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Explainable Machine Learning Classification to Identify Vulnerable Groups Among Parenting Mothers: Web-Based Cross-Sectional Questionnaire Study.

Akiko HanaiTetsuo IshikawaShoko SugaoMakoto FujiiKei HiraiHiroko WatanabeMasayo MatsuzakiGoji NakamotoToshihiro TakedaYasuji KitabatakeYuichi ItohMasayuki EndoTadashi KimuraEiryo Kawakami
Published in: JMIR formative research (2024)
The classifier, based on a combination of resilience and adaptation to the child's temperament and perceived support, was able identify psychosocial vulnerable groups in the newborn cohort, the start-up stage of childcare. Psychosocially vulnerable groups were also identified in qualitatively different infant and toddler cohorts, depending on their classifier. The vulnerable group identified in the infant cohort showed particularly high RP for depressed mood and poor sleep quality.
Keyphrases
  • sleep quality
  • machine learning
  • cross sectional
  • depressive symptoms
  • mental health
  • social support
  • physical activity
  • deep learning
  • artificial intelligence
  • bipolar disorder
  • climate change