Evidence map›Paper›PMID 39787191›Full record

ArticlePloS one2025

Predicting early cessation of exclusive breastfeeding using machine learning techniques.

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

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Freja Marie NejsumDepartment of Pediatrics, Copenhagen University Hospital-North Zealand, Hillerød, Denmark.ORCID 0000-0003-2142-4542
Rikke WiingreenDepartment of Pediatrics, Copenhagen University Hospital-North Zealand, Hillerød, Denmark.ORCID 0000-0002-6794-436X
Andreas Kryger JensenDepartment of Public Health, University of Copenhagen, Copenhagen, Denmark.
Ellen Christine Leth LøkkegaardDepartment of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
Bo Mølholm HansenDepartment of Pediatrics, Copenhagen University Hospital-North Zealand, Hillerød, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentification 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.

methodsUtilizing 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.

resultsThe 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.

conclusionsThe 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.

Indexed as

Breast FeedingMachine LearningAdultDenmarkFemaleHumansInfantInfant, NewbornMaleMothersPregnancyROC Curve

Identifiers

PMID39787191
PMCPMC11717195

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.