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Explainable Machine Learning Classification to Identify Vulnerable Groups Among Parenting Mothers: Web-Based

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Parenting resilience varies, impacting mothers' psychosocial well-being. A machine learning classifier identified vulnerable mothers experiencing higher rates of depressed mood, bonding issues, and poor sleep quality.

Keywords:
adaptationantenatalchildrendepressiondigital healthexplainable machine-learninginfantmachine learningmaternalmobile phonemothermother’s healthnewbornparentingparentsperceived supportpostpartumpsychosocialresiliencesurveyunsupervised clusteringweb-basedwomen

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Area of Science:

  • Maternal mental health
  • Psychosocial adaptation
  • Machine learning in healthcare

Background:

  • Parenting is a significant life event demanding resilience and adaptation.
  • Maternal resilience and perceived support during child-rearing are variable, creating an unclear real-world picture.
  • Postpartum checkups may not fully capture the spectrum of maternal challenges.

Purpose of the Study:

  • To explore the psychosocial status of mothers from newborn to toddler stages.
  • To develop a machine learning classifier identifying mothers' resilience and adaptation characteristics.
  • To pinpoint vulnerable maternal populations during the child-rearing period.

Main Methods:

  • A web-based cross-sectional survey was conducted.
  • Explainable k-means clustering was used to build a classifier for resilience and adaptation.
  • The classifier was applied to newborn, infant, and toddler cohorts to assess psychosocial status (EPDS, PBQ, PSQI).

Main Results:

  • A classifier stratified 1559 participants into 5 groups based on resilience and adaptation.
  • Mothers with the greatest difficulties in resilience and adaptation showed higher incidences of depressed mood, bonding issues, and poor sleep quality.
  • Vulnerable groups in infant and toddler cohorts exhibited significantly higher relative prevalence of psychosocial problems compared to those with no difficulties.

Conclusions:

  • A classifier integrating resilience, adaptation, and perceived support effectively identified psychosocially vulnerable mothers.
  • Vulnerable groups were identified across newborn, infant, and toddler stages, with distinct profiles.
  • The infant cohort's vulnerable group displayed particularly high relative prevalence of depressed mood and poor sleep quality.