From mother to infant: predicting infant temperament using maternal mental health measures and tabular machine learning models.

Background: Negative emotionality is a core dimension of infant temperament, characterized by heightened distress, reactivity, and difficulty with self-regulation. It has been consistently associated with later behavioral and emotional difficulties. Emerging evidence suggests that maternal mental health (MMH) in the postpartum period may influence infant temperament. However, few studies have applied machine learning (ML) methods to examine the predictive capacity of MMH profiles for early infant emotional development. Objectives: This study aimed to investigate whether postpartum maternal depression, anxiety, and birth-related trauma, along with sociodemographic factors, can predict infant negative emotionality during the first year postpartum using tabular ML models. Methods: Data were obtained from 410 mother-infant dyads. Infant temperament was assessed using the Negative Emotionality subscale of the Infant Behavior Questionnaire-Revised (IBQ-R). MMH symptoms were measured via the Edinburgh Postnatal Depression Scale (EPDS), the Hospital Anxiety and Depression Scale (HADS), and the City Birth Trauma Scale (City BiTS). Six tabular ML models were trained using MMH and demographic features: Tabular Prior-Data Fitted Network (TabPFN), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Random Forest, and Support Vector Machine (SVM). Performance was evaluated using Receiver Operating Characteristic Area Under The Curve (ROC-AUC), Precision-Recall Area Under the Curve (PR-AUC), F1-score, sensitivity, and specificity. Results: Postpartum MMH symptoms and maternal-infant characteristics moderately predicted infant negative emotionality. LightGBM achieved the highest performance across ROC-AUC (0.76), F1-score (0.72), sensitivity (0.71), and specificity (0.73). TabPFN yielded the highest PR-AUC (0.78). Key predictors included gestational age, infant's age, EPDS score, mother's age, HADS score, and City BiTS score. Conclusions: These findings highlight the potential of ML tools in early identification of at-risk infants and the importance of integrating MMH screening into postnatal care. Such predictive insights can inform timely, personalized interventions that address the unique emotional needs of both mother and infant, ultimately fostering healthier developmental trajectories and enhancing overall family well being.