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Breastfeeding Prediction Using Machine Learning: Insights into Key Predictors and Model Performance.

Feng Wei1, Zihan Sun2

  • 1Department of Sociology, Hopkins Population Center, Johns Hopkins University, Baltimore, Maryland, USA.

Breastfeeding Medicine : the Official Journal of the Academy of Breastfeeding Medicine
|May 22, 2026
PubMed
Summary

Maternal socioeconomic factors strongly predict breastfeeding initiation and duration. Machine learning models can forecast breastfeeding outcomes, though duration remains challenging to predict accurately.

Keywords:
Lasso logistic regressionbreastfeedingearly-life disparitiesmachine learningmaternal socioeconomic status

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Area of Science:

  • Public Health
  • Maternal and Child Health
  • Machine Learning Applications in Healthcare

Background:

  • Breastfeeding disparities persist due to complex, multifaceted factors, challenging traditional predictive models.
  • Understanding predictors is crucial for developing targeted interventions to support breastfeeding practices.

Purpose of the Study:

  • To identify key predictors of ever breastfeeding and longer breastfeeding duration.
  • To evaluate the efficacy of multiple machine learning models in predicting breastfeeding outcomes.

Main Methods:

  • Analysis of nationally representative U.K. Household Longitudinal Study data.
  • Application of diverse machine learning models: Lasso logistic regression, decision tree, random forest, XGBoost, support vector machine, and neural network.
  • Examination of factors associated with breastfeeding initiation and continuation at or after 3 months.

Main Results:

  • Maternal socioeconomic factors, particularly education and ethnicity, are the strongest predictors of breastfeeding.
  • Models achieved better performance in predicting breastfeeding initiation compared to breastfeeding duration.
  • Various machine learning models demonstrated comparable performance across the studied outcomes.

Conclusions:

  • Breastfeeding outcomes are predictable, with maternal socioeconomic characteristics being the primary drivers.
  • Machine learning offers a viable approach for identifying individuals needing targeted breastfeeding support.
  • Lasso logistic regression presents a practical, transparent, and effective choice for breastfeeding prediction due to its comparable performance with complex models.