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Updated: Jan 9, 2026

A Common Marmoset Model of Mother-Infant Intervention for Breastfeeding Disorders in the Presence of Paternal Inhibition and Maternal Neglect

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Using the Machine Learning Model in Breastfeeding Continuity: Nursing Training Intervention and Follow-Up Study.

Eda Yol Unlu¹, Sibel Kucuk

¹Author Affiliations: Department of Pediatric Health and Diseases, Ankara Training and Research Hospital, Ankara Provincial Health Directorate, Ankara, Türkiye (Dr Yol Unlu); and Faculty of Health Sciences, Department of Nursing, Ankara Yıldırım Beyazit University, Ankara, Türkiye (Dr Kucuk).

The Journal of Perinatal & Neonatal Nursing

|December 9, 2025

Summary

Machine learning (ML)-based breastfeeding training (MLBT) improved breastfeeding knowledge and continuation rates in mothers at risk of early cessation. This approach enhances healthcare quality through personalized solutions for sustained breastfeeding support.

Keywords:

breastfeeding follow-up intervention machine learning prediction model

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Area of Science:

Nursing
Public Health
Artificial Intelligence in Healthcare

Background:

Early cessation of breastfeeding poses risks to maternal and infant health.
Identifying mothers at risk of early breastfeeding cessation is crucial for timely intervention.
Innovative approaches are needed to support sustained breastfeeding practices.

Purpose of the Study:

To evaluate the impact of machine learning (ML)-based breastfeeding training (MLBT) on breastfeeding knowledge.
To assess the effect of MLBT on breastfeeding continuation rates among mothers at risk of early cessation.
To determine the efficacy of an ML model in identifying mothers likely to cease breastfeeding early.

Main Methods:

A quasi-experimental, pre-posttest study involving 90 mothers (45 intervention, 45 control).
Phase 1: Development of an ML model to predict early breastfeeding cessation risk.
Phase 2: Delivery of MLBT to the intervention group based on identified risk profiles; data analyzed using chi-square, t-tests, and ANOVA.

Main Results:

The intervention group demonstrated significant improvements in breastfeeding knowledge across all MLBT modules (P < .01) and total scores (P < .001).
Full and partial breastfeeding rates were significantly higher in the intervention group at 2 and 4 months postpartum (P < .005).

Conclusions:

MLBT effectively enhances breastfeeding knowledge and supports breastfeeding continuation in mothers identified by an ML model.
Integrating ML into nursing practice offers efficient, personalized solutions to improve healthcare quality.
This study provides a framework for developing and enhancing programs promoting sustained breastfeeding for improved maternal and infant health.