AUTHOR=Lin Yangyang , Zhou Dongqin 

TITLE=A method for predicting postpartum depression via an ensemble neural network model

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1571522

DOI=10.3389/fpubh.2025.1571522

ISSN=2296-2565

ABSTRACT=IntroductionPostpartum depression (PPD) has numerous adverse impacts on the families of new mothers and society at large. Early identification and intervention are of great significance. Although there are many existing machine learning classifiers for PPD prediction, the requirements for high accuracy and the interpretability of models present new challenges.MethodsThis paper designs an ensemble neural network model for predicting PPD, which combines a Fully Connected Neural Network (FCNN) and a Neural Network with Dropout mechanism (DNN). The weights of FCNN and DNN in the proposed model are determined by their accuracies on the training set and respective Dropout values. The structure of the FCNN is simple and straightforward. The connection pattern among the neurons of the FCNN makes it easy to understand the relationship between the features and the target feature, endowing the proposed model with interpretability. Moreover, the proposed model does not directly rely on the Dropout mechanism to prevent overfitting. Its structure is more stable than that of the DNN, which weakens the negative impact of the Dropout mechanism on the interpretability of the proposed model. At the same time, the Dropout mechanism of the DNN reduces the overfitting risk of the proposed model and enhances its generalization ability, enabling the proposed model to better adapt to different clinical data.ResultsThe proposed model achieved the following performance metrics on the PPD dataset: accuracy of 0.933, precision of 0.958, recall of 0.939, F1-score of 0.948, Matthews Correlation Coefficient (MCC) of 0.855, specificity of 0.923, Negative Predictive Value (NPV) of 0.889, False Positive Rate (FPR) of 0.077, and False Negative Rate (FNR) of 0.061. Compared with 10 classic machine learning classifiers, under different dataset split ratios, the proposed model outperforms in terms of indicators such as accuracy, precision, recall, and F1-score, and also has high stability.DiscussionThe research results show that the proposed model effectively improves the prediction performance of PPD, which can provide guiding suggestions for relevant medical staff and postpartum women in clinical decision-making. In the future, plans include collecting more disease datasets, using the proposed model to predict these diseases, and constructing an online disease prediction platform to embed the proposed model, which will help with real-time disease prediction.