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Summary
Childhood emotional and behavioral difficulties have a profound impact on later life outcomes, making it crucial to identify early-life risk factors that predict emotional and behavioral difficulties. However, much of the existing research has concentrated on diagnosing, rather than predicting, emotional and behavioral difficulties, and has often focused on adolescents rather than younger children. This study employs machine learning (ML) techniques to construct an interpretable predictive model using data from the UK Household Longitudinal Study, aiming to identify key risk factors that influence children's emotional and behavioral difficulties during childhood. We examined maternal habits during pregnancy and parent-reported data on birth, breastfeeding and regulatory problems during the newborn stage. Our findings highlighted lack of breastfeeding, low birthweight and maternal smoking during pregnancy as the three most significant predictors of emotional behavioral difficulties. Other important factors were related to infant regulatory problems. Heterogeneity analysis revealed gender differences in predictive power, with maternal smoking during pregnancy being a stronger predictor for boys, and the amount of fussing in infancy having a greater impact on girls. This study highlights the importance of comprehensive prenatal and postnatal care, advocates for early screening of emotional and behavioral difficulties, and calls for gender-specific approaches in assessing and addressing emotional and behavioral difficulties in children.
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