Predicting exclusive breastfeeding in maternity wards using machine learning techniques.

Journal: Computer methods and programs in biomedicine
PMID:

Abstract

BACKGROUND AND OBJECTIVE: Adequate support in maternity wards is decisive for breastfeeding outcomes during the first year of life. Quality improvement interventions require the identification of the factors influencing hospital benchmark indicators. Machine Learning (ML) models and post-hoc Explainable Artificial Intelligence (XAI) techniques allow accurate predictions and explaining them. This study aimed to predict exclusive breastfeeding during the in-hospital postpartum stay by ML algorithms and explain the ML model's behaviour to support decision making.

Authors

  • Antonio Oliver-Roig
    Department of Nursing, University of Alicante, Spain.
  • Juan Ramón Rico-Juan
    Department of Software and Computing Systems, University of Alicante, Spain. Electronic address: juanramonrico@ua.es.
  • Miguel Richart-Martínez
    Department of Nursing, University of Alicante, Spain.
  • Julio Cabrero-García
    Department of Nursing, University of Alicante, Spain.