw Tadele,
  • Asebe Hagos,
  • Misganaw Guadie Tiruneh,
  • Jenberu Mekurianew Kelkay

摘要

Background

Malaria remains a major public health challenge, particularly in sub-Saharan Africa, where women of reproductive age are especially vulnerable during pregnancy and childbirth. To identify key predictors and improve predictive accuracy, machine learning algorithms such as Random Forest were applied, along with SHAP analysis, to a large multi-country DHS dataset, with class imbalance addressed using Tomek Links and Random Over-Sampling.

Methods

This study employed a weighted dataset of 153,015 participants from the Demographic and Health Survey (DHS) conducted across ten sub-Saharan African countries. Data preprocessing and analysis were carried out using STATA version 17 and Python 3.10. Feature scaling was applied to standardize numerical variables, ensuring uniform weighting across predictors and improving model stability. An 80:20 data split ratio was applied, and class imbalance was addressed using Tomek Links combined with Random Over-Sampling. Eight models were selected and trained using both balanced and unbalanced datasets. The model performance was evaluated using metrics such as ROC-AUC, accuracy, recall, F1 score, and precision.

Results

The Random Forest algorithm performed best in this study, with an accuracy of 83%, an F1 score of 82%, recall of 80%, precision of 84%, and an AUC of 88%. Fifty-five percent of participants used mosquito nets. The SHAP analysis showed that Age above 34, being employed, frequent social media use, higher education, institutional deliveries, and female-headed households increased bed net use, while fewer ANC visits and being divorced decreased its use.

Conclusion

Age above 34, being employed, frequent social media use, higher education, institutional deliveries, and female-headed households increased bed net use, while fewer ANC visits and being divorced decreased its use. Strengthening social media use for health information, promoting women's education, encouraging institutional delivery, motivate for antenatal care services, and providing support to socially and economically vulnerable women are essential strategies to enhance mosquito net utilization.