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

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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: = 0.147). SHAP analysis revealed that acoustic features dominated model predictions, with MFCC and energy-related features ranking highest. Despite suboptimal probability calibration, decision curve analysis demonstrated positive net benefit across a clinically relevant range of threshold probabilities. 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.

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