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Predicting early cessation of exclusive breastfeeding using machine learning techniques

Author

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  • Freja Marie Nejsum
  • Rikke Wiingreen
  • Andreas Kryger Jensen
  • Ellen Christine Leth Løkkegaard
  • Bo Mølholm Hansen

Abstract

Background: Identification of mother-infant pairs predisposed to early cessation of exclusive breastfeeding is important for delivering targeted support. Machine learning techniques enable development of transparent prediction models that enhance clinical applicability. We aimed to develop and validate two models to predict cessation of exclusive breastfeeding within one month among infants born after 35 weeks gestation using machine learning techniques. Methods: Utilizing a nationwide dataset from Statistics Denmark, including infants born between the 1st of January 2014 and the 31st of December 2015, we employed random forest machine learning to develop two predictive models. The first model included 11 well-established factors associated with cessation of exclusive breastfeeding within one month. The second model was expanded to include 21 additional factors associated with complications during pregnancy and delivery that potentially impede breastfeeding. Feature importance was applied to elucidate the factors driving model predictions. Results: The dataset comprised 110,206 infants and 106,835 mothers. The first model predicted cessation of exclusive breastfeeding within one month with an area under the receiver operating curve of 62.0% (95% confidence interval 61.3% - 62.7%) and an accuracy of 60.4% (95% confidence interval 59.8% - 61.0%). The second model predicted cessation of exclusive breastfeeding within one month with an area under the receiver operating curve of 62.2% (95% confidence interval 61.5% - 62.9%) and an accuracy of 60.0% (95% confidence interval 59.3% - 60.6%). In both models, birthplace, maternal education, delivery mode, and maternal body mass index were the most important factors influencing the overall model performance. Conclusions: The two models could not accurately predict cessation of exclusive breastfeeding within one month among infants born after 35 weeks gestation. Contrary to our expectations, including additional factors in the model did not increase model performance.

Suggested Citation

  • Freja Marie Nejsum & Rikke Wiingreen & Andreas Kryger Jensen & Ellen Christine Leth Løkkegaard & Bo Mølholm Hansen, 2025. "Predicting early cessation of exclusive breastfeeding using machine learning techniques," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-17, January.
    • Handle: RePEc:plo:pone00:0312238
    DOI: 10.1371/journal.pone.0312238
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    3. Freja Marie Nejsum & Ragnhild Måstrup & Christian Torp-Pedersen & Ellen Christine Leth Løkkegaard & Rikke Wiingreen & Bo Mølholm Hansen, 2023. "Exclusive breastfeeding: Relation to gestational age, birth weight, and early neonatal ward admission. A nationwide cohort study of children born after 35 weeks of gestation," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-16, May.
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