A multimodal feature fusion model integrating voice acoustic features and early key risk factors for early screening of postpartum depression: a prospective short-term longitudinal observational study.

Background: Postpartum depression (PPD) is a prevalent perinatal mental health disorder, yet early identification remains challenging due to the reliance on subjective self-report screening. Voice acoustic features offer a promising objective alternative, but their utility for early PPD screening in perinatal populations is underexplored. Methods: A prospective short-term longitudinal study was conducted among 64 postpartum women (32 screening-positive, 32 screening-negative). Speech recordings and socio-ecological risk factors were collected on the day before hospital discharge, and PPD screening status was determined at 42 days postpartum using the Edinburgh Postnatal Depression Scale (EPDS ≥ 10). Sixty-five acoustic features, including mel-frequency cepstral coefficients (MFCCs), zero-crossing rate, fundamental frequency, and energy parameters, were extracted and combined with seven early key risk factors. Seven machine learning classifiers were evaluated under 10-fold GroupKFold cross-validation, and model performance was assessed using AUC, sensitivity, specificity, and related metrics. The best-performing model was interpreted using SHapley Additive exPlanations (SHAP). Results: Voice-only models substantially outperformed risk-only models across all classifiers. The fusion of voice and risk factors achieved the highest AUC of 0.871 (95% CI, 0.818-0.920) for the random forest model; however, this improvement over the best voice-only model was not statistically significant ( Conclusion: Voice acoustic features collected before hospital discharge show potential as objective markers for early PPD screening, although further validation in larger, independent cohorts is required before clinical application.